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Science: On Principles (Quotes)

noreply@blogger.com (Adrian) — Sat, 20 Nov 2021 02:00:00 GMT

"In all disciplines in which there is systematic knowledge of things with principles, causes, or elements, it arises from a grasp of those: we think we have knowledge of a thing when we have found its primary causes and principles, and followed it back to its elements." (Aristotle, "Physics", cca. 350 BC)

"The chief forms of beauty are order and symmetry and definiteness, which the mathematical sciences demonstrate in a special degree. And since these (e.g. order and definiteness) are obviously causes of many things, evidently these sciences must treat this sort of causative principle also (i.e. the beautiful) as in some sense a cause." (Aristotle, "Metaphysica", cca. 350 BC)

"Reflexion is careful and laborious thought, and watchful attention directed to the agreeable effect of one's plan. Invention, on the other hand, is the solving of intricate problems and the discovery of new principles by means of brilliancy and versatility." (Marcus Vitruvius Pollio, "De architectura" ["On Architecture], cca. 15BC)

"[…] the least initial deviation from the truth is multiplied later a thousand-fold. Admit, for instance, the existence of a minimum magnitude, and you will find that the minimum which you have introduced, small as it is, causes the greatest truths of mathematics to totter. The reason is that a principle is great rather in power than in extent; hence that which was small at the start turns out a giant at the end." (St. Thomas Aquinas, "De Ente et Essentia", cca. 1252)

"All that is required between cognizer and cognized is a likeness in terms of representation, not a likeness in terms of an agreement in nature. For it's plain that the form of a stone in the soul is of a far higher nature than the form of a stone in matter. But that form, insofar as it represents the stone, is to that extent the principle leading to its cognition." (Thomas Aquinas, "Quaestiones disputatae de veritate", cca. 1256-1259)

"It is superfluous to suppose that what can be accounted for by a few principles has been produced by many." (Thomas Aquinas, "Summa Theologica", cca. 1266-1273)

"Reason may be employed in two ways to establish a point: first for the purpose of furnishing sufficient proof of some principle, as in natural science, where sufficient proof can be brought to show that the movement of the heavens is always of uniform velocity. Reason is employed in another way, not as furnishing a sufficient proof of a principle, but as confirming an already established principle, by showing the congruity of its results […]" (Saint Thomas Aquinas, "Summa Theologica", cca. 1266-1273)

"There are and can be only two ways of searching into and discovering truth. The one flies from the senses and particulars to the most general axioms, and from these principles, the truth of which it takes for settled and immovable, proceeds to judgment and to the discovery of middle axioms. And this way is now in fashion. The other derives axioms from the senses and particulars, rising by a gradual and unbroken ascent, so that it arrives at the most general axioms last of all. This is the true way, but as yet untried." (Francis Bacon, "Novum Organum", 1620)

"Reality cannot be found except in One single source, because of the interconnection of all things with one another. […] It is a good thing to proceed in order and to establish propositions (principles). This is the way to gain ground and to progress with certainty." (Gottfried Leibniz, 1670)

"Principles taken upon trust, consequences lamely deduced from them, want of coherence in the parts, and of evidence in the whole, these are every where to be met with in the systems of the most eminent philosophers, and seem to have drawn disgrace upon philosophy itself." (David Hume, "A Treatise of Human Nature", 1739-40)

"Men are often led into errors by the love of simplicity, which disposes us to reduce things to few principles, and to conceive a greater simplicity in nature than there really is." (Thomas Reid, "Essays on the Intellectual Powers of Man", 1785)

"Every science has for its basis a system of principles as fixed and unalterable as those by which the universe is regulated and governed. Man cannot make principles; he can only discover them." (Thomas Paine, "The Age of Reason", 1794)

"The aim of every science is foresight. For the laws of established observation of phenomena are generally employed to foresee their succession. All men, however little advanced make true predictions, which are always based on the same principle, the knowledge of the future from the past." (Auguste Compte, "Plan des travaux scientifiques nécessaires pour réorganiser la société", 1822)

"It is true that of far the greater part of things, we must content ourselves with such knowledge as description may exhibit, or analogy supply; but it is true likewise, that these ideas are always incomplete, and that at least, till we have compared them with realities, we do not know them to be just. As we see more, we become possessed of more certainties, and consequently gain more principles of reasoning, and found a wider base of analogy." (Samuel Johnson, 1825)

"To invent without scruple a new principle to every new phenomenon, instead of adapting it to the old; to overload our hypothesis with a variety of this kind, are certain proofs that none of these principles is the just one, and that we only desire, by a number of falsehoods, to cover our ignorance of the truth." (David Hume, "Of the passions", 1826)

"For one person who is blessed with the power of invention, many will always be found who have the capacity of applying principles." (Charles Babbage, "Reflections on the Decline of Science in England, and on Some of Its Causes", 1830)

"A maxim is a conclusion upon observation of matters of fact, and is merely speculative; a ‘principle’ carries knowledge within itself, and is prospective." (Samuel T Coleridge, "The Table Talk and Omniana of Samuel Taylor Coleridge", 1831)

"The function of theory is to put all this in systematic order, clearly and comprehensively, and to trace each action to an adequate, compelling cause. […] Theory should cast a steady light on all phenomena so that we can more easily recognize and eliminate the weeds that always spring from ignorance; it should show how one thing is related to another, and keep the important and the unimportant separate. If concepts combine of their own accord to form that nucleus of truth we call a principle, if they spontaneously compose a pattern that becomes a rule, it is the task of the theorist to make this clear." (Carl von Clausewitz, "On War", 1832)

"The insights gained and garnered by the mind in its wanderings among basic concepts are benefits that theory can provide. Theory cannot equip the mind with formulas for solving problems, nor can it mark the narrow path on which the sole solution is supposed to lie by planting a hedge of principles on either side. But it can give the mind insight into the great mass of phenomena and of their relationships, then leave it free to rise into the higher realms of action."(Carl von Clausewitz, "On War", 1832)

"[…] in order to observe, our mind has need of some theory or other. If in contemplating phenomena we did not immediately connect them with principles, not only would it be impossible for us to combine these isolated observations, and therefore to derive profit from them, but we should even be entirely incapable of remembering facts, which would for the most remain unnoted by us." (Auguste Comte, "Cours de Philosophie Positive", 1830-1842)

"In truth, ideas and principles are independent of men; the application of them and their illustration is man's duty and merit." (Edward Forbes, 1847)

"It is easily seen from a consideration of the nature of demonstration and analysis that there can and must be truths which cannot be reduced by any analysis to identities or to the principle of contradiction but which involve an infinite series of reasons which only God can see through." (Gottfried W Leibniz, "Nouvelles lettres et opuscules inédits", 1857)

"It is, after all, a principle of logic not to multiply entities unnecessarily." (Antoine-Laurent Lavoisier, "Réflexions sur le phlogistique", 1862)

"The more man inquires into the laws which regulate the material universe, the more he is convinced that all its varied forms arise from the action of a few simple principles. These principles themselves converge, with accelerating force, towards some still more comprehensive law to which all matter seems to be submitted. Simple as that law may possibly be, it must be remembered that it is only one amongst an infinite number of simple laws: that each of these laws has consequences at least as extensive as the existing one, and therefore that the Creator who selected the present law must have foreseen the consequences of all other laws." (Charles Babbage, "Passages From the Life of a Philosopher", 1864)

"As in the experimental sciences, truth cannot be distinguished from error as long as firm principles have not been established through the rigorous observation of facts." (Louis Pasteur, "Étude sur la maladie des vers à soie", 1870)

"I say that a manifold (a collection, a set) of elements that belong to any conceptual sphere is well-defined, when on the basis of its definition and as a consequence of the logical principle of excluded middle it must be regarded as internally determined, both whether an object pertaining to the same conceptual sphere belongs or not as an element to the manifold, and whether two objects belonging to the set are equal to each other or not, despite formal differences in the ways of determination." (Georg Cantor, "Ober unendliche, lineare Punktmannichfaltigkeiten", 1879)

"It is of the nature of true science to take nothing on trust or on authority. Every fact must be established by accurate observation, experiment, or calculation. Every law and principle must rest on inductive argument. The apostolic motto, ‘Prove all things, hold fast that which is good’, is thoroughly scientific. It is true that the mere reader of popular science must often be content to take that on testimony which he cannot personally verify; but it is desirable that even the most cursory reader should fully comprehend the modes in which facts are ascertained and the reasons on which the conclusions are based." (Sir John W Dawson, "The Chain of Life in Geological Time", 1880)

"[…] not only a knowledge of the ideas that have been accepted and cultivated by subsequent teachers is necessary for the historical understanding of a science, but also that the rejected and transient thoughts of the inquirers, nay even apparently erroneous notions, may be very important and very instructive. The historical investigation of the development of a science is most needful, lest the principles treasured up in it become a system of half-understood prescripts, or worse, a system of prejudices." (Ernst Mach, "The Science of Mechanics", 1883)

"It goes without saying that the laws of nature are in themselves independent of the properties of the instruments with which they are measured. Therefore in every observation of natural phenomena we must remember the principle that the reliability of the measuring apparatus must always play an important role." (Max Planck,"Where is Science Going?", 1932)

"I think that we shall have to get accustomed to the idea that we must not look upon science as a 'body of knowledge,' but rather as a system of hypotheses; that is to say, as a system of guesses or anticipations which in principle cannot be justified, but with which we work as long as they stand up to tests, and of which we are never justified in saying that we know they are 'true' or 'more or less certain' or even 'probable’." (Karl R Popper, "The Logic of Scientific Discovery", 1934)

"The fundamental gospel of statistics is to push back the domain of ignorance, prejudice, rule-of-thumb, arbitrary or premature decisions, tradition, and dogmatism and to increase the domain in which decisions are made and principles are formulated on the basis of analyzed quantitative facts." (Robert W Burgess, "The Whole Duty of the Statistical Forecaster", Journal of the American Statistical Association , Vol. 32, No. 200, 1937)

"When an active individual of sound common sense perceives the sordid state of the world, desire to change it becomes the guiding principle by which he organizes given facts and shapes them into a theory. The methods and categories as well as the transformation of the theory can be understood only in connection with his taking of sides. This, in turn, discloses both his sound common sense and the character of the world. Right thinking depends as much on right willing as right willing on right thinking." (Max Horkheimer, "The Latest Attack on Metaphysics", 1937)

"Of course we have still to face the question why these analogies between different mechanisms - these similarities of relation-structure - should exist. To see common principles and simple rules running through such complexity is at first perplexing though intriguing. When, however, we find that the apparently complex objects around us are combinations of a few almost indestructible units, such as electrons, it becomes less perplexing." (Kenneth Craik, "The Nature of Explanation", 1943)

"We can put it down as one of the principles learned from the history of science that a theory is only overthrown by a better theory, never merely by contradictory facts." (James B Conant, "On Understanding Science", 1947)

"It is always more easy to discover and proclaim general principles than it is to apply them." (Winston Churchill, "The Second World War: The gathering storm", 1948)

"It is more important to have a clear understanding of general principles, without, however, thinking of them as fixed laws, than to load the mind with a mass of detailed technical information which can readily be found in reference books or card indexes." (William I B Beveridge, "The Art of Scientific Investigation", 1950)

"Thus, there exist models, principles, and laws that apply to generalized systems or their subclasses, irrespective of their particular kind, the nature of their component elements, and the relations or 'forces' between them. It seems legitimate to ask for a theory, not of systems of a more or less special kind, but of universal principles applying to systems in general. In this way we postulate a new discipline called General System Theory. Its subject matter is the formulation and derivation of those principles which are valid for ‘systems’ in general." (Ludwig von Bertalanffy, „General System Theory: Foundations, Development, Applications", 1968)

"No theory ever agrees with all the facts in its domain, yet it is not always the theory that is to blame. Facts are constituted by older ideologies, and a clash between facts and theories may be proof of progress. It is also a first step in our attempt to find the principles implicit in familiar observational notions." (Paul K Feyerabend, "Against Method: Outline of an Anarchistic Theory of Knowledge", 1975)

"The conception of the mental construction which is the fully analysed proof as being an infinite structure must, of course, be interpreted in the light of the intuitionist view that all infinity is potential infinity: the mental construction consists of a grasp of general principles according to which any finite segment of the proof could be explicitly constructed." (Michael Dummett, "The philosophical basis of intuitionistic logic", 1975)

"Real progress in understanding nature is rarely incremental. All important advances are sudden intuitions, new principles, new ways of seeing." (Marilyn Ferguson, "The Aquarian Conspiracy: Personal and Social Transformation in the 1980s", 1980)

"Principles are the territory. Values are maps. When we value correct principles, we have truth - a knowledge of things as they are." (Stephen R Covey, "The 7 Habits of Highly Effective People", 1989)

"The ‘objective reality’, or the territory itself, is composed of ‘lighthouse’ principles that govern human growth and happiness - natural laws that are woven into the fabric of every civilized society throughout history and comprise the roots of every family and institution that has endured and prospered. The degree to which our mental maps accurately describe the territory does not alter its existence." (Stephen Covey, "The 7 Habits of Highly Effective People", 1989)

"The word theory, as used in the natural sciences, doesn’t mean an idea tentatively held for purposes of argument - that we call a hypothesis. Rather, a theory is a set of logically consistent abstract principles that explain a body of concrete facts. It is the logical connections among the principles and the facts that characterize a theory as truth. No one element of a theory [...] can be changed without creating a logical contradiction that invalidates the entire system. Thus, although it may not be possible to substantiate directly a particular principle in the theory, the principle is validated by the consistency of the entire logical structure." (Alan Cromer, "Uncommon Sense: The Heretical Nature of Science", 1993)

"Within image theory, it is suggested that important components of decision-making processes are the different 'images' that a person may use to evaluate choice options. Images may represent a person's principles, goals, or plans. Decision options may then match or not match these images and be adopted, rejected, considered further, depending on circumstances." (Deborah J Terry & Michael A Hogg, "Attitudes, Behavior, and Social Context: The Role of Norms and Group Membership", 1999)

Knowledge Representation: On Strategy (Quotes)

noreply@blogger.com (Adrian) — Sun, 14 Nov 2021 22:56:00 GMT

"Strategy is a system of expedients. It is more than science, it is the translation of science into practical life, the development of an original leading thought in accordance with the ever-changing circumstances." (Helmuth von Moltke, "On Strategy", 1871)

"Strategy is concerned with the setting of an aim and the forming of schemes. Tactics are concerned with the execution of the schemes. Strategy is abstract, tactics are concrete. Expressing it in a popular way: Strategy requires thought, tactics require observation." (Dr. Max Euwe, "Strategy & Tactices in chess", 1937)

"A natural companion to the competitive advantage is the synergy component of strategy. This requires that opportunities within the scope possess characteristics which will enhance synergy." (Igor Ansoff, "Corporate Strategy", 1965)

"The act of making a decision consists of selecting one course of action, or strategy, from among the set of admissible strategies." (Richard A Epstein, "The Theory of Gambling and Statistical Logic", 1977)

"Risk is a function of how poorly a strategy will perform if the 'wrong' scenario occurs." (Michael Porter, "Competitive Advantage: Creating and Sustaining Superior Performance", 1985)

"It is possible to learn strategic flexibility [...] however, that it is difficult to teach it. It is not a matter of learning a few readily grasped general principles, but of learning a lot of small, 'local' rules, each of which is applicable in a limited area. The point is not to learn how to drive a steamroller with which one can flatten all problems in the same way, but to learn the adroitness of a puppeteer, who at one time holds many strings in his hands and who is able to adapt his movements to the given circumstances in the most sophisticated ways." (Dietrich Dörner, "The Logic of Failure", Philosophical Transactions of the Royal Society of London (B), 1990)

"Strategy means abstract thinking and planning, as opposed to tactics, which are the individual operations used to implement strategy. Tactics are specific; strategy is general. Tactics tend to be immediate, strategy long-term." (Bruce Pandolfini, "Weapons of Chess: An omnibus of chess strategy", 1989)

"A strategy is a set of hypotheses about cause and effect. The measurement system should make the relationships (hypotheses) among objectives (and measures) in the various perspectives explicit so that they can be managed and validated." (Robert S Kaplan & David P Norton, "The Balanced Scorecard", Harvard Business Review, 1996)

"[...] a general-purpose universal optimization strategy is theoretically impossible, and the only way one strategy can outperform another is if it is specialized to the specific problem under consideration." Yu-Chi Ho & David L Pepyne, "Simple explanation of the no-free-lunch theorem and its implications", Journal of Optimization Theory and Applications 115, 2002)

"Strategy is about stretching limited resources to fit ambitious aspirations." (Coimbatore K Prahalad, "Don Soderquist", 2005)

"Strategy-making is an immensely complex process involving the most sophisticated, subtle, and at times subconscious of human cognitive and social processes." (Henry Mintzberg, "Strategy Safari: A Guided Tour Through The Wilds of Strategic Mangement", 2005)

"Strategy is a constant reconciling of possibilities, means and ends." (Bernard Jenkin, 2010)

"A strategy coordinates action to address a specific challenge." (Richard Rumelt, "Good Strategy/Bad Strategy", 2011)

"Any strategy that involves crossing a valley - accepting short-term losses to reach a higher hill in the distance - will soon be brought to a halt by the demands of a system that celebrates short-term gains and tolerates stagnation, but condemns anything else as failure. In short, a world where big stuff can never get done." (Neal Stephenson, "Innovation Starvation," World Policy Journal, 2011)

"First develop a strategy that utilizes everything around you. The best way to prepare for a challenge is to cultivate the ability to call on an infinite variety of responses." (Paulo Coelho, "Aleph", 2011)

"The central issue is never strategy, structure, culture, or systems. The core of the matter is always about changing the behavior of people." (John Kotter, "The Heart of Change: Real-Life Stories of How People Change Their Organizations", 2012)

"By and large, strategy comes into play when there is actual or potential conflict, when interests collide and forms of resolution are required." (Lawrence Freedman, “Strategy: A history”, 2013)

"It is hard to avoid the conclusion that while strategy is undoubtedly a good thing to have, it is a hard thing to get right. […] So what turns something that is not quite strategy into strategy is a sense of actual or imminent instability, a changing context that induces a sense of conflict. Strategy therefore starts with an existing state of affairs and only gains meaning by an awareness of how, for better or worse, it could be different." (Lawrence Freedman, “Strategy: A history”, 2013)

"A strategy that doesn't take into account resources is doomed to failure." (John C Maxwell, "JumpStart Your Thinking: A 90-Day Improvement Plan", 2015)

"Somebody once told me, 'Manage the top line, and the bottom line will follow.' What's the top line? It's things like, why are we doing this in the first place? What's our strategy? What are customers saying? How responsive are we? Do we have the best products and the best people? Those are the kind of questions you have to focus on." (Steve Jobs, "Motivating Thoughts of Steve Jobs", 2016)

"Strategy is a style of thinking, a conscious and deliberate process, an intensive implementation system, the science of insuring future success." (Pete Johnson)

Knowledge Representation: On Intelligence (Quotes)

noreply@blogger.com (Adrian) — Mon, 01 Nov 2021 10:39:00 GMT

"When we have intelligence resulting from sincerity, this condition is to be ascribed to nature; when we have sincerity resulting from intelligence, this condition is to be ascribed to instruction. But given the sincerity, and there shall be the intelligence; given the intelligence, and there shall be the sincerity." (Confucius, "The Doctrine of the Mean", cca. 5th century)

"That which is apprehended by intelligence and reason is always in the same state; but that which is conceived by opinion with the help of sensation and without reason, is always in a process of becoming and perishing and never really is." (Plato, "Timaeus", cca. 360 BC)

"For verily not by design did the first-beginnings of things station themselves each in its right place guided by keen intelligence, nor did they bargain sooth to say what motions each should assume, but because many in number and shifting about in many ways throughout the universe they are driven and tormented by blows during infinite time past, after trying motions and unions of every kind at length they fall into arrangements such as those out of which our sum of things has been formed […]" (Lucretius, "On the Nature of Things", 1st century BC)

"Either all things proceed from one intelligent source and come together as in one body, and the part ought not to find fault with what is done for the benefit of the whole; or there are only atoms, and nothing else than a mixture and dispersion. Why, then, art thou disturbed? Say to this ruling faculty, Art thou dead, art thou corrupted, art thou playing the hypocrite, art thou become a beast, dost thou herd and feed with the rest?" (Marcus Aurelius, "Meditations". cca. 121–180 AD)

"We see that things which lack intelligence, such as natural bodies, act for an end, and this is evident from their acting always, or nearly always, in the same way, so as to obtain the best result. Hence it is plain that not fortuitously, but designedly, do they achieve their end. Now whatever lacks intelligence cannot move towards an end, unless it be directed by some being endowed with knowledge and intelligence; as the arrow is shot to its mark by the archer. Therefore some intelligent being exists by whom all natural things are directed to their end; and this being we call God." (St. Thomas Aquinas, "Summa Theologia", cca. 1266-1273)

"Geometry enlightens the intellect and sets one's mind right. All of its proofs are very clear and orderly. It is hardly possible for errors to enter into geometrical reasoning, because it is well arranged and orderly. Thus, the mind that constantly applies itself to geometry is not likely to fall into error. In this convenient way, the person who knows geometry acquires intelligence." (Ibn Khaldun, cca. 14th century)

"There are three kinds of intelligence: one kind understands things for itself, the other appreciates what others can understand, the third understands neither for itself nor through others. This first kind is excellent, the second good, and the third kind useless." (Niccolò Machiavelli, "The Prince", 1532)

"Intelligence is to genius as the whole is in proportion to its part." (Jean de La Bruyère, "The Characters", 1687)

"Look round the world: contemplate the whole and every part of it: You will find it to be nothing but one great machine, subdivided into an infinite number of lesser machines, which again admit of subdivisions, to a degree beyond what human senses and faculties can trace and explain. All these various machines, and even their most minute parts, are adjusted to each other with an accuracy, which ravishes into admiration all men, who have ever contemplated them. The curious adapting of means to ends, throughout all nature, resembles exactly, though it much exceeds, the productions of human contrivance; of human design, thought, wisdom, and intelligence." (David Hume, "Dialogues Concerning Natural Religion Dialogues Concerning Natural Religion", 1779)

"There cannot be design without a designer; contrivance without a contriver; order without choice; arrangement, without any thing capable of arranging; subserviency and relation to a purpose, without that which could intend a purpose; means suitable to an end, without the end ever having been contemplated, or the means accommodated to it. Arrangement, disposition of parts, subserviency of means to an end, relation of instruments to an use, imply the presence of intelligence and mind." (William Paley, "Natural Theology or Evidences of the Existence and Attributes of the Deity", 1802)

"Metaphysical inquiry attempts to trace things to the very first stage in which they can, even to the most penetrating intelligences, be the subjects of a thought, a doubt, or a proposition; that profoundest abstraction, where they stand on the first step of distinction from nonentity, and where that one question might be put concerning them, the answer to which would leave no further question possible. And having thus abstracted and penetrated to the state of pure entity, the speculation would come back, tracing it into all its modes and relations; till at last metaphysical truth, approaching nearer and nearer to the sphere of our immediate knowledge, terminates on the confines of distinct sciences and obvious realities. Now, it would seem evident that this inquiry into primary truth must surpass, in point of dignity, all other speculations. If any man could carry his discoveries as far, and make his proofs as strong, in the metaphysical world, as Newton did in the physical, he would be an incomparably greater man than even Newton." (John Foster, "Essays", cca. 1805)

"We ought then to consider the present state of the universe as the effect of its previous state and as the cause of that which is to follow. An intelligence that, at a given instant, could comprehend all the forces by which nature is animated and the respective situation of the beings that make it up, if moreover it were vast enough to submit these data to analysis, would encompass in the same formula the movements of the greatest bodies of the universe and those of the lightest atoms. For such an intelligence nothing would be uncertain, and the future, like the past, would be open to its eyes." (Pierre-Simon de Laplace, "Essai philosophique sur les probabilités", 1814)

"To be able to discern that what is true is true, and that what is false is false, - this is the mark and character of intelligence." (Ralph W Emerson, "Essays", 1841)

"We study the complex in the simple; and only from the intuition of the lower can we safely proceed to the intellection of the higher degrees. The only danger lies in the leaping from low to high, with the neglect of the intervening gradations." (Samuel T Coleridge, "Physiology of Life", 1848)

"Nature shows that with the growth of intelligence comes increased capacity for pain, and it is only with the highest degree of intelligence that suffering reaches its supreme point." (Arthur Schopenhauer, "The Wisdom of Life", 1851)

"As long as men inquire, they will find opportunities to know more upon these topics than those who have gone before them, so inexhaustibly rich is nature in the innermost diversity of her treasures of beauty, order, and intelligence." (J Louis R Agassiz, "Essay on Classification", 1859)

"Are our systems the inventions of naturalists, or only their reading of the Book of Nature? and can that book have more than one reading? If these classifications are not mere inventions, if they are not an attempt to classify for our own convenience the objects we study, then they are thoughts which, whether we detect them or not, are expressed in Nature, - then Nature is the work of thought, the production of intelligence carried out according to plan, therefore premeditated, - and in our study of natural objects we are approaching the thoughts of the Creator, reading His conceptions, interpreting a system that is His and not ours." (Jean L R Agassiz, "Methods of Study in Natural History", 1863)

"It is through a conviction of the inadequacy of all formulas to cover the facts of nature, it is by a constant recollection of the fallibility of the best instructed intelligence, and by an unintermittent skepticism which goes out of its way to look for difficulties, that scientific progress has been made possible." (James A Froude, "Short Studies on Great Subjects" Vol. 2, 1867)

"The accidental causes of science are only 'accidents' relatively to the intelligence of a man." (Chauncey Wright, "The Genesis of Species", North American Review, 1871)

"The object of pure Physic is the unfolding of the laws of the intelligible world; the object of pure Mathematic that of the unfolding the laws of human intelligence." (James J Sylvester, "On a Theorem Connected with Newton’s Rule", cca. 1870-1883)

"[…] the process of evolution on this earth, so far as we can judge, has been carried out neither with intelligence nor truth, but entirely through the routine of various sequences, commonly called 'laws', established or necessitated we know not how." (Sir Francis Galton, "Inquiries into Human Faculty and Its Development", 1883)

"It is a common and necessary feature of human intelligence that we can neither conceive of things nor define them conceptually without adding attributes to them that simply do not exist. This applies not only to every thought and imagination of ordinary life, even the sciences do not proceed otherwise. Only philosophy seeks and finds the difference between things that exist and things that we perceive, and also sees the necessity of this difference. […] What we add are therefore not incorrect conceptions but the conditions for such conceptions in general. We cannot simply remove them and replace them with better ones; either we must add them, or we must abstain from all conceptions of this kind." (Heinrich Hertz, "Die Prinzipien der Mechanik in neuem Zusammenhange dargestellt", 1894)

"It is a law of nature we overlook, that intellectual versatility is the compensation for change, danger, and trouble. An animal perfectly in harmony with its environment is a perfect mechanism. Nature never appeals to intelligence until habit and instinct are useless. There is no intelligence where there is no change and no need of change. Only those animals partake of intelligence that have a huge variety of needs and dangers." (Herbert G Wells, "The Time Machine", 1895)

"Does the harmony the human intelligence thinks it discovers in nature exist outside of this intelligence? No, beyond doubt, a reality completely independent of the mind which conceives it, sees or feels it, is an impossibility." (Henri Poincaré, "The Value of Science", 1905)

"No one can predict how far we shall be enabled by means of our limited intelligence to penetrate into the mysteries of a universe immeasurably vast and wonderful; nevertheless, each step in advance is certain to bring new blessings to humanity and new inspiration to greater endeavor." (Theodore W Richards, "The Fundamental Properties of the Elements", [Faraday lecture] 1911)

"It [science] involves an intelligent and persistent endeavor to revise current beliefs so as to weed out what is erroneous, to add to their accuracy, and, above all, to give them such shape that the dependencies of the various facts upon one another may be as obvious as possible." (John Dewey, "Democracy and Education", 1916)

"It may be impossible for human intelligence to comprehend absolute truth, but it is possible to observe Nature with an unbiased mind and to bear truthful testimony of things seen." (Sir Richard A Gregory, "Discovery, Or, The Spirit and Service of Science", 1916)

"Reason is experimental intelligence, conceived after the pattern of science, and used in the creation of social arts; it has something to do. It liberates man from the bondage of the past, due to ignorance and accident hardened into custom. It projects a better future and assists man in its realization. And its operation is always subject to test in experience. […] The principles which man projects as guides […] are not dogmas. They are hypotheses to be worked out in practice, and to be rejected, corrected and expanded as they fail or succeed in giving our present experience the guidance it requires. We may call them programmes of action, but since they are to be used in making our future acts less blind, more directed, they are flexible. Intelligence is not something possessed once for all. It is in constant process of forming, and its retention requires constant alertness in observing consequences, an open-minded will to learn and courage in re-adjustment." (John Dewey, "Reconstruction in Philosophy", 1920)

"For 'Natural Selection' has no moral significance: it deals with that part of evolution which has no purpose, no intelligence, and might more appropriately be called accidental selection, or better still, Unnatural Selection, since nothing is more unnatural than an accident. If it could be proved that the whole universe had been produced by such Selection, only fools and rascals could bear to live." (George B Shaw, "Back to Methuselah", 1921)

"Collective wisdom, alas, is no adequate substitute for the intelligence of individuals. Individuals who opposed received opinions have been the source of all progress, both moral and intellectual. They have been unpopular, as was natural." (Bertrand Russell, "Why I Am Not a Christian", 1927)

"In our recognition that order is universal, a fact confirmed by myriads of observations of patient, indefatigable, and devoted investigators, the old saying that 'an irreverent astronomer is mad' can apply with equal force to the physicist. Man learns something of his own minute and colossal stature, and he comes to feel that his own intelligence, which enables him to make such sublime discoveries, is the supreme achievement of evolution." (Harvey B Lemon, "Atomic Structure", 1927)

"Science is either an important statement of systematic theory correlating observations of a common world or is the daydream of a solitary intelligence with a taste for the daydream of publication." (Alfred N Whitehead, "Process and Reality: An Essay in Cosmology", 1929)

"The collective intelligence of any group of people who are thinking as a 'herd' rather than individually is no higher than the intelligence of the stupidest members. (Mary Day Winn, "Adam's Rib", 1931)

"It is enough for me to contemplate the mystery of conscious life perpetuating itself through all eternity, to reflect upon the marvelous structure of the universe which we dimly perceive, and to try humbly to comprehend an infinitesimal part of the intelligence manifested in nature." (Albert Einstein, "Mein Weltbild" ["My Worldview"] (1931)

"It takes a certain amount of intelligence and imagination to realize the extraordinary queerness and mysteriousness of the world in which we live." (Aldous Huxley, "Rotunda: A selection from the works of Aldous Huxley", 1932)

"In the study of ideas, it is necessary to remember that insistence on hard-headed clarity issues from sentimental feeling, as it were a mist, cloaking the perplexities of fact. Insistence on clarity at all costs is based on sheer superstition as to the mode in which human intelligence functions. Our reasonings grasp at straws for premises and float on gossamers for deductions." (Alfred N Whitehead, "Adventures of Ideas", 1933)

"[…] physics tries to discover the pattern of events which controls the phenomena we observe. But we can never know what this pattern means or how it originates; and even if some superior intelligence were to tell us, we should find the explanation unintelligible. Our studies can never put us into contact with reality, and its true meaning and nature must be for ever hidden from us." (James H Jeans, "Physics and Philosophy" 3rd Ed., 1943)

"In other words then, if a machine is expected to be infallible, it cannot also be intelligent. There are several theorems which say almost exactly that. But these theorems say nothing about how much intelligence may be displayed if a machine makes no pretense at infallibility." (Alan M Turing, 1946)

"Since the real world, in the absolute sense of the word, is independent of individual personalities, and in fact of all human intelligence, every discovery made by an individual acquires a completely universal significance. This gives the inquirer, wrestling with his problem in quiet seclusion, the assurance that every discovery will win the unhesitating recognition of all experts throughout the entire world, and in this feeling of the importance of his work lies his happiness. It compensates him fully for many a sacrifice which he must make in his daily life." (Max Planck, "The Meaning and Limits of Exact Science", 1949)

"A computer would deserve to be called intelligent if it could deceive a human into believing that it was human." (Alan Turing, "Computing Machinery and Intelligence" , Mind Vol. 59, 1950)

"All intelligent endeavor stands with one foot on observation and the other on contemplation." (Gerald Holton & Duane H D Roller, "Foundations of Modern Physical Science", 1950)

"Perhaps the most majestic feature of our whole existence is that while our intelligences are powerful enough to penetrate deeply into the evaluation of this quite incredible Universe, we still have not the smallest clue to our own fate." (Sir Fred Hoyle, "The Nature of the Universe", 1950)

"What in fact is the schema of the object? In one essential respect it is a schema belonging to intelligence. To have the concept of an object is to attribute the perceived figure to a substantial basis, so that the figure and the substance that it thus indicates continue to exist outside the perceptual field. The permanence of the object seen from this viewpoint is not only a product of intelligence, but constitutes the very first of those fundamental ideas of conservation which we shall see developing within the thought process." (Jean Piaget, "The Psychology of Intelligence", 1950)

"[…] observation is not enough, and it seems to me that in science, as in the arts, there is very little worth having that does not require the exercise of intuition as well as of intelligence, the use of imagination as well as of information." (Kathleen Lonsdale, "Facts About Crystals", American Scientist Vol. 39 (4), 1951)

"Concepts are for me specific mental abilities exercised in acts of judgment, and expressed in the intelligent use of words (though not exclusively in such use). There is no reason to ascribe concepts (in this sense) to brutes." (Peter T Geach, "Mental Acts: Their Content and their Objects", 1954)

"The following are some aspects of the artificial intelligence problem: […] If a machine can do a job, then an automatic calculator can be programmed to simulate the machine. […] It may be speculated that a large part of human thought consists of manipulating words according to rules of reasoning and rules of conjecture. From this point of view, forming a generalization consists of admitting a new word and some rules whereby sentences containing it imply and are implied by others. This idea has never been very precisely formulated nor have examples been worked out. […] How can a set of (hypothetical) neurons be arranged so as to form concepts. […] to get a measure of the efficiency of a calculation it is necessary to have on hand a method of measuring the complexity of calculating devices which in turn can be done. […] Probably a truly intelligent machine will carry out activities which may best be described as self-improvement. […] A number of types of 'abstraction' can be distinctly defined and several others less distinctly. […] the difference between creative thinking and unimaginative competent thinking lies in the injection of a some randomness. The randomness must be guided by intuition to be efficient." (John McCarthy et al, "A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence", 1955)

"Solving problems is the specific achievement of intelligence." (George Polya, 1957)

"We are terribly clever people, we moderns: we bend Nature to our will in countless ways. We move mountains, we make caves, fly at speeds no other organism can achieve and tap the power of the atom. We are terribly clever. The essentially religious feeling of subserviency to a power greater than ourselves comes hard to us clever people. But by our intelligence we are now beginning to make out the limits of our cleverness, the impotence principles that say what can and cannot be. In an operational sense, we are experiencing a return to a religious orientation toward the world." (Garrett Hardin, "Nature and Man’s Fate", 1959)

"Computers do not decrease the need for mathematical analysis, but rather greatly increase this need. They actually extend the use of analysis into the fields of computers and computation, the former area being almost unknown until recently, the latter never having been as intensively investigated as its importance warrants. Finally, it is up to the user of computational equipment to define his needs in terms of his problems, In any case, computers can never eliminate the need for problem-solving through human ingenuity and intelligence." (Richard E Bellman & Paul Brock, "On the Concepts of a Problem and Problem-Solving", American Mathematical Monthly 67, 1960)

"The modern mind is in complete disarray. Knowledge has stretched itself to the point where neither the world nor our intelligence can find any foot-hold. It is a fact that we are suffering from nihilism." (Albert Camus, "Carnets: 1942-1951", 1963)

"Engineering is the art of skillful approximation; the practice of gamesmanship in the highest form. In the end it is a method broad enough to tame the unknown, a means of combing disciplined judgment with intuition, courage with responsibility, and scientific competence within the practical aspects of time, of cost, and of talent. This is the exciting view of modern-day engineering that a vigorous profession can insist be the theme for education and training of its youth. It is an outlook that generates its strength and its grandeur not in the discovery of facts but in their application; not in receiving, but in giving. It is an outlook that requires many tools of science and the ability to manipulate them intelligently. In the end, it is a welding of theory and practice to build an early, strong, and useful result. Except as a valuable discipline of the mind, a formal education in technology is sterile until it is applied." (Ronald B Smith, "Professional Responsibility of Engineering", Mechanical Engineering Vol. 86 (1), 1964)

"The future offers very little hope for those who expect that our new mechanical slaves will offer us a world in which we may rest from thinking. Help us they may, but at the cost of supreme demands upon our honesty and intelligence. The world of the future will be an ever more demanding struggle against the limitations of our intelligence, not a comfortable hammock in which we can lay down to be waited upon by our robot slaves." (Norbert Wiener, "God and Golem, Inc.: A Comment on Certain Points Where Cybernetics Impinges on Religion", 1964)

"If science is to progress, what we need is the ability to experiment, honestly in reporting the results - the results must be reported without somebody saying what they would like the results to have been - and finally - an important thing - the intelligence to interpret the results. An important point about this intelligence is that it should not be sure ahead of time what must be. It cannot be prejudiced, and say 'That is very unlikely; I don’t like that.'" (Richard P Feynman, The Character of Physical Law, 1965)

"Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an 'intelligence explosion:, and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make." (Irving J Good, "Speculations Concerning the First Ultraintelligent Machine", Advances in Computers Vol. 6, 1965)

"When intelligent machines are constructed, we should not be surprised to find them as confused and as stubborn as men in their convictions about mind-matter, consciousness, free will, and the like." (Marvin Minsky, "Matter, Mind, and Models", Proceedings of the International Federation of Information Processing Congress Vol. 1 (49), 1965)

"Artificial intelligence is the science of making machines do things that would require intelligence if done by men." (Marvin Minsky, 1968)

"Intelligence has two parts, which we shall call the epistemological and the heuristic. The epistemological part is the representation of the world in such a form that the solution of problems follows from the facts expressed in the representation. The heuristic part is the mechanism that on the basis of the information solves the problem and decides what to do." (John McCarthy & Patrick J Hayes, "Some Philosophical Problems from the Standpoint of Artificial Intelligence", Machine Intelligence 4, 1969)

"Questions are the engines of intellect, the cerebral machines which convert energy to motion, and curiosity to controlled inquiry." (David H Fischer, "Historians’ Fallacies", 1970)

"There is no reason to assume that the universe has the slightest interest in intelligence - or even in life. Both may be random accidental by-products of its operations like the beautiful patterns on a butterfly's wings. The insect would fly just as well without them […]" (Arthur C Clarke, "The Lost Worlds of 2001", 1972)

"Any intelligent fool can make things bigger, more complex, and more violent. It takes a touch of genius - and a lot of courage to move in the opposite direction." (Ernst F Schumacher, "Small is Beautiful", 1973)

"Man is not a machine, [...] although man most certainly processes information, he does not necessarily process it in the way computers do. Computers and men are not species of the same genus. [...] No other organism, and certainly no computer, can be made to confront genuine human problems in human terms. [...] However much intelligence computers may attain, now or in the future, theirs must always be an intelligence alien to genuine human problems and concerns." (Joesph Weizenbaum, Computer Power and Human Reason: From Judgment to Calculation, 1976)

"Any living thing possesses an enormous amount of 'intelligence' [...] Today, this 'intelligence' is called 'information', but it is still the same thing. [...] This 'intelligence' is the sine qua non of life. If absent, no living being is imaginable. Where does it come from? This is a problem which concerns both biologists and philosophers, and, at present, science seems incapable of solving it." (Pierre P Grassé, "Evolution of Living Organisms: Evidence for a New Theory of Transformation", 1977)

"Play is the only way the highest intelligence of humankind can unfold." (Joseph C Pearce, "Magical Child: Rediscovering Nature's Plan for Our Children", 1977)

"Because of mathematical indeterminancy and the uncertainty principle, it may be a law of nature that no nervous system is capable of acquiring enough knowledge to significantly predict the future of any other intelligent system in detail. Nor can intelligent minds gain enough self-knowledge to know their own future, capture fate, and in this sense eliminate free will." (Edward O Wilson, "On Human Nature", 1978)

"Collective intelligence emerges when a group of people work together effectively. Collective intelligence can be additive (each adds his or her part which together form the whole) or it can be synergetic, where the whole is greater than the sum of its parts." (Trudy and Peter Johnson-Lenz, "Groupware: Orchestrating the Emergence of Collective Intelligence", cca. 1980)

"Knowing a great deal is not the same as being smart; intelligence is not information alone but also judgement, the manner in which information is coordinated and used." (Carl Sagan, "Cosmos", 1980)

"The basic idea of cognitive science is that intelligent beings are semantic engines - in other words, automatic formal systems with interpretations under which they consistently make sense. We can now see why this includes psychology and artificial intelligence on a more or less equal footing: people and intelligent computers (if and when there are any) turn out to be merely different manifestations of the same underlying phenomenon. Moreover, with universal hardware, any semantic engine can in principle be formally imitated by a computer if only the right program can be found." (John Haugeland, "Semantic Engines: An introduction to mind design", 1981)

"There is a tendency to mistake data for wisdom, just as there has always been a tendency to confuse logic with values, intelligence with insight. Unobstructed access to facts can produce unlimited good only if it is matched by the desire and ability to find out what they mean and where they lead." (Norman Cousins, "Human Options : An Autobiographical Notebook", 1981)

"Cybernetic information theory suggests the possibility of assuming that intelligence is a feature of any feedback system that manifests a capacity for learning." (Paul Hawken et al, "Seven Tomorrows", 1982)

"We lose all intelligence by averaging." (John Naisbitt, "Megatrends: Ten New Directions Transforming Our Lives", 1982)

"Artificial intelligence is based on the assumption that the mind can be described as some kind of formal system manipulating symbols that stand for things in the world. Thus it doesn't matter what the brain is made of, or what it uses for tokens in the great game of thinking. Using an equivalent set of tokens and rules, we can do thinking with a digital computer, just as we can play chess using cups, salt and pepper shakers, knives, forks, and spoons. Using the right software, one system (the mind) can be mapped onto the other (the computer)." (George Johnson, Machinery of the Mind: Inside the New Science of Artificial Intelligence, 1986)

"Cybernetics is simultaneously the most important science of the age and the least recognized and understood. It is neither robotics nor freezing dead people. It is not limited to computer applications and it has as much to say about human interactions as it does about machine intelligence. Today’s cybernetics is at the root of major revolutions in biology, artificial intelligence, neural modeling, psychology, education, and mathematics. At last there is a unifying framework that suspends long-held differences between science and art, and between external reality and internal belief." (Paul Pangaro, "New Order From Old: The Rise of Second-Order Cybernetics and Its Implications for Machine Intelligence", 1988)

"A popular myth says that the invention of the computer diminishes our sense of ourselves, because it shows that rational thought is not special to human beings, but can be carried on by a mere machine. It is a short stop from there to the conclusion that intelligence is mechanical, which many people find to be an affront to all that is most precious and singular about their humanness." (Jeremy Campbell, "The improbable machine", 1989)

"Fuzziness, then, is a concomitant of complexity. This implies that as the complexity of a task, or of a system for performing that task, exceeds a certain threshold, the system must necessarily become fuzzy in nature. Thus, with the rapid increase in the complexity of the information processing tasks which the computers are called upon to perform, we are reaching a point where computers will have to be designed for processing of information in fuzzy form. In fact, it is the capability to manipulate fuzzy concepts that distinguishes human intelligence from the machine intelligence of current generation computers. Without such capability we cannot build machines that can summarize written text, translate well from one natural language to another, or perform many other tasks that humans can do with ease because of their ability to manipulate fuzzy concepts." (Lotfi A Zadeh, "The Birth and Evolution of Fuzzy Logic", 1989)

"Modeling underlies our ability to think and imagine, to use signs and language, to communicate, to generalize from experience, to deal with the unexpected, and to make sense out of the raw bombardment of our sensations. It allows us to see patterns, to appreciate, predict, and manipulate processes and things, and to express meaning and purpose. In short, it is one of the most essential activities of the human mind. It is the foundation of what we call intelligent behavior and is a large part of what makes us human. We are, in a word, modelers: creatures that build and use models routinely, habitually – sometimes even compulsively – to face, understand, and interact with reality." (Jeff Rothenberg, "The Nature of Modeling. In: Artificial Intelligence, Simulation, and Modeling", 1989)

"We haven't worked on ways to develop a higher social intelligence […] We need this higher intelligence to operate socially or we're not going to survive. […] If we don't manage things socially, individual high intelligence is not going to make much difference. [...] Ordinary thought in society is incoherent - it is going in all sorts of directions, with thoughts conflicting and canceling each other out. But if people were to think together in a coherent way, it would have tremendous power." (David Bohm, "New Age Journal", 1989)

"Information exists. It does not need to be perceived to exist. It does not need to be understood to exist. It requires no intelligence to interpret it. It does not have to have meaning to exist. It exists." (Tom Stonier, "Information and the Internal Structure of the Universe: An Exploration into Information Physics", 1990)

"[Language comprehension] involves many components of intelligence: recognition of words, decoding them into meanings, segmenting word sequences into grammatical constituents, combining meanings into statements, inferring connections among statements, holding in short-term memory earlier concepts while processing later discourse, inferring the writer’s or speaker’s intentions, schematization of the gist of a passage, and memory retrieval in answering questions about the passage. [… The reader] constructs a mental representation of the situation and actions being described. […] Readers tend to remember the mental model they constructed from a text, rather than the text itself." (Gordon H Bower & Daniel G Morrow, 1990)

"Civilization is to groups what intelligence is to individuals. It is a means of combining the intelligence of many to achieve ongoing group adaptation. […] Civilization, like intelligence, may serve well, serve adequately, or fail to serve its adaptive function. When civilization fails to serve, it must disintegrate unless it is acted upon by unifying internal or external forces." (Octavia E Butler, "Parable of the Sower", 1993)

"The insight at the root of artificial intelligence was that these 'bits' (manipulated by computers) could just as well stand as symbols for concepts that the machine would combine by the strict rules of logic or the looser associations of psychology." (Daniel Crevier, "AI: The tumultuous history of the search for artificial intelligence", 1993)

"The leading edge of growth of intelligence is at the cultural and societal level. It is like a mind that is struggling to wake up. This is necessary because the most difficult problems we face are now collective ones. They are caused by complex global interactions and are beyond the scope of individuals to understand and solve. Individual mind, with its isolated viewpoints and narrow interests, is no longer enough." (Jeff Wright, "Basic Beliefs", [email] 1995)

"Adaptation is the process of changing a system during its operation in a dynamically changing environment. Learning and interaction are elements of this process. Without adaptation there is no intelligence." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Artificial intelligence comprises methods, tools, and systems for solving problems that normally require the intelligence of humans. The term intelligence is always defined as the ability to learn effectively, to react adaptively, to make proper decisions, to communicate in language or images in a sophisticated way, and to understand." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Learning is the process of obtaining new knowledge. It results in a better reaction to the same inputs at the next session of operation. It means improvement. It is a step toward adaptation. Learning is a major characteristic of intelligent systems." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Intelligence is: (a) the most complex phenomenon in the Universe; or (b) a profoundly simple process. The answer, of course, is (c) both of the above. It's another one of those great dualities that make life interesting." (Ray Kurzweil, "The Age of Spiritual Machines: When Computers Exceed Human Intelligence", 1999)

"It [collective intelligence] is a form of universally distributed intelligence, constantly enhanced, coordinated in real time, and resulting in the effective mobilization of skills. I'll add the following indispensable characteristic to this definition: The basis and goal of collective intelligence is mutual recognition and enrichment of individuals rather than the cult of fetishized or hypostatized communities." (Pierre Levy, "Collective Intelligence", 1999)

"It is, however, fair to say that very few applications of swarm intelligence have been developed. One of the main reasons for this relative lack of success resides in the fact that swarm-intelligent systems are hard to 'program', because the paths to problem solving are not predefined but emergent in these systems and result from interactions among individuals and between individuals and their environment as much as from the behaviors of the individuals themselves. Therefore, using a swarm-intelligent system to solve a problem requires a thorough knowledge not only of what individual behaviors must be implemented but also of what interactions are needed to produce such or such global behavior." (Eric Bonabeau et al, "Swarm Intelligence: From Natural to Artificial Systems", 1999)

"Once a computer achieves human intelligence it will necessarily roar past it." (Ray Kurzweil, "The Age of Spiritual Machines: When Computers Exceed Human Intelligence", 1999)

"[…] when software systems become so intractable that they can no longer be controlled, swarm intelligence offers an alternative way of designing an ‘intelligent’ systems, in which autonomy, emergence, and distributed functioning replace control, preprogramming, and centralization." (Eric Bonabeau et al, "Swarm Intelligence: From Natural to Artificial Systems", 1999)

"With the growing interest in complex adaptive systems, artificial life, swarms and simulated societies, the concept of 'collective intelligence' is coming more and more to the fore. The basic idea is that a group of individuals (e. g. people, insects, robots, or software agents) can be smart in a way that none of its members is. Complex, apparently intelligent behavior may emerge from the synergy created by simple interactions between individuals that follow simple rules." (Francis Heylighen, "Collective Intelligence and its Implementation on the Web", 1999)

"Ecological rationality uses reason – rational reconstruction – to examine the behavior of individuals based on their experience and folk knowledge, who are ‘naïve’ in their ability to apply constructivist tools to the decisions they make; to understand the emergent order in human cultures; to discover the possible intelligence embodied in the rules, norms and institutions of our cultural and biological heritage that are created from human interactions but not by deliberate human design. People follow rules without being able to articulate them, but they can be discovered." (Vernon L Smith, "Constructivist and ecological rationality in economics", 2002)

"But intelligence is not just a matter of acting or behaving intelligently. Behavior is a manifestation of intelligence, but not the central characteristic or primary definition of being intelligent. A moment's reflection proves this: You can be intelligent just lying in the dark, thinking and understanding. Ignoring what goes on in your head and focusing instead on behavior has been a large impediment to understanding intelligence and building intelligent machines." (Jeff Hawkins, "On Intelligence", 2004)

"Evolution moves towards greater complexity, greater elegance, greater knowledge, greater intelligence, greater beauty, greater creativity, and greater levels of subtle attributes such as love. […] Of course, even the accelerating growth of evolution never achieves an infinite level, but as it explodes exponentially it certainly moves rapidly in that direction." (Ray Kurzweil, "The Singularity is Near", 2005)

"Swarm Intelligence can be defined more precisely as: Any attempt to design algorithms or distributed problem-solving methods inspired by the collective behavior of the social insect colonies or other animal societies. The main properties of such systems are flexibility, robustness, decentralization and self-organization." ("Swarm Intelligence in Data Mining", Ed. Ajith Abraham et al, 2006))

"Swarm intelligence is sometimes also referred to as mob intelligence. Swarm intelligence uses large groups of agents to solve complicated problems. Swarm intelligence uses a combination of accumulation, teamwork, and voting to produce solutions. Accumulation occurs when agents contribute parts of a solution to a group. Teamwork occurs when different agents or subgroups of agents accidentally or purposefully work on different parts of a large problem. Voting occurs when agents propose solutions or components of solutions and the other agents vote explicitly by rating the proposal’s quality or vote implicitly by choosing whether to follow the proposal." (Michael J North & Charles M Macal, "Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation", 2007)

"The brain and its cognitive mental processes are the biological foundation for creating metaphors about the world and oneself. Artificial intelligence, human beings’ attempt to transcend their biology, tries to enter into these scenarios to learn how they function. But there is another metaphor of the world that has its own particular landscapes, inhabitants, and laws. The brain provides the organic structure that is necessary for generating the mind, which in turn is considered a process that results from brain activity." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"Cultures are never merely intellectual constructs. They take form through the collective intelligence and memory, through a commonly held psychology and emotions, through spiritual and artistic communion." (Tariq Ramadan, "Islam and the Arab Awakening", 2012)

"An intuition is neither caprice nor a sixth sense but a form of unconscious intelligence." (Gerd Gigerenzer, "Risk Savvy", 2015)

"Artificial intelligence is the elucidation of the human learning process, the quantification of the human thinking process, the explication of human behavior, and the understanding of what makes intelligence possible." (Kai-Fu Lee, "AI Superpowers: China, Silicon Valley, and the New World Order", 2018)

"Deep learning has instead given us machines with truly impressive abilities but no intelligence. The difference is profound and lies in the absence of a model of reality." (Judea Pearl, "The Book of Why: The New Science of Cause and Effect", 2018)

"AI won‘t be fool proof in the future since it will only as good as the data and information that we give it to learn. It could be the case that simple elementary tricks could fool the AI algorithm and it may serve a complete waste of output as a result." (Zoltan Andrejkovics, "Together: AI and Human. On the Same Side", 2019)

Knowledge Representation: On Artificial Intelligence (Quotes)

noreply@blogger.com (Adrian) — Mon, 25 Oct 2021 06:28:00 GMT

"There is no security against the ultimate development of mechanical consciousness, in the fact of machines possessing little consciousness now. A mollusc has not much consciousness. Reflect upon the extraordinary advance which machines have made during the last few hundred years, and note how slowly the animal and vegetable kingdoms are advancing. The more highly organized machines are creatures not so much of yesterday, as of the last five minutes, so to speak, in comparison with past time." (Samuel Butler, "Erewhon: Or, Over the Range", 1872)

"A computer would deserve to be called intelligent if it could deceive a human into believing that it was human." (Alan Turing, "Computing Machinery and Intelligence", 1950)

"The original question, 'Can machines think?:, I believe too meaningless to deserve discussion. Nevertheless I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted." (Alan M Turing, 1950)

"The view that machines cannot give rise to surprises is due, I believe, to a fallacy to which philosophers and mathematicians are particularly subject. This is the assumption that as soon as a fact is presented to a mind all consequences of that fact spring into the mind simultaneously with it. It is a very useful assumption under many circumstances, but one too easily forgets that it is false. A natural consequence of doing so is that one then assumes that there is no virtue in the mere working out of consequences from data and general principles." (Alan Turing, "Computing Machinery and Intelligence", Mind Vol. 59, 1950)

"We shall therefore say that a program has common sense if it automatically deduces for itself a sufficient wide class of immediate consequences of anything it is told and what it already knows. [...] Our ultimate objective is to make programs that learn from their experience as effectively as humans do." (John McCarthy, "Programs with Common Sense", 1958)

"Although it sounds implausible, it might turn out that above a certain level of complexity, a machine ceased to be predictable, even in principle, and started doing things on its own account, or, to use a very revealing phrase, it might begin to have a mind of its own." (John R Lucas, "Minds, Machines and Gödel", 1959)

"When intelligent machines are constructed, we should not be surprised to find them as confused and as stubborn as men in their convictions about mind-matter, consciousness, free will, and the like." (Marvin Minsky, "Matter, Mind, and Models", Proceedings of the International Federation of Information Processing Congress Vol. 1 (49), 1965)

"Artificial intelligence is the science of making machines do things that would require intelligence if done by men." (Marvin Minsky, 1968)

"There are now machines in the world that think, that learn and create. Moreover, their ability to do these things is going to increase rapidly until - in the visible future - the range of problems they can handle will be coextensive with the range to which the human mind has been applied." (Allen Newell & Herbert A Simon, "Human problem solving", 1976)

"It is essential to realize that a computer is not a mere 'number cruncher', or supercalculating arithmetic machine, although this is how computers are commonly regarded by people having no familiarity with artificial intelligence. Computers do not crunch numbers; they manipulate symbols. [...] Digital computers originally developed with mathematical problems in mind, are in fact general purpose symbol manipulating machines." (Margaret A Boden, "Minds and mechanisms", 1981)

"The digital-computer field defined computers as machines that manipulated numbers. The great thing was, adherents said, that everything could be encoded into numbers, even instructions. In contrast, scientists in AI [artificial intelligence] saw computers as machines that manipulated symbols. The great thing was, they said, that everything could be encoded into symbols, even numbers." (Allen Newell, "Intellectual Issues in the History of Artificial Intelligence", 1983)

"The cybernetics phase of cognitive science produced an amazing array of concrete results, in addition to its long-term (often underground) influence: the use of mathematical logic to understand the operation of the nervous system; the invention of information processing machines (as digital computers), thus laying the basis for artificial intelligence; the establishment of the metadiscipline of system theory, which has had an imprint in many branches of science, such as engineering (systems analysis, control theory), biology (regulatory physiology, ecology), social sciences (family therapy, structural anthropology, management, urban studies), and economics (game theory); information theory as a statistical theory of signal and communication channels; the first examples of self-organizing systems. This list is impressive: we tend to consider many of these notions and tools an integrative part of our life […]" (Francisco Varela, "The Embodied Mind", 1991)

"The deep paradox uncovered by AI research: the only way to deal efficiently with very complex problems is to move away from pure logic. [...] Most of the time, reaching the right decision requires little reasoning.[...] Expert systems are, thus, not about reasoning: they are about knowing. [...] Reasoning takes time, so we try to do it as seldom as possible. Instead we store the results of our reasoning for later reference." (Daniel Crevier, "The Tree of Knowledge", 1993)

"From a historical viewpoint, computationalism is a sophisticated version of behaviorism, for it only interpolates the computer program between stimulus and response, and does not regard novel programs as brain creations. [...] The root of computationalism is of course the actual similarity between brains and computers, and correspondingly between natural and artificial intelligence. The two are indeed similar because the artifacts in question have been designed to perform analogs of certain brain functions. And the computationalist program is an example of the strategy of treating similars as identicals." (Mario Bunge, "Matter and Mind: A Philosophical Inquiry", 2010)

"Artificial intelligence is a concept that obscures accountability. Our problem is not machines acting like humans - it's humans acting like machines." (John Twelve Hawks, "Spark", 2014)

"AI failed (at least relative to the hype it had generated), and it’s partly out of embarrassment on behalf of their discipline that the term 'artificial intelligence' is rarely used in computer science circles (although it’s coming back into favor, just without the over-hyping). We are as far away from mimicking human intelligence as we have ever been, partly because the human brain is fantastically more complicated than a mere logic engine." (Field Cady, "The Data Science Handbook", 2017)

"AI ever allows us to truly understand ourselves, it will not be because these algorithms captured the mechanical essence of the human mind. It will be because they liberated us to forget about optimizations and to instead focus on what truly makes us human: loving and being loved." (Kai-Fu Lee, "AI Superpowers: China, Silicon Valley, and the New World Order", 2018)

"Artificial intelligence is defined as the branch of science and technology that is concerned with the study of software and hardware to provide machines the ability to learn insights from data and the environment, and the ability to adapt in changing situations with high precision, accuracy and speed." (Amit Ray, "Compassionate Artificial Intelligence", 2018)

"Artificial Intelligence is not just learning patterns from data, but understanding human emotions and its evolution from its depth and not just fulfilling the surface level human requirements, but sensitivity towards human pain, happiness, mistakes, sufferings and well-being of the society are the parts of the evolving new AI systems." (Amit Ray, "Compassionate Artificial Intelligence", 2018)

"It is the field of artificial intelligence in which the population is in the form of agents which search in a parallel fashion with multiple initialization points. The swarm intelligence-based algorithms mimic the physical and natural processes for mathematical modeling of the optimization algorithm. They have the properties of information interchange and non-centralized control structure." (Sajad A Rather & P Shanthi Bala, "Analysis of Gravitation-Based Optimization Algorithms for Clustering and Classification", 2020)

"A significant factor missing from any form of artificial intelligence is the inability of machines to learn based on real life experience. Diversity of life experience is the single most powerful characteristic of being human and enhances how we think, how we learn, our ideas and our ability to innovate. Machines exist in a homogeneous ecosystem, which is ok for solving known challenges, however even Artificial General Intelligence will never challenge humanity in being able to acquire the knowledge, creativity and foresight needed to meet the challenges of the unknown." (Tom Golway, 2021)

"Every machine has artificial intelligence. And the more advanced a machine gets, the more advanced artificial intelligence gets as well. But, a machine cannot feel what it is doing. It only follows instructions - our instructions - instructions of the humans. So, artificial intelligence will not destroy the world. Our irresponsibility will destroy the world." (Abhijit Naskar)

Science: Principles (Quotes)

noreply@blogger.com (Adrian) — Sun, 17 Oct 2021 11:28:00 GMT

"It is superfluous to suppose that what can be accounted for by a few principles has been produced by many." (Thomas Aquinas, "Summa Theologica", cca. 1266-1273)

"In the original discovery of a proposition of practical utility, by deduction from general principles and from experimental data, a complex algebraical investigation is often not merely useful, but indispensable; but in expounding such a proposition as a part of practical science, and applying it to practical purposes, simplicity is of the importance: - and […] the more thoroughly a scientific man has studied higher mathematics, the more fully does he become aware of this truth – and […] the better qualified does he become to free the exposition and application of principles from mathematical intricacy." (William J M Rankine, "On the Harmony of Theory and Practice in Mechanics", 1856)

"The more man inquires into the laws which regulate the material universe, the more he is convinced that all its varied forms arise from the action of a few simple principles." (Charles Babbage, "Passages From the Life of a Philosopher", 1864)

"A modern mathematical proof is not very different from a modern machine, or a modern test setup: the simple fundamental principles are hidden and almost invisible under a mass of technical details." (Hermann Weyl, "Unterrichtsblätter für Mathematik und Naturwissenschaften", 1932)

"It is always more easy to discover and proclaim general principles than it is to apply them." (Winston Churchill, "The Second World War: The gathering storm", 1948)

"The method of guessing the equation seems to be a pretty effective way of guessing new laws. This shows again that mathematics is a deep way of expressing nature, and any attempt to express nature in philosophical principles, or in seat-of-the-pants mechanical feelings, is not an efficient way." (Richard Feynman, "The Character of Physical Law", 1965)

"Engineering is the application of scientific principles toward practical ends. If the engineering isn't practical, it's bad engineering." (Steve McConnell, "After the Gold Rush: Creating a True Profession of Software Engineering", 1999)

"A small error in the beginning (or in principles) leads to a big error in the end (or in conclusions)." (ancient axiom)

Cybernetics: On Computers (Quotes)

noreply@blogger.com (Adrian) — Mon, 20 Sep 2021 20:10:00 GMT

"Let it be remarked [...] that an important difference between the way in which we use the brain and the machine is that the machine is intended for many successive runs, either with no reference to each other, or with a minimal, limited reference, and that it can be cleared between such runs; while the brain, in the course of nature, never even approximately clears out its past records. Thus the brain, under normal circumstances, is not the complete analogue of the computing machine but rather the analogue of a single run on such a machine." (Norbert Wiener, "Cybernetics: Or Control and Communication in the Animal and the Machine", 1948)

"A computer would deserve to be called intelligent if it could deceive a human into believing that it was human." (Alan Turing, "Computing Machinery and Intelligence" , Mind Vol. 59, 1950)

"A computer is a person or machine that is able to take in information (problems and data), perform reasonable operations on the iformation, and put out answers. A computer is identified by the fact that it (or he) handles information reasonably." (Edmund C Berkeley & Lawrence Wainwright, Computers: Their Operation and Applications", 1956)

"There are two types of systems engineering - basis and applied. [...] Systems engineering is, obviously, the engineering of a system. It usually, but not always, includes dynamic analysis, mathematical models, simulation, linear programming, data logging, computing, optimating, etc., etc. It connotes an optimum method, realized by modern engineering techniques. Basic systems engineering includes not only the control system but also all equipment within the system, including all host equipment for the control system. Applications engineering is - and always has been - all the engineering required to apply the hardware of a hardware manufacturer to the needs of the customer. Such applications engineering may include, and always has included where needed, dynamic analysis, mathematical models, simulation, linear programming, data logging, computing, and any technique needed to meet the end purpose - the fitting of an existing line of production hardware to a customer's needs. This is applied systems engineering." (Instruments and Control Systems Vol. 31, 1958)

"An information retrieval system is therefore defined here as any device which aids access to documents specified by subject, and the operations associated with it. The documents can be books, journals, reports, atlases, or other records of thought, or any parts of such records - articles, chapters, sections, tables, diagrams, or even particular words. The retrieval devices can range from a bare list of contents to a large digital computer and its accessories. The operations can range from simple visual scanning to the most detailed programming." (Brian C Vickery, "The Structure of Information Retrieval Systems", 1959)

"There is the very real danger that a number of problems which could profitably be subjected to analysis, and so treated by simpler and more revealing techniques. will instead be routinely shunted to the computing machines [...] The role of computing machines as a mathematical tool is not that of a panacea for all computational ills." (Richard E Bellman & Paul Brock, "On the Concepts of a Problem and Problem-Solving", American Mathematical Monthly 67, 1960)

"Cybernetics is concerned primarily with the construction of theories and models in science, without making a hard and fast distinction between the physical and the biological sciences. The theories and models occur both in symbols and in hardware, and by 'hardware’ we shall mean a machine or computer built in terms of physical or chemical, or indeed any handleable parts. Most usually we shall think of hardware as meaning electronic parts such as valves and relays. Cybernetics insists, also, on a further and rather special condition that distinguishes it from ordinary scientific theorizing: it demands a certain standard of effectiveness. In this respect it has acquired some of the same motive power that has driven research on modern logic, and this is especially true in the construction and application of artificial languages and the use of operational definitions. Always the search is for precision and effectiveness, and we must now discuss the question of effectiveness in some detail. It should be noted that when we talk in these terms we are giving pride of place to the theory of automata at the expense, at least to some extent, of feedback and information theory." (Frank H George, "The Brain As A Computer", 1962)

"These machines have no common sense; they have not yet learned to 'think', and they do exactly as they are told, no more and no less. This fact is the hardest concept to grasp when one first tries to use a computer." (Donald Knuth, "The Art of Computer Programming, Volume 1: Fundamental Algorithms", 1968)

"Because the subject matter of cybernetics is the propositional or informational aspect of the events and objects in the natural world, this science is forced to procedures rather different from those of the other sciences. The differentiation, for example, between map and territory, which the semanticists insist that scientists shall respect in their writings must, in cybernetics, be watched for in the very phenomena about which the scientist writes. Expectably, communicating organisms and badly programmed computers will mistake map for territory; and the language of the scientist must be able to cope with such anomalies." (Gregory Bateson, "Steps to an Ecology of Mind", 1972)

"Computers can do better than ever what needn't be done at all. Making sense is still a human monopoly." (Marshall McLuhan, "Take Today: The Executive as Dropout", 1972)

"Everything we think we know about the world is a model. Every word and every language is a model. All maps and statistics, books and databases, equations and computer programs are models. So are the ways I picture the world in my head - my mental models. None of these is or ever will be the real world. […] Our models usually have a strong congruence with the world. That is why we are such a successful species in the biosphere. Especially complex and sophisticated are the mental models we develop from direct, intimate experience of nature, people, and organizations immediately around us." (Donella Meadows, "Limits to Growth", 1972)

"It follows from this that man's most urgent and pre-emptive need is maximally to utilize cybernetic science and computer technology within a general systems framework, to build a meta-systemic reality which is now only dimly envisaged. Intelligent and purposeful application of rapidly developing telecommunications and teleprocessing technology should make possible a degree of worldwide value consensus heretofore unrealizable." (Richard F Ericson, "Visions of Cybernetic Organizations", 1972)

"The mind is defined as the sum total of all the programs and the metaprograms of a given human computer, whether or not they are immediately elicitable, detectable, and visibly operational to the self or to others." (John C Lilly "Programming and Metaprogramming in the Human Biocomputer" 2nd Ed., 1972)

"When the phenomena of the universe are seen as linked together by cause-and-effect and energy transfer, the resulting picture is of complexly branching and interconnecting chains of causation. In certain regions of this universe (notably organisms in environments, ecosystems, thermostats, steam engines with governors, societies, computers, and the like), these chains of causation form circuits which are closed in the sense that causal interconnection can be traced around the circuit and back through whatever position was (arbitrarily) chosen as the starting point of the description. In such a circuit, evidently, events at any position in the circuit may be expected to have effect at all positions on the circuit at later times." (Gregory Bateson, "Steps to an Ecology of Mind", 1972)

"The pseudo approach to uncertainty modeling refers to the use of an uncertainty model instead of using a deterministic model which is actually (or at least theoretically) available. The uncertainty model may be desired because it results in a simpler analysis, because it is too difficult (expensive) to gather all the data necessary for an exact model, or because the exact model is too complex to be included in the computer." (Fred C Scweppe, "Uncertain dynamic systems", 1973)

"Computers make possible an entirely new relationship between theories and models. I have already said that theories are texts. Texts are written in a language. Computer languages are languages too, and theories may be written in them. Indeed, for the present purpose we need not restrict our attention to machine languages or even to the kinds of 'higher-level' languages we have discussed. We may include all languages, specifically also natural languages, that computers may be able to interpret. The point is precisely that computers do interpret texts given to them, in other words, that texts determine computers' behavior. Theories written in the form of computer programs are ordinary theories as seen from one point of view." (Joseph Weizenbaum, "Computer power and human reason: From judgment to calculation" , 1976)

"The connection between a model and a theory is that a model satisfies a theory; that is, a model obeys those laws of behavior that a corresponding theory explicitly states or which may be derived from it. [...] Computers make possible an entirely new relationship between theories and models. [...] A theory written in the form of a computer program is [...] both a theory and, when placed on a computer and run, a model to which the theory applies." (Joseph Weizenbaum, "Computer power and human reason: From judgment to calculation" , 1976)

"Computers and robots replace humans in the exercise of mental functions in the same way as mechanical power replaced them in the performance of physical tasks. As time goes on, more and more complex mental functions will be performed by machines. Any worker who now performs his task by following specific instructions can, in principle, be replaced by a machine. This means that the role of humans as the most important factor of production is bound to diminish - in the same way that the role of horses in agricultural production was first diminished and then eliminated by the introduction of tractors." (Wassily Leontief, National perspective: The definition of problem and opportunity, 1983)

"If arithmetical skill is the measure of intelligence, then computers have been more intelligent than all human beings all along. If the ability to play chess is the measure, then there are computers now in existence that are more intelligent than any but a very few human beings. However, if insight, intuition, creativity, the ability to view a problem as a whole and guess the answer by the “feel” of the situation, is a measure of intelligence, computers are very unintelligent indeed. Nor can we see right now how this deficiency in computers can be easily remedied, since human beings cannot program a computer to be intuitive or creative for the very good reason that we do not know what we ourselves do when we exercise these qualities." (Isaac Asimov, "Machines That Think", 1983)

"Computation offers a new means of describing and investigating scientific and mathematical systems. Simulation by computer may be the only way to predict how certain complicated systems evolve." (Stephen Wolfram, "Computer Software in Science and Mathematics", 1984)

"In the real world, none of these assumptions are uniformly valid. Often people want to know why mathematics and computers cannot be used to handle the meaningful problems of society, as opposed, let us say, to the moon boondoggle and high energy-high cost physics. The answer lies in the fact that we don't know how to describe the complex systems of society involving people, we don't understand cause and effect, which is to say the consequences of decisions, and we don't even know how to make our objectives reasonably precise. None of the requirements of classical science are met. Gradually, a new methodology for dealing with these 'fuzzy' problems is being developed, but the path is not easy." (Richard E Bellman, "Eye of the Hurricane: An Autobiography", 1984)

"Let us change our traditional attitude to the construction of programs: Instead of imagining that our main task is to instruct a computer what to do, let us concentrate rather on explaining to human beings what we want a computer to do." (Donald E Knuth, "Literate Programming", 1984)

"Scientific laws give algorithms, or procedures, for determining how systems behave. The computer program is a medium in which the algorithms can be expressed and applied. Physical objects and mathematical structures can be represented as numbers and symbols in a computer, and a program can be written to manipulate them according to the algorithms. When the computer program is executed, it causes the numbers and symbols to be modified in the way specified by the scientific laws. It thereby allows the consequences of the laws to be deduced." (Stephen Wolfram, "Computer Software in Science and Mathematics", 1984)

"The trouble with an analog computer is that one begins to construct mathematical models which can be treated using an analog computer. In many cases this is not realistic." (Richard E Bellman, "Eye of the Hurricane: An Autobiography", 1984)

"Under pressure from the computer, the question of mind in relation to machine is becoming a central cultural preoccupation." (Sherry Turkle, "The Second Self: Computers and the Human Spirit", 1984)

"A computer is an interpreted automatic formal system - that is to say, a symbol-manipulating machine." (John Haugeland, "Artificial intelligence: The very idea", 1985)

"Just like a computer, we must remember things in the order in which entropy increases. This makes the second law of thermodynamics almost trivial. Disorder increases with time because we measure time in the direction in which disorder increases." (Stephen Hawking, "A Brief History of Time", 1988)

"The principle of maximum diversity operates both at the physical and at the mental level. It says that the laws of nature and the initial conditions are such as to make the universe as interesting as possible. As a result, life is possible but not too easy. Always when things are dull, something new turns up to challenge us and to stop us from settling into a rut. Examples of things which make life difficult are all around us: comet impacts, ice ages, weapons, plagues, nuclear fission, computers, sex, sin and death. Not all challenges can be overcome, and so we have tragedy. Maximum diversity often leads to maximum stress. In the end we survive, but only by the skin of our teeth." (Freeman Dyson, "Infinite in All Directions", 1988)

"Looking at ourselves from the computer viewpoint, we cannot avoid seeing that natural language is our most important 'programming language'. This means that a vast portion of our knowledge and activity is, for us, best communicated and understood in our natural language. [...] One could say that natural language was our first great original artifact and, since, as we increasingly realize, languages are machines, so natural language, with our brains to run it, was our primal invention of the universal computer. One could say this except for the sneaking suspicion that language isn’t something we invented but something we became, not something we constructed but something in which we created, and recreated, ourselves. (Justin Leiber, "Invitation to cognitive science", 1991)

"A computer terminal is not some clunky old television with a typewriter in front of it. It is an interface where the mind and body can connect with the universe and move bits of it about." (Douglas N Adams, "Mostly Harmless", 1992)

"Finite Nature is a hypothesis that ultimately every quantity of physics, including space and time, will turn out to be discrete and finite; that the amount of information in any small volume of space-time will be finite and equal to one of a small number of possibilities. [...] We take the position that Finite Nature implies that the basic substrate of physics operates in a manner similar to the workings of certain specialized computers called cellular automata." (Edward Fredkin, "A New Cosmogony", PhysComp ’92: Proceedings of the Workshop on Physics and Computation, 1993)

"At first glance the theory of numbers is deprived of any geometricity. But this is actually not the case. At the contemporary stage of development of computers it has become possible to explain to a wide range of readers that visual geometry helps not only to illustrate some abstract situations from the number theory, but sometimes also to solve new problems." (Anatolij Fomenko, "Visual Geometry and Topology", 1994)

"On the other hand, those who design and build computers know exactly how the machines are working down in the hidden depths of their semiconductors. Computers can be taken apart, scrutinized, and put back together. Their activities can be tracked, analyzed, measured, and thus clearly understood - which is far from possible with the brain. This gives rise to the tempting assumption on the part of the builders and designers that computers can tell us something about brains, indeed, that the computer can serve as a model of the mind, which then comes to be seen as some manner of information processing machine, and possibly not as good at the job as the machine. (Theodore Roszak, "The Cult of Information", 1994)

"And in computer life, where the term 'species' does not yet have meaning, we see no cascading emergence of entirely new kinds of variety beyond an initial burst. In the wild, in breeding, and in artificial life, we see the emergence of variation. But by the absence of greater change, we also clearly see that the limits of variation appear to be narrowly bounded, and often bounded within species." (Kevin Kelly, "Out of Control: The New Biology of Machines, Social Systems and the Economic World", 1995)

"Self-organization refers to the spontaneous formation of patterns and pattern change in open, nonequilibrium systems. […] Self-organization provides a paradigm for behavior and cognition, as well as the structure and function of the nervous system. In contrast to a computer, which requires particular programs to produce particular results, the tendency for self-organization is intrinsic to natural systems under certain conditions." (J A Scott Kelso, "Dynamic Patterns : The Self-organization of Brain and Behavior", 1995)

"Representation is the process of transforming existing problem knowledge to some of the known knowledge-engineering schemes in order to process it by applying knowledge-engineering methods. The result of the representation process is the problem knowledge base in a computer format." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Shearing away detail is the very essence of model building. Whatever else we require, a model must be simpler than the thing modeled. In certain kinds of fiction, a model that is identical with the thing modeled provides an interesting device, but it never happens in reality. Even with virtual reality, which may come close to this literary identity one day, the underlying model obeys laws which have a compact description in the computer - a description that generates the details of the artificial world." (John H Holland, "Emergence" , Philosophica 59, 1997)

"Modelling techniques on powerful computers allow us to simulate the behaviour of complex systems without having to understand them. We can do with technology what we cannot do with science. […] The rise of powerful technology is not an unconditional blessing. We have to deal with what we do not understand, and that demands new ways of thinking." (Paul Cilliers,"Complexity and Postmodernism: Understanding Complex Systems", 1998)

"For most problems found in mathematics textbooks, mathematical reasoning is quite useful. But how often do people find textbook problems in real life? At work or in daily life, factors other than strict reasoning are often more important. Sometimes intuition and instinct provide better guides; sometimes computer simulations are more convenient or more reliable; sometimes rules of thumb or back-of-the-envelope estimates are all that is needed." (Lynn A Steen,"Twenty Questions about Mathematical Reasoning", 1999)

"Once a computer achieves human intelligence it will necessarily roar past it." (Ray Kurzweil, "The Age of Spiritual Machines: When Computers Exceed Human Intelligence", 1999)

"The classic example of chaos at work is in the weather. If you could measure the positions and motions of all the atoms in the air at once, you could predict the weather perfectly. But computer simulations show that tiny differences in starting conditions build up over about a week to give wildly different forecasts. So weather predicting will never be any good for forecasts more than a few days ahead, no matter how big (in terms of memory) and fast computers get to be in the future. The only computer that can simulate the weather is the weather; and the only computer that can simulate the Universe is the Universe." (John Gribbin, "The Little Book of Science", 1999)

"Conventional wisdom, fooled by our misleading 'physical intuition', is that the real world is continuous, and that discrete models are necessary evils for approximating the 'real' world, due to the innate discreteness of the digital computer." (Doron Zeilberger, "'Real' Analysis is a Degenerate Case of Discrete Analysis", 2001)

"The randomness of the card-shuffle is of course caused by our lack of knowledge of the precise procedure used to shuffle the cards. But that is outside the chosen system, so in our practical sense it is not admissible. If we were to change the system to include information about the shuffling rule – for example, that it is given by some particular computer code for pseudo-random numbers, starting with a given ‘seed value’ – then the system would look deterministic. Two computers of the same make running the same ‘random shuffle’ program would actually produce the identical sequence of top cards."(Ian Stewart, "Does God Play Dice: The New Mathematics of Chaos", 2002)

"There are endless examples of elaborate structures and apparently complex processes being generated through simple repetitive rules, all of which can be easily simulated on a computer. It is therefore tempting to believe that, because many complex patterns can be generated out of a simple algorithmic rule, all complexity is created in this way." (F David Peat, "From Certainty to Uncertainty", 2002)

"We build models to increase productivity, under the justified assumption that it's cheaper to manipulate the model than the real thing. Models then enable cheaper exploration and reasoning about some universe of discourse. One important application of models is to understand a real, abstract, or hypothetical problem domain that a computer system will reflect. This is done by abstraction, classification, and generalization of subject-matter entities into an appropriate set of classes and their behavior." (Stephen J Mellor, "Executable UML: A Foundation for Model-Driven Architecture", 2002)

"Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algorithms are more scalable than statisticians ever thought possible. Formal statistical theory is more pervasive than computer scientists had realized." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"Computers bootstrap their own offspring, grow so wise and incomprehensible that their communiqués assume the hallmarks of dementia: unfocused and irrelevant to the barely-intelligent creatures left behind. And when your surpassing creations find the answers you asked for, you can't understand their analysis and you can't verify their answers. You have to take their word on faith." (Peter Watts, "Blindsight", 2006)

"In specific cases, we think by applying mental rules, which are similar to rules in computer programs. In most of the cases, however, we reason by constructing, inspecting, and manipulating mental models. These models and the processes that manipulate them are the basis of our competence to reason. In general, it is believed that humans have the competence to perform such inferences error-free. Errors do occur, however, because reasoning performance is limited by capacities of the cognitive system, misunderstanding of the premises, ambiguity of problems, and motivational factors. Moreover, background knowledge can significantly influence our reasoning performance. This influence can either be facilitation or an impedance of the reasoning process." (Carsten Held et al, "Mental Models and the Mind", 2006)

"A neural network is a particular kind of computer program, originally developed to try to mimic the way the human brain works. It is essentially a computer simulation of a complex circuit through which electric current flows." (Keith J Devlin & Gary Lorden, "The Numbers behind NUMB3RS: Solving crime with mathematics", 2007)

"The burgeoning field of computer science has shifted our view of the physical world from that of a collection of interacting material particles to one of a seething network of information. In this way of looking at nature, the laws of physics are a form of software, or algorithm, while the material world - the hardware - plays the role of a gigantic computer." (Paul C W Davies, "Laying Down the Laws", New Scientist, 2007)

"We tend to form mental models that are simpler than reality; so if we create represented models that are simpler than the actual implementation model, we help the user achieve a better understanding. […] Understanding how software actually works always helps someone to use it, but this understanding usually comes at a significant cost. One of the most significant ways in which computers can assist human beings is by putting a simple face on complex processes and situations. As a result, user interfaces that are consistent with users’ mental models are vastly superior to those that are merely reflections of the implementation model." (Alan Cooper et al, "About Face 3: The Essentials of Interaction Design", 2007)

"An algorithm refers to a successive and finite procedure by which it is possible to solve a certain problem. Algorithms are the operational base for most computer programs. They consist of a series of instructions that, thanks to programmers’ prior knowledge about the essential characteristics of a problem that must be solved, allow a step-by-step path to the solution." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"We evolved to be good at learning and using rules of thumb, not at searching for ultimate causes and making fine distinctions. Still less did we evolve to spin out long chains of calculation that connect fundamental laws to observable consequences. Computers are much better at it!" (Frank Wilczek,"The Lightness of Being – Mass, Ether and the Unification of Forces", 2008)

"Chess, as a game of zero sum and total information is, theoretically, a game that can be solved. The problem is the immensity of the search tree: the total number of positions surpasses the number of atoms in our galaxy. When there are few pieces on the board, the search space is greatly reduced, and the problem becomes trivial for computers’ calculation capacity." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"Generally, these programs fall within the techniques of reinforcement learning and the majority use an algorithm of temporal difference learning. In essence, this computer learning paradigm approximates the future state of the system as a function of the present state. To reach that future state, it uses a neural network that changes the weight of its parameters as it learns." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"[...] we also distinguish knowledge from information, because some pieces of information, such as questions, orders, and absurdities do not constitute knowledge. And also because computers process information but, since they lack minds, they cannot be said to know anything." (Mario Bunge, "Matter and Mind: A Philosophical Inquiry", 2010)

"System dynamics models have little impact unless they change the way people perceive a situation. A model must help to organize information in a more understandable way. A model should link the past to the present by showing how present conditions arose, and extend the present into persuasive alternative futures under a variety of scenarios determined by policy alternatives. In other words, a system dynamics model, if it is to be effective, must communicate with and modify the prior mental models. Only people's beliefs - that is, their mental models - will determine action. Computer models must relate to and improve mental models if the computer models are to fill an effective role." (Jay W Forrester, "Modeling for What Purpose?", The Systems Thinker Vol. 24 (2), 2013)

"A computer makes calculations quickly and correctly, but doesn’t ask if the calculations are meaningful or sensible. A computer just does what it is told." (Gary Smith, "Standard Deviations", 2014)

"Now think about the prospect of competition from computers instead of competition from human workers. On the supply side, computers are far more different from people than any two people are different from each other: men and machines are good at fundamentally different things. People have intentionality - we form plans and make decisions in complicated situations. We’re less good at making sense of enormous amounts of data. Computers are exactly the opposite: they excel at efficient data processing, but they struggle to make basic judgments that would be simple for any human." (Peter Thiel & Blake Masters, "Zero to One: Notes on Startups, or How to Build the Future", 2014)

"With fast computers and plentiful data, finding statistical significance is trivial. If you look hard enough, it can even be found in tables of random numbers." (Gary Smith, "Standard Deviations", 2014)

"Working an integral or performing a linear regression is something a computer can do quite effectively. Understanding whether the result makes sense - or deciding whether the method is the right one to use in the first place - requires a guiding human hand. When we teach mathematics we are supposed to be explaining how to be that guide. A math course that fails to do so is essentially training the student to be a very slow, buggy version of Microsoft Excel." (Jordan Ellenberg, "How Not to Be Wrong: The Power of Mathematical Thinking", 2014)

"The term data, unlike the related terms facts and evidence, does not connote truth. Data is descriptive, but data can be erroneous. We tend to distinguish data from information. Data is a primitive or atomic state (as in ‘raw data’). It becomes information only when it is presented in context, in a way that informs. This progression from data to information is not the only direction in which the relationship flows, however; information can also be broken down into pieces, stripped of context, and stored as data. This is the case with most of the data that’s stored in computer systems. Data that’s collected and stored directly by machines, such as sensors, becomes information only when it’s reconnected to its context." (Stephen Few, "Signal: Understanding What Matters in a World of Noise", 2015)

"[…] the usefulness of mathematics is by no means limited to finite objects or to those that can be represented with a computer. Mathematical concepts depending on the idea of infinity, like real numbers and differential calculus, are useful models for certain aspects of physical reality." (Alfred S Posamentier & Bernd Thaller, "Numbers: Their tales, types, and treasures", 2015)

"The human mind isn’t a computer; it cannot progress in an orderly fashion down a list of candidate moves and rank them by a score down to the hundredth of a pawn the way a chess machine does. Even the most disciplined human mind wanders in the heat of competition. This is both a weakness and a strength of human cognition. Sometimes these undisciplined wanderings only weaken your analysis. Other times they lead to inspiration, to beautiful or paradoxical moves that were not on your initial list of candidates." (Garry Kasparov, "Deep Thinking", 2017)

"There are other problems with Big Data. In any large data set, there are bound to be inconsistencies, misclassifications, missing data - in other words, errors, blunders, and possibly lies. These problems with individual items occur in any data set, but they are often hidden in a large mass of numbers even when these numbers are generated out of computer interactions." (David S Salsburg, "Errors, Blunders, and Lies: How to Tell the Difference", 2017)

Knowledge Representation: The Filtering Mind (Quotes)

noreply@blogger.com (Adrian) — Tue, 14 Sep 2021 05:05:00 GMT

"Because of the extended time image and the extended relationship images, man is capable of ‘rational behavior,’ that is to say, his response is not to an immediate stimulus but to an image of the future filtered through an elaborate value system. His image contains not only what is, but what might be." (Kenneth E Boulding, "The Image: Knowledge in life and society", 1956)

"We say the map is different from the territory. But what is the territory? Operationally, somebody went out with a retina or a measuring stick and made representations which were then put on paper. What is on the paper map is a representation of what was in the retinal representation of the man who made the map; and as you push the question back, what you find is an infinite regress, an infinite series of maps. The territory never gets in at all. […] Always, the process of representation will filter it out so that the mental world is only maps of maps, ad infinitum." (Gregory Bateson, "Steps to an Ecology of Mind", 1972)

"Nature is not ‘given’ to us - our minds are never virgin in front of reality. Whatever we say we see or observe is biased by what we already know, think, believe, or wish to see. Some of these thoughts, beliefs and knowledge can function as an obstacle to our understanding of the phenomena." (Anna Sierpinska, "Understanding in Mathematics", 1994)

"The abstractions of science are stereotypes, as two-dimensional and as potentially misleading as everyday stereotypes. And yet they are as necessary to the process of understanding as filtering is to the process of perception." (K C Cole, "First You Build a Cloud and Other Reflections on Physics as a Way of Life", 1999)

"We all would like to know more and, at the same time, to receive less information. In fact, the problem of a worker in today's knowledge industry is not the scarcity of information but its excess. The same holds for professionals: just think of a physician or an executive, constantly bombarded by information that is at best irrelevant. In order to learn anything we need time. And to make time we must use information filters allowing us to ignore most of the information aimed at us. We must ignore much to learn a little." (Mario Bunge, "Philosophy in Crisis: The Need for Reconstruction", 2001)

"The receiver decodes the symbols to interpret the meaning of the message. Encoding and decoding are potential sources for communication errors because knowledge, attitudes, and context act as filters and create noise when translating from symbols to meaning. Finally, feedback occurs when the receiver responds to the sender’s communication with a return message. Without feedback, the communication is one-way; with feedback, it is two-way. Feedback is a powerful aid to communication effectiveness because it enables the sender to determine whether the receiver correctly interpreted the message." (Richard L Daft & Dorothy Marcic, "Understanding Management" 5th Ed., 2006)

"Your mental models shape the way you see the world. They help you to quickly make sense of the noises that filter in from outside, but they can also limit your ability to see the true picture." (Colin Cook & Yoram R Wind, "The Power of Impossible Thinking: Transform the Business of Your Life and the Life of Your Business", 2006)

"Actually, around 80% of the data we use to make decisions is already in our heads before we engage with a situation. Our power to perceive is governed and limited by cognitive filters, sometimes termed our ‘mental model’. Mental models are formed as a result of past experience, knowledge and attitudes. They are deeply ingrained, often subconscious, structures that limit what we perceive and also colour our interpretation of supposed facts." (Robina Chatham & Brian Sutton, "Changing the IT Leader’s Mindset", 2010)

"[…] our strong mental models tend to make us blind to certain possibilities, and therefore we unknowingly engage in biased listening. Whenever we interpret information, we subconsciously access three filters based upon how we feel about the content, the information source and situation (or context) in which we receive the information." (Robina Chatham & Brian Sutton, "Changing the IT Leader’s Mindset", 2010)

"Perception and memory are imprecise filters of information, and the way in which information is presented, that is, the frame, influences how it is received. Because too much information is difficult to deal with, people have developed shortcuts or heuristics in order to come up with reasonable decisions. Unfortunately, sometimes these heuristics lead to bias, especially when used outside their natural domains." (Lucy F Ackert & Richard Deaves, "Behavioral Finance: Psychology, Decision-Making, and Markets", 2010)

"Mental models bind our awareness within a particular scaffold and then selectively can filter the content we subsequently receive. Through recalibration using revised mental models, we argue, we cultivate strategies anew, creating new habits, and galvanizing more intentional and evolved mental models. This recalibration often entails developing a strong sense of self and self-worth, realizing that each of us has a range of moral choices that may deviate from those in authority, and moral imagination." (Patricia H Werhane et al, "Obstacles to Ethical: Decision-Making Mental Models, Milgram and the Problem of Obedience", 2013)

"In the absence of clear information - in the absence of reliable statistics - people did what they had always done: filtered available information through the lens of their worldview." (Zachary Karabell, "The Leading Indicators: A short history of the numbers that rule our world", 2014)

"Images are generally resistant to change and ignore messages that do not conform to their internal settings. Sometimes, however, they do react and can alter in an incremental or even revolutionary manner. Humans can talk about and share their images and, in the symbolic universe they create, reflect upon what is and what might be." (Michael C Jackson, "Critical Systems Thinking and the Management of Complexity", 2019)

"We filter new information. If it accords with what we expect, we’ll be more likely to accept it. […] Our brains are always trying to make sense of the world around us based on incomplete information. The brain makes predictions about what it expects, and tends to fill in the gaps, often based on surprisingly sparse data." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

Science: On Data Visualization (Quotes)

noreply@blogger.com (Adrian) — Tue, 31 Aug 2021 17:54:00 GMT

"When visualization tools act as a catalyst to early visual thinking about a relatively unexplored problem, neither the semantics nor the pragmatics of map signs is a dominant factor. On the other hand, syntactics (or how the sign-vehicles, through variation in the visual variables used to construct them, relate logically to one another) are of critical importance." (Alan M MacEachren, "How Maps Work: Representation, Visualization, and Design", 1995)

"The nature of maps and of their use in science and society is in the midst of remarkable change - change that is stimulated by a combination of new scientific and societal needs for geo-referenced information and rapidly evolving technologies that can provide that information in innovative ways. A key issue at the heart of this change is the concept of ‘visualization’." (Alan M MacEachren, "Exploratory cartographic visualization: advancing the agenda", 1997)

"Visualizations can be used to explore data, to confirm a hypothesis, or to manipulate a viewer. [...] In exploratory visualization the user does not necessarily know what he is looking for. This creates a dynamic scenario in which interaction is critical. [...] In a confirmatory visualization, the user has a hypothesis that needs to be tested. This scenario is more stable and predictable. System parameters are often predetermined." (Usama Fayyad et al, "Information Visualization in Data Mining and Knowledge Discovery", 2002)

"Dashboards and visualization are cognitive tools that improve your 'span of control' over a lot of business data. These tools help people visually identify trends, patterns and anomalies, reason about what they see and help guide them toward effective decisions. As such, these tools need to leverage people's visual capabilities. With the prevalence of scorecards, dashboards and other visualization tools now widely available for business users to review their data, the issue of visual information design is more important than ever." (Richard Brath & Michael Peters, "Dashboard Design: Why Design is Important," DM Direct, 2004)

"Data visualization [...] expresses the idea that it involves more than just representing data in a graphical form (instead of using a table). The information behind the data should also be revealed in a good display; the graphic should aid readers or viewers in seeing the structure in the data. The term data visualization is related to the new field of information visualization. This includes visualization of all kinds of information, not just of data, and is closely associated with research by computer scientists." (Antony Unwin et al, "Introduction" [in "Handbook of Data Visualization"], 2008)

"The main goal of data visualization is its ability to visualize data, communicating information clearly and effectively. It doesn’t mean that data visualization needs to look boring to be functional or extremely sophisticated to look beautiful. To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex dataset by communicating its key aspects in a more intuitive way. Yet designers often tend to discard the balance between design and function, creating gorgeous data visualizations which fail to serve its main purpose - communicate information." (Vitaly Friedman, "Data Visualization and Infographics", Smashing Magazine, 2008)

"With the ever increasing amount of empirical information that scientists from all disciplines are dealing with, there exists a great need for robust, scalable and easy to use clustering techniques for data abstraction, dimensionality reduction or visualization to cope with and manage this avalanche of data." (Jörg Reichardt, "Structure in Complex Networks", 2009)

"So what is the difference between a chart or graph and a visualization? […] a chart or graph is a clean and simple atomic piece; bar charts contain a short story about the data being presented. A visualization, on the other hand, seems to contain much more ʻchart junkʼ, with many sometimes complex graphics or several layers of charts and graphs. A visualization seems to be the super-set for all sorts of data-driven design." (Brian Suda, "A Practical Guide to Designing with Data", 2010)

"All graphics present data and allow a certain degree of exploration of those same data. Some graphics are almost all presentation, so they allow just a limited amount of exploration; hence we can say they are more infographics than visualization, whereas others are mostly about letting readers play with what is being shown, tilting more to the visualization side of our linear scale. But every infographic and every visualization has a presentation and an exploration component: they present, but they also facilitate the analysis of what they show, to different degrees." (Alberto Cairo, "The Functional Art", 2011)

"The first and main goal of any graphic and visualization is to be a tool for your eyes and brain to perceive what lies beyond their natural reach." (Alberto Cairo, "The Functional Art", 2011)

"Thinking of graphics as art leads many to put bells and whistles over substance and to confound infographics with mere illustrations." (Alberto Cairo, "The Functional Art", 2011)

"Good infographic design is about storytelling by combining data visualization design and graphic design." (Randy Krum, "Good Infographics: Effective Communication with Data Visualization and Design", 2013)

"Good visualization is a winding process that requires statistics and design knowledge. Without the former, the visualization becomes an exercise only in illustration and aesthetics, and without the latter, one of only analyses. On their own, these are fine skills, but they make for incomplete data graphics. Having skills in both provides you with the luxury - which is growing into a necessity - to jump back and forth between data exploration and storytelling." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"The biggest thing to know is that data visualization is hard. Really difficult to pull off well. It requires harmonization of several skills sets and ways of thinking: conceptual, analytic, statistical, graphic design, programmatic, interface-design, story-telling, journalism - plus a bit of 'gut feel'. The end result is often simple and beautiful, but the process itself is usually challenging and messy." (David McCandless, 2013)

"Visualization can be appreciated purely from an aesthetic point of view, but it’s most interesting when it’s about data that’s worth looking at. That’s why you start with data, explore it, and then show results rather than start with a visual and try to squeeze a dataset into it. It’s like trying to use a hammer to bang in a bunch of screws. […] Aesthetics isn’t just a shiny veneer that you slap on at the last minute. It represents the thought you put into a visualization, which is tightly coupled with clarity and affects interpretation." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"Visualization is what happens when you make the jump from raw data to bar graphs, line charts, and dot plots. […] In its most basic form, visualization is simply mapping data to geometry and color. It works because your brain is wired to find patterns, and you can switch back and forth between the visual and the numbers it represents. This is the important bit. You must make sure that the essence of the data isn’t lost in that back and forth between visual and the value it represents because if you can’t map back to the data, the visualization is just a bunch of shapes." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"What is good visualization? It is a representation of data that helps you see what you otherwise would have been blind to if you looked only at the naked source. It enables you to see trends, patterns, and outliers that tell you about yourself and what surrounds you. The best visualization evokes that moment of bliss when seeing something for the first time, knowing that what you see has been right in front of you, just slightly hidden. Sometimes it is a simple bar graph, and other times the visualization is complex because the data requires it." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"Just because data is visualized doesn’t necessarily mean that it is accurate, complete, or indicative of the right course of action. Exhibiting a healthy skepticism is almost always a good thing." (Phil Simon, "The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions", 2014)

"To be sure, data doesn’t always need to be visualized, and many data visualizations just plain suck. Look around you. It’s not hard to find truly awful representations of information. Some work in concept but fail because they are too busy; they confuse people more than they convey information [...]. Visualization for the sake of visualization is unlikely to produce desired results - and this goes double in an era of Big Data. Bad is still bad, even and especially at a larger scale." (Phil Simon, "The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions", 2014)

"We are all becoming more comfortable with data. Data visualization is no longer just something we have to do at work. Increasingly, we want to do it as consumers and as citizens. Put simply, visualizing helps us understand what’s going on in our lives - and how to solve problems." (Phil Simon, "The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions", 2014)

"Apart from the technical challenge of working with the data itself, visualization in big data is different because showing the individual observations is just not an option. But visualization is essential here: for analysis to work well, we have to be assured that patterns and errors in the data have been spotted and understood. That is only possible by visualization with big data, because nobody can look over the data in a table or spreadsheet." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"As a first principle, any visualization should convey its information quickly and easily, and with minimal scope for misunderstanding. Unnecessary visual clutter makes more work for the reader’s brain to do, slows down the understanding (at which point they may give up) and may even allow some incorrect interpretations to creep in." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"One very common problem in data visualization is that encoding numerical variables to area is incredibly popular, but readers can’t translate it back very well." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"There is often no one 'best' visualization, because it depends on context, what your audience already knows, how numerate or scientifically trained they are, what formats and conventions are regarded as standard in the particular field you’re working in, the medium you can use, and so on. It’s also partly scientific and partly artistic, so you get to express your own design style in it, which is what makes it so fascinating." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"The best approach is to build visualizations in the most digestible form, fitted to how that executive thinks. You will have to interact with executives, show them different visualizations, and see how they react in order to learn which forms work best for them. Be ready to fail often and learn fast, particularly with visualizations." (John Lucker)

"Visualisation is fundamentally limited by the number of pixels you can pump to a screen. If you have big data, you have way more data than pixels, so you have to summarise your data. Statistics gives you lots of really good tools for this." (Hadley Wickham)

"We often think of visualization as a design and programming task, but the process starts further back with the data. You have to understand the data - its trends and patterns, along with its flaws and imperfections - and the rest follows." (Nathan Yau)

Science: On Dimensionality (Quotes)

noreply@blogger.com (Adrian) — Tue, 31 Aug 2021 17:51:00 GMT

"[…] the intrinsic value of a small-scale model is that it compensates for the renunciation of sensible dimensions by the acquisition of intelligible dimensions." (Claude Levi- Strauss, "The Savage Mind", 1962)

"The idea of knowledge as an improbable structure is still a good place to start. Knowledge, however, has a dimension which goes beyond that of mere information or improbability. This is a dimension of significance which is very hard to reduce to quantitative form. Two knowledge structures might be equally improbable but one might be much more significant than the other." (Kenneth E Boulding, "Beyond Economics: Essays on Society", 1968)

"A time series is a sequence of observations, usually ordered in time, although in some cases the ordering may be according to another dimension. The feature of time series analysis which distinguishes it from other statistical analysis is the explicit recognition of the importance of the order in which the observations are made. While in many problems the observations are statistically independent, in time series successive observations may be dependent, and the dependence may depend on the positions in the sequence. The nature of a series and the structure of its generating process also may involve in other ways the sequence in which the observations are taken." (Theodore W Anderson, "The Statistical Analysis of Time Series", 1971)

"The number of information-carrying (variable) dimensions depicted should not exceed the number of dimensions in the data." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"In addition to dimensionality requirements, chaos can occur only in nonlinear situations. In multidimensional settings, this means that at least one term in one equation must be nonlinear while also involving several of the variables. With all linear models, solutions can be expressed as combinations of regular and linear periodic processes, but nonlinearities in a model allow for instabilities in such periodic solutions within certain value ranges for some of the parameters." (Courtney Brown, "Chaos and Catastrophe Theories", 1995)

"The dimensionality and nonlinearity requirements of chaos do not guarantee its appearance. At best, these conditions allow it to occur, and even then under limited conditions relating to particular parameter values. But this does not imply that chaos is rare in the real world. Indeed, discoveries are being made constantly of either the clearly identifiable or arguably persuasive appearance of chaos. Most of these discoveries are being made with regard to physical systems, but the lack of similar discoveries involving human behavior is almost certainly due to the still developing nature of nonlinear analyses in the social sciences rather than the absence of chaos in the human setting." (Courtney Brown, "Chaos and Catastrophe Theories", 1995)

"A system may be called complex here if its dimension (order) is too high and its model (if available) is nonlinear, interconnected, and information on the system is uncertain such that classical techniques can not easily handle the problem." (M Jamshidi, "Autonomous Control on Complex Systems: Robotic Applications", Current Advances in Mechanical Design and Production VII, 2000)

"The greatest plus of data modeling is that it produces a simple and understandable picture of the relationship between the input variables and responses [...] different models, all of them equally good, may give different pictures of the relation between the predictor and response variables [...] One reason for this multiplicity is that goodness-of-fit tests and other methods for checking fit give a yes–no answer. With the lack of power of these tests with data having more than a small number of dimensions, there will be a large number of models whose fit is acceptable. There is no way, among the yes–no methods for gauging fit, of determining which is the better model." (Leo Breiman, "Statistical modeling: The two cultures" Statistical Science 16(3), 2001)

"The more dimensions used in quantitative comparisons, the larger are the disparities that can be accommodated. As irony would have it, however, the ease of comparison generally diminishes in direct proportion to the number of dimensions involved." (Joel Katz, "Designing Information: Human factors and common sense in information design", 2012)

"Dimensionality reduction is essential for coping with big data - like the data coming in through your senses every second. A picture may be worth a thousand words, but it’s also a million times more costly to process and remember. [...] A common complaint about big data is that the more data you have, the easier it is to find spurious patterns in it. This may be true if the data is just a huge set of disconnected entities, but if they’re interrelated, the picture changes." (Pedro Domingos, "The Master Algorithm", 2015)

"The correlational technique known as multiple regression is used frequently in medical and social science research. This technique essentially correlates many independent (or predictor) variables simultaneously with a given dependent variable (outcome or output). It asks, 'Net of the effects of all the other variables, what is the effect of variable A on the dependent variable?' Despite its popularity, the technique is inherently weak and often yields misleading results. The problem is due to self-selection. If we don’t assign cases to a particular treatment, the cases may differ in any number of ways that could be causing them to differ along some dimension related to the dependent variable. We can know that the answer given by a multiple regression analysis is wrong because randomized control experiments, frequently referred to as the gold standard of research techniques, may give answers that are quite different from those obtained by multiple regression analysis." (Richard E Nisbett, "Mindware: Tools for Smart Thinking", 2015)

"Dimensionality reduction is a way of reducing a large number of different measures into a smaller set of metrics. The intent is that the reduced metrics are a simpler description of the complex space that retains most of the meaning." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"The higher the dimension, in other words, the higher the number of possible interactions, and the more disproportionally difficult it is to understand the macro from the micro, the general from the simple units. This disproportionate increase of computational demands is called the curse of dimensionality." (Nassim N Taleb, "Skin in the Game: Hidden Asymmetries in Daily Life", 2018)

Science: On Control (Quotes)

noreply@blogger.com (Adrian) — Tue, 31 Aug 2021 17:48:00 GMT

"An inference, if it is to have scientific value, must constitute a prediction concerning future data. If the inference is to be made purely with the help of the distribution theory of statistics, the experiments that constitute evidence for the inference must arise from a state of statistical control; until that state is reached, there is no universe, normal or otherwise, and the statistician’s calculations by themselves are an illusion if not a delusion. The fact is that when distribution theory is not applicable for lack of control, any inference, statistical or otherwise, is little better than a conjecture. The state of statistical control is therefore the goal of all experimentation. (William E Deming, "Statistical Method from the Viewpoint of Quality Control", 1939)

"Sampling is the science and art of controlling and measuring the reliability of useful statistical information through the theory of probability." (William E Deming, "Some Theory of Sampling", 1950)

"The well-known virtue of the experimental method is that it brings situational variables under tight control. It thus permits rigorous tests of hypotheses and confidential statements about causation. The correlational method, for its part, can study what man has not learned to control. Nature has been experimenting since the beginning of time, with a boldness and complexity far beyond the resources of science. The correlator’s mission is to observe and organize the data of nature’s experiments." (Lee J Cronbach, "The Two Disciplines of Scientific Psychology", The American Psychologist Vol. 12, 1957)

"In complex systems cause and effect are often not closely related in either time or space. The structure of a complex system is not a simple feedback loop where one system state dominates the behavior. The complex system has a multiplicity of interacting feedback loops. Its internal rates of flow are controlled by nonlinear relationships. The complex system is of high order, meaning that there are many system states (or levels). It usually contains positive-feedback loops describing growth processes as well as negative, goal-seeking loops. In the complex system the cause of a difficulty may lie far back in time from the symptoms, or in a completely different and remote part of the system. In fact, causes are usually found, not in prior events, but in the structure and policies of the system." (Jay W Forrester, "Urban dynamics", 1969)

"To adapt to a changing environment, the system needs a variety of stable states that is large enough to react to all perturbations but not so large as to make its evolution uncontrollably chaotic. The most adequate states are selected according to their fitness, either directly by the environment, or by subsystems that have adapted to the environment at an earlier stage. Formally, the basic mechanism underlying self-organization is the (often noise-driven) variation which explores different regions in the system’s state space until it enters an attractor. This precludes further variation outside the attractor, and thus restricts the freedom of the system’s components to behave independently. This is equivalent to the increase of coherence, or decrease of statistical entropy, that defines self-organization." (Francis Heylighen, "The Science Of Self-Organization And Adaptivity", 1970)

"Science consists simply of the formulation and testing of hypotheses based on observational evidence; experiments are important where applicable, but their function is merely to simplify observation by imposing controlled conditions." (Henry L Batten, "Evolution of the Earth", 1971)

"Uncontrolled variation is the enemy of quality." (W Edwards Deming, 1980)

"The methods of science include controlled experiments, classification, pattern recognition, analysis, and deduction. In the humanities we apply analogy, metaphor, criticism, and (e)valuation. In design we devise alternatives, form patterns, synthesize, use conjecture, and model solutions." (Béla H Bánáthy, "Designing Social Systems in a Changing World", 1996)

"A mathematical model uses mathematical symbols to describe and explain the represented system. Normally used to predict and control, these models provide a high degree of abstraction but also of precision in their application." (Lars Skyttner, "General Systems Theory: Ideas and Applications", 2001)

"A model is an imitation of reality and a mathematical model is a particular form of representation. We should never forget this and get so distracted by the model that we forget the real application which is driving the modelling. In the process of model building we are translating our real world problem into an equivalent mathematical problem which we solve and then attempt to interpret. We do this to gain insight into the original real world situation or to use the model for control, optimization or possibly safety studies." (Ian T Cameron & Katalin Hangos, "Process Modelling and Model Analysis", 2001)

"The methodology of feedback design is borrowed from cybernetics (control theory). It is based upon methods of controlled system model’s building, methods of system states and parameters estimation (identification), and methods of feedback synthesis. The models of controlled system used in cybernetics differ from conventional models of physics and mechanics in that they have explicitly specified inputs and outputs. Unlike conventional physics results, often formulated as conservation laws, the results of cybernetical physics are formulated in the form of transformation laws, establishing the possibilities and limits of changing properties of a physical system by means of control." (Alexander L Fradkov, "Cybernetical Physics: From Control of Chaos to Quantum Control", 2007)

"Put simply, statistics is a range of procedures for gathering, organizing, analyzing and presenting quantitative data. […] Essentially […], statistics is a scientific approach to analyzing numerical data in order to enable us to maximize our interpretation, understanding and use. This means that statistics helps us turn data into information; that is, data that have been interpreted, understood and are useful to the recipient. Put formally, for your project, statistics is the systematic collection and analysis of numerical data, in order to investigate or discover relationships among phenomena so as to explain, predict and control their occurrence." (Reva B Brown & Mark Saunders, "Dealing with Statistics: What You Need to Know", 2008)

"One technique employing correlational analysis is multiple regression analysis (MRA), in which a number of independent variables are correlated simultaneously (or sometimes sequentially, but we won’t talk about that variant of MRA) with some dependent variable. The predictor variable of interest is examined along with other independent variables that are referred to as control variables. The goal is to show that variable A influences variable B 'net of' the effects of all the other variables. That is to say, the relationship holds even when the effects of the control variables on the dependent variable are taken into account." (Richard E Nisbett, "Mindware: Tools for Smart Thinking", 2015)

"The theory behind multiple regression analysis is that if you control for everything that is related to the independent variable and the dependent variable by pulling their correlations out of the mix, you can get at the true causal relation between the predictor variable and the outcome variable. That’s the theory. In practice, many things prevent this ideal case from being the norm." (Richard E Nisbett, "Mindware: Tools for Smart Thinking", 2015)

"Too little attention is given to the need for statistical control, or to put it more pertinently, since statistical control (randomness) is so rarely found, too little attention is given to the interpretation of data that arise from conditions not in statistical control." (William E Deming)

Systems Thinking: On Loops (Quotes)

noreply@blogger.com (Adrian) — Sun, 29 Aug 2021 22:35:00 GMT

"Even in a complex system only one or a few loops dominate the behavior of a variable of interest over an interval of time. […] These loops that dominate the behavior of a variable shift and usually produce different characteristic behavior due to the shift." (Carl V Swanson, "Notions Useful for the Analysis of Complex Feedback Systems", 1968)

"Like all systems, the complex system is an interlocking structure of feedback loops [...] This loop structure surrounds all decisions public or private, conscious or unconscious. The processes of man and nature, of psychology and physics, of medicine and engineering all fall within this structure [...]" (Jay W Forrester, "Urban Dynamics", 1969)

"The structure of a complex system is not a simple feedback loop where one system state dominates the behavior. The complex system has a multiplicity of interacting feedback loops. Its internal rates of flow are controlled by non‐linear relationships. The complex system is of high order, meaning that there are many system states (or levels). It usually contains positive‐feedback loops describing growth processes as well as negative, goal‐seeking loops." (Jay F Forrester, "Urban Dynamics", 1969)

"To model the dynamic behavior of a system, four hierarchies of structure should be recognized: closed boundary around the system; feedback loops as the basic structural elements within the boundary; level variables representing accumulations within the feedback loops; rate variables representing activity within the feedback loops." (Jay W Forrester, "Urban Dynamics", 1969)

"Whatever the system, adaptive change depends upon feedback loops, be it those provided by natural selection or those of individual reinforcement. In all cases, then, there must be a process of trial and error and a mechanism of comparison. […] By superposing and interconnecting many feedback loops, we (and all other biological systems) not only solve particular problems but also form habits which we apply to the solution of classes of problems." (Gregory Bateson, "Steps to an Ecology of Mind", 1972)

"A nonlinear relationship causes the feedback loop of which it is a part to vary in strength, depending on the state of the system. Linked nonlinear feedback loops thus form patterns of shifting loop dominance- under some conditions one part of the system is very active, and under other conditions another set of relationships takes control and shifts the entire system behavior. A model composed of several feedback loops linked nonlinearly can produce a wide variety of complex behavior patterns." (Jørgen Randers, "Elements of the System Dynamics Method", 1980)

"The term closed loop-learning process refers to the idea that one learns by determining what s desired and comparing what is actually taking place as measured at the process and feedback for comparison. The difference between what is desired and what is taking place provides an error indication which is used to develop a signal to the process being controlled." (Harold Chestnut, 1984)

"When loops are present, the network is no longer singly connected and local propagation schemes will invariably run into trouble. [...] If we ignore the existence of loops and permit the nodes to continue communicating with each other as if the network were singly connected, messages may circulate indefinitely around the loops and process may not converges to a stable equilibrium. […] Such oscillations do not normally occur in probabilistic networks […] which tend to bring all messages to some stable equilibrium as time goes on. However, this asymptotic equilibrium is not coherent, in the sense that it does not represent the posterior probabilities of all nodes of the network." (Judea Pearl, "Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference", 1988)

"The term chaos is used in a specific sense where it is an inherently random pattern of behaviour generated by fixed inputs into deterministic (that is fixed) rules (relationships). The rules take the form of non-linear feedback loops. Although the specific path followed by the behaviour so generated is random and hence unpredictable in the long-term, it always has an underlying pattern to it, a 'hidden' pattern, a global pattern or rhythm. That pattern is self-similarity, that is a constant degree of variation, consistent variability, regular irregularity, or more precisely, a constant fractal dimension. Chaos is therefore order (a pattern) within disorder (random behaviour)." (Ralph D Stacey, "The Chaos Frontier: Creative Strategic Control for Business", 1991)

"[…] self-organization is the spontaneous emergence of new structures and new forms of behavior in open systems far from equilibrium, characterized by internal feedback loops and described mathematically by nonlinear equations." (Fritjof Capra, "The web of life: a new scientific understanding of living systems", 1996)

"System dynamics is a method for studying the world around us. Unlike other scientists, who study the world by breaking it up into smaller and smaller pieces, system dynamicists look at things as a whole. The central concept to system dynamics is understanding how all the objects in a system interact with one another. A system can be anything from a steam engine, to a bank account, to a basketball team. The objects and people in a system interact through 'feedback' loops, where a change in one variable affects other variables over time, which in turn affects the original variable, and so on." (Edward Yourdon, "Death March", 1997)

"All dynamics arise from the interaction of just two types of feedback loops, positive (or self-reinforcing) and negative (or self-correcting) loops. Positive loops tend to reinforce or amplify whatever is happening in the system […] Negative loops counteract and oppose change." (John D Sterman, "Business Dynamics: Systems thinking and modeling for a complex world", 2000)

"Even if our cognitive maps of causal structure were perfect, learning, especially double-loop learning, would still be difficult. To use a mental model to design a new strategy or organization we must make inferences about the consequences of decision rules that have never been tried and for which we have no data. To do so requires intuitive solution of high-order nonlinear differential equations, a task far exceeding human cognitive capabilities in all but the simplest systems." (John D Sterman, "Business Dynamics: Systems thinking and modeling for a complex world", 2000)

"[...] information feedback about the real world not only alters our decisions within the context of existing frames and decision rules but also feeds back to alter our mental models. As our mental models change we change the structure of our systems, creating different decision rules and new strategies. The same information, processed and interpreted by a different decision rule, now yields a different decision. Altering the structure of our systems then alters their patterns of behavior. The development of systems thinking is a double-loop learning process in which we replace a reductionist, narrow, short-run, static view of the world with a holistic, broad, long-term, dynamic view and then redesign our policies and institutions accordingly." (John D Sterman, "Business dynamics: Systems thinking and modeling for a complex world", 2000)

"The mental models people use to guide their decisions are dynamically deficient. […] people generally adopt an event-based, open-loop view of causality, ignore feedback processes, fail to appreciate time delays between action and response and in the reporting of information, do not understand stocks and flows and are insensitive to nonlinearities that may alter the strengths of different feedback loops as a system evolves." (John D Sterman, "Business Dynamics: Systems thinking and modeling for a complex world", 2000)

"The phenomenon of emergence takes place at critical points of instability that arise from fluctuations in the environment, amplified by feedback loops." (Fritjof Capra, "The Hidden Connections: A Science for Sustainable Living", 2002)

"Deep change in mental models, or double-loop learning, arises when evidence not only alters our decisions within the context of existing frames, but also feeds back to alter our mental models. As our mental models change, we change the structure of our systems, creating different decision rules and new strategies. The same information, interpreted by a different model, now yields a different decision. Systems thinking is an iterative learning process in which we replace a reductionist, narrow, short-run, static view of the world with a holistic, broad, long-term, dynamic view, reinventing our policies and institutions accordingly." (John D Sterman, "Learning in and about complex systems", Systems Thinking Vol. 3 2003)

"Thus, nonlinearity can be understood as the effect of a causal loop, where effects or outputs are fed back into the causes or inputs of the process. Complex systems are characterized by networks of such causal loops. In a complex, the interdependencies are such that a component A will affect a component B, but B will in general also affect A, directly or indirectly. A single feedback loop can be positive or negative. A positive feedback will amplify any variation in A, making it grow exponentially. The result is that the tiniest, microscopic difference between initial states can grow into macroscopically observable distinctions." (Carlos Gershenson, "Design and Control of Self-organizing Systems", 2007)

"In physical, exponentially growing systems, there must be at least one reinforcing loop driving growth and at least one balancing feedback loop constraining growth, because no system can grow forever in a finite environment." (Donella H Meadows, “Thinking in Systems: A Primer”, 2008)

"The butterfly effect demonstrates that complex dynamical systems are highly responsive and interconnected webs of feedback loops. It reminds us that we live in a highly interconnected world. Thus our actions within an organization can lead to a range of unpredicted responses and unexpected outcomes. This seriously calls into doubt the wisdom of believing that a major organizational change intervention will necessarily achieve its pre-planned and highly desired outcomes. Small changes in the social, technological, political, ecological or economic conditions can have major implications over time for organizations, communities, societies and even nations." (Elizabeth McMillan, "Complexity, Management and the Dynamics of Change: Challenges for practice", 2008)

"System dynamics is an approach to understanding the behaviour of over time. It deals with internal feedback loops and time delays that affect the behaviour of the entire system. It also helps the decision maker untangle the complexity of the connections between various policy variables by providing a new language and set of tools to describe. Then it does this by modeling the cause and effect relationships among these variables." (Raed M Al-Qirem & Saad G Yaseen, "Modelling a Small Firm in Jordan Using System Dynamics", 2010)

"Complexity is a relative term. It depends on the number and the nature of interactions among the variables involved. Open loop systems with linear, independent variables are considered simpler than interdependent variables forming nonlinear closed loops with a delayed response." (Jamshid Gharajedaghi, "Systems Thinking: Managing Chaos and Complexity A Platform for Designing Business Architecture" 3rd Ed., 2011)

"Cyberneticists argue that positive feedback may be useful, but it is inherently unstable, capable of causing loss of control and runaway. A higher level of control must therefore be imposed upon any positive feedback mechanism: self-stabilising properties of a negative feedback loop constrain the explosive tendencies of positive feedback. This is the starting point of our journey to explore the role of cybernetics in the control of biological growth. That is the assumption that the evolution of self-limitation has been an absolute necessity for life forms with exponential growth." (Tony Stebbing, "A Cybernetic View of Biological Growth: The Maia Hypothesis", 2011)

"Cybernetics is the study of systems which can be mapped using loops (or more complicated looping structures) in the network defining the flow of information. Systems of automatic control will of necessity use at least one loop of information flow providing feedback." (Alan Scrivener, "A Curriculum for Cybernetics and Systems Theory", 2012)

"System Dynamics is a dynamic modelling approach at system level which is primarily used to understand interconnected systems and their evolution over time. Basic elements to represent the systems are internal feedback loops as well as stocks and flows." (Catalina Spataru et al, "Multi-Scale, Multi-Dimensional Modelling of Future Energy Systems", 2015)

"System dynamics [...] uses models and computer simulations to understand behavior of an entire system, and has been applied to the behavior of large and complex national issues. It portrays the relationships in systems as feedback loops, lags, and other descriptors to explain dynamics, that is, how a system behaves over time. Its quantitative methodology relies on what are called 'stock-and-flow diagrams' that reflect how levels of specific elements accumulate over time and the rate at which they change. Qualitative systems thinking constructs evolved from this quantitative discipline." (Karen L Higgins, "Economic Growth and Sustainability: Systems Thinking for a Complex World", 2015)

"To remedy chaotic situations requires a chaotic approach, one that is non-linear, constantly morphing, and continually sharpening its competitive edge with recurring feedback loops that build upon past experiences and lessons learned. Improvement cannot be sustained without reflection. Chaos arises from myriad sources that stem from two origins: internal chaos rising within you, and external chaos being imposed upon you by the environment. The result of this push/pull effect is the disequilibrium [...]." (Jeff Boss, "Navigating Chaos: How to Find Certainty in Uncertain Situations", 2015)

Science: On Change (Quotes)

noreply@blogger.com (Adrian) — Sun, 29 Aug 2021 08:18:00 GMT

"A law of nature, however, is not a mere logical conception that we have adopted as a kind of memoria technical to enable us to more readily remember facts. We of the present day have already sufficient insight to know that the laws of nature are not things which we can evolve by any speculative method. On the contrary, we have to discover them in the facts; we have to test them by repeated observation or experiment, in constantly new cases, under ever-varying circumstances; and in proportion only as they hold good under a constantly increasing change of conditions, in a constantly increasing number of cases with greater delicacy in the means of observation, does our confidence in their trustworthiness rise." (Hermann von Helmholtz, "Popular Lectures on Scientific Subjects", 1873)

"It is clear that one who attempts to study precisely things that are changing must have a great deal to do with measures of change." (Charles Cooley, "Observations on the Measure of Change", Journal of the American Statistical Association (21), 1893)

"Given any object, relatively abstracted from its surroundings for study, the behavioristic approach consists in the examination of the output of the object and of the relations of this output to the input. By output is meant any change produced in the surroundings by the object. By input, conversely, is meant any event external to the object that modifies this object in any manner." (Arturo Rosenblueth, Norbert Wiener & Julian Bigelow, "Behavior, Purpose and Teleology", Philosophy of Science 10, 1943)

"The general method involved may be very simply stated. In cases where the equilibrium values of our variables can be regarded as the solutions of an extremum (maximum or minimum) problem, it is often possible regardless of the number of variables involved to determine unambiguously the qualitative behavior of our solution values in respect to changes of parameters." (Paul Samuelson, "Foundations of Economic Analysis", 1947)

"A common and very powerful constraint is that of continuity. It is a constraint because whereas the function that changes arbitrarily can undergo any change, the continuous function can change, at each step, only to a neighbouring value." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"As a simple trick, the discrete can often be carried over into the continuous, in a way suitable for practical purposes, by making a graph of the discrete, with the values shown as separate points. It is then easy to see the form that the changes will take if the points were to become infinitely numerous and close together." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"The discrete change has only to become small enough in its jump to approximate as closely as is desired to the continuous change. It must further be remembered that in natural phenomena the observations are almost invariably made at discrete intervals; the 'continuity' ascribed to natural events has often been put there by the observer's imagina- tion, not by actual observation at each of an infinite number of points. Thus the real truth is that the natural system is observed at discrete points, and our transformation represents it at discrete points. There can, therefore, be no real incompatibility." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"A satisfactory prediction of the sequential properties of learning data from a single experiment is by no means a final test of a model. Numerous other criteria - and some more demanding - can be specified. For example, a model with specific numerical parameter values should be invariant to changes in independent variables that explicitly enter in the model." (Robert R Bush & Frederick Mosteller,"A Comparison of Eight Models?", Studies in Mathematical Learning Theory, 1959)

"Prediction of the future is possible only in systems that have stable parameters like celestial mechanics. The only reason why prediction is so successful in celestial mechanics is that the evolution of the solar system has ground to a halt in what is essentially a dynamic equilibrium with stable parameters. Evolutionary systems, however, by their very nature have unstable parameters. They are disequilibrium systems and in such systems our power of prediction, though not zero, is very limited because of the unpredictability of the parameters themselves. If, of course, it were possible to predict the change in the parameters, then there would be other parameters which were unchanged, but the search for ultimately stable parameters in evolutionary systems is futile, for they probably do not exist… Social systems have Heisenberg principles all over the place, for we cannot predict the future without changing it." (Kenneth E Boulding, Evolutionary Economics, 1981)

"Model is used as a theory. It becomes theory when the purpose of building a model is to understand the mechanisms involved in the developmental process. Hence as theory, model does not carve up or change the world, but it explains how change takes place and in what way or manner. This leads to build change in the structures." (Laxmi K Patnaik, "Model Building in Political Science", The Indian Journal of Political Science Vol. 50 (2), 1989)

"A useful description relates the systematic variation to one or more factors; if the residuals dwarf the effects for a factor, we may not be able to relate variation in the data to changes in the factor. Furthermore, changes in the factor may bring no important change in the response. Such comparisons of residuals and effects require a measure of the variation of overlays relative to each other." (Christopher H Schrnid, "Value Splitting: Taking the Data Apart", 1991)

"[…] continuity appears when we try to mathematically express continuously changing phenomena, and differentiability is the result of expressing smoothly changing phenomena." (Kenji Ueno & Toshikazu Sunada, "A Mathematical Gift, III: The Interplay Between Topology, Functions, Geometry, and Algebra", Mathematical World Vol. 23, 1996)

"How deep truths can be defined as invariants – things that do not change no matter what; how invariants are defined by symmetries, which in turn define which properties of nature are conserved, no matter what. These are the selfsame symmetries that appeal to the senses in art and music and natural forms like snowflakes and galaxies. The fundamental truths are based on symmetry, and there’s a deep kind of beauty in that." (K C Cole, "The Universe and the Teacup: The Mathematics of Truth and Beauty", 1997)

"There is a new science of complexity which says that the link between cause and effect is increasingly difficult to trace; that change (planned or otherwise) unfolds in non-linear ways; that paradoxes and contradictions abound; and that creative solutions arise out of diversity, uncertainty and chaos." (Andy P Hargreaves & Michael Fullan, "What’s Worth Fighting for Out There?", 1998)

"We analyze numbers in order to know when a change has occurred in our processes or systems. We want to know about such changes in a timely manner so that we can respond appropriately. While this sounds rather straightforward, there is a complication - the numbers can change even when our process does not. So, in our analysis of numbers, we need to have a way to distinguish those changes in the numbers that represent changes in our process from those that are essentially noise." (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"Each of the most basic physical laws that we know corresponds to some invariance, which in turn is equivalent to a collection of changes which form a symmetry group. […] whilst leaving some underlying theme unchanged. […] for example, the conservation of energy is equivalent to the invariance of the laws of motion with respect to translations backwards or forwards in time […] the conservation of linear momentum is equivalent to the invariance of the laws of motion with respect to the position of your laboratory in space, and the conservation of angular momentum to an invariance with respect to directional orientation… discovery of conservation laws indicated that Nature possessed built-in sustaining principles which prevented the world from just ceasing to be." (John D Barrow, "New Theories of Everything", 2007)

"Causal inference is different, because a change in the system is contemplated - an intervention. Descriptive statistics tell you about the data that you happen to have. Causal models claim to tell you what will happen to some of the numbers if you intervene to change other numbers." (David A. Freedman, "Statistical Models: Theory and Practice", 2009)

"The concept of symmetry is used widely in physics. If the laws that determine relations between physical magnitudes and a change of these magnitudes in the course of time do not vary at the definite operations (transformations), they say, that these laws have symmetry (or they are invariant) with respect to the given transformations. For example, the law of gravitation is valid for any points of space, that is, this law is in variant with respect to the system of coordinates." (Alexey Stakhov et al, "The Mathematics of Harmony", 2009)

"After you visualize your data, there are certain things to look for […]: increasing, decreasing, outliers, or some mix, and of course, be sure you’re not mixing up noise for patterns. Also note how much of a change there is and how prominent the patterns are. How does the difference compare to the randomness in the data? Observations can stand out because of human or mechanical error, because of the uncertainty of estimated values, or because there was a person or thing that stood out from the rest. You should know which it is." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"In negative feedback regulation the organism has set points to which different parameters (temperature, volume, pressure, etc.) have to be adapted to maintain the normal state and stability of the body. The momentary value refers to the values at the time the parameters have been measured. When a parameter changes it has to be turned back to its set point. Oscillations are characteristic to negative feedback regulation […]" (Gaspar Banfalvi, "Homeostasis - Tumor – Metastasis", 2014)

"Regression does not describe changes in ability that happen as time passes […]. Regression is caused by performances fluctuating about ability, so that performances far from the mean reflect abilities that are closer to the mean." (Gary Smith, "Standard Deviations", 2014)

"When memorization happens, you may have the illusion that everything is working well because your machine learning algorithm seems to have fitted the in sample data so well. Instead, problems can quickly become evident when you start having it work with out-of-sample data and you notice that it produces errors in its predictions as well as errors that actually change a lot when you relearn from the same data with a slightly different approach. Overfitting occurs when your algorithm has learned too much from your data, up to the point of mapping curve shapes and rules that do not exist [...]. Any slight change in the procedure or in the training data produces erratic predictions." (John P Mueller & Luca Massaron, Machine Learning for Dummies, 2016)

"Data analysis and data mining are concerned with unsupervised pattern finding and structure determination in data sets. The data sets themselves are explicitly linked as a form of representation to an observational or otherwise empirical domain of interest. 'Structure' has long been understood as symmetry which can take many forms with respect to any transformation, including point, translational, rotational, and many others. Symmetries directly point to invariants, which pinpoint intrinsic properties of the data and of the background empirical domain of interest. As our data models change, so too do our perspectives on analysing data." (Fionn Murtagh, "Data Science Foundations: Geometry and Topology of Complex Hierarchic Systems and Big Data Analytics", 2018)

Science: On Invariance (Quotes)

noreply@blogger.com (Adrian) — Sat, 28 Aug 2021 20:09:00 GMT

"The calculation of probabilities is of the utmost value, […] but in statistical inquiries there is need not so much of mathematical subtlety as of a precise statement of all the circumstances. The possible contingencies are too numerous to be covered by a finite number of experiments, and exact calculation is, therefore, out of the question. Although nature has her habits, due to the recurrence of causes, they are general, not invariable. Yet empirical calculation, although it is inexact, may be adequate in affairs of practice." (Gottfried W Leibniz [letter to Bernoulli], 1703)

"[…] as every law of nature implies the existence of an invariant, it follows that every law of nature is a constraint. […] Science looks for laws; it is therefore much concerned with looking for constraints. […] the world around us is extremely rich in constraints. We are so familiar with them that we take most of them for granted, and are often not even aware that they exist. […] A world without constraints would be totally chaotic." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"Many of the activities of living organisms permit this double aspect. On the one hand the observer can notice the great deal of actual movement and change that occurs, and on the other hand he can observe that throughout these activities, so far as they are coordinated or homeostatic, there are invariants and constancies that show the degree of regulation that is being achieved." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"Through all the meanings runs the basic idea of an 'invariant': that although the system is passing through a series of changes, there is some aspect that is unchanging; so some statement can be made that, in spite of the incessant changing, is true unchangingly." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"[...] the existence of any invariant over a set of phenomena implies a constraint, for its existence implies that the full range of variety does not occur. The general theory of invariants is thus a part of the theory of constraints. Further, as every law of nature implies the existence of an invariant, it follows that every law of nature is a constraint." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"We know many laws of nature and we hope and expect to discover more. Nobody can foresee the next such law that will be discovered. Nevertheless, there is a structure in laws of nature which we call the laws of invariance. This structure is so far-reaching in some cases that laws of nature were guessed on the basis of the postulate that they fit into the invariance structure." (Eugene P Wigner, "The Role of Invariance Principles in Natural Philosophy", 1963)

"[..] principle of equipresence: A quantity present as an independent variable in one constitutive equation is so present in all, to the extent that its appearance is not forbidden by the general laws of Physics or rules of invariance. […] The principle of equipresence states, in effect, that no division of phenomena is to be laid down by constitutive equations." (Clifford Truesdell, "Six Lectures on Modern Natural Philosophy", 1966)

"It is now natural for us to try to derive the laws of nature and to test their validity by means of the laws of invariance, rather than to derive the laws of invariance from what we believe to be the laws of nature." (Eugene P Wigner, "Symmetries and Reflections", 1967)

"As a metaphor - and I stress that it is intended as a metaphor - the concept of an invariant that arises out of mutually or cyclically balancing changes may help us to approach the concept of self. In cybernetics this metaphor is implemented in the ‘closed loop’, the circular arrangement of feedback mechanisms that maintain a given value within certain limits. They work toward an invariant, but the invariant is achieved not by a steady resistance, the way a rock stands unmoved in the wind, but by compensation over time. Whenever we happen to look in a feedback loop, we find the present act pitted against the immediate past, but already on the way to being compensated itself by the immediate future. The invariant the system achieves can, therefore, never be found or frozen in a single element because, by its very nature, it consists in one or more relationships - and relationships are not in things but between them." (Ernst von Glasersfeld German, "Cybernetics, Experience and the Concept of Self", 1970)

"Because of its foundation in topology, catastrophe theory is qualitative, not quantitative. Just as geometry treated the properties of a triangle without regard to its size, so topology deals with properties that have no magnitude, for example, the property of a given point being inside or outside a closed curve or surface. This property is what topologists call 'invariant' -it does not change even when the curve is distorted. A topologist may work with seven-dimensional space, but he does not and cannot measure (in the ordinary sense) along any of those dimensions. The ability to classify and manipulate all types of form is achieved only by giving up concepts such as size, distance, and rate. So while catastrophe theory is well suited to describe and even to predict the shape of processes, its descriptions and predictions are not quantitative like those of theories built upon calculus. Instead, they are rather like maps without a scale: they tell us that there are mountains to the left, a river to the right, and a cliff somewhere ahead, but not how far away each is, or how large." (Alexander Woodcock & Monte Davis, "Catastrophe Theory", 1978)

"An essential condition for a theory of choice that claims normative status is the principle of invariance: different representations of the same choice problem should yield the same preference. That is, the preference between options should be independent of their description. Two characterizations that the decision maker, on reflection, would view as alternative descriptions of the same problem should lead to the same choice-even without the benefit of such reflection." (Amos Tversky & Daniel Kahneman, "Rational Choice and the Framing of Decisions", The Journal of Business Vol. 59 (4), 1986)

"Axiomatic theories of choice introduce preference as a primitive relation, which is interpreted through specific empirical procedures such as choice or pricing. Models of rational choice assume a principle of procedure invariance, which requires strategically equivalent methods of elicitation to yield the same preference order." (Amos Tversky et al, "The Causes of Preference Reversal", The American Economic Review Vol. 80 (1), 1990)

"Symmetry is basically a geometrical concept. Mathematically it can be defined as the invariance of geometrical patterns under certain operations. But when abstracted, the concept applies to all sorts of situations. It is one of the ways by which the human mind recognizes order in nature. In this sense symmetry need not be perfect to be meaningful. Even an approximate symmetry attracts one's attention, and makes one wonder if there is some deep reason behind it." (Eguchi Tohru & ‎K Nishijima ," Broken Symmetry: Selected Papers Of Y Nambu", 1995)

"The cliché became, erroneously, 'everything is relative'; whereas the point is that out of the vast flux one can distill the very opposite: 'some things are invariant'." (Gerald Holton, "Einstein, History, and Other Passions: The Rebellion Against Science at the End of the Twentieth Century", 1996)

"Cybernetics is the science of effective organization, of control and communication in animals and machines. It is the art of steersmanship, of regulation and stability. The concern here is with function, not construction, in providing regular and reproducible behaviour in the presence of disturbances. Here the emphasis is on families of solutions, ways of arranging matters that can apply to all forms of systems, whatever the material or design employed. [...] This science concerns the effects of inputs on outputs, but in the sense that the output state is desired to be constant or predictable – we wish the system to maintain an equilibrium state. It is applicable mostly to complex systems and to coupled systems, and uses the concepts of feedback and transformations (mappings from input to output) to effect the desired invariance or stability in the result." (Chris Lucas, "Cybernetics and Stochastic Systems", 1999)

"The concept of symmetry (invariance) with its rigorous mathematical formulation and generalization has guided us to know the most fundamental of physical laws. Symmetry as a concept has helped mankind not only to define ‘beauty’ but also to express the ‘truth’. Physical laws tries to quantify the truth that appears to be ‘transient’ at the level of phenomena but symmetry promotes that truth to the level of ‘eternity’." (Vladimir G Ivancevic & Tijana T Ivancevic,"Quantum Leap", 2008)

"In dynamical systems, a bifurcation occurs when a small smooth change made to the parameter values (the bifurcation parameters) of a system causes a sudden 'qualitative' or topological change in its behaviour. Generally, at a bifurcation, the local stability properties of equilibria, periodic orbits or other invariant sets changes." (Gregory Faye, "An introduction to bifurcation theory", 2011)

"The invariance principle states that the result of counting a set does not depend on the order imposed on its elements during the counting process. Indeed, a mathematical set is just a collection without any implied ordering. A set is the collection of its elements - nothing more." (Alfred S Posamentier & Bernd Thaller, "Numbers: Their tales, types, and treasures", 2015)

Science: On Constraints (Quotes)

noreply@blogger.com (Adrian) — Wed, 25 Aug 2021 07:21:00 GMT

"Constraints are of high importance in cybernetics […] because when a constraint exists advantage can usually be taken of it." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"Formulating consists of determining the system inputs, outputs, requirements, objectives, constraints. Structuring the system provides one or more methods of organizing the solution, the method of operation, the selection of parts, and the nature of their performance requirements. It is evident that the processes of formulating a system and structuring it are strongly related." (Harold Chestnut, "Systems Engineering Tools", 1965)

"In general, we can say that the larger the system becomes, the more the parts interact, the more difficult it is to understand environmental constraints, the more obscure becomes the problem of what resources should be made available, and deepest of all, the more difficult becomes the problem of the legitimate values of the system." (C West Churchman, "The Systems Approach", 1968)

"A physical theory must accept some actual data as inputs and must be able to generate from them another set of possible data (the output) in such a way that both input and output match the assumptions of the theory - laws, constraints, etc. This concept of matching involves relevance: thus boundary conditions are relevant only to field-like theories such as hydrodynamics and quantum mechanics. But matching is more than relevance: it is also logical compatibility." (Mario Bunge, "Philosophy of Physics", 1973)

"Physics is like that. It is important that the models we construct allow us to draw the right conclusions about the behaviour of the phenomena and their causes. But it is not essential that the models accurately describe everything that actually happens; and in general it will not be possible for them to do so, and for much the same reasons. The requirements of the theory constrain what can be literally represented. This does not mean that the right lessons cannot be drawn. Adjustments are made where literal correctness does not matter very much in order to get the correct effects where we want them; and very often, as in the staging example, one distortion is put right by another. That is why it often seems misleading to say that a particular aspect of a model is false to reality: given the other constraints that is just the way to restore the representation." (Nancy Cartwright, "How the Laws of Physics Lie", 1983)

"Indeed, except for the very simplest physical systems, virtually everything and everybody in the world is caught up in a vast, nonlinear web of incentives and constraints and connections. The slightest change in one place causes tremors everywhere else. We can't help but disturb the universe, as T.S. Eliot almost said. The whole is almost always equal to a good deal more than the sum of its parts. And the mathematical expression of that property - to the extent that such systems can be described by mathematics at all - is a nonlinear equation: one whose graph is curvy." (M Mitchell Waldrop, "Complexity: The Emerging Science at the Edge of Order and Chaos", 1992)

"Many of the basic functions performed by neural networks are mirrored by human abilities. These include making distinctions between items (classification), dividing similar things into groups (clustering), associating two or more things (associative memory), learning to predict outcomes based on examples (modeling), being able to predict into the future (time-series forecasting), and finally juggling multiple goals and coming up with a good- enough solution (constraint satisfaction)." (Joseph P Bigus,"Data Mining with Neural Networks: Solving business problems from application development to decision support", 1996)

"A conceptual model is a representation of the system expertise using this formalism. An internal model is derived from the conceptual model and from a specification of the system transactions and the performance constraints." (Zbigniew W. Ras & Andrzej Skowron [Eds.], Foundations of Intelligent Systems: 10th International Symposium Vol 10, 1997)

"Whereas formal systems apply inference rules to logical variables, neural networks apply evolutive principles to numerical variables. Instead of calculating a solution, the network settles into a condition that satisfies the constraints imposed on it." (Paul Cilliers, "Complexity and Postmodernism: Understanding Complex Systems", 1998)

"What it means for a mental model to be a structural analog is that it embodies a representation of the spatial and temporal relations among, and the causal structures connecting the events and entities depicted and whatever other information that is relevant to the problem-solving talks. […] The essential points are that a mental model can be nonlinguistic in form and the mental mechanisms are such that they can satisfy the model-building and simulative constraints necessary for the activity of mental modeling." (Nancy J Nersessian, "Model-based reasoning in conceptual change", 1999)

"To develop a Control, the designer should find aspect systems, subsystems, or constraints that will prevent the negative interferences between elements (friction) and promote positive interferences (synergy). In other words, the designer should search for ways of minimizing frictions that will result in maximization of the global satisfaction" (Carlos Gershenson, "Design and Control of Self-organizing Systems", 2007)

"[chaos theory] presents a universe that is at once deterministic and obeys the fundamental physical laws, but is capable of disorder, complexity, and unpredictability. It shows that predictability is a rare phenomenon operating only within the constraints that science has filtered out from the rich diversity of our complex world." (Ziauddin Sardar & Iwona Abrams, "Introducing Chaos: A Graphic Guide", 2008)

"Cybernetics is the art of creating equilibrium in a world of possibilities and constraints. This is not just a romantic description, it portrays the new way of thinking quite accurately. Cybernetics differs from the traditional scientific procedure, because it does not try to explain phenomena by searching for their causes, but rather by specifying the constraints that determine the direction of their development." (Ernst von Glasersfeld, "Partial Memories: Sketches from an Improbable Life", 2010)

"Optimization is more than finding the best simulation results. It is itself a complex and evolving field that, subject to certain information constraints, allows data scientists, statisticians, engineers, and traders alike to perform reality checks on modeling results." (Chris Conlan, "Automated Trading with R: Quantitative Research and Platform Development", 2016)

"Exponentially growing systems are prevalent in nature, spanning all scales from biochemical reaction networks in single cells to food webs of ecosystems. How exponential growth emerges in nonlinear systems is mathematically unclear. […] The emergence of exponential growth from a multivariable nonlinear network is not mathematically intuitive. This indicates that the network structure and the flux functions of the modeled system must be subjected to constraints to result in long-term exponential dynamics." (Wei-Hsiang Lin et al, "Origin of exponential growth in nonlinear reaction networks", PNAS 117 (45), 2020)

Science: On Classification (Quotes)

noreply@blogger.com (Adrian) — Sun, 22 Aug 2021 17:08:00 GMT

"No occupation is more worthy of an intelligent and enlightened mind, than the study of Nature and natural objects; and whether we labour to investigate the structure and function of the human system, whether we direct our attention to the classification and habits of the animal kingdom, or prosecute our researches in the more pleasing and varied field of vegetable life, we shall constantly find some new object to attract our attention, some fresh beauties to excite our imagination, and some previously undiscovered source of gratification and delight." (Sir Joseph Paxton, "A Practical Treatise on the Cultivation of the Dahlia", 1838)

"Are our systems the inventions of naturalists, or only their reading of the Book of Nature? and can that book have more than one reading? If these classifications are not mere inventions, if they are not an attempt to classify for our own convenience the objects we study, then they are thoughts which, whether we detect them or not, are expressed in Nature, - then Nature is the work of thought, the production of intelligence carried out according to plan, therefore premeditated, - and in our study of natural objects we are approaching the thoughts of the Creator, reading His conceptions, interpreting a system that is His and not ours." (Jean L R Agassiz, "Methods of Study in Natural History", 1863)

"Science is the systematic classification of experience." (George H Lewes, "The Physical Basis of Mind", 1877)

"The classification of facts, the recognition of their sequence and relative significance is the function of science, and the habit of forming a judgment upon these facts unbiased by personal feeling is characteristic of what may be termed the scientific frame of mind." (Karl Pearson, "The Grammar of Science", 1892)

"The sole purpose of physical theory is to provide a representation and classification of experimental laws; the only test permitting us to judge a physical theory and pronounce it good or bad is the comparison between the consequences of this theory and the experimental laws it has to represent and classify." (Pierre-Maurice-Marie Duhem, “The Aim and Structure of Physical Theory”, 1908)

"Science works by the slow method of the classification of data, arranging the detail patiently in a periodic system into groups of facts, in series like the strata of the rocks. For each series there must be a vocabulary of special words which do not always make good sense when used in another series. But the laws of periodicity seem to hold throughout, among the elements and in every sphere of thought, and we must learn to co-ordinate the whole through our new conception of the reign of relativity." (William H Pallister, "Poems of Science", 1931)

"A […] difference between most system-building in the social sciences and systems of thought and classification of the natural sciences is to be seen in their evolution. In the natural sciences both theories and descriptive systems grow by adaptation to the increasing knowledge and experience of the scientists. In the social sciences, systems often issue fully formed from the mind of one man. Then they may be much discussed if they attract attention, but progressive adaptive modification as a result of the concerted efforts of great numbers of men is rare." (Lawrence J Henderson, "The Study of Man", 1941)

"Statistics is the fundamental and most important part of inductive logic. It is both an art and a science, and it deals with the collection, the tabulation, the analysis and interpretation of quantitative and qualitative measurements. It is concerned with the classifying and determining of actual attributes as well as the making of estimates and the testing of various hypotheses by which probable, or expected, values are obtained. It is one of the means of carrying on scientific research in order to ascertain the laws of behavior of things - be they animate or inanimate. Statistics is the technique of the Scientific Method." (Bruce D Greenschields & Frank M Weida, "Statistics with Applications to Highway Traffic Analyses", 1952)

"It might be reasonable to expect that the more we know about any set of statistics, the greater the confidence we would have in using them, since we would know in which directions they were defective; and that the less we know about a set of figures, the more timid and hesitant we would be in using them. But, in fact, it is the exact opposite which is normally the case; in this field, as in many others, knowledge leads to caution and hesitation, it is ignorance that gives confidence and boldness. For knowledge about any set of statistics reveals the possibility of error at every stage of the statistical process; the difficulty of getting complete coverage in the returns, the difficulty of framing answers precisely and unequivocally, doubts about the reliability of the answers, arbitrary decisions about classification, the roughness of some of the estimates that are made before publishing the final results. Knowledge of all this, and much else, in detail, about any set of figures makes one hesitant and cautious, perhaps even timid, in using them." (Ely Devons, "Essays in Economics", 1961)

"The purpose of a classification scheme is to arrange information, in documents on shelves or on cards in indexes, in a sequence that will be helpful to the user." (Douglas J Foskett, Classification and indexing in the social sciences, 1963)

"Ultimately, discovery and invention are both problems of classification, and classification is fundamentally a problem of finding sameness. When we classify, we seek to group things that have a common structure or exhibit a common behavior." (Grady Booch, "Object-oriented design: With Applications", 1991)

"While classification is important, it can certainly be overdone. Making too fine a distinction between things can be as serious a problem as not being able to decide at all. Because we have limited storage capacity in our brain (we still haven't figured out how to add an extender card), it is important for us to be able to cluster similar items or things together. Not only is clustering useful from an efficiency standpoint, but the ability to group like things together (called chunking by artificial intelligence practitioners) is a very important reasoning tool. It is through clustering that we can think in terms of higher abstractions, solving broader problems by getting above all of the nitty-gritty details." (Joseph P Bigus,"Data Mining with Neural Networks: Solving business problems from application development to decision support", 1996)

"Compared to traditional statistical studies, which are often hindsight, the field of data mining finds patterns and classifications that look toward and even predict the future. In summary, data mining can (1) provide a more complete understanding of data by finding patterns previously not seen and (2) make models that predict, thus enabling people to make better decisions, take action, and therefore mold future events." (Robert Nisbet et al, "Handbook of statistical analysis and data mining applications", 2009)

"A problem in data mining when random variations in data are misclassified as important patterns. Overfitting often occurs when the data set is too small to represent the real world." (Microsoft, "SQL Server 2012 Glossary", 2012)

"The power of deep learning models comes from their ability to classify or predict nonlinear data using a modest number of parallel nonlinear steps4. A deep learning model learns the input data features hierarchy all the way from raw data input to the actual classification of the data. Each layer extracts features from the output of the previous layer." (N D Lewis, "Deep Learning Made Easy with R: A Gentle Introduction for Data Science", 2016)

"Decision trees are important for a few reasons. First, they can both classify and regress. It requires literally one line of code to switch between the two models just described, from a classification to a regression. Second, they are able to determine and share the feature importance of a given training set." (Russell Jurney, "Agile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark", 2017)

"Multilayer perceptrons share with polynomial classifiers one unpleasant property. Theoretically speaking, they are capable of modeling any decision surface, and this makes them prone to overfitting the training data." (Miroslav Kubat," An Introduction to Machine Learning" 2nd Ed., 2017)

"The main reason why pruning tends to improve classification performance on future examples is that the removal of low-level tests, which have poor statistical support, usually reduces the danger of overfitting. This, however, works only up to a certain point. If overdone, a very high extent of pruning can (in the extreme) result in the decision being replaced with a single leaf labeled with the majority class." (Miroslav Kubat," An Introduction to Machine Learning" 2nd Ed., 2017)

"An advantage of random forests is that it works with both regression and classification trees so it can be used with targets whose role is binary, nominal, or interval. They are also less prone to overfitting than a single decision tree model. A disadvantage of a random forest is that they generally require more trees to improve their accuracy. This can result in increased run times, particularly when using very large data sets." (Richard V McCarthy et al, "Applying Predictive Analytics: Finding Value in Data", 2019)

"The classifier accuracy would be extra ordinary when the test data and the training data are overlapping. But when the model is applied to a new data it will fail to show acceptable accuracy. This condition is called as overfitting." (Jesu V Nayahi J & Gokulakrishnan K, "Medical Image Classification", 2019)

Science: On Engineering (Quotes)

noreply@blogger.com (Adrian) — Sun, 22 Aug 2021 09:38:00 GMT

"The engineer must be able not only to design, but to execute. A draftsman may be able to design, but unless he is able to execute his designs to successful operation he cannot be classed as an engineer. The production engineer must be able to execute his work as he has planned it. This requires two qualifications in addition to technical engineering ability: He must know men, and he must have creative ability in applying good statistical, accounting, and 'system' methods to any particular production work he may undertake." (Hugo Diemer, "Industrial Engineering", 1905)

"In fact 'engineering' now often signifies a new system of thought, a fresh method of attack upon the world’s problems the antithesis of traditionalism, with its precedents and dogmas. (Alfred D Flinn, "Leadership in Economic Progress", Civil Engineering Vol. 2 (4), 1932)

"There may be said to be two kinds of engineering, that which is essentially creative, and that which is practiced in pursuit of known methods." (William L Emmet, "The Autobiography of an Engineer", 1940)

"Science acquires knowledge but has no interest in its practical applications. The applications are the work of engineers." (Edwin P Hubble, "The Nature of Science and Other Lectures", 1954)

"An engineering science aims to organize the design principles used in engineering practice into a discipline and thus to exhibit the similarities between different areas of engineering practice and to emphasize the power of fundamental concepts. In short, an engineering science is predominated by theoretical analysis and very often uses the tool of advanced mathematics." (Qian Xuesen, "Engineering cybernetics", 1954)

"The characteristic feature of our age results from the wedding of science and engineering. It is the working together of disciplined curiosity and purposeful ingenuity to create new materials, new forces, and new opportunities which powerfully affect our manner of living and ways of thinking." (Karl T Compton, "A Scientist Speaks: Excerpts from Addresses by Karl Taylor Compton - During the Years 1930-1949", 1955)

"Doing engineering is practicing the art of the organized forcing of technological change." (George Spencer-Brown, Electronics, Vol. 32 (47), 1959)

"Science aims at the discovery, verification, and organization of fact and information [...] engineering is fundamentally committed to the translation of scientific facts and information to concrete machines, structures, materials, processes, and the like that can be used by men." (Eric A Walker, "Engineers and/or Scientists", Journal of Engineering Education Vol. 51, 1961)

"What, then, is science according to common opinion? Science is what scientists do. Science is knowledge, a body of information about the external world. Science is the ability to predict. Science is power, it is engineering. Science explains, or gives causes and reasons." (John Bremer "What Is Science?" [in "Notes on the Nature of Science"], 1962)

"Engineering is the art of skillful approximation; the practice of gamesmanship in the highest form. In the end it is a method broad enough to tame the unknown, a means of combing disciplined judgment with intuition, courage with responsibility, and scientific competence within the practical aspects of time, of cost, and of talent. This is the exciting view of modern-day engineering that a vigorous profession can insist be the theme for education and training of its youth. It is an outlook that generates its strength and its grandeur not in the discovery of facts but in their application; not in receiving, but in giving. It is an outlook that requires many tools of science and the ability to manipulate them intelligently In the end, it is a welding of theory and practice to build an early, strong, and useful result. Except as a valuable discipline of the mind, a formal education in technology is sterile until it is applied." (Ronald B Smith, "Professional Responsibility of Engineering", Mechanical Engineering Vol. 86 (1), 1964)

"Engineering is a method and a philosophy for coping with that which is uncertain at the earliest possible moment and to the ultimate service to mankind. It is not a science struggling for a place in the sun. Engineering is extrapolation from existing knowledge rather than interpolation between known points. Because engineering is science in action - the practice of decision making at the earliest moment - it has been defined as the art of skillful approximation. No situation in engineering is simple enough to be solved precisely, and none worth evaluating is solved exactly. Never are there sufficient facts, sufficient time, or sufficient money for an exact solution, for if by chance there were, the answer would be of academic and not economic interest to society. These are the circumstances that make engineering so vital and so creative." (Ronald B Smith, "Engineering Is…", Mechanical Engineering Vol. 86 (5), 1964)

"Engineering is knowledge work. That is, although the goal of engineering may be to produce useful objects, engineers do not construct such object themselves. Rather they aim to generate knowledge that will allow such objects to be built." (Dorothy A Winsor, "Writing Like an Engineer: A Rhetorical Education", 1966)

"Engineering is a profession, an art of action and synthesis and not simply a body of knowledge. Its highest calling is to invent and innovate." (Daniel V DeSimone & Hardy Cross, "Education for Innovation", 1968)

"Technological invention and innovation are the business of engineering. They are embodied in engineering change." (Daniel V DeSimone & Hardy Cross, "Education for Innovation", 1968)

"[...] it is rather more difficult to recapture directness and simplicity than to advance in the direction of ever more sophistication and complexity. Any third-rate engineer or researcher can increase complexity; but it takes a certain flair of real insight to make things simple again." (Ernst F Schumacher, "Small Is Beautiful", 1973)

"Engineering is a profession, an art of action and synthesis and not simply a body of knowledge. Its highest calling is to invent and innovate." (Hardy Cross, "Education for Innovation", 1968)

"Engineering is superficial only to those who view it superficially. At the heart of engineering lies existential joy." (Samuel C Florman, "The Existential Pleasures of Engineering", 1976)

"From the point of view of modern science, design is nothing, but from the point of view of engineering, design is everything. It represents the purposive adaptation of means to reach a preconceived end, the very essence of engineering." (Edwin T Layton Jr., "American Ideologies of Science and Engineering", Technology and Culture No. 4, 1976)

"[An engineer's] invention causes things to come into existence from ideas, makes world conform to thought; whereas science, by deriving ideas from observation, makes thought conform to existence." (Carl Mitcham, "Types of Technology", Research in Philosophy & Technology Vol. 1, 1978)

"A good scientist is a person with original ideas. A good engineer is a person who makes a design that works with as few original ideas as possible. There are no prima donnas in engineering." (Freeman Dyson, "Disturbing the Universe", 1979)

"Engineering or Technology is the making of things that did not previously exist, whereas science is the discovering of things that have long existed. Technological results are forms that exist only because people want to make them, whereas scientific results are formulations of what exists independently of human intentions." (David Billington, "The Tower and the Bridge: The New Art of Structural Engineering", 1983)

"As engineering becomes increasingly central to the shaping of society, it is ever more important that engineers become introspective. Rather than merely revel in our technical successes, we should intensify our efforts to explore, define, and improve the philosophical foundations of our professions." (Samuel C Florman, "The Civilized Engineer", 1985)

"Engineering, like poetry, is an attempt to approach perfection. And engineers, like poets, are seldom completely satisfied with their creations. They notice, even if no one else does, the word that is not quite 'le mot juste' or the hairline crack that blemishes the structure." (Henry Petroski, "To Engineer Is Human: The Role of Failure in Successful Design", 1985)

"[...] without imagination, heightened awareness, moral sense, and some reference to the general culture, the engineering experience becomes less meaningful, less fulfilling than it should be." (Samuel C Florman, "The Civilized Engineer", 1985)

"Science can amuse and fascinate us all, but it is engineering that changes the world." (Isaac Asimov, "Isaac Asimov’s Book of Science and Nature Quotations", 1988)

"Engineering knowledge reflects the fact that design does not take place for its own sake and in isolation." (Walter G Vincenti, "What Engineers Know and How They Know It", 1990)

"Engineers use knowledge primarily to design, produce, and operate artifacts. [...] Scientists, by contrast, use knowledge primarily to generate more knowledge." (Walter Vincenti, What Engineers Know and How They Know It, 1990)

"All of engineering involves some creativity to cover the parts not known, and almost all of science includes some practical engineering to translate the abstractions into practice." (Richard W Hamming, "The Art of Probability for Scientists and Engineers", 1991)

"No matter how vigorously a 'science' of design may be pushed, the successful design of real things in a contingent world will always be based more on art than on science. Unquantifiable judgments and choices are the elements that determine the way a design comes together. Engineering design is simply that kind of process. It always has been; it always will be. (Eugene S Ferguson , "Engineering and the Mind’s Eye", 1992)

"Good engineering is not a matter of creativity or centering or grounding or inspiration or lateral thinking, as useful as those might be, but of decoding the clever, even witty, messages the solution space carves on the corpses of the ideas in which you believed with all your heart, and then building the road to the next message." (Fred Hapgood, "Up the infinite Corridor: MIT and the Technical Imagination", 1993)

"Engineering is quite different from science. Scientists try to understand nature. Engineers try to make things that do not exist in nature. Engineers stress invention. To embody an invention the engineer must put his idea in concrete terms, and design something that people can use. That something can be a device, a gadget, a material, a method, a computing program, an innovative experiment, a new solution to a problem, or an improvement on what is existing. Since a design has to be concrete, it must have its geometry, dimensions, and characteristic numbers. Almost all engineers working on new designs find that they do not have all the needed information. Most often, they are limited by insufficient scientific knowledge. Thus they study mathematics, physics, chemistry, biology and mechanics. Often they have to add to the sciences relevant to their profession. Thus engineering sciences are born." (Yuan-Cheng Fung & Pin Tong, "Classical and Computational Solid Mechanics", 2001)

"Engineering isn't about perfect solutions; it's about doing the best you can with limited resources." (Randy Pausch, "The Last Lecture", 2008)

"The central activity of engineering, as distinguished from science, is the design of new devices, processes and systems." (Myron Tribus, "Rational Descriptions, Decisions and Designs", 2016)

"Designers need to be part engineer. Good design only exists in concert with engineering. That is because form has to follow function, so you focus on function and then give the object a shape to make it appealing." (Ferdinand A Porsche)

"Engineering is a living branch of human activity and its frontiers are by no means exhausted." (Igor I Sikorsky)

"Engineering is an art of simplification, and the rules - when and how to simplify - are a matter of experience and intuition." (Olle I Elgerd)

"Engineering is not merely knowing and being knowledgeable, like a walking encyclopedia; engineering is not merely analysis; engineering is not merely the possession of the capacity to get elegant solutions to non-existent engineering problems; engineering is practicing the art of the organizing forces of technological change. [...] Engineers operate at the interface between science and society." (Gordon S Brown)

"Engineering is the conscious application of science to the problems of economic production." (Halbert P Gillette)

"Engineering is the art or science of utilizing, directing or instructing others in the utilization of the principles, forces, properties and substances of nature in the production, manufacture, construction, operation and use of things [...] or of means, methods, machines, devices and structures [...]" (Alfred W Kiddle)

"Engineering is the professional and systematic application of science to the efficient utilization of natural resources to produce wealth." (T J Hoover & J C L Fish)

"Indeed, the most important part of engineering work - and also of other scientific work - is the determination of the method of attacking the problem, whatever it may be." (Charles P Steinmetz)

"The essence of engineering consists not so much in the mere construction of the spectacular layouts or developments, but in the invention required - the analysis of the problem, the design, the solution by the mind which directs it all." (William Hood)

"The scientist describes what is; the engineer creates what never was." (Theodore von Kármán)

Science: On Scientific Method (Quotes)

noreply@blogger.com (Adrian) — Sat, 21 Aug 2021 22:25:00 GMT

"Nothing is more certain in scientific method than that approximate coincidence alone can be expected. In the measurement of continuous quantity perfect correspondence must be accidental, and should give rise to suspicion rather than to satisfaction." (William S Jevons, "The Principles of Science: A Treatise on Logic and Scientific Method", 1874)

"There is no short cut to truth, no way to gain a knowledge of the universe except through the gateway of scientific method." (Karl Pearson, "The Grammar of Science", 1892)

“It must be gently but firmly pointed out that analogy is the very corner-stone of scientific method. A root-and-branch condemnation would invalidate any attempt to explain the unknown in terms of the known, and thus prune away every hypothesis.” (Archie E Heath, “On Analogy”, The Cambridge Magazine, 1918)

"The fundamental difference between engineering with and without statistics boils down to the difference between the use of a scientific method based upon the concept of laws of nature that do not allow for chance or uncertainty and a scientific method based upon the concepts of laws of probability as an attribute of nature." (Walter A Shewhart, 1940)

"When the number of factors coming into play in a phenomenological complex is too large, scientific method in most cases fails us. One need only think of the weather, in which case prediction even for a few days ahead is impossible. Nevertheless no one doubts that we are confronted with a causal connection whose causal components are in the main known to us. Occurrences in this domain are beyond the reach of exact prediction because of the variety of factors in operation, not because of any lack of order in nature." (Albert Einstein, "Science and Religion", 1941)

"Besides electrical engineering theory of the transmission of messages, there is a larger field [cybernetics] which includes not only the study of language but the study of messages as a means of controlling machinery and society, the development of computing machines and other such automata, certain reflections upon psychology and the nervous system, and a tentative new theory of scientific method." (Norbert Wiener, "Cybernetics", 1948)

"The construction of hypotheses is a creative act of inspiration, intuition, invention; its essence is the vision of something new in familiar material. The process must be discussed in psychological, not logical, categories; studied in autobiographies and biographies, not treatises on scientific method; and promoted by maxim and example, not syllogism or theorem." (Milton Friedman, "Essays in Positive Economics", 1953)

"Science cannot be based on dogma or authority of any kind, nor on any institution or revelation, unless indeed it be of the Book of Nature that lies open before our eyes. We need not dwell on the processes of acquiring knowledge by observation, experiment, and inductive and deductive reasoning. The study of scientific method both in theory and practice is of great importance. It is inherent in the philosophy that the record may be imperfect and the conceptions erroneous; the potential fallibility of our science is not only acknowledged but also insisted upon." (Sir Robert Robinson, "Science and the Scientist", Nature Vol. 176 (4479), 1955)

"Science no longer confronts nature as an objective observer, but sees itself as an actor in this interplay between man and nature. The scientific method of analysing, explaining, and classifying has become conscious of its limitations. […] Method and object can no longer be separated." (Werner K Heisenberg, "Das Naturbild der heutigen Physik" ["The Physicist's Conception of Nature"], 1955)

"Scientific method is the way to truth, but it affords, even in principle, no unique definition of truth. Any so-called pragmatic definition of truth is doomed to failure equally." (Willard v O Quine, "Word and Object", 1960)

"Model-making, the imaginative and logical steps which precede the experiment, may be judged the most valuable part of scientific method because skill and insight in these matters are rare. Without them we do not know what experiment to do. But it is the experiment which provides the raw material for scientific theory. Scientific theory cannot be built directly from the conclusions of conceptual models." (Herbert G Andrewartha, "Introduction to the Study of Animal Population", 1961)

"Statistics is the branch of scientific method which deals with the data obtained by counting or measuring the properties of populations of natural phenomena." (Sir Maurice G Kendall & Alan Stuart, "The Advanced Theory of Statistics", 1963)

"Observation, reason, and experiment make up what we call the scientific method. (Richard Feynman, "Mainly mechanics, radiation, and heat", 1963)

"When terms [...] evolve and change definition with time; and when the social reality which terms are intended to organize and render intelligible is also seen to be in flux, capturing the truth in a net of words becomes a matter of intuition and style more than of any scientific method that can be replicated by others and made to achieve the same result every time someone asks the same question, or undertakes the same operations." (William H McNeill, "Discrepancies among the Social Sciences", 1981)

"Science and mathematics […] have added little to our understanding of such things as Truth, Beauty, and Justice. There may be definite limits to the applicability of the scientific method." (Richard W Hamming, "Methods of Mathematics Applied to Calculus, Probability, and Statistics", 1985)

"The heart of the scientific method is the problem-hypothesis-test process. And, necessarily, the scientific method involves predictions. And predictions, to be useful in scientific methodology, must be subject to test empirically." (Paul Davies, "The Cosmic Blueprint: New Discoveries in Nature's Creative Ability to, Order the Universe", 1988)

"Scientists use mathematics to build mental universes. They write down mathematical descriptions - models - that capture essential fragments of how they think the world behaves. Then they analyse their consequences. This is called 'theory'. They test their theories against observations: this is called 'experiment'. Depending on the result, they may modify the mathematical model and repeat the cycle until theory and experiment agree. Not that it's really that simple; but that's the general gist of it, the essence of the scientific method." (Ian Stewart & Martin Golubitsky, "Fearful Symmetry: Is God a Geometer?", 1992)

"But our ways of learning about the world are strongly influenced by the social preconceptions and biased modes of thinking that each scientist must apply to any problem. The stereotype of a fully rational and objective ‘scientific method’, with individual scientists as logical (and interchangeable) robots, is self-serving mythology." (Stephen J Gould, "This View of Life: In the Mind of the Beholder", "Natural History", Vol. 103, No. 2, 1994)

"The traditional, scientific method for studying such systems is known as reductionism. Reductionism sees the parts as paramount and seeks to identify the parts, understand the parts and work up from an understanding of the parts to an understanding of the whole. The problem with this is that the whole often seems to take on a form that is not recognizable from the parts. The whole emerges from the interactions between the parts, which affect each other through complex networks of relationships. Once it has emerged, it is the whole that seems to give meaning to the parts and their interactions." (Michael C Jackson, "Systems Thinking: Creative Holism for Managers", 2003)

"Scientists pursue ideas in an ill-defined but effective way that is often called the scientific method. There is no strict rule of procedure that will lead you from a good idea to a Nobel prize or even to a publishable discovery. Some scientists are meticulously careful; others are highly creative. The best scientists are probably both careful and creative. Although there are various scientific methods in use, a typical approach consists of a series of steps." (Peter Atkins et al, "Chemical Principles: The Quest for Insight" 6th ed., 2013)

"Science, at its core, is simply a method of practical logic that tests hypotheses against experience. Scientism, by contrast, is the worldview and value system that insists that the questions the scientific method can answer are the most important questions human beings can ask, and that the picture of the world yielded by science is a better approximation to reality than any other." (John M Greer, "After Progress: Reason and Religion at the End of the Industrial Age", 2015)

"The scientific method does not begin with the injunction to use reason and logic, and to obey the principle of sufficient reason and Occam’s razor. Instead, it begins with the word 'Observe'. In other words, if reality in itself is unobservable - which is of course the case - then the scientific method automatically fails to tell us a single thing about it." (Thomas Stark, "God Is Mathematics: The Proofs of the Eternal Existence of Mathematics", 2018)

"One of the severest tests of a scientific mind is to discern the limits of the legitimate application of the scientific method." (James C Maxwell)

Cybernetics: On Machines (Quotes)

noreply@blogger.com (Adrian) — Sat, 21 Aug 2021 19:29:00 GMT

"Thanks to the psycho-physical reversibility, we can materialize the act of creation. Undoubtedly, the inventive machine has not yet been created, but we can see its creation soon." (Stefan Odobleja, "Consonant Psychology", 1938)

"Every time one combines and records facts in accordance with established logical processes, the creative aspect of thinking is concerned only with the selection of the data and the process to be employed, and the manipulation thereafter is repetitive in nature and hence a fit matter to be relegated to the machines." (Vannevar Bush, "As We May Think", 1945)

"A machine can handle information; it can calculate, conclude, and choose; it can perform reasonable operations with information. A machine. therefore, can think." (Edmund C Berkeley, "Giant Brains or Machines that Think", 1949)

"From a narrow point of view, a machine that only thinks produces only information. It takes in information in one state, and it puts out information in another state. From this viewpoint, information in itself is harmless; it is just an arrangement of marks; and accordingly, a machine that thinks is harmless, and no control is necessary." (Edmund C Berkeley, "Giant Brains or Machines that Think", 1949)

"Now when we speak of a machine that thinks, or a mechanical brain, what do we mean? Essentially, a mechanical brain is a machine that handles information, transfers information automatically from one part of the machine to another, and has a flexible control over the sequence of its operations. No human being is needed around such a machine to pick up a physical piece of information produced in one part of the machine, personally move it to another part of the machine, and there put it in again. Nor is any human being needed to give the machine instructions from minute to minute. Instead, we can write out the whole program to solve a problem, translate the program into machine language, and put the program into the machine." (Edmund C Berkeley, "Giant Brains or Machines that Think", 1949)

"Probably the foremost problem which machines that think can solve is automatic control over all sorts of other machines. This involves controlling a machine that is running so that it will do the right thing at the right time in response to information." (Edmund C Berkeley, "Giant Brains or Machines that Think", 1949)

"A computer would deserve to be called intelligent if it could deceive a human into believing that it was human." (Alan Turing, "Computing Machinery and Intelligence", 1950)

"When intelligent machines are constructed, we should not be surprised to find them as confused and as stubborn as men in their convictions about mind-matter, consciousness, free will, and the like." (Marvin Minsky, "Matter, Mind, and Models", Proceedings of the International Federation of Information Processing Congress Vol. 1 (49), 1965)

"A smart machine will first consider which is more worth its while: to perform the given task or, instead, to figure some way out of it." (Stanisław Lem, "The Futurological Congress The Futurological Congress", 1971)

"It's difficult to be rigorous about whether a machine really 'knows', 'thinks', etc., because we're hard put to define these things. We understand human mental processes only slightly better than a fish understands swimming." (John McCarthy, "The Little Thoughts of Thinking Machines", Psychology Today, 1983)

"The hardest problems we have to face do not come from philosophical questions about whether brains are machines or not. There is not the slightest reason to doubt that brains are anything other than machines with enormous numbers of parts that work in perfect accord with physical laws. As far as anyone can tell, our minds are merely complex processes. The serious problems come from our having had so little experience with machines of such complexity that we are not yet prepared to think effectively about them." (Marvin Minsky, 1986)

"Once a computer achieves human intelligence it will necessarily roar past it." (Ray Kurzweil, "The Age of Spiritual Machines: When Computers Exceed Human Intelligence", 1999)

"As machines slip from human control they will do more than become conscious. They will become spiritual beings, whose inner life is no more limited by conscious thought than ours. Not only will they think and have emotions. They will develop the errors and illusions that go with self-awareness." (John Gray, "Straw Dogs: Thoughts on Humans and Other Animals", 2002)

Systems Thinking: On Social Systems (Quotes)

noreply@blogger.com (Adrian) — Tue, 17 Aug 2021 08:18:00 GMT

"The determination of the average man is not merely a matter of speculative curiosity; it may be of the most important service to the science of man and the social system. It ought necessarily to precede every other inquiry into social physics, since it is, as it were, the basis. The average man, indeed, is in a nation what the centre of gravity is in a body; it is by having that central point in view that we arrive at the apprehension of all the phenomena of equilibrium and motion." (Adolphe Quetelet, "A Treatise on Man and the Development of his Faculties", 1842)

"Social structures are the products of social synergy, i.e., of the interaction of different social forces, all of which, in and of themselves, are destructive, but whose combined effect, mutually checking, constraining, and equilibrating one another, is to produce structures. The entire drift is toward economy, conservatism, and the prevention of waste. Social structures are mechanisms for the production of results, and the results cannot be secured without them. They are reservoirs of power." (James Q Dealey & Lester F Ward, "A Text-book of Sociology", 1905)

"Social organizations are flagrantly open systems in that the input of energies and the conversion of output into further energy input consists of transactions between the organization and its environment." (Daniel Katz, "The Social Psychology of Organizations", 1929)

"For the state centralisation is the appropriate form of organisation, since it aims at the greatest possible uniformity in social life for the maintenance of political and social equilibrium. But for a movement whose very existence depends on prompt action at any favourable moment and on the independent thought and action of its supporters, centralism could but be a curse by weakening its power of decision and systematically repressing all immediate action. [...] Organisation is, after all, only a means to an end. When it becomes an end in itself, it kills the spirit and the vital initiative of its members and sets up that domination by mediocrity which is the characteristic of all bureaucracies." (Rudolf Rocker, "Anarcho-Syndicalism", 1938)

"Every social system is a functioning entity. That is, it is a system of interdependent structures and processes such that it tends to maintain a relative stability and distinctiveness of pattern and behaviour as an entity by contrast with its - social or other - environment, and with it a relative independence from environmental forces. It 'responds' , to be sure, to the environmental stimuli, but is not completely assimilated to its environment, maintaining rather an element of distinctiveness in the face of variations in environmental conditions. To this extent it is analogous to an organism." (Talcott Parsons, "Propaganda and Social Control", 1942)

"The technology is the independent variable, the social system the dependent variable. Social systems are therefore determined by systems of technology; as the latter change, so do the former." (Leslie White, "The Science of Culture", 1949)

"A social system is a mode of organization of action elements relative to the persistence or ordered processes of change of the interactive patterns of a plurality of individual actors." (Talcott Parsons, "The social system", 1951)

"Culture has been distinguished from the other elements of action by the fact that it is intrinsically transmissible from one action system to another from personality to personality by learning and from social system to social system by diffusion. This is because culture is constituted by 'ways of orienting and acting', these ways being 'embodied in' meaningful symbols." (Talcott Parsons, "Toward a general theory of action", 1951)

"The thing that distinguishes social systems from physical or even biological systems is their incomparable (and embarrassing) richness in special cases. Generalizations in the social sciences are mere pathways which lead through a riotous forest of individual trees, each a species unto itself." (Kenneth Boulding, "Evidences for an Administrative Science: A review of the Administrative", Science Quarterly Vol. 1-2, 1958)

"An organization which depends solely upon its blueprints of prescribed behavior is a very fragile social system." (Daniel Katz, "The motivational basis of organizational behavior", Behavioral science, 1964)

"Negative feedback is the form normally encountered in the control of physical systems. Yet, positive feedback dominates in the growth and decline patterns of social systems." (Jay W Forrester, "Modeling the Dynamic Processes of Corporate Growth", 1964)

"Our social systems are highly nonlinear. It seems likely that such nonlinearities, coupled with the unstable tendencies caused by amplifications and time delays, create the characteristic modes of behavior […]" (Jay W Forrester, "Modeling the Dynamic Processes of Corporate Growth", 1964)

"System theory is basically concerned with problems of relationships, of structure, and of interdependence rather than with the constant attributes of objects. In general approach it resembles field theory except that its dynamics deal with temporal as well as spatial patterns. Older formulations of system constructs dealt with the closed systems of the physical sciences, in which relatively self-contained structures could be treated successfully as if they were independent of external forces. But living systems, whether biological organisms or social organizations, are acutely dependent on their external environment and so must be conceived of as open systems." (Daniel Katz, "The Social Psychology of Organizations", 1966)

"A system of opinions which, being founded on a system of accepted values, determines the attitudes and behavior of men with respect to desired objectives of development of the society, social group or individual." (Adam Schaff, "Functional Definition, Ideology, and the Problem of the 'fin du siècle' of Ideology, L'Homme et la Société, 1967)

"Trust is an important lubricant of a social system. It is extremely efficient; it saves a lot of trouble to have a fair degree of reliance on other people's word. Unfortunately this is not a commodity which can be bought very easily. If you have to buy it, you already have some doubts about what you have bought." (Kenneth Arrow, "The Limits of Organization", 1974)

"When you are confronted by any complex social system […] with things about it that you’re dissatisfied with and anxious to fix, you cannot just step in and set about fixing with much hope of helping. This realization is one of the sore discouragements of our century […] You cannot meddle with one part of a complex system from the outside without the almost certain risk of setting off disastrous events that you hadn’t counted on in other, remote parts. If you want to fix something you are first obliged to understand […] the whole system. […] Intervening is a way of causing trouble." (Lewis Thomas, "The Medusa and the Snail: More Notes of a Biology Watcher", 1974)

"In a biological or social system each holon must assert its individuality in order to maintain the system's stratified order, but it must also submit to the demands of the whole in order to make the system viable. These two tendencies are opposite but complementary. In a healthy system - an individual, a society, or an ecosystem - there is a balance between integration and self-assertion. This balance is not static but consists of a dynamic interplay between the two complementary tendencies, which makes the whole system flexible and open to change." (Fritjof Capra, "The Turning Point: Science, Society, and the Turning Culture", 1982)

"The world is a complex, interconnected, finite, ecological–social–psychological–economic system. We treat it as if it were not, as if it were divisible, separable, simple, and infinite. Our persistent, intractable global problems arise directly from this mismatch." (Donella Meadows, "Whole Earth Models and Systems", 1982)

"Organizations are (1) social entities that (2) are goal-directed, (3) are designed as deliberately structured and coordinated activity systems, and (4) are linked to the external environment." (Richard L Daft, "Organization Theory and Design", 1983)

"Markets are social organizations, structured and regulated by more or less well-defined social rule systems." (Tom R Burns, "The shaping of social organization", 1987)

"Coherence and harmony in relations and interactions between the members of a human social system are due to the coherence and harmony of their growth in it, in an ongoing social learning which their own social (linguistic) operation defines and which is possible thanks to the genetic and ontogenetic processes that permit structural plasticity of the members." (Humberto Maturana, "The tree of Knowledge", 1992)

"There must be, however, cybernetic or homeostatic mechanisms for preventing the overall variables of the social system from going beyond a certain range. There must, for instance, be machinery for controlling the total numbers of the population; there must be machinery for controlling conflict processes and for preventing perverse social dynamic processes of escalation and inflation. One of the major problems of social science is how to devise institutions which will combine this overall homeostatic control with individual freedom and mobility." (Kenneth Boulding, "Economics of the coming spaceship Earth", 1994)

"First, social systems are inherently insensitive to most policy changes that people choose in an effort to alter the behavior of systems. In fact, social systems draw attention to the very points at which an attempt to intervene will fail. Human intuition develops from exposure to simple systems. In simple systems, the cause of a trouble is close in both time and space to symptoms of the trouble. If one touches a hot stove, the burn occurs here and now; the cause is obvious. However, in complex dynamic systems, causes are often far removed in both time and space from the symptoms. True causes may lie far back in time and arise from an entirely different part of the system from when and where the symptoms occur. However, the complex system can mislead in devious ways by presenting an apparent cause that meets the expectations derived from simple systems."(Jay W Forrester, "Counterintuitive Behavior of Social Systems", 1995)

"Second, social systems seem to have a few sensitive influence points through which behavior can be changed. These high-influence points are not where most people expect. Furthermore, when a high-influence policy is identified, the chances are great that a person guided by intuition and judgment will alter the system in the wrong direction." (Jay W Forrester, "Counterintuitive Behavior of Social Systems", 1995)

"Third, social systems exhibit a conflict between short-term and long-term consequences of a policy change. A policy that produces improvement in the short run is usually one that degrades a system in the long run. Likewise, policies that produce long-run improvement may initially depress behavior of a system. This is especially treacherous. The short run is more visible and more compelling. Short-run pressures speak loudly for immediate attention. However, sequences of actions all aimed at short-run improvement can eventually burden a system with long-run depressants so severe that even heroic short-run measures no longer suffice. Many problems being faced today are the cumulative result of short-run measures taken in prior decades." (Jay W Forrester, "Counterintuitive Behavior of Social Systems", 1995)

"Networks constitute the new social morphology of our societies, and the diffusion of networking logic substantially modifies the operation and outcomes in processes of production, experience, power, and culture. While the networking form of social organization has existed in other times and spaces, the new information technology paradigm provides the material basis for its pervasive expansion throughout the entire social structure." (Manuel Castells, "The Rise of the Network Society", 1996)

[systems dynamics:] "A field of study that includes a methodology for constructing computer simulation models to achieve better under-standing of social and corporate systems. It draws on organizational studies, behavioral decision theory, and engineering to provide a theoretical and empirical base for structuring the relationships in complex systems." (Virginia Anderson & Lauren Johnson, "Systems Thinking Basics: From Concepts to Casual Loops", 1997)

"In a closed system, the change in entropy must always be 'positive', meaning toward death. However, in open biological or social systems, entropy can be arrested and may even be transformed into negative entropy - a process of more complete organization and enhanced ability to transform resources. Why? Because the system imports energy and resources from its environment, leading to renewal. This is why education and learning are so important, as they provide new and stimulating input (termed neg-entropy) that can transform each of us." (Stephen G Haines, "The Managers Pocket Guide to Systems Thinking & Learning", 1998)

"[...] synergy is the consequence of the energy expended in creating order. It is locked up in the viable system created, be it an organism or a social system. It is at the level of the system. It is not discernible at the level of the system. It is not discernible at the level of the system’s components. Whenever the system is dismembered to examine its components, this binding energy dissipates." (J-C Spender, "Organizational Knowledge, Collective Practice and Penrose Rents", 1999)

"The tipping point is that magic moment when an idea, trend, or social behavior crosses a threshold, tips, and spreads like wildfire." (Malcolm T Gladwell, "The Tipping Point: How Little Things Can Make a Big Difference", 2000)

"The theory of Tipping Points requires, however, that we reframe the way we think about the world. [...] We have trouble estimating dramatic, exponential change. [...] There are abrupt limits to the number of cognitive categories we can make and the number of people we can truly love and the number of acquaintances we can truly know. We throw up our hands at a problem phrased in an abstract way, but have no difficulty at all solving the same problem rephrased as a social dilemma. All of these things are expressions of the peculiarities of the human mind and heart, a refutation of the notion that the way we function and communicate and process information is straightforward and transparent. It is not. It is messy and opaque." (Malcolm T Gladwell, "The Tipping Point: How Little Things Can Make a Big Difference", 2000)

"Defined from a societal standpoint, information may be seen as an entity which reduces maladjustment between system and environment. In order to survive as a thermodynamic entity, all social systems are dependent upon an information flow. This explanation is derived from the parallel between entropy and information where the latter is regarded as negative entropy (negentropy). In more common terms information is a form of processed data or facts about objects, events or persons, which are meaningful for the receiver, inasmuch as an increase in knowledge reduces uncertainty." (Lars Skyttner, "General Systems Theory: Ideas and Applications", 2001)

"Chaos theory explains the ways in which natural and social systems organize themselves into stable entities that have the ability to resist small disturbances and perturbations. It also shows that when you push such a system too far it becomes balanced on a metaphoric knife-edge. Step back and it remains stable; give it the slightest nudge and it will move into a radically new form of behavior such as chaos." (F David Peat, "From Certainty to Uncertainty", 2002)

"Organizations are complex social systems that sometimes perform remarkably well and sometimes fail miserably. Organizational psychology is a subfield within the larger domain of industrial/organizational psychology that seeks to facilitate a greater understanding of social processes in organizations. Organizational psychologists also seek to use these insights to enhance the effectiveness of organizations - a goal that is potentially beneficial to all." (Steve M Jex, "Organizational Psychology", 2002)

"A physical system is just that: a physical system. What is systematized is matter itself, and the processes in which the system is realized are also material. But a biological system is more complex: it is both biological and physical — it is matter with the added component of life; and a social system is more complex still: it is physical, and biological, with the added component of social order, or value." (Michael Halliday, 2005)

"Social ecology is based on the conviction that nearly all of our present ecological problems originate in deep-seated social problems. It follows, from this view, that these ecological problems cannot be understood, let alone solved, without a careful understanding of our existing society and the irrationalities that dominate it. To make this point more concrete: economic, ethnic, cultural, and gender conflicts, among many others, lie at the core of the most serious ecological dislocations we face today—apart, to be sure, from those that are produced by natural catastrophes." (Murray Bookchin, "Social Ecology and Communalism", 2007)

"Any system, biological, economic, or social, that gets so encrusted that it cannot self-evolve, a system that systematically scorns experimentation and wipes out the raw material of innovation, is doomed over the long term on this highly variable planet." (Donella Meadows, "Thinking in Systems: A Primer", 2008)

"Enterprise engineering is an emerging discipline that studies enterprises from an engineering perspective. The first paradigm of this discipline is that enterprises are purposefully designed and implemented systems. Consequently, they can be re-designed and re-implemented if there is a need for change. The second paradigm of enterprise engineering is that enterprises are social systems. This means that the system elements are social individuals, and that the essence of an enterprise's operation lies in the entering into and complying with commitments between these social individuals." (Erik Proper, "Advances in Enterprise Engineering II", 2009)

"In short, synergy is the consequence of the energy expended in creating order. It is locked up in the viable system created, be it an organism or a social system. It is at the level of the system. It is not discernible at the level of the system. It is not discernible at the level of the system's components. Whenever the system is dismembered to examine its components, this binding energy dissipates." (J-C Spender, "Organizational Knowledge, Collective Practice and Penrose Rents", 2009)

"Complex systems defy intuitive solutions. Even a third-order, linear differential equation is unsolvable by inspection. Yet, important situations in management, economics, medicine, and social behavior usually lose reality if simplified to less than fifth-order nonlinear dynamic systems. Attempts to deal with nonlinear dynamic systems using ordinary processes of description and debate lead to internal inconsistencies. Underlying assumptions may have been left unclear and contradictory, and mental models are often logically incomplete. Resulting behavior is likely to be contrary to that implied by the assumptions being made about' underlying system structure and governing policies." (Jay W Forrester, "Modeling for What Purpose?", The Systems Thinker Vol. 24 (2), 2013)

"The universe of all things that exist may be understood as a universe of systems where a system is defined as any set of related and interacting elements. This concept is primitive and powerful and has been used increasingly over the last half-century to organize knowledge in virtually all domains of interest to investigators. As human inventions and social interactions grow more complex, general conceptual frameworks that integrate knowledge among different disciplines studying those emerging systems grow more important. Living systems theory (LST) instructs integrative research among biological and social sciences and related academic disciplines." (G A Swanson & James G Miller, "Living Systems Theory", 2013)

"Systems thinking is a discipline or process that considers how individual elements interact with one another as part of a whole entity. As an approach to solving problems, systems thinking uses relationships among individual elements and the dynamics of these relationships to explain the behavior of systems such as an ecosystem, social system, or organization." (Karen L Higgins, "Economic Growth and Sustainability: Systems Thinking for a Complex World", 2015)

"In the physics [entropy is the] rate of system´s messiness or disorder in a physical system. In the social systems theory - social entropy is a sociological theory that evaluates social behaviors using a method based on the second law of thermodynamics." (Justína Mikulášková et al, "Spiral Management: New Concept of the Social Systems Management", 2020)

Systems Thinking: On Fractals (Quotes)

noreply@blogger.com (Adrian) — Sun, 15 Aug 2021 08:20:00 GMT

"A fractal is a mathematical set or concrete object that is irregular or fragmented at all scales [...]" (Benoît Mandelbrot, "The Fractal Geometry of Nature", 1982)

"A fractal is by definition a set for which the Hausdorff-Besicovitch dimension strictly exceeds the topological dimension." (Benoît Mandelbrot, "The Fractal Geometry of Nature", 1982)

"In the mind's eye, a fractal is a way of seeing infinity." (James Gleick, "Chaos: Making a New Science, A Geometry of Nature", 1987)

"The chaos theory will require scientists in all fields to, develop sophisticated mathematical skills, so that they will be able to better recognize the meanings of results. Mathematics has expanded the field of fractals to help describe and explain the shapeless, asymmetrical find randomness of the natural environment." (Theoni Pappas, "More Joy of Mathematics: Exploring mathematical insights & concepts", 1991)

"Some fractals come close to qualifying as chaos by being produced by uncomplicated rules while appearing highly intricate and not just unfamiliar in structure. There is, however, one very close liaison between fractality and chaos; strange attractors are fractals." (Edward N Lorenz, "The Essence of Chaos", 1993)

"It is time to employ fractal geometry and its associated subjects of chaos and nonlinear dynamics to study systems engineering methodology (SEM). [...] Fractal geometry and chaos theory can convey a new level of understanding to systems engineering and make it more effective." (Arthur D Hall, "The fractal architecture of the systems engineering method", "Systems, Man and Cybernetics", Vol. 28 (4), 1998)

"The self-similarity of fractal structures implies that there is some redundancy because of the repetition of details at all scales. Even though some of these structures may appear to teeter on the edge of randomness, they actually represent complex systems at the interface of order and disorder." (Edward Beltrami, "What is Random?: Chaos and Order in Mathematics and Life", 1999)

"If financial markets aren't efficient, then what are they? According to the 'fractal market hypothesis', they are highly unstable dynamic systems that generate stock prices which appear random, but behind which lie deterministic patterns." (Steve Keen, "Debunking Economics: The Naked Emperor Of The Social Sciences", 2001)

"Mathematical fractals are generated by repeating the same simple steps at ever decreasing scales. In this way an apparently complex shape, containing endless detail, can be generated by the repeated application of a simple algorithm. In turn these fractals mimic some of the complex forms found in nature. After all, many organisms and colonies also grow though the repetition of elementary processes such as, for example, branching and division." (F David Peat, "From Certainty to Uncertainty", 2002)

"Do I claim that everything that is not smooth is fractal? That fractals suffice to solve every problem of science? Not in the least. What I'm asserting very strongly is that, when some real thing is found to be un-smooth, the next mathematical model to try is fractal or multi-fractal. A complicated phenomenon need not be fractal, but finding that a phenomenon is 'not even fractal' is bad news, because so far nobody has invested anywhere near my effort in identifying and creating new techniques valid beyond fractals. Since roughness is everywhere, fractals - although they do not apply to everything - are present everywhere. And very often the same techniques apply in areas that, by every other account except geometric structure, are separate." (Benoît Mandelbrot, "A Theory of Roughness", 2004)

"In plain English, fractal geometry is the geometry of the irregular, the geometry of nature, and, in general, fractals are characterized by infinite detail, infinite length, and the absence of smoothness or derivative." (Philip Tetlow, "The Web’s Awake: An Introduction to the Field of Web Science and the Concept of Web Life", 2007)

"Wherever we look in our world the complex systems of nature and time seem to preserve the look of details at finer and finer scales. Fractals show a holistic hidden order behind things, a harmony in which everything affects everything else, and, above all, an endless variety of interwoven patterns. Fractal geometry allows bounded curves of infinite length, as well as closed surfaces with infinite area. It even allows curves with positive volume and arbitrarily large groups of shapes with exactly the same boundary." (Philip Tetlow, "The Web’s Awake: An Introduction to the Field of Web Science and the Concept of Web Life", 2007)

"The economy is a nonlinear fractal system, where the smallest scales are linked to the largest, and the decisions of the central bank are affected by the gut instincts of the people on the street." (David Orrell, "The Other Side Of The Coin", 2008)

"Only at the edge of chaos can complex systems flourish. This threshold line, that edge between anarchy and frozen rigidity, is not a like a fence line, it is a fractal line; it possesses nonlinearity." (Stephen H Buhner, "Plant Intelligence and the Imaginal Realm: Beyond the Doors of Perception into the Dreaming of Earth", 2014)

"Geometric pattern repeated at progressively smaller scales, where each iteration is about a reproduction of the image to produce completely irregular shapes and surfaces that can not be represented by classical geometry. Fractals are generally self-similar (each section looks at all) and are not subordinated to a specific scale. They are used especially in the digital modeling of irregular patterns and structures in nature." (Mauro Chiarella, "Folds and Refolds: Space Generation, Shapes, and Complex Components", 2016)

Systems Thinking: On Attractors (Quotes)

noreply@blogger.com (Adrian) — Sat, 14 Aug 2021 14:17:00 GMT

"Cellular automata are discrete dynamical systems with simple construction but complex self-organizing behaviour. Evidence is presented that all one-dimensional cellular automata fall into four distinct universality classes. Characterizations of the structures generated in these classes are discussed. Three classes exhibit behaviour analogous to limit points, limit cycles and chaotic attractors. The fourth class is probably capable of universal computation, so that properties of its infinite time behaviour are undecidable." (Stephen Wolfram, "Nonlinear Phenomena, Universality and complexity in cellular automata", Physica 10D, 1984)

"Cellular automata may be considered as discrete dynamical systems. In almost all cases, cellular automaton evolution is irreversible. Trajectories in the configuration space for cellular automata therefore merge with time, and after many time steps, trajectories starting from almost all initial states become concentrated onto 'attractors'. These attractors typically contain only a very small fraction of possible states. Evolution to attractors from arbitrary initial states allows for 'self-organizing' behaviour, in which structure may evolve at large times from structureless initial states. The nature of the attractors determines the form and extent of such structures." (Stephen Wolfram, "Nonlinear Phenomena, Universality and complexity in cellular automata", Physica 10D, 1984)

"Regarding stability, the state trajectories of a system tend to equilibrium. In the simplest case they converge to one point (or different points from different initial states), more commonly to one (or several, according to initial state) fixed point or limit cycle(s) or even torus(es) of characteristic equilibrial behaviour. All this is, in a rigorous sense, contingent upon describing a potential, as a special summation of the multitude of forces acting upon the state in question, and finding the fixed points, cycles, etc., to be minima of the potential function. It is often more convenient to use the equivalent jargon of 'attractors' so that the state of a system is 'attracted' to an equilibrial behaviour. In any case, once in equilibrial conditions, the system returns to its limit, equilibrial behaviour after small, arbitrary, and random perturbations." (Gordon Pask, "Different Kinds of Cybernetics", 1992)

"Systems, acting dynamically, produce (and incidentally, reproduce) their own boundaries, as structures which are complementary (necessarily so) to their motion and dynamics. They are liable, for all that, to instabilities chaos, as commonly interpreted of chaotic form, where nowadays, is remote from the random. Chaos is a peculiar situation in which the trajectories of a system, taken in the traditional sense, fail to converge as they approach their limit cycles or 'attractors' or 'equilibria'. Instead, they diverge, due to an increase, of indefinite magnitude, in amplification or gain." (Gordon Pask, "Different Kinds of Cybernetics", 1992)

"A strange attractor, when it exists, is truly the heart of a chaotic system. If a concrete system has been in existence for some time, states other than those extremely close to the attractor might as well not exist; they will never occur. For one special complicated chaotic system - the global weather - the attractor is simply the climate, that is, the set of weather patterns that have at least some chance of occasionally occurring." (Edward N Lorenz, "The Essence of Chaos", 1993)

"An attractor that consists of an infinite number of curves, surfaces, or higher-dimensional manifolds - generalizations of surfaces to multidimensional space - often occurring in parallel sets, with a gap between any two members of the set, is called a strange attractor." (Edward N Lorenz, "The Essence of Chaos", 1993)

"When a system has more than one attractor, the points in phase space that are attracted to a particular attractor form the basin of attraction for that attractor. Each basin contains its attractor, but consists mostly of points that represent transient states. Two contiguous basins of attraction will be separated by a basin boundary." (Edward N Lorenz, "The Essence of Chaos", 1993)

"One reason nature pleases us is its endless use of a few simple principles: the cube-square law; fractals; spirals; the way that waves, wheels, trig functions, and harmonic oscillators are alike; the importance of ratios between small primes; bilateral symmetry; Fibonacci series, golden sections, quantization, strange attractors, path-dependency, all the things that show up in places where you don’t expect them [...] these rules work with and against each other ceaselessly at all levels, so that out of their intrinsic simplicity comes the rich complexity of the world around us. That tension - between the simple rules that describe the world and the complex world we see - is itself both simple in execution and immensely complex in effect. Thus exactly the levels, mixtures, and relations of complexity that seem to be hardwired into the pleasure centers of the human brain - or are they, perhaps, intrinsic to intelligence and perception, pleasant to anything that can see, think, create? - are the ones found in the world around us." (John Barnes, "Mother of Storms", 1994)

"As with subtle bifurcations, catastrophes also involve a control parameter. When the value of that parameter is below a bifurcation point, the system is dominated by one attractor. When the value of that parameter is above the bifurcation point, another attractor dominates. Thus the fundamental characteristic of a catastrophe is the sudden disappearance of one attractor and its basin, combined with the dominant emergence of another attractor. Any type of attractor static, periodic, or chaotic can be involved in this. Elementary catastrophe theory involves static attractors, such as points. Because multidimensional surfaces can also attract (together with attracting points on these surfaces), we refer to them more generally as attracting hypersurfaces, limit sets, or simply attractors." (Courtney Brown, "Chaos and Catastrophe Theories", 1995)

"Fundamental to catastrophe theory is the idea of a bifurcation. A bifurcation is an event that occurs in the evolution of a dynamic system in which the characteristic behavior of the system is transformed. This occurs when an attractor in the system changes in response to change in the value of a parameter. A catastrophe is one type of bifurcation. The broader framework within which catastrophes are located is called dynamical bifurcation theory." (Courtney Brown, "Chaos and Catastrophe Theories", 1995)

"Knowledge, truth, and information flow in networks and swarm systems. I have always been interested in the texture of scientific knowledge because it appears to be lumpy and uneven. Much of what we collectively know derives from a few small areas, yet between them lie vast deserts of ignorance. I can interpret that observation now as the effect of positive feedback and attractors. A little bit of knowledge illuminates much around it, and that new illumination feeds on itself, so one corner explodes. The reverse also holds true: ignorance breeds ignorance. Areas where nothing is known, everyone avoids, so nothing is discovered. The result is an uneven landscape of empty know-nothing interrupted by hills of self-organized knowledge." (Kevin Kelly, "Out of Control: The New Biology of Machines, Social Systems and the Economic World", 1995)

"Self-organization is seen as the process by which systems of many components tend to reach a particular state, a set of cycling states, or a small volume of their state space (attractor basins), with no external interference." (Luis M Rocha, "Syntactic Autonomy", Proceedings of the Joint Conference on the Science and Technology of Intelligent Systems, 1998)

"Indeed a deterministic die behaves very much as if it has six attractors, the steady states corresponding to its six faces, all of whose basins are intertwined. For technical reasons that can't quite be true, but it is true that deterministic systems with intertwined basins are wonderful substitutes for dice; in fact they're super-dice, behaving even more ‘randomly’ - apparently - than ordinary dice. Super-dice are so chaotic that they are uncomputable. Even if you know the equations for the system perfectly, then given an initial state, you cannot calculate which attractor it will end up on. The tiniest error of approximation – and there will always be such an error - will change the answer completely." (Ian Stewart, "Does God Play Dice: The New Mathematics of Chaos", 2002)

"A sudden change in the evolutive dynamics of a system (a ‘surprise’) can emerge, apparently violating a symmetrical law that was formulated by making a reduction on some (or many) finite sequences of numerical data. This is the crucial point. As we have said on a number of occasions, complexity emerges as a breakdown of symmetry (a system that, by evolving with continuity, suddenly passes from one attractor to another) in laws which, expressed in mathematical form, are symmetrical. Nonetheless, this breakdown happens. It is the surprise, the paradox, a sort of butterfly effect that can highlight small differences between numbers that are very close to one another in the continuum of real numbers; differences that may evade the experimental interpretation of data, but that may increasingly amplify in the system’s dynamics." (Cristoforo S Bertuglia & Franco Vaio, "Nonlinearity, Chaos, and Complexity: The Dynamics of Natural and Social Systems", 2003)

"Physically, the stability of the dynamics is characterized by the sensitivity to initial conditions. This sensitivity can be determined for statistically stationary states, e.g. for the motion on an attractor. If this motion demonstrates sensitive dependence on initial conditions, then it is chaotic. In the popular literature this is often called the 'Butterfly Effect', after the famous 'gedankenexperiment' of Edward Lorenz: if a perturbation of the atmosphere due to a butterfly in Brazil induces a thunderstorm in Texas, then the dynamics of the atmosphere should be considered as an unpredictable and chaotic one. By contrast, stable dependence on initial conditions means that the dynamics is regular." (Ulrike Feudel et al, "Strange Nonchaotic Attractors", 2006)

"Although the potential for chaos resides in every system, chaos, when it emerges, frequently stays within the bounds of its attractor(s): No point or pattern of points is ever repeated, but some form of patterning emerges, rather than randomness. Life scientists in different areas have noticed that life seems able to balance order and chaos at a place of balance known as the edge of chaos. Observations from both nature and artificial life suggest that the edge of chaos favors evolutionary adaptation." (Terry Cooke-Davies et al, "Exploring the Complexity of Projects", 2009)

"Strange attractors, unlike regular ones, are geometrically very complicated, as revealed by the evolution of a small phase-space volume. For instance, if the attractor is a limit cycle, a small two-dimensional volume does not change too much its shape: in a direction it maintains its size, while in the other it shrinks till becoming a 'very thin strand' with an almost constant length. In chaotic systems, instead, the dynamics continuously stretches and folds an initial small volume transforming it into a thinner and thinner 'ribbon' with an exponentially increasing length." (Massimo Cencini et al, "Chaos: From Simple Models to Complex Systems", 2010)

Science: On Parameters (Quotes)

noreply@blogger.com (Adrian) — Fri, 13 Aug 2021 18:41:00 GMT

"The essential feature is that we express ignorance of whether the new parameter is needed by taking half the prior probability for it as concentrated in the value indicated by the null hypothesis and distributing the other half over the range possible." (Harold Jeffreys, "Theory of Probablitity", 1939)

"An economic system is not a linear system, and [...] this fact stands in the way of the determination of the parameters of the system by methods that presume linearity, and [...] it introduces great difficulties in the extrapolation from past behaviour for purposes of prediction. [...] Actual economic systems are constantly subjected to change and disturbances, which would result in irregularity." (Arnold Tustin, "The Mechanism of Economic System", 1953)

"A primary goal of any learning model is to predict correctly the learning curve - proportions of correct responses versus trials. Almost any sensible model with two or three free parameters, however, can closely fit the curve, and so other criteria must be invoked when one is comparing several models." (Robert R Bush & Frederick Mosteller, "A Comparison of Eight Models?", Studies in Mathematical Learning Theory, 1959)

"Clearly, if the state of the system is coupled to parameters of an environment and the state of the environment is made to modify parameters of the system, a learning process will occur. Such an arrangement will be called a Finite Learning Machine, since it has a definite capacity. It is, of course, an active learning mechanism which trades with its surroundings. Indeed it is the limit case of a self-organizing system which will appear in the network if the currency supply is generalized." (Gordon Pask, "The Natural History of Networks", 1960)

"The usefulness of the models in constructing a testable theory of the process is severely limited by the quickly increasing number of parameters which must be estimated in order to compare the predictions of the models with empirical results" (Anatol Rapoport, "Prisoner's Dilemma: A study in conflict and cooperation", 1965)

"Since all models are wrong the scientist cannot obtain a ‘correct’ one by excessive elaboration. On the contrary following William of Occam he should seek an economical description of natural phenomena. Just as the ability to devise simple but evocative models is the signature of the great scientist so overelaboration and overparameterization is often the mark of mediocrity." (George Box, "Science and Statistics", Journal of the American Statistical Association 71, 1976)

"Mathematical model making is an art. If the model is too small, a great deal of analysis and numerical solution can be done, but the results, in general, can be meaningless. If the model is too large, neither analysis nor numerical solution can be carried out, the interpretation of the results is in any case very difficult, and there is great difficulty in obtaining the numerical values of the parameters needed for numerical results." (Richard E Bellman, "Eye of the Hurricane: An Autobiography", 1984)

"A mechanistic model has the following advantages: 1. It contributes to our scientiﬁc understanding of the phenomenon under study. 2. It usually provides a better basis for extrapolation (at least to conditions worthy of further experimental investigation if not through the entire range of all input variables). 3. It tends to be parsimonious (i.e, frugal) in the use of parameters and to provide better estimates of the response." (George E P Box, "Empirical Model-Building and Response Surfaces", 1987)

"Whenever parameters can be quantified, it is usually desirable to do so." (Norman R Augustine, "Augustine's Laws", 1987)

"Perhaps the most exciting implication [of CA representation of biological phenomena] is the possibility that life had its origin in the vicinity of a phase transition and that evolution reflects the process by which life has gained local control over a successively greater number of environmental parameters affecting its ability to maintain itself at a critical balance point between order and chaos." ( Chris G Langton, "Computation at the Edge of Chaos: Phase Transitions and Emergent Computation", Physica D (42), 1990)

"A conceptual model is a qualitative description of the system and includes the processes taking place in the system, the parameters chosen to describe the processes, and the spatial and temporal scales of the processes." (A Avogadro & R C Ragaini, "Technologies for Environmental Cleanup", 1993)

"Initially, it may seem that such systems constitute a very special class of processes. And, in fact, that is indeed the case. However, nature has providentially worked things out so that a lot of processes of practical concern just happen to belong to this class - including many of the systems of classical physics like passive electrical circuits, damped vibrating springs, and bending beams. Moreover, when we observe these kinds of processes in real life, what we usually see is the system when it is at or very near to equilibrium. For these reasons catastrophe theory can be of great value in helping us understand how these kinds of systems can shift abruptly from one equilibrium state to another as various parameters, like spring constants or unemployment rates, are varied just a little bit." (John L Casti, "Five Golden Rules", 1995)

"The existence of equilibria or steady periodic solutions is not sufficient to determine if a system will actually behave that way. The stability of these solutions must also be checked. As parameters are changed, a stable motion can become unstable and new solutions may appear. The study of the changes in the dynamic behavior of systems as parameters are varied is the subject of bifurcation theory. Values of the parameters at which the qualitative or topological nature of the motion changes are known as critical or bifurcation values." (Francis C Moona, "Nonlinear Dynamics", 2003)

"Bayesian inference is appealing when prior information is available since Bayes’ theorem is a natural way to combine prior information with data. Some people find Bayesian inference psychologically appealing because it allows us to make probability statements about parameters. […] In parametric models, with large samples, Bayesian and frequentist methods give approximately the same inferences. In general, they need not agree." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"The Bayesian approach is based on the following postulates: (B1) Probability describes degree of belief, not limiting frequency. As such, we can make probability statements about lots of things, not just data which are subject to random variation. […] (B2) We can make probability statements about parameters, even though they are fixed constants. (B3) We make inferences about a parameter θ by producing a probability distribution for θ. Inferences, such as point estimates and interval estimates, may then be extracted from this distribution." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"The important thing is to understand that frequentist and Bayesian methods are answering different questions. To combine prior beliefs with data in a principled way, use Bayesian inference. To construct procedures with guaranteed long run performance, such as confidence intervals, use frequentist methods. Generally, Bayesian methods run into problems when the parameter space is high dimensional." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"Feedback and its big brother, control theory, are such important concepts that it is odd that they usually find no formal place in the education of physicists. On the practical side, experimentalists often need to use feedback. Almost any experiment is subject to the vagaries of environmental perturbations. Usually, one wants to vary a parameter of interest while holding all others constant. How to do this properly is the subject of control theory. More fundamentally, feedback is one of the great ideas developed (mostly) in the last century, with particularly deep consequences for biological systems, and all physicists should have some understanding of such a basic concept." (John Bechhoefer, "Feedback for physicists: A tutorial essay on control", Reviews of Modern Physics Vol. 77, 2005)

"Each fuzzy set is uniquely defined by a membership function. […] There are two approaches to determining a membership function. The first approach is to use the knowledge of human experts. Because fuzzy sets are often used to formulate human knowledge, membership functions represent a part of human knowledge. Usually, this approach can only give a rough formula of the membership function and fine-tuning is required. The second approach is to use data collected from various sensors to determine the membership function. Specifically, we first specify the structure of membership function and then fine-tune the parameters of membership function based on the data." (Huaguang Zhang & Derong Liu, "Fuzzy Modeling and Fuzzy Control", 2006)

"In parametrized dynamical systems a bifurcation occurs when a qualitative change is invoked by a change of parameters. In models such a qualitative change corresponds to transition between dynamical regimes. In the generic theory a finite list of cases is obtained, containing elements like ‘saddle-node’, ‘period doubling’, ‘Hopf bifurcation’ and many others." (Henk W Broer & Heinz Hanssmann, "Hamiltonian Perturbation Theory (and Transition to Chaos)", 2009)

"The concept of bifurcation, present in the context of non-linear dynamic systems and theory of chaos, refers to the transition between two dynamic modalities qualitatively distinct; both of them are exhibited by the same dynamic system, and the transition (bifurcation) is promoted by the change in value of a relevant numeric parameter of such system. Such parameter is named 'bifurcation parameter', and in highly non-linear dynamic systems, its change can produce a large number of bifurcations between distinct dynamic modalities, with self-similarity and fractal structure. In many of these systems, we have a cascade of numberless bifurcations, culminating with the production of chaotic dynamics." (Emilio Del-Moral-Hernandez, "Chaotic Neural Networks", Encyclopedia of Artificial Intelligence, 2009)

"Traditional statistics is strong in devising ways of describing data and inferring distributional parameters from sample. Causal inference requires two additional ingredients: a science-friendly language for articulating causal knowledge, and a mathematical machinery for processing that knowledge, combining it with data and drawing new causal conclusions about a phenomenon." (Judea Pearl, "Causal inference in statistics: An overview", Statistics Surveys 3, 2009)

"In dynamical systems, a bifurcation occurs when a small smooth change made to the parameter values (the bifurcation parameters) of a system causes a sudden 'qualitative' or topological change in its behaviour. Generally, at a bifurcation, the local stability properties of equilibria, periodic orbits or other invariant sets changes." (Greegory Faye, "An introduction to bifurcation theory", 2011)

"Principle of Equifinality: If a steady state is reached in an open system, it is independent of the initial conditions, and determined only by the system parameters, i.e. rates of reaction and transport." (Kevin Adams & Charles Keating, "Systems of systems engineering", 2012)

"Roughly spoken, bifurcation theory describes the way in which dynamical system changes due to a small perturbation of the system-parameters. A qualitative change in the phase space of the dynamical system occurs at a bifurcation point, that means that the system is structural unstable against a small perturbation in the parameter space and the dynamic structure of the system has changed due to this slight variation in the parameter space." (Holger I Meinhardt, "Cooperative Decision Making in Common Pool Situations", 2012)

"Bifurcation theory is the mathematical study of changes in the qualitative or topological structure of a given family, such as the integral curves of a family of vector fields, and the solutions of a family of differential equations. Most commonly applied to the mathematical study of dynamical systems, a bifurcation occurs when a small smooth change made to the parameter values (the bifurcation parameters) of a system causes a sudden 'qualitative' or topological change in its behavior. Bifurcations can occur in both continuous systems (described by ODEs, DDEs, or PDEs) and discrete systems (described by maps)." (Tianshou Zhou, "Bifurcation", 2013)

"Today we routinely learn models with millions of parameters, enough to give each elephant in the world his own distinctive wiggle. It’s even been said that data mining means 'torturing the data until it confesses'." (Pedro Domingos, "The Master Algorithm", 2015)

"An estimate (the mathematical definition) is a number derived from observed values that is as close as we can get to the true parameter value. Useful estimators are those that are 'better' in some sense than any others." (David S Salsburg, "Errors, Blunders, and Lies: How to Tell the Difference", 2017)

"Estimators are functions of the observed values that can be used to estimate specific parameters. Good estimators are those that are consistent and have minimum variance. These properties are guaranteed if the estimator maximizes the likelihood of the observations." (David S Salsburg, "Errors, Blunders, and Lies: How to Tell the Difference", 2017)

"One kind of probability - classic probability - is based on the idea of symmetry and equal likelihood […] In the classic case, we know the parameters of the system and thus can calculate the probabilities for the events each system will generate. […] A second kind of probability arises because in daily life we often want to know something about the likelihood of other events occurring […]. In this second case, we need to estimate the parameters of the system because we don’t know what those parameters are. […] A third kind of probability differs from these first two because it’s not obtained from an experiment or a replicable event - rather, it expresses an opinion or degree of belief about how likely a particular event is to occur. This is called subjective probability […]." (Daniel J Levitin, "Weaponized Lies", 2017)

"What properties should a good statistical estimator have? Since we are dealing with probability, we start with the probability that our estimate will be very close to the true value of the parameter. We want that probability to become greater and greater as we get more and more data. This property is called consistency. This is a statement about probability. It does not say that we are sure to get the right answer. It says that it is highly probable that we will be close to the right answer." (David S Salsburg, "Errors, Blunders, and Lies: How to Tell the Difference", 2017)

Systems Thinking: On Simplicity (Quotes)

noreply@blogger.com (Adrian) — Thu, 12 Aug 2021 22:13:00 GMT

"Cultivate simplicity or rather should I say banish elaborateness, for simplicity springs spontaneous from the heart." (Charles Lamb, [Letter to Coleridge] 1790)

"Nature does nothing in vain when less will serve; for Nature is pleased with simplicity and affects not the pomp of superfluous causes." (Sir Isaac Newton, "The Mathematical Principles of Natural Philosophy", Voll. II, 1803)

"[…] we must not measure the simplicity of the laws of nature by our facility of conception; but when those which appear to us the most simple, accord perfectly with observations of the phenomena, we are justified in supposing them rigorously exact." (Pierre-Simon Laplace, "The System of the World", 1809)

"Simplicity and precision ought to be the characteristics of a scientific nomenclature: words should signify things, or the analogies of things, and not opinions." (Sir Humphry Davy, Elements of Chemical Philosophy", 1812)

"Discoveries are not generally made in the order of their scientific arrangement: their connexions and relations are made out gradually; and it is only when the fermentation of invention has subsided that the whole clears into simplicity and order. " (William Whewell, "An Elementary Treatise on Mechanics" Vol. I, 1819)

"Simplicity of structure means organic unity, whether the organism be simple or complex; and hence in all times the emphasis which critics have laid upon Simplicity, though they have not unfrequently confounded it with narrowness of range." (George H Lewes, "The Principles of Success in Literature", 1865)

"The first obligation of Simplicity is that of using the simplest means to secure the fullest effect. But although the mind instinctively rejects all needless complexity, we shall greatly err if we fail to recognise the fact, that what the mind recoils from is not the complexity, but the needlessness." (George H Lewes, "The Principles of Success in Literature", 1865)

"[...] he simplicity of nature which we at present grasp is really the result of infinite complexity; and that below the uniformity there underlies a diversity whose depths we have not yet probed, and whose secret places are still beyond our reach." (William Spottiswoode, [Report of the Forty-eighth Meeting of the British Association for the, Advancement of Science] 1878)

"The aim of science is always to reduce complexity to simplicity." (William James, "The Principles of Psychology", 1890)

"[…] we cannot a priori demand from nature simplicity, nor can we judge what in her opinion is simple." (Heinrich Hertz, "The Principles of Mechanics Presented in a New Form", 1894)

"[…] it is an error to believe that rigor in the proof is the enemy of simplicity." (David Hilbert, [Paris International Congress] 1900)

"If we study the history of science we see happen two inverse phenomena […] Sometimes simplicity hides under complex appearances; sometimes it is the simplicity which is apparent, and which disguises extremely complicated realities. […] No doubt, if our means of investigation should become more and more penetrating, we should discover the simple under the complex, then the complex under the simple, then again the simple under the complex, and so on, without our being able to foresee what will be the last term. We must stop somewhere, and that science may be possible, we must stop when we have found simplicity. This is the only ground on which we can rear the edifice of our generalizations." (Henri Poincaré, "Science and Hypothesis", 1901)

"Let us notice first of all, that every generalization implies in some measure the belief in the unity and simplicity of nature." (Jules H Poincaré, "Science and Hypothesis", 1905)

"The aim of science is not things themselves, as the dogmatists in their simplicity imagine, but the relation between things. (Henri Poincaré, "Science and Hypothesis", 1905)

"Elegance may produce the feeling of the unforeseen by the unexpected meeting of objects we are not accustomed to bring together; there again it is fruitful, since it thus unveils for us kinships before unrecognized. It is fruitful even when it results only from the contrast between the simplicity of the means and the complexity of the problem set; it makes us then think of the reason for this contrast and very often makes us see that chance is not the reason; that it is to be found in some unexpected law. In a word, the feeling of mathematical elegance is only the satisfaction due to any adaptation of the solution to the needs of our mind, and it is because of this very adaptation that this solution can be for us an instrument. Consequently this esthetic satisfaction is bound up with the economy of thought." (Jules Henri Poincaré, "The Future of Mathematics", Monist Vol. 20, 1910)

"The facts of greatest outcome are those we think simple; may be they really are so, because they are influenced only by a small number of well-defined circumstances, may be they take on an appearance of simplicity because the various circumstances upon which they depend obey the laws of chance and so come to mutually compensate." (Henri Poincaré, "The Foundations of Science", 1913)

"But it is just this characteristic of simplicity in the laws of nature hitherto discovered which it would be fallacious to generalize, for it is obvious that simplicity has been a part cause of their discovery, and can, therefore, give no ground for the supposition that other undiscovered laws are equally simple." (Bertrand Russell, "'On the Scientific Method in Philosophy", 1918)

"The aim of science is to seek the simplest explanations of complex facts. We are apt to fall into the error of thinking that the facts are simple because simplicity is the goal of our quest. The guiding motto in the life of every natural philosopher should be, ‘Seek simplicity and distrust it’." (Alfred N Whitehead, "The Concept of Nature", 1919)

"The researcher worker, in his efforts to express the fundamental laws of Nature in mathematical form, should strive mainly for mathematical beauty. He should still take simplicity into consideration in a subordinate way to beauty. […] It often happens that the requirements of simplicity and beauty are the same, but where they clash the latter must take precedence." (Paul A M Dirac, "The Relation Between Mathematics and Physics", Proceedings of the Royal Society , Volume LIX, 1939)

"A theory is the more impressive the greater the simplicity of its premises is, the more different kinds of things it relates, and the more extended is its area of applicability. Therefore the deep impression which classical thermodynamics made upon me. It is the only physical theory of universal content concerning which I am convinced that, within the framework of the applicability of its basic concepts, it will never be overthrown (for the special attention of those who are skeptics on principle)." (Albert Einstein, "Autobiographical Notes", 1949)

"In products of the human mind, simplicity marks the end of a process of refining, while complexity marks a primitive stage." (Eric Hoffer, 1954)

"As shorthand, when the phenomena are suitably simple, words such as equilibrium and stability are of great value and convenience. Nevertheless, it should be always borne in mind that they are mere shorthand, and that the phenomena will not always have the simplicity that these words presuppose." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"Scientists whose work has no clear, practical implications would want to make their decisions considering such things as: the relative worth of (1) more observations, (2) greater scope of his conceptual model, (3) simplicity, (4) precision of language, (5) accuracy of the probability assignment." (C West Churchman, "Costs, Utilities, and Values", 1956)

"Nature is pleased with simplicity, and affects not the pomp of superfluous causes." (Morris Kline, "Mathematics and the Physical World", 1959)

"The central task of a natural science is to make the wonderful commonplace: to show that complexity, correctly viewed, is only a mask for simplicity; to find pattern hidden in apparent chaos." (Herbert A Simon, "The Sciences of the Artificial", 1969)

"For if as scientists we seek simplicity, then obviously we try the simplest surviving theory first, and retreat from it only when it proves false. Not this course, but any other, requires explanation. If you want to go somewhere quickly, and several alternate routes are equally likely to be open, no one asks why you take the shortest. The simplest theory is to be chosen not because it is the most likely to be true but because it is scientifically the most rewarding among equally likely alternatives. We aim at simplicity and hope for truth." (Nelson Goodman, "Problems and Projects", 1972)

"The beauty of physics lies in the extent which seemingly complex and unrelated phenomena can be explained and correlated through a high level of abstraction by a set of laws which are amazing in their simplicity." (Melvin Schwartz, "In Principles of Electrodynamics", 1972)

"The systems view is the emerging contemporary view of organized complexity, one step beyond the Newtonian view of organized simplicity, and two steps beyond the classical world views of divinely ordered or imaginatively envisaged complexity." (Ervin László, "Introduction to Systems Philosophy", 1972)

"Science attempts to find logic and simplicity in nature. Mathematics attempts to establish order and simplicity in human thought." (Edward Teller, "The Pursuit of Simplicity", 1980)

"Simplicity does not precede complexity, but follows it." (Alan Perlis, "Epigrams on Programming", 1982)

"It is often the scientist’s experience that he senses the nearness of truth when such connections are envisioned. A connection is a step toward simplification, unification. Simplicity is indeed often the sign of truth and a criterion of beauty." (Mahlon B Hoagland, "Toward the Habit of Truth", 1990)

"It is important to emphasize the value of simplicity and elegance, for complexity has a way of compounding difficulties and as we have seen, creating mistakes. My definition of elegance is the achievement of a given functionality with a minimum of mechanism and a maximum of clarity." (Fernando J Corbató, "On Building Systems That Will Fail", 1991)

"It is not merely the truth of science that makes it beautiful, but its simplicity." (Walker Percy, "Signposts in a Strange Land", 1991)

"The amount of understanding produced by a theory is determined by how well it meets the criteria of adequacy - testability, fruitfulness, scope, simplicity, conservatism - because these criteria indicate the extent to which a theory systematizes and unifies our knowledge." (Theodore Schick Jr., "How to Think about Weird Things: Critical Thinking for a New Age", 1995)

"[…] the simplest hypothesis proposed as an explanation of phenomena is more likely to be the true one than is any other available hypothesis, that its predictions are more likely to be true than those of any other available hypothesis, and that it is an ultimate a priori epistemic principle that simplicity is evidence for truth." (Richard Swinburne, "Simplicity as Evidence for Truth", 1997)

"I seek […] to show that - other things being equal - the simplest hypothesis proposed as an explanation of phenomena is more likely to be the true one than is any other available hypothesis, that its predictions are more likely to be true than those of any other available hypothesis, and that it is an ultimate a priori epistemic principle that simplicity is evidence for truth." (Richard Swinburne, "Simplicity as Evidence for Truth", 1997)

"[…] some systems (system is just a jargon for anything, like the swinging pendulum or the Solar System, or water dripping from a tap) are very sensitive to their starting conditions, so that a tiny difference in the initial ‘push’ you give them causes a big difference in where they end up, and there is feedback, so that what a system does affects its own behavior."(John Gribbin, "Deep Simplicity", 2004)

"The immediate evidence from the natural world may seem to be chaotic and without any inner regularity, but mathematics reveals that under the surface the world of nature has an unexpected simplicity - an extraordinary beauty and order." (William Byers, "How Mathematicians Think", 2007)

"Nature is capable of building complex structures by processes of self-organization; simplicity begets complexity." (Victor J Stenger, "God: The Failed Hypothesis", 2010)

"Complexity is the prodigy of the world. Simplicity is the sensation of the universe. Behind complexity, there is always simplicity to be revealed. Inside simplicity, there is always complexity to be discovered." (Gang Yu, "in Data Warehousing in the Age of Big Data", 2013)

"Decentralized systems are the quintessential patrons of simplicity. They allow complexity to rise to a level at which it is sustainable, and no higher." (Lawrence K. Samuels, "Defense of Chaos: The Chaology of Politics, Economics and Human Action", 2013)

"I think there is a profound and enduring beauty in simplicity; in clarity, in efficiency. True simplicity is derived from so much more than just the absence of clutter and ornamentation. It's about bringing order to complexity." (Jonathan Ive, 2013)

"Simplicity in a system tends to increase that system’s efficiency. Because less can go wrong with fewer parts, less will. Complexity in a system tends to increase that system’s inefficiency; the greater the number of variables, the greater the probability of those variables clashing, and in turn, the greater the potential for conflict and disarray. Because more can go wrong, more will. That is why centralized systems are inclined to break down quickly and become enmeshed in greater unintended consequences." (Lawrence K Samuels,"Defense of Chaos: The Chaology of Politics, Economics and Human Action", 2013)

"It is the last lesson of modern science that the highest simplicity of structure is produced, not by few elements, but by the highest complexity." (Ralph W Emerson)

"It would be simple enough, if only simplicity were not the most difficult of all things." (Carl G Jung)

"It would seem that more than function itself, simplicity is the deciding factor in the aesthetic equation. One might call the process beauty through function and simplification." (Raymond Loewy)

"The art of simplicity is a puzzle of complexity." (Douglas Horton)

"The beauty in the laws of physics is the fantastic simplicity that they have." (John A Wheeler)

"The beauty of simplicity is the complexity it attracts." (Tom Robbins)

"The equations that really work in describing nature with the most generality and the greatest simplicity are very elegant and subtle." (Edward Witten)

Science: On Events (Quotes)

noreply@blogger.com (Adrian) — Sun, 08 Aug 2021 15:47:00 GMT

"[…] as it is thus demonstrable that there are, in the constitution of things, certain Laws according to which Events happen, it is no less evident from Observation, that these Laws serve to wise, useful and beneficent purposes, to preserve the steadfast Order of the Universe, to propagate the several Species of Beings, and furnish to the sentient Kind such degrees of happiness as are suited to their State." (Abraham de Moivre, "The Doctrine of Chances: or, A Method of Calculating the Probabilities of Events in Play", 1718)

"Further, the same Arguments which explode the Notion of Luck, may, on the other side, be useful in some Cases to establish a due comparison between Chance and Design: We may imagine Chance and Design to be, as it were, in Competition with each other, for the production of some sorts of Events, and many calculate what Probability there is, that those Events should be rather be owing to the one than to the other." (Abraham de Moivre, "The Doctrine of Chances", 1718)

"[…] chance, that is, an infinite number of events, with respect to which our ignorance will not permit us to perceive their causes, and the chain that connects them together. Now, this chance has a greater share in our education than is imagined. It is this that places certain objects before us and, in consequence of this, occasions more happy ideas, and sometimes leads us to the greatest discoveries […]" (Claude A Helvetius, "On Mind", 1751)

"But ignorance of the different causes involved in the production of events, as well as their complexity, taken together with the imperfection of analysis, prevents our reaching the same certainty about the vast majority of phenomena. Thus there are things that are uncertain for us, things more or less probable, and we seek to compensate for the impossibility of knowing them by determining their different degrees of likelihood. So it was that we owe to the weakness of the human mind one of the most delicate and ingenious of mathematical theories, the science of chance or probability." (Pierre-Simon Laplace, "Recherches, 1º, sur l'Intégration des Équations Différentielles aux Différences Finies, et sur leur Usage dans la Théorie des Hasards", 1773)

"[…] determine the probability of a future or unknown event not on the basis of the number of possible combinations resulting in this event or in its complementary event, but only on the basis of the knowledge of order of familiar previous events of this kind" (Marquis de Condorcet, "Essai sur l'application de l'analyse à la probabilité des décisions rendues à la pluralité des voix", 1785)

"It is contrary to the usual order of things, that events so harmonious as those of the system of the world, should depend on such diversified agents as are supposed to exist in our artificial arrangements; and there is reason to anticipate a great reduction in the number of undecompounded bodies, and to expect that the analogies of nature will be found conformable to the refined operations of art. The more the phenomena of the universe are studied, the more distinct their connection appears, and the more simple their causes, the more magnificent their design, and the more wonderful the wisdom and power of their Author." (Sir Humphry Davy, "Elements of Chemical Philosophy", 1812)

"Probability has reference partly to our ignorance, partly to our knowledge [..] The theory of chance consists in reducing all the events of the same kind to a certain number of cases equally possible, that is to say, to such as we may be equally undecided about in regard to their existence, and in determining the number of cases favorable to the event whose probability is sought. The ratio of this number to that of all cases possible is the measure of this probability, which is thus simply a fraction whose number is the number of favorable cases and whose denominator is the number of all cases possible." (Pierre-Simon Laplace, "Philosophical Essay on Probabilities", 1814)

"Things of all kinds are subject to a universal law which may be called the law of large numbers. It consists in the fact that, if one observes very considerable numbers of events of the same nature, dependent on constant causes and causes which vary irregularly, sometimes in one direction, sometimes in the other, it is to say without their variation being progressive in any definite direction, one shall find, between these numbers, relations which are almost constant." (Siméon-Denis Poisson, "Poisson’s Law of Large Numbers", 1837)

"The very events which in their own nature appear most capricious and uncertain, and which in any individual case no attainable degree of knowledge would enable us to foresee, occur, when considerable numbers are taken into account, with a degree of regularity approaching to mathematical." (John S Mills, "A System of Logic", 1862)

"When we merely note and record the phenomena which occur around us in the ordinary course of nature we are said to observe. When we change the course of nature by the intervention of our will and muscular powers, and thus produce unusual combinations and conditions of phenomena, we are said to experiment. […] an experiment differs from a mere observation in the fact that we more or less influence the character of the events which we observe." (William S Jevons, "The Principles of Science: A Treatise on Logic and Scientific Method", 1874)

"Some of the common ways of producing a false statistical argument are to quote figures without their context, omitting the cautions as to their incompleteness, or to apply them to a group of phenomena quite different to that to which they in reality relate; to take these estimates referring to only part of a group as complete; to enumerate the events favorable to an argument, omitting the other side; and to argue hastily from effect to cause, this last error being the one most often fathered on to statistics. For all these elementary mistakes in logic, statistics is held responsible." (Sir Arthur L Bowley, "Elements of Statistics", 1901)

"The theory of chance consists in reducing all the events of the same kind to a certain number of cases equally possible, that is to say, to such as we may be equally undecided about in regard to their existence, and in determining the number of cases favorable to the event whose probability is sought." (Pierre-Simon de Laplace, "Philosophical Essay on Probabilities", 1902)

"Now a system is nothing but a mental connexion applied to a number of isolated events." (William Smith, The Quarterly review, 1906)

"The second law of thermodynamics appears solely as a law of probability, entropy as a measure of the probability, and the increase of entropy is equivalent to a statement that more probable events follow less probable ones." (Max Planck, "A Survey of Physics", 1923)

"Every theory of the course of events in nature is necessarily based on some process of simplification and is to some extent, therefore, a fairy tale." (Sir Napier Shaw, "Manual of Meteorology", 1932)

"The most important application of the theory of probability is to what we may call 'chance-like' or 'random' events, or occurrences. These seem to be characterized by a peculiar kind of incalculability which makes one disposed to believe - after many unsuccessful attempts - that all known rational methods of prediction must fail in their case. We have, as it were, the feeling that not a scientist but only a prophet could predict them. And yet, it is just this incalculability that makes us conclude that the calculus of probability can be applied to these events." (Karl R Popper, "The Logic of Scientific Discovery", 1934)

"A permanent state is reached, in which no observable events occur. The physicist calls this the state of thermodynamical equilibrium, or of ‘maximum entropy’. Practically, a state of this kind is usually reached very rapidly. Theoretically, it is very often not yet an absolute equilibrium, not yet the true maximum of entropy. But then the final approach to equilibrium is very slow. It could take anything between hours, years, centuries […]." (Erwin Schrödinger, "What is Life?", 1944)

"A fundamental value in the scientific outlook is concern with the best available map of reality. The scientist will always seek a description of events which enables him to predict most by assuming least. He thus already prefers a particular form of behavior. If moralities are systems of preferences, here is at least one point at which science cannot be said to be completely without preferences. Science prefers good maps." (Anatol Rapoport, "Science and the goals of man: a study in semantic orientation", 1950)

"It is indeed wrong to think that the poetry of Nature’s moods in all their infinite variety is lost on one who observes them scientifically, for the habit of observation refines our sense of beauty and adds a brighter hue to the richly coloured background against which each separate fact is outlined. The connection between events, the relation of cause and effect in different parts of a landscape, unite harmoniously what would otherwise be merely a series of detached sciences." (Marcel Minnaert, "The Nature of Light and Colour in the Open Air", 1954)

"Multiple equilibria are not necessarily useless, but from the standpoint of any exact science the existence of a uniquely determined equilibrium is, of course, of the utmost importance, even if proof has to be purchased at the price of very restrictive assumptions; without any possibility of proving the existence of (a) uniquely determined equilibrium - or at all events, of a small number of possible equilibria - at however high a level of abstraction, a field of phenomena is really a chaos that is not under analytical control." (Joseph A Schumpeter, "History of Economic Analysis", 1954)

"In fact, it is empirically ascertainable that every event is actually produced by a number of factors, or is at least accompanied by numerous other events that are somehow connected with it, so that the singling out involved in the picture of the causal chain is an extreme abstraction. Just as ideal objects cannot be isolated from their proper context, material existents exhibit multiple interconnections; therefore the universe is not a heap of things but a system of interacting systems." (Mario Bunge, "Causality: The place of the casual principles in modern science", 1959)

"Certain properties are necessary or sufficient conditions for other properties, and the network of causal relations thus established will make the occurrence of one property at least tend, subject to the presence of other properties, to promote or inhibit the occurrence of another. Arguments from models involve those analogies which can be used to predict the occurrence of certain properties or events, and hence the relevant relations are causal, at least in the sense of implying a tendency to co-occur." (Mary B Hesse," Models and Analogies in Science", 1963)

"For Science in its totality, the ultimate goal is the creation of a monistic system in which - on the symbolic level and in terms of the inferred components of invisibility and intangibly fine structure - the world’s enormous multiplicity is reduced to something like unity, and the endless successions of unique events of a great many different kinds get tidied and simplified into a single rational order. Whether this goal will ever be reached remains to be seen. Meanwhile we have the various sciences, each with its own system coordinating concepts, its own criterion of explanation." (Aldous Huxley, "Literature and Science", 1963)

"The subject matter of the scientist is a crowd of natural events at all times; he presupposes that this crowd is not real but apparent, and seeks to discover the true place of events in the system of nature. The subject matter of the poet is a crowd of historical occasions of feeling recollected from the past; he presupposes that this crowd is real but should not be, and seeks to transform it into a community. Both science and art are primarily spiritual activities, whatever practical applications may be derived from their results. Disorder, lack of meaning, are spiritual not physical discomforts, order and sense spiritual not physical satisfactions." (Wystan H Auden, "The Dyer’s Hand and Other Essays", 1965)

"There are different levels of organization in the occurrence of events. You cannot explain the events of one level in terms of the events of another. For example, you cannot explain life in terms of mechanical concepts, nor society in terms of individual psychology. Analysis can only take you down the scale of organization. It cannot reveal the workings of things on a higher level. To some extent the holistic philosophers are right." (Anatol Rapoport, "General Systems" Vol. 14, 1969)

"[I]n probability theory we are faced with situations in which our intuition or some physical experiments we have carried out suggest certain results. Intuition and experience lead us to an assignment of probabilities to events. As far as the mathematics is concerned, any assignment of probabilities will do, subject to the rules of mathematical consistency." (Robert Ash, "Basic probability theory", 1970)

"This parallel, between cybernetic explanation and the tactics of logical or mathematical proof, is of more than trivial interest. Outside of cybernetics, we look for explanation, but not for anything which would simulate logical proof. This simulation of proof is something new. We can say, however, with hindsight wisdom, that explanation by simulation of logical or mathematical proof was expectable. After all, the subject matter of cybernetics is not events and objects but the information 'carried' by events and objects. We consider the objects or events only as proposing facts, propositions, messages, percepts, and the like. The subject matter being propositional, it is expectable that explanation would simulate the logical." (Gregory Bateson, "Steps to an Ecology of Mind", 1972)

"General systems theory deals with the most fundamental concepts and aspects of systems. Many theories dealing with more specific types of systems (e. g., dynamical systems, automata, control systems, game-theoretic systems, among many others) have been under development for quite some time. General systems theory is concerned with the basic issues common to all these specialized treatments. Also, for truly complex phenomena, such as those found predominantly in the social and biological sciences, the specialized descriptions used in classical theories (which are based on special mathematical structures such as differential or difference equations, numerical or abstract algebras, etc.) do not adequately and properly represent the actual events. Either because of this inadequate match between the events and types of descriptions available or because of the pure lack of knowledge, for many truly complex problems one can give only the most general statements, which are qualitative and too often even only verbal. General systems theory is aimed at providing a description and explanation for such complex phenomena." (Mihajlo D. Mesarovic & Yasuhiko Takahare, "General Systems Theory: Mathematical foundations", 1975)

"[…] there is an external world which can in principle be exhaustively described in scientific language. The scientist, as both observer and language-user, can capture the external facts of the world in propositions that are true if they correspond to the facts and false if they do not. Science is ideally a linguistic system in which true propositions are in one-to-one relation to facts, including facts that are not directly observed because they involve hidden entities or properties, or past events or far distant events. These hidden events are described in theories, and theories can be inferred from observation, that is, the hidden explanatory mechanism of the world can be discovered from what is open to observation. Man as scientist is regarded as standing apart from the world and able to experiment and theorize about it objectively and dispassionately." (Mary B Hesse, "Revolutions and Reconstructions in the Philosophy of Science", 1980)

"Perhaps randomness is not merely an adequate description for complex causes that we cannot specify. Perhaps the world really works this way, and many events are uncaused in any conventional sense of the word." (Stephen Jay Gould,"Hen's Teeth and Horse's Toes", 1983)

"If you perceive the world as some place where things happen at random - random events over which you have sometimes very little control, sometimes fairly good control, but still random events - well, one has to be able to have some idea of how these things behave. […] People who are not used to statistics tend to see things in data - there are random fluctuations which can sometimes delude them - so you have to understand what can happen randomly and try to control whatever can be controlled. You have to expect that you are not going to get a clean-cut answer. So how do you interpret what you get? You do it by statistics." (Lucien LeCam, [interview] 1988)

"In the end, each life is no more than the sum of contingent facts, a chronicle of chance intersections, of flukes, of random events that divulge nothing but their own lack of purpose." (Paul Auster, "The Locked Room", 1988)

"This transition from uncertainty to near certainty when we observe long series of events, or large systems, is an essential theme in the study of chance." (David Ruelle, "Chance and Chaos", 1991)

"According to the narrower definition of randomness, a random sequence of events is one in which anything that can ever happen can happen next. Usually it is also understood that the probability that a given event will happen next is the same as the probability that a like event will happen at any later time. [...] According to the broader definition of randomness, a random sequence is simply one in which any one of several things can happen next, even though not necessarily anything that can ever happen can happen next." (Edward N Lorenz, "The Essence of Chaos", 1993)

"At the other far extreme, we find many systems ordered as a patchwork of parallel operations, very much as in the neural network of a brain or in a colony of ants. Action in these systems proceeds in a messy cascade of interdependent events. Instead of the discrete ticks of cause and effect that run a clock, a thousand clock springs try to simultaneously run a parallel system. Since there is no chain of command, the particular action of any single spring diffuses into the whole, making it easier for the sum of the whole to overwhelm the parts of the whole. What emerges from the collective is not a series of critical individual actions but a multitude of simultaneous actions whose collective pattern is far more important. This is the swarm model." (Kevin Kelly, "Out of Control: The New Biology of Machines, Social Systems and the Economic World", 1995)

"So we pour in data from the past to fuel the decision-making mechanisms created by our models, be they linear or nonlinear. But therein lies the logician's trap: past data from real life constitute a sequence of events rather than a set of independent observations, which is what the laws of probability demand.[...] It is in those outliers and imperfections that the wildness lurks." (Peter L Bernstein, "Against the Gods: The Remarkable Story of Risk", 1996)

"Paradigms are the most general-rather like a philosophical or ideological framework. Theories are more specific, based on the paradigm and designed to describe what happens in one of the many realms of events encompassed by the paradigm. Models are even more specific providing the mechanisms by which events occur in a particular part of the theory's realm. Of all three, models are most affected by empirical data - models come and go, theories only give way when evidence is overwhelmingly against them and paradigms stay put until a radically better idea comes along." (Lee R Beach, "The Psychology of Decision Making: People in Organizations", 1997)

"Is a random outcome completely determined, and random only by virtue of our ignorance of the most minute contributing factors? Or are the contributing factors unknowable, and therefore render as random an outcome that can never be determined? Are seemingly random events merely the result of fluctuations superimposed on a determinate system, masking its predictability, or is there some disorderliness built into the system itself?" (Deborah J Bennett, "Randomness", 1998)

"Events may appear to us to be random, but this could be attributed to human ignorance about the details of the processes involved." (Brain S Everitt, "Chance Rules", 1999)

"It is in the nature of exponential growth that events develop extremely slowly for extremely long periods of time, but as one glides through the knee of the curve, events erupt at an increasingly furious pace. And that is what we will experience as we enter the twenty-first century." (Ray Kurzweil, "The Age of Spiritual Machines: When Computers Exceed Human Intelligence", 1999)

"Randomness is the very stuff of life, looming large in our everyday experience. […] The fascination of randomness is that it is pervasive, providing the surprising coincidences, bizarre luck, and unexpected twists that color our perception of everyday events." (Edward Beltrami, "What is Random?: Chaos and Order in Mathematics and Life", 1999)

"The Law of Accelerating Returns: As order exponentially increases, time exponentially speeds up (that is, the time interval between salient events grows shorter as time passes)." (Ray Kurzweil, "The Age of Spiritual Machines: When Computers Exceed Human Intelligence", 1999)

"The subject of probability begins by assuming that some mechanism of uncertainty is at work giving rise to what is called randomness, but it is not necessary to distinguish between chance that occurs because of some hidden order that may exist and chance that is the result of blind lawlessness. This mechanism, figuratively speaking, churns out a succession of events, each individually unpredictable, or it conspires to produce an unforeseeable outcome each time a large ensemble of possibilities is sampled." (Edward Beltrami, "What is Random?: Chaos and Order in Mathematics and Life", 1999)

"Entropy [...] is the amount of disorder or randomness present in any system. All non-living systems tend toward disorder; left alone they will eventually lose all motion and degenerate into an inert mass. When this permanent stage is reached and no events occur, maximum entropy is attained. A living system can, for a finite time, avert this unalterable process by importing energy from its environment. It is then said to create negentropy, something which is characteristic of all kinds of life." (Lars Skyttner, "General Systems Theory: Ideas and Applications", 2001)

"One can be highly functionally numerate without being a mathematician or a quantitative analyst. It is not the mathematical manipulation of numbers (or symbols representing numbers) that is central to the notion of numeracy. Rather, it is the ability to draw correct meaning from a logical argument couched in numbers. When such a logical argument relates to events in our uncertain real world, the element of uncertainty makes it, in fact, a statistical argument." (Eric R Sowey, "The Getting of Wisdom: Educating Statisticians to Enhance Their Clients' Numeracy", The American Statistician 57(2), 2003)

"Randomness is a difficult notion for people to accept. When events come in clusters and streaks, people look for explanations and patterns. They refuse to believe that such patterns - which frequently occur in random data - could equally well be derived from tossing a coin. So it is in the stock market as well." (Didier Sornette, "Why Stock Markets Crash: Critical events in complex financial systems", 2003)

"What appear to be the most valuable aspects of the theoretical physics we have are the mathematical descriptions which enable us to predict events. These equations are, we would argue, the only realities we can be certain of in physics; any other ways we have of thinking about the situation are visual aids or mnemonics which make it easier for beings with our sort of macroscopic experience to use and remember the equations." (Celia Green, "The Lost Cause", 2003)

"[…] we would like to observe that the butterfly effect lies at the root of many events which we call random. The final result of throwing a dice depends on the position of the hand throwing it, on the air resistance, on the base that the die falls on, and on many other factors. The result appears random because we are not able to take into account all of these factors with sufficient accuracy. Even the tiniest bump on the table and the most imperceptible move of the wrist affect the position in which the die finally lands. It would be reasonable to assume that chaos lies at the root of all random phenomena." (Iwo Bialynicki-Birula & Iwona Bialynicka-Birula, "Modeling Reality: How Computers Mirror Life", 2004)

"The basic concept of complexity theory is that systems show patterns of organization without organizer (autonomous or self-organization). Simple local interactions of many mutually interacting parts can lead to emergence of complex global structures. […] Complexity originates from the tendency of large dynamical systems to organize themselves into a critical state, with avalanches or 'punctuations' of all sizes. In the critical state, events which would otherwise be uncoupled became correlated." (Jochen Fromm, "The Emergence of Complexity", 2004)

"[...] in probability theory we are faced with situations in which our intuition or some physical experiments we have carried out suggest certain results. Intuition and experience lead us to an assignment of probabilities to events. As far as the mathematics is concerned, any assignment of probabilities will do, subject to the rules of mathematical consistency." (Robert Ash, "Basic Probability Theory", 2008)

"Prior to the discovery of the butterfly effect it was generally believed that small differences averaged out and were of no real significance. The butterfly effect showed that small things do matter. This has major implications for our notions of predictability, as over time these small differences can lead to quite unpredictable outcomes. For example, first of all, can we be sure that we are aware of all the small things that affect any given system or situation? Second, how do we know how these will affect the long-term outcome of the system or situation under study? The butterfly effect demonstrates the near impossibility of determining with any real degree of accuracy the long term outcomes of a series of events." (Elizabeth McMillan, Complexity, "Management and the Dynamics of Change: Challenges for practice", 2008)

"Regression toward the mean. That is, in any series of random events an extraordinary event is most likely to be followed, due purely to chance, by a more ordinary one." (Leonard Mlodinow, "The Drunkard’s Walk: How Randomness Rules Our Lives", 2008)

"You can’t navigate well in an interconnected, feedback-dominated world unless you take your eyes off short-term events and look for long term behavior and structure; unless you are aware of false boundaries and bounded rationality; unless you take into account limiting factors, nonlinearities and delays." (Donella H Meadow, "Thinking in Systems: A Primer", 2008)

"In the network society, the space of flows dissolves time by disordering the sequence of events and making them simultaneous in the communication networks, thus installing society in structural ephemerality: being cancels becoming." (Manuel Castells, "Communication Power", 2009)

"Because the question for me was always whether that shape we see in our lives was there from the beginning or whether these random events are only called a pattern after the fact. Because otherwise we are nothing." (Cormac McCarthy, "All the Pretty Horses", 2010)

"Without precise predictability, control is impotent and almost meaningless. In other words, the lesser the predictability, the harder the entity or system is to control, and vice versa. If our universe actually operated on linear causality, with no surprises, uncertainty, or abrupt changes, all future events would be absolutely predictable in a sort of waveless orderliness." (Lawrence K Samuels, "Defense of Chaos: The Chaology of Politics, Economics and Human Action", 2013)

"The problem of complexity is at the heart of mankind’s inability to predict future events with any accuracy. Complexity science has demonstrated that the more factors found within a complex system, the more chances of unpredictable behavior. And without predictability, any meaningful control is nearly impossible. Obviously, this means that you cannot control what you cannot predict. The ability ever to predict long-term events is a pipedream. Mankind has little to do with changing climate; complexity does." (Lawrence K Samuels, "The Real Science Behind Changing Climate", 2014)

Knowledge Representation: Events (Quotes)

noreply@blogger.com (Adrian) — Sun, 08 Aug 2021 15:10:00 GMT

"Man’s mind cannot grasp the causes of events in their completeness, but the desire to find those causes is implanted in man’s soul. And without considering the multiplicity and complexity of the conditions any one of which taken separately may seem to be the cause, he snatches at the first approximation to a cause that seems to him intelligible and says: ‘This is the cause!’" (Leo Tolstoy, "War and Peace", 1867)

"Thus representations of the external world are images of the lawlike temporal succession of natural events, and if they are correctly formed in accordance with the laws of our thinking, and we are able correctly to translate them back again into actuality through our actions, then the representations that we have are also the uniquely true [ones] for our faculty of thought; all others would be false." (Hermann von Helmholtz, "Handbuch der physiologischen Optik" Vol. 3, 1867)

"Things and events explain themselves, and the business of thought is to brush aside the verbal and conceptual impediments which prevent them from doing so. Start with the notion that it is you who explain the Object, and not the Object that explains itself, and you are bound to end in explaining it away. It ceases to exist, its place being taken by a parcel of concepts, a string of symbols, a form of words, and you find yourself contemplating, not the thing, but your theory of the thing." (Lawrence P Jacks, "The Usurpation Of Language", 1910)

"Thought interferes with the probability of events, and, in the long run therefore, with entropy." (David L Watson, 1930)

"If the organism carries a ‘small-scale model’ of external reality and of its possible actions within its head, it is able to try out various alternatives, conclude which is the best of them, react to future simulations before they arise, utilize the knowledge of past events in dealing with the present and the future, and in every way to react in a much fuller, safer, and more competent manner to the emergencies which face it." (Kenneth Craik, "The Nature of Explanation", 1943)

"Thus we do not try to prove the existence of the external world – we discover it, because the fundamental power of words or other symbols to represent events [...] permits us to put forward hypotheses and test their truth by reference to experience." (Kenneth Craik, "The Nature of Explanation", 1943)

"Early scientific thinking was holistic, but speculative - the modern scientific temper reacted by being empirical, but atomistic. Neither is free from error, the former because it replaces factual inquiry with faith and insight, and the latter because it sacrifices coherence at the altar of facticity. We witness today another shift in ways of thinking: the shift toward rigorous but holistic theories. This means thinking in terms of facts and events in the context of wholes, forming integrated sets with their own properties and relationships."(Ervin László, "Introduction to Systems Philosophy", 1972)

"A schema, then is a data structure for representing the generic concepts stored in memory. There are schemata representing our knowledge about all concepts; those underlying objects, situations, events, sequences of events, actions and sequences of actions. A schema contains, as part of its specification, the network of interrelations that is believed to normally hold among the constituents of the concept in question. A schema theory embodies a prototype theory of meaning. That is, inasmuch as a schema underlying a concept stored in memory corresponds to the meaning of that concept, meanings are encoded in terms of the typical or normal situations or events that instantiate that concept." (David E Rumelhart, "Schemata: The building blocks of cognition", 1980)

"Since mental models can take many forms and serve many purposes, their contents are very varied. They can contain nothing but tokens that represent individuals and identities between them, as in the sorts of models that are required for syllogistic reasoning. They can represent spatial relations between entities, and the temporal or causal relations between events. A rich imaginary model of the world can be used to compute the projective relations required for an image. Models have a content and form that fits them to their purpose, whether it be to explain, to predict, or to control." (Philip Johnson-Laird, "Mental models: Toward a cognitive science of language, inference, and consciousness", 1983)

"The basic idea is that schemata are data structures for representing the generic concepts stored in memory. There are schemata for generalized concepts underlying objects, situations, events, sequences of events, actions, and sequences of actions. Roughly, schemata are like models of the outside world. To process information with the use of a schema is to determine which model best fits the incoming information. Ultimately, consistent configurations of schemata are discovered which, in concert, offer the best account for the input. This configuration of schemata together constitutes the interpretation of the input. (David E Rumelhart, Paul Smolensky, James L McClelland & Geoffrey E Hinton, "Schemata and sequential thought processes in PDP models", 1986)

"There is no coherent knowledge, i.e. no uniform comprehensive account of the world and the events in it. There is no comprehensive truth that goes beyond an enumeration of details, but there are many pieces of information, obtained in different ways from different sources and collected for the benefit of the curious. The best way of presenting such knowledge is the list - and the oldest scientific works were indeed lists of facts, parts, coincidences, problems in several specialized domains." (Paul K Feyerabend, "Farewell to Reason", 1987)

"We build mental models that represent significant aspects of our physical and social world, and we manipulate elements of those models when we think, plan, and try to explain events of that world. The ability to construct and manipulate valid models of reality provides humans with our distinctive adaptive advantage; it must be considered one of the crowning achievements of the human intellect." (Gordon H Bower & Daniel G Morrow, 1990)

"[Schemata are] knowledge structures that represent objects or events and provide default assumptions about their characteristics, relationships, and entailments under conditions of incomplete information." (Paul J DiMaggio, "Culture and Cognition", Annual Review of Sociology No. 23, 1997)

"According to the representational format claim, images constitute a class of mental models that is particularly suited to represent visually perceptible information. More specifically, an image can be conceived of as a viewer centered projection of an underlying mental model which, in turn, represents spatiotemporal aspects of external objects or events. Unlike propositional or network representations, images depict, rather than describe, a particular state of affairs." (Gert Rickheit & Lorenz Sichelschmidt, „Mental Models: Some Answers, Some Questions, Some Suggestions", 1999)

"In broad terms, a mental model is to be understood as a dynamic symbolic representation of external objects or events on the part of some natural or artificial cognitive system. Mental models are thought to have certain properties which make them stand out against other forms of symbolic representations." (Gert Rickheit & Lorenz Sichelschmidt, "Mental Models: Some Answers, Some Questions, Some Suggestions", 1999)

"Bounded rationality simultaneously constrains the complexity of our cognitive maps and our ability to use them to anticipate the system dynamics. Mental models in which the world is seen as a sequence of events and in which feedback, nonlinearity, time delays, and multiple consequences are lacking lead to poor performance when these elements of dynamic complexity are present." (John D Sterman, "Business Dynamics: Systems thinking and modeling for a complex world", 2000)

"We must begin by distinguishing between visual mental imagery and visual perception: Visual perception occurs while a stimulus is being viewed, and includes functions such as visual recognition (i. e., registering that a stimulus is familiar) and identification (i. e., recalling the name, context, or other information associated with the object). Two types of mechanisms are used in visual perception: ‘bottom-up’ mechanisms are driven by the input from the eyes; in contrast, ‘top-down’ mechanisms make use of stored information (such as knowledge, belief, expectations, and goals). Visual mental imagery is a set of representations that gives rise to the experience of viewing a stimulus in the absence of appropriate sensory input. In this case, information in memory underlies the internal events that produce the experience. Unlike afterimages, mental images are relatively prolonged." (Stephen M Kosslyn, "Mental images and the brain", Cognitive Neuropsychology 22, 2005)

"At all events, our world pictures may have components of all three kinds: perceptual, conceptual, and praxiological (action-theoretical). This is because there are three gates to the outer world: perception, conception, and action. However, ordinarily only one or two of them need be opened: combinations of all three, as in building a house according to a blueprint, are the exception. We may contemplate a landscape without forming either a conceptual model of it or a plan to act upon it. And we may build a theoretical model of an imperceptible thing, such as an invisible extrasolar planet, on which we cannot act." (Mario Bunge, "Chasing Reality: Strife over Realism", 2006)

"[…] our mental models fail to take into account the complications of the real world - at least those ways that one can see from a systems perspective. It is a warning list. Here is where hidden snags lie. You can’t navigate well in an interconnected, feedback-dominated world unless you take your eyes off short-term events and look for long-term behavior and structure; unless you are aware of false boundaries and bounded rationality; unless you take into account limiting factors, nonlinearities and delays. You are likely to mistreat, misdesign, or misread systems if you don’t respect their properties of resilience, self-organization, and hierarchy." (Donella H Meadows, "Thinking in Systems: A Primer", 2008)

A 2020 Success Story & Four Important Changes for 2021

etale — Sat, 02 Jan 2021 00:59:41 GMT

I’m excited to share four important announcements as we begin the new year. Before I do that, I’m compelled to offer a few paragraphs of reflection on 2020. If you want to skip right to the news, please feel free to scroll to the bottom and look for the four bold items.

2020 was quite the year, wasn’t it? If you are anything like me, I will likely be striving to make sense of last year for a very long time. There were some aspects of the year that we might identify as shared experiences, but even more of 2020 was a distinct journey for each of us. We experienced varied calls to adventure, each struggled with with those calls in different ways. We found ourselves in unfamiliar places, facing often deeply individual tests, encountered various allies and detractors. For most of us, the journey has not ended.

For me, much of 2020 was joining the Goddard College community in what might be described as a battle for our continued existence, and an ongoing challenge to ensure that what we are doing continues to provide something distinct, if not truly unique, in the higher education ecosystem; being a champion for the significance and relevance of learner-driven models of higher education, ones where the educational practices flow from a celebration and cultivation of learner voice, choice, ownership, and agency.

And as many of you know, Goddard College remains on the journey, but with many incredible and positive achievements in 2020. When I arrived at Goddard in 2018, we were on probation with the accreditors, on track for over a million dollar deficit, struggling from multiple semesters of declining enrollment, and then in 2020 a global pandemic was added to the mix. Today, Goddard is no longer on probation, with a ten year accreditation renewal. We are operating from a balanced budget with a growing cash reserve, and we just welcomed the largest fall class since 2016. It has been the most challenging professional adventure of my career, risks remain (as they do in all of higher education), and there is more work to be accomplished. Yet, I am humbled and grateful to have been trusted to serve as President of this community as we navigated such times, and I am so proud of how the community came together to achieve such favorable results so far. I’ve shared this news in may other places, but for those longtime and loyal readers of Etale, which has obviously not received much attention during the last two years, I offer you this background to share a bit of what occupied my time in 2020.

Yet, writing and exploring educational ideas with readers like you is an important part of my life’s work and calling, and I’m resolved to return to some of that work in 2021, recognizing that my time is more limited while serving as a College president. With that in mind, here is what you can expect in 2021.

The digital world continues to evolve and while blogging is still valued by some of us, many more people in education are consuming content through video and audio. This is not an opinion. The data is undeniable. As such, I’ve decided to focus my content creation and rough draft exploration of ideas in some new forums this year.

Much of How I Used Etale in the Past is Moving to YouTube in 2021

Check it out and subscribe here: https://www.youtube.com/c/bernardbull

You will still find an occasional blog post on Etale, but for 2021, I launched a new YouTube channel, tentatively called Inspired Learning. In terms of content, it is very much the type of thing that I’ve written on Etale in the past, but with less on education policy and a greater focus upon designing hopeful, humane, and inspiring learning experiences (for both personal learning and in formal learning communities). These will be short videos of five to fifteen minutes, rough draft, exploring ideas that matter in education, and challenging us to move from idea to action. I pre-posted a few videos to launch the channel, but I plan to add one new short video each week. Please check it out and hit the subscribe button if you like what you see. I would be grateful for your help in spreading the word about this change, inviting others to subscribe as well, but only if it is something that you think others will find valuable.

The Edu Futures Podcast is Returning for Season Two

Check out season one and subscribe for season two here: https://podcasts.apple.com/us/podcast/the-edu-futures-podcast/id1497857342 (This link is for iTunes, but it is on most other platforms as well.).

Amid the almost all-consuming but meaning-rich tasks at Goddard College this past year, I found brief evening escapes to enjoy a new endeavor, the launch of the Edu Futures podcast, interviews that explore forecasts, frontiers, and futures in education. Season one, which is already available online, included a delightful array of guests, people like Angela Duckworth, Tony Wagner, James Lang, Rohit Bhargava, Robert Pondiscio, Sarah Fine, Howard Rheingold, Ozan Varol, Bryan Alexander, Tom Vander Ark, Michael Staton, Thomas Frey, David Staley, Michael Horn, and many others. Season two is scheduled to launch in the first few months of 2021, with the goal of releasing at least 40 new episodes for the second season. If you want to be notified when the episodes for the new season arrive, just subscribe using the link above.

New Books

Thanks to many of you who bought and offered feedback on my last book, Breathe a Vison and Framework for Human-Centered Learning Environments. Yet, another part of my scholarship that was largely put aside amid the complexities of my work at Goddard was my writing. As such, I renegotiated due dates for several books, but I remain committed to finishing them in 2021, while starting a couple new project as well.

The book that I’ve been promising readers that I will get to this for years, will be finished this year. Learning Beyond Letter Grades, a call to cultivating cultures of learning over cultures of earning, is a top writing priority. I’m addition, I’m slowly working on finishing anther book that is specifically designed to help struggling private and faith-based schools work through their challenges, engage in honest and important reflection on their future, and create a path forward.

These days I commit to a daily evening/night habit of writing 250 words, nothing like what I’ve done in the past, but I’m getting closer and closer to completion on these two projects. Once they are complete, I’m venturing into two more book projects that I’ve mentioned in the past, The Lincoln Test: A Legacy of Learning Beyond Credentials, and Made by Measurement: Education Priorities Revisited. I’ve spoken to a few editors about these, but have not secured publishers. As such, chances are that you will not see these released until 2022, but 2021 is the year that most of the writing gets accomplished.

I’ve Accepted a New Challenge

It was a difficult decision, as I believe deeply in the learner-driven model and mission at Goddard college, but I recently accepted the call to become the next President of Concordia University Nebraska (CUNE). If all goes according to plan, I finish my time at Goddard August 1 and transition to CUNE soon after that. Lutheran education is my professional home, so when the call was extended, I found myself deeply honored to give back to the education system that has been there for me throughout much of my life, even amid challenges like the death of my father when I was twelve years old. I am sad to leave the Goddard community so quickly, but honored to take on this new opportunity for service.

Those are the four big pieces of news as I enter 2021. I have one other, but the timing is not quite right to share it. Of course, you will all be among the first to know once I am able to say more.

Until then, I wish you all a meaning-rich new year!

With grit and gratitude,

Bernard

Poem: Our Assorted Quarantines

ekswitaj — Sun, 14 Jun 2020 02:06:55 GMT

My poem, Our Assorted Quarantines, about living on an island COVID-19 has not (yet) reached while friends and family around the world have to isolate themselves, was published as part of the Indolent Books’ online poetry project, What Rough Beast. Read the poem at: https://www.indolentbooks.com/what-rough-beast-covid-19-edition-05-26-20-elizabeth-kate-switaj/

7 Game Changers for Private & Residential Colleges

etale — Tue, 26 May 2020 03:22:07 GMT

If you work at or lead a private residential college, things are going to get interesting. As we move through 2020 and beyond, these colleges have ten years (or much less with some of the items in the list) to figure out how they are going to respond or adapt to the following changes.

American higher education is the story of change, struggle, adaptation, extinction, flourishing, fumbling, innovation, and tradition. As such, the following list might include new challenges, but most are no greater than the challenges that shaped and reshaped higher education in the past. The question is whether colleges will carefully, thoughtfully, strategically plan and adapt; or whether they will wait until it is too late, only to find themselves struggling for a response at the last minute.

Tuition Free College – Like it or not (I have a number of concerns about all of the existing proposals that I’ve seen), there will be political ebbs and flows and there is a high likelihood of a bill passed that makes tuition free for 2-year degrees, and possibly 4-year degrees at public higher education institutions. If you are a selective or highly selective college, you might be insulated, but for the rest, this is going to impact your enrollment. In addition, the reallocation of federal funds for tuition-free college has the chance of removing or decreasing federal financial aid for undergraduate students in private colleges. This might not happen in the next 5 years, but schools are wise to at least have a potential plan should this occurs in the next 10-15 years.
Online Degree Programs – Nothing new here, right? Wrong. I first got involved with online learning in the 1990s and there was already well over fifty years of research and practice on distance learning. We’ve only seen the beginning of online learning. The adoption rate is on the rise as is the growing comfort of spending more of our lives behind a screen. The stigma is on a decline, and while there are mixed views about how the pandemic will impact people’s perceptions of online learning, every indication is that it is on the rise. In the next decade we will see double digit declines in people choosing a residential college experience over an online (or largely online) alternative, at least for the academic portion of their higher education experience.
Declining Value for Higher Education – We can ignore or dislike the statistics, but there is a steady increase in the number of young people (and people of all ages) who don’t consider traditional college as important as the last couple generations.
Higher Education Alternatives with Equally Promising Career Trajectories – This combined with the last one is well on its way to creating a perfect storm. Education innovation is on the rise, but much of it is not confined to the work of regionally accredited residential undergraduate colleges. Many in academia may well dismiss these outsider educational movements as lower quality fads that will fade, and that is just how these innovations often begin. High or low quality, they are growing.
AI-Powered Adaptive Learning – “Computers will never replace teachers” has been a common quote to comfort and appease educators for decades, but it isn’t true. Computers have already begun to replace teachers. Or, rather, the development of adaptive learning software has done so. We might not see a rapid reduction in full-time equivalent employees due to the growth of innovations like adaptive learning software right away, but it is only a matter of time before we begin to see student learning outcomes through computer-aided-instruction exceeding the outcomes of teacher-led instruction. Such results will create change. When an AI-powered piece of software can read and adapt to the slightest shifts and needs of every learner, we will see people wanting to use it.
Shifting Demographics – Read Nathan Grawe’s Demographics and the Demand for Higher Education. If your focus is traditional aged residential undergraduate students, read it twice.
The New Normal – Will life return to normal post-pandemic? Yes, but it will be a new normal. Memories, habits, ways of thinking, and ways of being may well persist for a generation or longer. Some of these changes might be so subtle that we fail to notice them on a daily basis, but all of those little changes can add up to sizable shifts over years. Among many possibilities, people‘s connection to space and place is likely to shift.

These have potential to significantly change how people think and go about higher education, but they don’t mean the end of the residential college experience. Full-time and residential college has always and will continue to be the minority option in the United States, but that does not mean that the future is bleak. In fact, there are countless promising possibilities for such schools. They can and will continue to play a valued role in life of Americans and society, if only they are willing to listen, learn, respond, adapt, differentiate, and embrace new ways and possibilities.

What if These 50+ Activities Made Up 90% of Every School Day?

etale — Thu, 26 Dec 2019 03:25:53 GMT

Given that I’m persistently arguing for reframing the nature of learning environments (ala the new book, Breathe: A Vision and Framework for Human-Centered Learning Environments), someone recently asked me what I want to see instead of traditional classrooms with desks rows, letter grades, and teacher’s directing and dictating while students have the primary roles of achieving expertise in compliance and conformity.

Here is my quick response…

I envision learning environments where one or more of the following 50+ items make up the bulk of every school day. I see schools where learning is rich, inspiring, meaningful, and transformational; and where the dragons of tests and grades no longer demand fear and submission. I envision a learning environment that is deeply human and humane, one that is responsive to the needs, callings, passions, proclivities, perspectives, and voices of all learners.

All of this is possible, but we must slay the industrial dragons that rule while boldly exploring and embracing the breadth of possibilities offered below (and beyond).

Note: There is plenty of overlap from one concept to the next in this list.

Adventure-Based Learning

Challenge-Based Learning

Quest-based Learning

Competition-Based Learning

Problem-Based Learning

Project-Based Learning

Experiential Learning

Phenomenon-Based Learning

Framing Study as Adventures & Quests

Engaged Citizenship

The Individual & Collective Pursuit of the Unknown & That Which has Never Been Accomplished Before

Service Learning

Acts of Service

Learner-Led Activities

Expeditionary Learning

Inquiry-based learning

Socratic Circles

Meaningful Engagement with Music, Art, Literature, & Performing Arts

Storytelling & Story-Making

Public Performances

Learning in Depth

Self-Designed Projects

Self-Directed Learning Plans

Apprenticeships

Passion Projects

Genius Hour

Authentic Collaboration, Cooperation, & Teamwork

Positive Psychology Interventions

Random Acts of Kindness

Game-Based Learning

Gameful Learning

Case-Based Learning

Gamification in Education

Real World Design Thinking Projects

Life Experiments

Hackathons

Startup Competitions

Games & Puzzles

Creation

Deliberate Practice Inspired by a Personal Goal or Aspiration

Deeply Meaningful Direct & Indirect Experiences with Mystery & Wonder

Rough and Tumble Play

Social Play

Communicative Play

Locomotor Play

Dramatic Play

Object Play

Explorative Play

Recapitulative Play

Deep Play

Creative Play

Socio-Dramatic Play

Symbolic Play

Mastery Play

Role Play

Fantasy & Imaginative Play

What if Experimentation & Play Were a Daily Part of the Classroom?

etale — Sat, 07 Dec 2019 23:41:52 GMT

This article is an early draft excerpt from the book, Breathe: A Vision and Framework for Human-Centered Learning Environments, available at Amazon and elsewhere.

“A child who does not play is not a child, but the man who does not play has lost forever the child who lived in him.” -Pablo Neruda

Imagine an activity that can increase productivity at work and school, speed the rate and depth of learning something new, increase well-being and satisfaction, decrease stress, enhance the bonding between two or more people, and strengthen connections and communication with others. With such a long list of benefits, who wouldn’t want to engage in such an activity? The activity that I’m describing is play.

Stuart Brown, a leading expert on the merits of play, argues that, “Play is a basic human need as essential to our well-being as sleep, so when we’re low on play, our minds and bodies notice…” If this is true, then play is certainly not just for children, nor is it best reserved for a special treat. If humans really are designed to crave play, then it is best made a part of our daily lives, and the daily lives of learners around the world.

Yet, there is an ongoing tension about the word play for many people. In both schools and work, there continue to be some who are skeptical about anything that uses the word play. School and work are about productivity and hard work, and people think of play as something different. Turning again to Stuart Brown, he reminds us that, “the opposite of play is not work, it is depression.”

When we diminish the value of play and playfulness in schools and workplaces, where many of us spend a significant part of our lives, we are depriving ourselves and others from something deeply inspiring and invigorating, something that we crave and that helps us to achieve well-being and higher levels of productivity.

While distinct, experimentation often flows out of play and playfulness. In imaginative play, we venture beyond the present world as we see and experience it. We find ourselves experimenting with other possibilities, even if only within the realm of our own minds. Experiments are, in one sense, tests that we conduct to explore some thesis, question, or examine a possibility. They often grow out of a willingness to ask and wonder. Some of the most powerful questions in human history led to both play and experimentation but went on to discovery and transformation.

Ray Bradbury once wrote that, “life is trying things to see if they work.” Ralph Waldo Emerson similarly wrote that, “All life is an experiment. The more experiments you make the better.” While the scientists among us have more narrow definitions for an experiment or what constitutes a good one, it is the orientation toward experimentation that we are talking about here. To experiment is to test something out, whenever possible, in the real world. You have an idea of how things might be, and you conduct one or more experiments. You observe and seek actionable insights that often leads to more experiments.

To experiment is to learn, and it taps into that drive for adventure that we already explored. Every true experiment is an adventure because you are going on a journey, and you don’t know the outcome. Experiments have that measure of wild, curiosity, uncertainty, and mystery; and these are things upon which thrive as people.

You don’t need formal training to start experimenting, although there are many tools that can help. In fact, you’ve been experimenting your entire life. You conducted an experiment the first time that you tried to walk, and each time after that. You experimented when you tried to reach out and touch that intriguing red stovetop. You experimented when you stuck your tongue out to catch your first snowflake, and to figure out how to get your bicycle to stay upright while you pedal it. You conducted an experiment each time that you tasted something new, tried to improve on a video game, or explored people’s reactions to your words and actions. Maybe you didn’t start each of these with a thesis that you were testing, but each of these are expressions of experimentation.

People continue to experiment throughout their lives, but over time we find comfort in that which doesn’t require experimentation. We develop rituals and habits. We find ourselves drawn to safe and stable situations where we are already confident about the outcomes. Think of how often we design classrooms in this same way? Experiments entail risks, and the yearning for safety and security competes with the equally important yearning for novelty, adventure, and learning.

There is nothing wrong with safety, and rituals are rich, beautiful, and meaningful parts of our lives. Or, even when they are not, they serve other useful purposes in our lives. The problem is when the pull for safety and security begins to close us off from the experimenting part of learning.

It helps to make experimentation a more planned and intentional part of the learning community. One way to do this is through what I call life experiments. These are simple experiments intended to test out new practices, ideas, and activities. For example, if you find yourself struggling with negativity, what if you conducted a simple experiment for 10 days in a row where you end each day writing down three things that went well and why. This particular “experiment” comes from Martin Seligman, the father of positive psychology, finding that something as simple as this can greatly improve the optimism and sense of well-being for many people. Of course, you don’t know if it will work for you unless you try it, perhaps you can test it out for 10 days and see for yourself. You can do the same thing with experiments around building new relationships, setting and achieving goals, managing your time, or getter better at a hobby or a skill for work. Now imagine a classroom or learning community of students who are persistently creating simple experiments for themselves and others, gaining new experiences and insights, and using that to learn and grow in new ways.

Some students will be hesitant, not having engaged in this sort of playfulness or experimentation. Here are three simple suggestions to help them get started. First, have them begin with exploring something that they want to understand or improve, or a problem that they want to solve. Maybe they want to better understand how to make money, get along with a sibling, improve a skill in a sport, address a troubling social issue, or how to develop a new skill.

Next, create a context or where they can read, talk to people, watch documentaries and YouTube videos, and gain some new knowledge about the area of interest. As they learn and explore, they will start to find possibilities and practices that intrigue them. That is where we go to step two. Have each student create a simple, time-based experiment that allows them to learn, through direct experience, how that practice or possibility might work in their life or the world. For example, several years ago, Martin Seligman and others popularized findings of a study that revealed the power of a simple bedtime practice. Before going to sleep, write down three things that went well that day and why. I read this and decided to give it a try. I committed to doing it daily for 4 weeks. At the end of each day, I also wrote down how I felt: bad, okay, good, or amazing. At the end of the four weeks, I went back, reviewed my “what went well?” statements, and I tallied up how many days I felt bad, okay, good, and amazing. I probably should have recorded how I felt daily for a month before starting the experiment. I didn’t. Regardless, the pattern was clear. In week 1 of my little life experiment, I felt bad two days, okay on four days, and good on one day. At the end of week four, I felt bad one day, okay one day, good three days, and amazing on two days. For my personal sense of well-being, that was a great outcome, so I decided to continue with the practice or different versions of it, which leads to the third suggestion.

Once students identify something to explore and conduct their personal experiment, they can make it their goal to gain actionable insight about themselves, the topic, the problem, or the world. It isn’t simple about whether it worked or not. There are lessons to be learned regardless of the outcome. This is where some form of personal reflection is valuable. This can be as simple as posing a few questions to oneself and pondering them. I tend to create times throughout the experiment for reflection, dedicating a more extended time at the end. In addition, I always include some sort of question like, “What next?” In other words, now that I completed this experiment, what do I want to do with the insights? I might continue the experiment, make some adjustment to my life in some way, or get an idea for a new or related experiment.

This should be fun, even playful. They are exploring and experimenting. Some might enjoy inviting others to join them in creating and conducting personal life experiments, sharing their lessons and insights along the way. Others might prefer keeping them private. Learners can chart their own course. I’m offering a few suggestions, but engage the learners to decide what to do, when to do it, and how to do it.

In some ways, this recipe approach might feel “industrial” in nature. As I’ve mentioned before, those are not bad values. We just need to gain control of them and make sure that we are prioritizing and celebrating the deeply human-centered ones. That is what we are doing here. We are creating a recipe that helps you prioritize more experimentation and play in your life.

By adding more play and experimentation in our learning communities, we are embracing a sense of possibility, and possibility breeds hope and a deeper sense of meaning. As Paul Rogat Loeb wrote, “Possibility is the oxygen upon which hope thrives.”

Here are a few questions for further consideration:

How much do learners presently play and experiment in the classroom or learning community?
What are simple ways for learners to engage in the content or learning goals through structured or unstructured play?
How can we infuse more playfulness into the classroom or learning community? What ideas might students have for this?
How can I invite learners to take a posture of experimentation about their own learning, but also about seeking understanding of other things that are important in their lives?

Are Predictions About College Closures Causing More Schools to Struggle? How Might Alternative Predictions Shape the Future of Higher Education?

etale — Mon, 02 Dec 2019 00:22:20 GMT

Anyone who follows contemporary issues in higher education is familiar with the provocative prediction from Clayton Christiansen (and others like Thomas Frey) that 50% of colleges will close over the 10-15 years. Will it happen? We are already several years into the prediction, so we don’t have to wait too long to find out. Only I’d like to posit three other questions.

First, does it have to happen?

Second, is the college closure prediction causing colleges to struggle and close?

Third, could the definition of “higher education“ be keeping us from recognizing that the more likely future is one of expanded higher education?

Let’s start with the first question. Does this closure prediction have to happen? Thomas Frey, a futurist whose work I follow and admire, is known for saying that, “the future creates the present.” In other words, our visions and musings about the future shape our thinking in the present. By thinking more deeply about a particular vision of the future, we may also be helping to make it a reality.

So, does it have to happen? At this point, I suspect that some version of the college closure prediction is likely to occur. In fact, the spirit of the prediction has already occurred. The number of closures is up, and there are many colleges struggling for survival. Did this have to happen? Yes and No. There could be other data-informed predictions and ways of thinking about the future that could create a very different future for higher education. Competing predictions could have shaped and redirected the public conversation in very different ways. Some colleges would have still closed, as they have always done. For a helpful historical perspective of the wild rise and demise of colleges in the United States, check out A Perfect Mess: The Unlikely Ascendancy of American Higher Education by David Labaree.

What about the second question? Is Christiansen’s prediction causing colleges to struggle and close? If Frey’s concept is accurate, that the future creates the present, does that mean that the prediction of the future is indeed helping to create the present circumstances? Or, to be fair, is the prediction of several futurists that many colleges will close soon helping to make that a reality?

Consider this example. There was recent rumor about a possible release of an article in a well known higher education news source that was going to provide a list of predictions about which colleges are likely to close in the next ten years. It turns out that this was not exactly the intent of the article and it has not released at this point, but there are indeed groups that are analyzing colleges based upon publicly available records, and they could release such a list.

What happens next? Suppose there is a college that is struggling but it is doing amazing work to stabilize, turn itself around, and re-imagine itself in ways that could be incredible, inspiring, and valuable to the world. Yet, the current circumstance of that organization is fragile. Then an article releases that predicts this college’s closure within 5-10 years. How do you think that will impact the college’s ability to recruit new students or raise new funds? How do you think it will impact its ability to build strategic partnerships? By publishing such a specific prediction, it didn’t just offer a neutral report. It helped make the prediction a more likely reality. It might have potentially undermined the present turnaround efforts of the institutions on that list. Of course, I live in a nation where there is freedom of press, and I would not want to censor such free exchange. I just want to acknowledge that such writing isn’t just describing. It is helping to create a particular future. It isn’t just informing. It is forming.

The same thing applies with broader or more general predictions about higher education. It would not be fair or accurate to claim that Clayton Christiansen’s prediction about college closures is the cause of so many recent college struggles and even closures, nor would it be accurate. There are many other factors. Yet, it is hard to deny that this prediction changed the contemporary discourse in higher education. Just scan the number of media headlines. If you go to higher education conferences, there is no doubt that you’ve heard countless people quoting Christiansen’s prediction or discussing it between sessions. Talk to groups of college presidents or to chairs of college boards, and you will find that this quote has influenced in will influence their thinking and choices. To publicly predict something is to help make it a more likely reality.

I don’t write this as a criticism. I support the fact that Christiansen wrote this. It sparked good and important conversation. It drew people’s attention to the many forces influencing the future of higher education. It also sparked new thinking and innovations.

Yet, when a well-respected Harvard professor makes such a prediction and it starts to gain traction, that is powerful force. It gets picked up by more media sources and gains the attention of people in places of influence. Just look at how Howard Gardner’s writing shifted an entire generation of educator’s view of intelligence. Ideas (and predictions are ideas) have consequences. They are never neutral. If they are shared and discussed, they influence. They influence policy. They influence the thinking and decision of key influencers in government, higher education leadership, accrediting agencies, think tanks, and more.

So, did the prediction that 50% of all colleges will close in the next 10-15 years help make that a more likely future? There is compelling case that the answer is “yes.” Would that future have occurred even without the prediction being shared? I think the answer that question is also “yes.”

This brings me to the third question. Could our definition of “higher education” be causing us to miss an opportunity to help create a possible future of rapidly expanding higher education?

Followers of my work know that I’ve challenged the college closure prediction, not because I think it is inaccurate, but because I think a more nuanced prediction could help us create a more hopeful future for learning beyond high school. I don’t doubt or question that many colleges will struggle and close, especially those resistant to fundamental changes. We already see that happening, and if they are not able to provide enough value to warrant the attendance of students and the support of donors, perhaps it is time for them to close. Yet, despite all of this, I made the following prediction in 2015:

Will that mean that only half survive? I think that is too simple of a picture. Many will have extreme makeovers, but will emerge with new life. Some will shrink while others expand. And alongside all of that, I am convinced that we will see an entirely new breed of higher education institution… I expect that, by 2030 [or maybe 2035], we might have two to three times as many higher education institutions as we have today, even as there will be more alternatives to the traditional college routes for people. Get ready for the higher education “startup” revolution. They might not all be higher education institutions as we’ve thought of them in the past, but they will be institutions [or communities] that provide education beyond the secondary level.
http://etale.org/main/2015/07/27/higher-education-in-2030-get-ready-for-the-highered-startup-revolution/

In fact, while many media sources are quick to join in helping to amplify the impact of the prediction about college closures, I continue to contend that we are missing the higher education revolution underway. People are drawn to the headlines of college closures and colleges on probation with their accreditors (I’m helping lead one of those colleges right now, and the future is admittedly unclear), and those make for provocative headlines. Yet, one need not look further than Clayton Christiansen’s own writing about how disruptive innovation works to know that the true disruptions are often missed, even ignored in early stages. Not only are they missed and ignored by the established institutions, they are also often missed by the media and larger system. That is happening today.

There is a rapid democratization of learning community creation, formation, and cultivation at work today. I’ve sometimes referred to this as “outsider higher education” because it resembles the development of outsider art. Outsider artists often didn’t (and don’t) even consider themselves artists. Yet, over time, outsider art became a part of the larger ecosystem. This is happening all over the place, especially in the digital landscape. Individual instances have an ebb and flow of media attention and discussion in higher education communities, but people rarely recognize how these individual instances represent a larger pattern and movement that is changing the nature of learning and education. Sometimes these individual instances resemble traditional higher education communities. Other times they don’t, and they don’t bother with such things as grades, transcripts, degrees, any many other characteristics that are part of our image of higher education.

As one who studies past, present, and future trends in education; I continue to be amazed at how little many of us in higher education know about our own history, and how short that history is when it comes to aspects of college or higher education experience. When we step back and look at the history of higher education, we see that change is the norm, sometimes drastic change.

The problem is that these new and emerging higher education communities look so different from what we think of as college that we don’t think to include them in our conversations about the future of the ecosystem. Yet, if we think of them in terms of the goals that they help people achieve (beyond earning a degree), there is a strong case to be made that they are indeed a part of the broader notion of higher education.

When I predict that we will have two to three times the number of higher education communities, I am not suggesting that these will be formal or regionally accredited colleges. Rather, I am suggesting that they will be communities that, at their essence, expand and extend people’s learning beyond the secondary level. If we are willing to broaden or perspective, we soon begin to see that this is a revolution that has been underway for fifty years, and it is growing exponentially.

So, while we already have predictions about college closures, I expect that the far more interesting and promising predictions relate to what forms of higher education will persist. By only focusing upon the college closure predictions, we risk contributing to a future where there are a smaller number of dominant institutions that lack the variety and diversity that exists today. If we instead broaden our definition of higher education and focus upon predictions of a rich, vibrant, diverse, and highly valued ecosystem; we have a much better chance of helping make that future a present reality.

A 9-Stage Continuum of Teacher-Centered to Learner-Led Classrooms & Communities

etale — Sun, 17 Nov 2019 20:05:42 GMT

Inspired by a recent thread about student-centered versus student-driven learning in a Facebook group and on Twitter, I turned to my keyboard to think through the topic. In recent years, it has become popular to champion what many refer to as student-centered classrooms and schools, often described in contrast to what people think of as teacher-centered contexts.

Yet, education is a messy field of study when it comes to definitions. Consider the phrase “student-centered learning.” One person’s understanding of student-centered learning might simply refer to teachers taking the time to get to know their students, adapting their teaching methods based upon the knowledge gained. For those teachers, student-centered learning is really a synonym for differentiated instruction. Another teacher might use the same “student-centered” phrase to describe a classroom where students are granted the authority to decide much of what and how they learn.

With so many competing definitions of the same terms, it can be challenging to make sense of the current landscape. So, I’ve decided to complicate the matter by offering my own definition of terms. I certainly don’t claim to be the definitive source for these terms. Instead, I offer them as working definitions to provide a way for us to add greater depth to the student-centered versus teacher-centered conversation in education.

While some people might think it would be nice to have a set of universally-accepted definitions, that is beyond the scope of this article. For now, it is enough for me to contrast different approaches, and I’m using the following terms to achieve that goal.

Also, while we might think of these terms as being part of a continuum, that is a bit too linear for me. The continuum construct is easier if you are just comparing and contrasting two terms, but it gets complicated when we add the others into the mix. As such, some people might choose to look at the following in a linear fashion, but in the real-world, there is often a mixture of these philosophies and perspectives in the same classroom and school. One concept might be the focus, but others are still present, influencing the culture and climate.

With that introduction established, consider the following.

Teacher-centered

Some people use this phrase to describe sage-on-the-stage teaching methods, instances where teachers lecture and students are expected to “sit and get.’ Yet, the more consistent use of the phrase in the literature relates to where the power resides in a classroom or school. A teacher-centered classroom is one where the teacher is in charge of deciding what to learn, how to learn it, what will be graded, and how it will be graded. The teacher chooses the pace of the learning as well as determines the learning pathway followed by each student.

This term is also used even when the teacher doesn’t actually have full say over what is learned or how it is learned. There are many classrooms where the teacher is given a set of standards or maybe even a pre-existing curriculum. That teacher might have some choice and voice in what to do and how to do it, but part of all of those decisions might have actually been made by an outside group or organization.

Teaching-Centered

A nuanced but important distinction from teacher-centered, the teaching-centered classroom puts the quality of teaching at the center of what happens. If you want to improve student outcomes, some argue, the best way to do that is the increase the quality of the teaching. So, while teaching-centered classrooms are sometimes also teacher-centered, the focus here is upon improving the quality of the school by celebrating and championing the quality of teaching. Get teachers to embrace and embody the best practices that are well supported by the research, and everything else will fall into place.

Content-centered

Content-centered is often teacher-led, but not necessarily. Instead, the focus is upon exposure to and experiences with a given body of content. These classrooms tend to be content heavy, but contrary to common straw-man arguments against content-centered education, these classrooms are usually about both content mastery and progress toward higher levels of thinking with that content. What is distinct about the content-centered classroom is that the focus is neither upon the teacher’s action or the student’s actions and interest. It is more about getting lost in what is being studied. Great content is at the heart of great schools.

Standards-centered

While some might think that the content-centered and standards-centered classrooms are similar, a standards-centered classroom is often agnostic to specific content (as in a set reading list). Rather, attention is placed upon concise statements (called standards) of what students are expected to know and be able to do at different stages or levels of their education.

The focus is upon student’s making progress toward mastery of academic standards in each academic area. By its very nature, this tends to put heightened attention on what is happening with each student, and assessment tends to became a greater focus. Diagnostics that allow one to track students movement toward mastery of standards gets much greater attention in this type of classroom.

Learning-centered

The learning-centered classroom makes student individual and collective learning the top priority. It could be focused upon students progressing toward mastery of standards, but it could also be about the relative progress (or improvement) of a student from the beginning of the semester to the end.

Because the focus is upon learning, there is obviously significant attention to what is happening with each learner. As such, it is common for people to describe the learning-centered classroom as also a learner-centered classroom.

Learner-centered

This is a classroom where the focus is placed upon the learner. What are the individual needs of each learner? What are the interests, goals, and aspirations of the learner? What is the prior knowledge that each learner brings to the classroom? What is the cultural background of the learner? What are the beliefs, values, joys, and fears of the learner?

Notice that this is not just about getting to know a group of learners. It is about getting to know each learner in a deep and substantive way, and then adapting the learning plan accordingly. At the same time, some who describe their classrooms as learner-centered don’t necessarily engage in an in-depth investigation of these factors. Yet, the idea is that knowledge about each learner is what shapes decisions about what students learn and how they learn it. In some cases, the teacher is still making most of the decisions. In other cases, students are given greater voice and choice (even though I’ve set aside a different term for classrooms where that is the focus).

Problem-centered

I thought twice about including this category, but without it, there is a significant gap. This represents classrooms where the focus is not actually upon the teacher, the learner, standards, or specific content. Of course, each of these are a part of the mix, but the problem-centered classroom is one that that is actually more focused upon engaging in acts of service, or understanding and engaging in solving real-world problems. Much learning happens, and in most schools or classrooms that embrace this approach, there are typically ways to document (if not assess) student learning along the way. Yet, the primary attention is upon doing something real in the world…and learning by doing that thing.

Learner-driven

In some ways, this is the most direct contrast to the teacher-centered classroom because it is about where the power resides. The learner-driven classroom is the one where learners are the primary decision-makers about what they learn, how they learn it, and maybe even how they demonstrate their learning. There are instances where each learner has almost complete control. In other instances, the learner has to fit plans within a set of standards (so a mix with a standards-centered classroom) or some pre-developed fences within which the learners are permitted to work. In other cases, the learner is co-creator of what and how to learn, doing that work with the teacher (mentor, coach, guide) and/or other classmates.

Learner-led

It is probably sufficient to simply acknowledge that there are different levels of learner-driven classrooms and schools, but I’m compelled to create this last category to acknowledge what some might describe as the most immersive expression of being learner-driven. That is when the learner (and collective of learners) not only has voice, choice, ownership, and agency of the learning process. The learner also has significant influence on what happens in the entire learning community. Learners have say on the rules, policies, practices, the physical environment, and more. In fact, while less common, there are examples of schools that are entirely learner-led, with no teachers, or where learners can vote on which teachers stay or go.

Conclusion

I’m sure that I missed other important distinctions, but my main goal here was to acknowledge and reflect upon a level of nuance that gets missed when we simply contrast teacher-centered versus student-centered learning. While breaking things into these nine categories was an exercise in organizing my own thinking as much as anything else, perhaps others will find it useful as well.

The Open Education Resource Movement Has Promise, but It Also Has This Significant Limitation

etale — Sat, 16 Nov 2019 17:10:31 GMT

Let’s get controversial. In my education innovation circles there is widespread support for Open Education Resources (OER). I value and support the promise, possibility and idea of OER, but there is a persistent equity issue in the OER world that doesn’t seem to get much attention. Before I get into that, how about a quick definition break?

For newcomers to the topic, here is a quick description of OER from CreativeCommons.org:

Open educational resources (OER) are free and openly licensed educational materials that can be used for teaching, learning, research, and other purposes.

Open Education “…is the simple and powerful idea that the world’s knowledge is a public good and that technology in general and the Web in particular provide an extraordinary opportunity for everyone to share, use, and reuse knowledge.”
—The William and Flora Hewlett Foundation

That sounds great, right? What is there to not like about free resources? There is potential to remove a financial barrier to education materials for more learners, thus lowering the cost of education. It gives faculty (students, and pretty much anyone) greater control over the teaching materials. OER also creates fertile soil for any number of potential teaching and learning innovations. From a philosophical lens, OER also contributes to a type of collaborative creation, co-creation, sharing, and use of education resources. All of those have affordances.

So why would I have an issue with OER? Allow me to offer a few scenarios about how open education resources often get created, drawn from my direct conversation with content creators. After that, I’ll explain.

The Grant-Funded Independent Scholar

A person seeks out a grant from a foundation, government source, or other organization. Essentially this grant money serves as pay for the person to create the content. In return, the person agrees to license the content so that it becomes an open education resource, free for others to use (and in this case) edit, revise, and reshare as they see fit. The creator of the original content doesn’t get royalties for ongoing use of the content, as one might through a traditional publisher, but as long as that person has another source of income, or is able to keep securing grant money for the next project, the person has a living wage.

The Tenured Professor

This person already has a solid, stable source of personal income. And when deciding to work on writing a book and related resources that she intended to make an open education resource, her University actually gave her an extra grant or stipend for the work. So, she not only got paid her regular salary for blocking out time of her day to do the writing. She essentially got bonus pay.

The Content Creation Team

Similarly, in this third scenario, a team of scholars at multiple Universities came together to work on a shared open education resource project. They secured a grant from an outside source that paid for a percentage of these faculty member’s salaries. In other words, each faculty member got a 10% or 20% release from their regular work to focus on this project, and the grant paid the college that percentage of each faculty member’s salary. So, while the faculty member didn’t get paid extra, the work on the OER was an integrated part of their workload at the University.

The OER But Pay-For-Print Author

This person, a teacher at a K-12 school, submitted a book proposal to a publisher that agrees to publish the book in digital format as OER, but also sells paper copies of the book. The author gets royalties on any book that is purchased. Typically that might amount to anywhere from $500 to $10,000 dollars over a 5 year period. So, it is a nice side-project for the author, but he does this more as a free gift to the world, depending upon his full-time job at the K-12 school for his primary income.

The Education Entrepreneur

This fourth category is incredibly rare, but not if we expand our thinking beyond formal OER, allowing ourselves the messiness of also including the creating and sharing of free content in general (ala bloggers like me). Imagine a person who writes and creates content on a blog or other online platform, making it freely available for others to read, use, or maybe even edit and reshare. This person doesn’t get paid for creating any of this content and doesn’t have another job. He just writes and creates content with much of his time because he wants to share ideas that matter with the world. Yet, how does he make a living wage? Well, he uses the creation of free content to get lots of people to visit his website. His reputation grows as a thought leader so much that a number of funding opportunities emerge:

Invited and paid speaking opportunities.
Paid consulting opportunities.
Traditional online and paper publishers notice his work and reach out to offer paid/contracted writing work (of course, that content is rarely OER).
As his reputation grows, he starts to get full-time job offers because of his expertise.
Vendors and others reach out about possible sponsorship deals, offering to subsidize his work in return for advertising or promoting products and services for them.
He even has a Patreon account where followers of his work make one-time or monthly donations to fund his ongoing writing and projects.

There are very few OER creators in this educational entrepreneur category. It is a tough road. While many can augment their full-time salary by doing this type of work, there is as much luck as there is skill in making this happen.

Who is Missing?

This brings me to a persistent concern in the OER movement. Right now, OER either depends upon the narrow band of people in the categories described above, or it relies upon people working for free. Add to the fact that there is a significant amount of passionate and evangelistic fervor among some advocates of OER with a thick layer or moralistic assertions. They celebrate poets, musicians, and others creatives who charge for their work; but the moment that someone’s creative energy is focused upon something defined as educational content, they decry the creative who might opt for a traditional publishing route.

There is a fervor among some, but certainly not all, that almost comes off as wanting to ban or abandon any education content that includes a price tag. Yet, doing this has a serious risk of turning education content creation into an elitist enterprise. There are lots of people out there who don’t fit neatly into any of the categories that I described earlier in this article, but they produce great content.

OER has its promise and benefits, but this is no small matter to address. Not only does it involve the problem of limiting content creation to a privileged few (or demanding that others content creators beyond this group work for free), it also underestimates the significant creative and intellectual resources that come from people who don’t work in places that will pay them to create OER.

So, as much as there is a place for OER, I expect that some of the best and most creative content will continue to have a price tag associated with it. Regardless of that fact, it would serve the OER community well to identify some creative solutions that increase access and opportunity for a broader array of content creators.

And just for a fun, paradoxical conclusion to this article…

This article is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. If you like part of it but not another part, make it better, but don’t forget to give attribution to the original content creator.

King Tide Twilight

ekswitaj — Sun, 22 Sep 2019 03:48:53 GMT

King Tide Twilight, a work of persona poetry, came out in Rougarou‘s Winter 2019 issue.

Why Innovation is Important in Education

etale — Mon, 08 Jul 2019 21:54:51 GMT

Have you seen the viral video of the man on the airplane, working with all of his might to fit his bag in the overhead compartment? After 45 seconds of trying, a flight attendant comes to help. She twists the bag and it slips into the compartment. At the very end, you can see the the onlookers laughing at the man, shaking their heads at his inability to see the obvious.

https://youtu.be/eY1khLZ3h54

We can find the video funny, but I’d like to suggest that we have all been the man in this video. We have an idea of how it is supposed to work. We try, and to our confusion, it doesn’t happen. So what do we do? We try again, and again, and maybe again. Perhaps we convince ourselves that grit, perseverance, and persistence will win the day. With that personal pep talk, we pick up the bag once more and try the exact same way, but to no avail.

Then someone comes to us and suggests another way. Maybe we are open to taking their advice? Or, maybe we reject it. Sometimes on a matter of principle, we persist with the method, protecting it like our only child.

“This is now how it is supposed to work.”

“This is the right way to do it.”

“It has worked for me this way before.”

“I’ve tried everything and it just doesn’t fit.”

This is why we need innovation in education. Innovation is not just a buzz word. It isn’t just about embracing new and trendy ideas. It is about embracing the breadth of possibilities, acknowledging that there might be a better way, being open to new ways of embodying our values and embracing our mission.

We’ve had enough of trying to stuff students in our educational compartments. We blame the students, the compartment, the people around us, or even ourselves when it doesn’t work out as we desired or expected. Maybe innovation is really about having the openness and humility to consider something new. And maybe it is sometimes as simple and subtle as twisting the idea on its side.

A Student Saves a Teacher’s Life & Reminds us all About The Power of Self-Directed and Connected Learning

etale — Thu, 09 May 2019 22:10:52 GMT

I’ve been writing about and advocating for the importance of learner voice and agency for years, but a young man in Michigan might have just taught us all more about the value of such attributes in less than a minute.

Did you read the news story about the fifth grade student who saved his teacher from choking? According to the news sources, Dylan saw that his teacher was choking, got up, and used the
Heimlich maneuver while other students ran for help.

This is an inspiring story in itself, but I’m particularly intrigued by how Dylan learned to use the Heimlich maneuver. According to the article, his mother is a nurse, so he had that going for him. Yet, when asked about it, Dylan explained that he learned it from a YouTuber, Jaiden Animations.

Think about that for a moment. An entertaining animated videos watched by a fifth grader during his free time actually equipped him to make a real and significant difference in the life of another person.

I also find it particularly intriguing that this free range learning via YouTube made a difference in a school, a place that does not typically formally acknowledge or incorporate such learning. Schools, as most people experience them, are places where learning is more planned, prescribed, and directed; and there are plenty of us who learned and valued what we took from such places. Nonetheless, I’m compelled to recognize that this recent event is a beautiful reminder that learning is so much bigger than schooling; that education exists before, after, and beyond the school day; that some of our most valuable lessons are not housed in formal lesson plans and carefully assessed on quizzes and tests; and that schools themselves can benefit from finding ways to re-imagine learning environments as places that build upon, support, celebrate, and incorporate the larger world of learning in the lives of each student.

As progressive educators have been embracing for a century, life and learning are inseparable. School walls, no matter how thick, are permeable, and that is a very good thing. Now amplified by the nature of life in a connected world, we have the exciting task of creating learning communities that are strengthened by embracing this reality, and re-imagining school accordingly. And just as we learned from young Dylan, the students have much to teach us. Maybe they will even lead us to such a future. Perhaps they are already doing it.

Writing Recipes for New Learning and Personal Growth

etale — Sat, 20 Apr 2019 14:02:45 GMT

Typically, I don’t follow recipes. I like to reference a few, come up with my own twist on how to make something, and then learn through trial and error. At other times, I follow the recipe as closely as possible. After building some confidence (and maybe a fraction of competence) making it a few times, then I start to experiment with other options. Don’t get me wrong. I’m rarely in the kitchen, and I tend to make things that don’t require much of a recipe. Recently, while transitioning jobs and living away from my family for an extended period, I lived off of 3 smoothies a day, the exact same smoothie for breakfast, lunch, and dinner. As much as I seek out and value new challenges and experiences, sometimes I have so much novelty and change in my life that it is nice to not have to think about something like what I will eat that day.

Over the last ten years, I’ve been spending a great deal of time creating and then following a different type of recipe. In fact, I’ve created and tested well over a hundred of them. I don’t typically use the word “recipe”, but I’ve come to learn that the word connects with people’s prior experience, making it easier to grasp than while I typically call them, which are “life experiments.”

It started when I discovered this beautiful intersection between three areas of interest: 1) emerging research about well-being from positive psychology, 2) my intrigue with alternative and innovative education practices, and 3) my ongoing value for ancient wisdom and practices that seem to transcend time and place. This occurred around the same time that my son was born and I suddenly experienced an existential crisis about my own mortality (that is a story for another time). Those closest to me know that it was not my best moment, but it did motivate me to explore research on well-being, gratitude, grit, having a growth mindset, and so much more.

The more I read, the more I wanted to read and learn. Only I knew that my current personal crisis needed more than reading and new knowledge. I needed to cultivate new habits and ways of being. That meant turning some of this knowledge into practical experiments that I could conduct to see if they could help me learn, grow, and work through some of this new anxiety and depression that competed for my time and attention. There is more to this story, but I’ll save that for the introduction to yet another book that I’m working on tentatively called The 12 Quests.

[For the record, I’ve never enjoyed a writing project more than this one, and it is completely different than anything that I’ve written before. Of course, true to form, it is slowed by the fact that I realized that I needed to write a second book to explain the vision and philosophy behind the first book, and that is the one that I’ll likely finish first. That one is tentatively called, Breathe: 7 Priorities for Inspired Living. If there are any editors or publishers reading this, no I don’t have a contract yet, and yes, I would love to explore the possibilities with you.]

Back to the point of this article. So I started to take these positive psychology (and other) ideas about well-being, and I wrote out recipes for how I could test them in my life. I came up with recipes for things like:

experiencing more wonder in my life by watching sunrises and sunsets,
cultivating more optimism by bedtime journaling,
gaining motivation and order by making my bed in the morning,
showing more appreciation and experiencing more connection with others by sending daily thank you messages to people,
creating more times to celebrate the small things in my life,
systematically overcoming specific fears,
adding more gratitude and mindfulness by taking daily pictures of things for which I am grateful,
cultivating and planning for new experiences (there is a TON of research about the importance and benefits of novelty and new experiences, by the way),
and the list goes on, to now what is well over a hundred different life experiments.

Each recipe or life experiment included 3 to 10 steps, and I tried to make any critical element explicit. For example, I quickly realized that, to ensure follow through, I needed to add steps in each recipe for planning and scheduling. That might mean a step like, “Create a list of 10 possibilities, and then narrow it down to the 1 that you want to use for this experiment.” and “Now that you have your plan, block off 30 minutes on your calendar for each of the next 10 days.” I also included steps that reminded me to pause and journal about what I’m observing, feeling, thinking, experiencing, and learning (an incredibly important step!). At the end of each recipe, I created a “tips” section where I recorded words of encouragement, suggestions for working through what I anticipated to be potential roadblocks, etc. I also added to the tips section after each experiment, giving myself reminders for the next time.

The more I wrote recipes, the more I figured out what worked best for me. I got it down to an art, science, or maybe a blend of the two. What I know for sure is that I become intrigued by writing recipes for myself and then testing them out, sometimes refining them a couple times. I rarely shared these experiments with others. I’ve historically shared so much about my life on this blog and elsewhere, that I enjoyed keeping this one part of my life to myself (that is until now, as I’m working on the new books).

I’ve also decided to start sharing my past, present, and some of my forthcoming recipes or life experiments on a separate blog to see if others might be interested in trying out some of the recipes as well. If that interests you, head over the the What is in the Air? Blog. At the time of writing this, What is in the Air? is less than a year old. Since I have so many recipes scribbled in a dozen or more of my old idea journals, I’m beginning to transfer some of them to the digital world, and I’m excited to see if others might like to try them out, give some feedback, or maybe even suggest some of their own recipes / life experiments.

So much of modern education is biased toward knowledge acquisition, but so much significant change happens when we convert knowledge into habits, practices, rituals, and direct experiences. This recipe / life experiment approach is my effort to bridge those two worlds.

Poem: Forest for the Trees

ekswitaj — Sun, 04 Feb 2018 10:57:08 GMT

You can now read my most recently published poem, Forest for the Trees, at Indianapolis Review. This poem came from my ongoing efforts to think through what it means to write in an age of surveillance and against the tide of state violence.

Two Short Stories: Game Night and When the Typhoon Came

ekswitaj — Sun, 31 Dec 2017 08:37:13 GMT

In this, the final week of the year, Singapore-based Eunoia Review haspublished two of my short stories: Game Night and When the Typhoon Came. But what the stories themselves are based upon back further than 2017, back before I moved to Majuro atoll, before my years in Belfast, before China, to my years in Japan. 2004 to 2006.There are feelings I have from that era of my life that are more immediate than those from any other set of years. And yet that time feels more unreachable and distant than a decade and change can justify. It has felt that way since mere months after I left. Trauma will cleave.

Poetry: Articulation III

ekswitaj — Fri, 08 Dec 2017 11:40:03 GMT

The latest piece to be published from my Articulations series can now be read at sidereal magazine.

Poetry: Venomous Fang Blenny

ekswitaj — Wed, 25 Oct 2017 12:44:42 GMT

A poem of mine inspired by recent discoveries about the venom of a fish appears in the latest issue of Corium.

Striped poison fang blenny – Meiacanthus grammistes (Photo credit: Brian Gratwicke)

Poetry: Articulations VIII, IX, and XII

ekswitaj — Sun, 22 Oct 2017 11:00:53 GMT

The first set from my series of poems making use of language found in pre-1914 medical texts can now be read at Figure 1. While the idea behind the poetry has its roots in books so physical I can smell them and feel their pages near crumble, it was the Internet Archive that served as point of access to the texts I actually used. The importance of both digital and physical text to to this project says as much about embodiment now as the books themselves did about embodiment in the time when they were printed.

A Very Gendered Apocalypse: The Book of the Unnamed Midwife and The Book of Etta

ekswitaj — Sun, 15 Oct 2017 07:14:44 GMT

Over the past few days, in shared taxis and before bed, I have read the first two books in Meg Elison’s Road to Nowhere trilogy which explores the former U.S. in the years after an autoimmune disease devastates the population, killing women disproportionately. These books are uneven novels. As the speed at which I read them suggests, they are true page turners. You want to know how characters survive, or don’t, and the narrative’s early following of characters after they leave the eponymous midwife promises the reader that those questions will always be answered.

Elison plays with cliches of apocalyptic fiction with limited success. For example, in the first book, the main character sleeps through the apocalypse with the fever though, as a nurse on a maternity ward, she witnesses its beginning. The trope is thus not fully followed. Still, it is a bit disappointing not to get more of those final moments of the old world in flashback.

What the main character of the first book, who might better be described as many-named than unnamed, discovers quickly upon waking is that social deterioration and the altered balance between the genders has turned most men into predators who hunt and enslave women. At this historical moment, with a sexual predator in the White House, that seems terribly believable Even the good men in The Book of the Unnamed Midwife tend to belong settlements that fall into gender essentialism in their efforts to repopulate the earth.

It is really only in the second book that this essentialism is fully explored and problematized. At the same time, however, The Book of Etta contains a number of plot weakness—specifically, deus ex machina characters with unexplained spiritual powers and a weapons cache.

The third book, The Book of Flora, is not yet published and I expect that I will read it, though it isn’t a release for which I will be counting down the days.

The Future of the Post-Apocalyptic (themillions.com)
Floridapocalypse: The End of the Sunshine State (themillions.com)

Teachable Moment: A Dialogue

ekswitaj — Sat, 30 Sep 2017 01:49:18 GMT

Background: One of the hazards of living in Majuro is the dog pack. These dogs are neither stray nor feral, but are not controlled or generally treated as pets.

I was talking with a student about a particular dog down the back road near campus who has attacked a number of faculty. One time when he went after me, I lost my keys and only got them back through the incredible kindness of a witness.

Student: Yeah, that’s my brother’s dog. We trained him to be that way. He keeps the kids away.

Me: Could you at least train him not to go after ri-bālle [foreigners]?

Student: laughs He’s always been that way. He likes white meat. He’s racist.

Me: No, he can’t be racist because institutional power doesn’t support his going after white people.

Framing the Flag Fight

ekswitaj — Tue, 26 Sep 2017 08:30:46 GMT

When someone criticizes NFL players who sit or kneel during the US national anthem for disrespecting veterans, it’s reasonable to reply that they are not disrespecting the military, because the protest is not, in fact, about the military. In the face disbelief, it is also reasonable to marshal the testimony of veterans who support the protest. These responses, however, leave dangerous assumptions untouched: namely, that respect for the military should have priority over all other considerations and that the opinions of those who have military experience are more valuable than those of of other citizens. These assumptions are not characteristic of a civil democracy. In a democracy, civil society takes priority; there is a reason that the Command In Chief is a civilian.

The better response when someone claims that a protest against unjust killings of citizens disrespects the military is “So what if it did?” A society that prioritizes the feelings of the military is already approaching military rule.

Of course, in this case, racism has enabled the focus on military feelings. Racism has made black people vulnerable not only to police brutality but also to having their lives regarded as less important than perceptions of respect for soldiers. That does not mean that the expectation that the military be venerated will never be placed above other citizens’ lives, or even above all civilian lives. Rather, it is a toehold, as toeholds for authoritarianism and other oppressions have often begun with already oppressed groups.

To be clear, I am not advocating for disrespect of individual soldiers or veterans any more than I would advocate for the disrespect of other individual citizens (Nazis being an exception). What I am doing is to point out the danger in accepting the terms of an argument in which deference to symbols associated with the military takes precedence over everything else.

Dan — Sat, 24 Sep 2016 16:55:00 GMT

Dan — Thu, 07 Apr 2016 06:58:16 GMT

Dan — Sat, 04 Apr 2015 13:39:47 GMT

Personal Knowledge Management Routine

noreply@blogger.com (Diana Samson (DJay)) — Wed, 12 Mar 2014 14:18:00 GMT

Jane Hart shared her daily PKM routine and Harold Jarche commented on it in a blog post called What is your PKM routine?

For an overview of PKM see my pervious blog Personal Knowledge Management

Personal Knowledge Management Workshop Intro 2013

As Harold points out Personal Knowledge Management is personal. Thus, to get into a routine you need to find something that works for you.

Here is Jane's diagram:

PKM routine:

Planned Obsolescence

noreply@blogger.com (Diana Samson (DJay)) — Sun, 23 Feb 2014 17:43:00 GMT

Week 6: Rhizomatic Learning

Week 6 Rhizo14 - Planned Obsolescence

Week 6 Challenge
How do we teach ourselves into uselessness? How do we empower people so they have the PERMISSION to learn without us?
Title partially stolen from Planned Obsolenscence

Week 6 - Planned Obsolescence

Text from unhangout

Hangout points:

When creating an open course don't fill it up with content but fill it up with space for participant contribution and engagement. Place to play.

Power structures: who has the power

The What is the How - experimenting with rhizomatic learning first hand

All good teachers want to be in the background eventually.

Most moocs are shallow - the web is shallow. Most conversations are shallow, you need to work to make it profound and deep.

How do we do away with power? Everyone has power. Power is a tempering device. You can't do away with power but you can share it.

Posts:

Dave Cormier:

MOOC as Networked Textbook and a look back at the feedbook
Talks about re-imagining the textbook so that it is engaging and relevant. He looks at how moocs are like textbooks - networked ones. moocs when participatory engage learners and give them the opportunity to be the content creators. The mooc becomes the textbook,the learners within the mooc create this textbook, a living textbook. The community becomes the curriculum.

Bonnie Stewart:

Lin Talks: Bonnie Stewart

Bonnie discusses networks, how they function, their importance for today's learner and digital literacy.

Aaron Davis:

A Homage to Rhizomatic Learning

Aaron states:

Lets do our job so we are no longer required.

To me, there will always be a need for an instigator, someone to stock the fire occasionally, keep it burning, but whether this needs to be a teacher or leader I am not so sure.

As was demonstrated by +Mariana Funes' post, much was left to the community to continue the learning.

In the end, what needs to change is putting learning at the heart of education. In this environment everyone has their part to play. If we all see ourselves as learners then surely that is most of the job done.

#Rhizo14 Six Weeks' Wonder

Collaborative Autoethnography for #rhizo14

Community As Curriculum

noreply@blogger.com (Diana Samson (DJay)) — Sat, 15 Feb 2014 18:36:00 GMT

Week 5: Rhizomatic Learning

Jaap Bosman Softskills - Rhizo14

Week 5 rhizo 14 - community as curriculum - what does it mean

This week we delve into the title of the course... well... one of the titles anyway. We are going to open up the bag of worms that is community. If you've not read it, this week builds directly on week 4 as presented here... feel free to ignore that if it works for you, but that's what I'm thinking about.

Week 5 - Community as Curriculum

UnHangout Week 5 chat transcript

Post:

Richard Sennett: "Il faut restaurer le vivre ensemble"

Richard Sennett reviews some basic issues of cooperation in America. He mentions the lack of trust of the banks, the "us against them" mentality, the use of sympathy as opposed to attempting to empathize which requires more work. He has hope that technology will provide opportunities to build social and political ties. He also mentions issues with the lack of basic consideration and support.

How to restore the spirit of the collective and cooperation, it is difficult and it takes all of us in every aspect of our lives in our work, school and communities to reinstate authentic social connections. Remember - reciprocate!

MOOCS AS A GATHERING PLACE - Dave Cormier

Here are two quoted paragraphs that say it all:

Access to information, however, is not the real transformative change that excites me about learning on the Internet. Information is cold, pre-processed stuff that, at one time, came from a human. As Socrates so elegantly put it in the Phaedrus; once it’s written down it can no longer defend itself. Now, we can reach out and find the person behind the information. We can reach people and hear their thoughts while they are still uncertain, while they still maintain their complexity. The real transformation that has happened is a near unlimited possibility for connection to actual people.

In my hometown the easiest way to start a gathering from amoungst those connections was to light a fire on the beach. We’d rustle together some driftwood, stack it up high and set a match to it. People saw it from further down the beach or from the road. At first, they gathered round the fire in order to find their bearings. Then they would look from face to face in the firelight, listen in on conversations and gradually gravitate to a group. As the evening wore on, the groups would start to separate a little more and standing away from each other. The fire became less central. This was because the fire was not the purpose of the party; it was the catalyst for connection. This massive potential for connection prompted by openness was what I meant by the word MOOC when I first used it.

Stephen Downes:
What can be a Network?

A node A and a node B are connected if and only if a change of state in A could result in a change of state in B. So - anything that can have an impact on something else can be connected.

There's no 'right way' to find patterns in a network. That's because patterns in a network exist only insofar as they are perceived (a pattern in one network perceived by another network is typically how networks interact with each other). Perception is a matter of perspective and intent. We see what we want to see, or what we need to see.

Dave Cormier:

Building an introductory physics course – cMOOC meets xMOOC

Penny Bentley - Rhizo14

Clarissa Bezerra - Rhizo14

Is Books Making Us Stupid?

noreply@blogger.com (Diana Samson (DJay)) — Sat, 08 Feb 2014 16:14:00 GMT

Week 4: Rhizomatic Learning

Week 4 Rhizo14 - Is books making us stupid?

Week 4 Challenge
Is books making us stupid? Have they always? What has the medium of print done to learning? What are the implications of this objective distance? How does it impact what we believe is valid in our society both inside learning and outside of it? In the words of Walter Ong - Orality is "empathetic and participatory rather than objectively distanced".

BONUS - focus on a project that your building as part of the course. Tell us about it.From Orality to Literacy to Hypertext: Back to the Future?

Is Google Making Us Stupid?

Week 4 #rhizo14 - Is books making us stupid?

Text document of dialogue

Is books making us stupid? behind the curtain of #rhizo14

To me, quality resources do not make students stupid. Students need a healthy mix of resources, collaboration with others, reflection, experimentation and creation.

Books making us stupid – too soon?

Posts:

Arcimboldo_Librarian_Stokholm

Stephen Fry Kinetic Typography - Language

The Branches of the Other 21st Century Skills

Seth Godin: Why I want you to steal my ideas and FB Discussion

I feel that remixing and Stealing are two different things. Nothing is really new - everything is a remix.

Chadi Aljundi:

All of us would be happy when our students use our ideas to reach new horizons, but when some body, even a student, claims our work to be his.. This is another issue, and if it is OK for you Jaap, me, I will not accept it and I will do the maximum to "teach" this student the honesty is a good thing in life.. And believe me, this is for his own good..

Is Books Making Us Stupid?

In a previous blog Aaron asks 'What's so Digital about Literacy Anyway?'

Comparing digital text to print text - just because it is different does it mean it is better or worse? I feel that although reading is important, responding is great. Engage with the inner voice.

As +Doug Belshaw pointed out in his ebook The Essential Elements of Digital Literacies, “I would argue that literacy is inherently a social phenomenon. In fact, I’d argue that, in isolation, an individual cannot be literate at all”

Digital print allows us to respond and aggregate the information in so many different ways. It also gives us the opportunity to collaborate, remix and share in various ways.

Luis López-Cano responds:

In any case, +Aaron Davis I see concepts like isolation and solitude as key states of mind/body which we need to build the social network properly. I think that they are really connected with identity, voicing and empathy, for example.

Print, Stupidity, and #rhizo14

Keith Hamon refers to an audio lecture with TVO audio lecture by Don Tapscott
Don discusses how student don't read books - end to end. The student treats books as Web documents: he links to what he needs, when he needs it. You don't approach a book like a record. When playing a record you focus on the tracks you like and avoid those that don't interest you. You engage with what you like. You don't force yourself to listen to the record from beginning to end. Songs follow you. They stick with you.

It is easy to see print texts as linear and limiting, or as Dave Cormier say in this week's Rhizo14 challenge: print moves toward objectivity and distance and remove and impartiality, something that is less participatory, that moves toward the definite and not toward the relational. Dave is quite correct, I think, that this is how most people have viewed printed texts. It is the way most of us were taught in school to write texts (beginning thesis with supporting details that leads logically and inevitably to a specific conclusion, and all that), but I don't think we must view texts this way.

I think it is quite possible—in fact, I think it is inevitable—to view even printed texts rhizomatically, as a function of complex, multi-scale networks. The Rhodes Scholar in Tapscott's lecture is viewing print that way: he enters the text from the arc of his own trajectory, his own interests and needs, not necessarily from the beginning, and he takes from the text what he needs, and he at last reconnects and recombines what he's taken with any variety of other sources and types of information.

Keith reminds us that today text is not static but dynamic. Websites change luring us back to them. Text is dynamic in that it lives on and reforms and transforms in the interactions we have with others as we reflect on what we have read. How when we return we get something new from the text. Our frame of reference and our interactions are new so we have new experiences and reflections from the same text.

All texts—certainly electronic texts, but even printed texts—are rhizomatic.

SURE Books “ARE” making us stupid

Chadi points out that

We MUST concentrate on human relationships and collaboration. This applies on internet, but also on books.

I feel that it is the old saying - too much of a good thing. It is like anything in life, it is how you use it. Quality resources do not make students stupid. Students need a healthy mix of resources, collaboration, reflection, critiquing, responding to critiques, experimentation and creation.

Chadi Aljundi: Even quality resources might make students stupid IF they do not learn how to use it.. If they do not learn the other qualities that you are talking about!

Textbooks are not a destiny
Open text, open media... EXCLUSIVE: UAE colleges to ditch books for iPads

Letting Go

noreply@blogger.com (Diana Samson (DJay)) — Mon, 03 Feb 2014 13:09:00 GMT

Letting Go and Embracing Uncertainty

"Happy Dancing Giraffes" by Matt West

I have been working on unlearning that there is a right answer, a right way, a right way to be, that I as a person am right. I needed to unlearn that there is always a "right".

Growing up the adults were right; my parents, the teachers, the church. In school I needed to get the right answer. In every aspect of my being I had to be right, that was the goal. As a child I thought that was the ultimate goal.

My problem as a child was that I was wrong and I needed to be right, I needed to be what I was supposed to be what was deemed right.

So I worked hard not to be wrong. I was pressured to be right in many facets of my life; as a person, my culture, my language, where I came from, my beliefs, my thoughts and my understanding and who I was as a being.

Once I accomplished not being wrong then I would be accepted, everything would be good at home, at school and nothing would be bad. If I was right I might avoid pain and suffering. I might be able to experience some peace and maybe feel happy. All I needed to do was dodge the bullet. Yes, always dodging bullets. Narrow escapes if I was lucky. So I played the "right" game as best I could. Be "right" and be "accepted".

So when I played the right game where did it lead me to, what behaviors accompany this game, what did I need to do to win the right game? I became a robot, never stepping out of my comfort zone, never ask "why", never tried new things or experimented, never dared to make a mistake, was totally dependent, was dependent on external motivation as a survival instinct and of course I was the pleaser.

Then came the rebellious teenage years which helped liberate me somewhat but I still held on to the this basic need to a certain extent. I went through a large chunk of my life not even realizing this was part my life in so many ways. It had an effect on every decision I made.

There comes a point in one's life when you reach a certain age and you have this sense of knowing yourself, you become more reflective and more forgiving. It is about forgiveness. Because being wrong brings with it blame and shame and it takes a large amount of growing up to let go of that. To let go of the toxic people, let in the healthy ones in your life and to embrace uncertainty as an adventure.

The challenge is saying no and avoiding the toxic people. Another challenge is of course being good with uncertainty. There is no right way to move from the need for certainty to being ok with uncertainty. But what helps is that for me it is liberating - it is a feel good place with feel good people. I view it as a big adventure and I am with some great people that are sharing it with me. I can't wait for the surprise at the other end.

My Uncertain life is way better than my certain "always need to be right" life. That was hell and now I am in a better place.

The Meaning of Life:

How Brené Brown, Seth Godin and 27 Other Change-Makers Define a "Good Life"

Embracing Uncertainty

noreply@blogger.com (Diana Samson (DJay)) — Sat, 01 Feb 2014 21:54:00 GMT

Week 3: Rhizomatic Learning

Week 3 - Embracing uncertainty #Rhizo14

How do we make embrace uncertainty in learning? How do we keep people encouraged about learning if there is no finite achievable goal? How do we teach when there are no answers, but only more questions?

BONUS challenge - If you are starting a final project for the course, this week would be a good time to start that development. Don't know what I'm talking about...? Watch this video:

Success In a mooc

Coping with Uncertainty

Timely: this post was made when this week started:
How to Cope with Uncomfortable Uncertainty | Scientific American Mind

Week 3: Unhangout

Rhizo14 Week 3 Embracing uncertainty

Highlights of the Unhangout discussion and my thoughts:

There is a pressure to know - need for certainty. As opposed to a freedom to have and be comfortable with uncertainty. Uncertainty is a part of life. Not knowing is what we all share.

Teaching is uncertain and different every time. When we accept the uncertainty we make the learning process authentic.

Uncertainty requires some structure. The Rhizome needs some structure.

Is all knowledge up for grabs or is some knowledge grounded. Some customs built inside of our of ideas and knowledge are so deeply ingrained and automatic we should talk about them like their grounded and not unravel them to make them useful. We need to keep it practical. Somethings can be unraveled and some should be grounded. Call it real, there is knowledge out there and we just haven't gotten there yet. Use guideposts to make conversation with others to manage and maintain knowledge in the classroom.

Managing within a mooc: anchor yourself, don't try to take it all in - you can't. Don't become overwhelmed with the abundance of knowledge. Be comfortable with uncertainty by anchoring yourself to a few ideas and stick with them or move onto new ones.

Shaping in curriculum is about creating certainty with scarcity. We need to open this up. Remove scarcity, move toward uncertainty for rhizomatic learning and thinking.

The world of certainty, of the right answer and the right way is an exception. It has cropped up and displaced the long tradition of rhizomatic learning that has been going on since the start of human existence. Ancient people up to the creation of print material used rhizomatic learning to solve everyday issues. They anchored themselves when entering new territory or facing new problems and rhizomatically found their way and solved problems. Creation of scarcity and the right answer has given rise to certainty giving way to the feeling of attainment of all the content. When in fact in real life one can never attain all the content. That is why the internet and rhizomatic learning's abundance and lack of a right answer is overwhelming. The unraveling of certainty is overwhelming. We made information scarce through the use of print.

Did we loose a connection to real life - did we start fabricating life in the classroom? How do we solve problems naturally.

How can we convince learners in a classroom that things are uncertain. Expose them to uncertainty over and over again? Once they come close to certainty throw them back into uncertainty?

Learning by doing, How we deal with theory is by doing. In rhizomatic learning the only way to come to know is by doing.

The rhizome includes structure - we need to build structure within the rhizome. We don't know how long the structure will last and that is the unknown and the uncertainty.

Blogs

Model one: maps: Cath Ellis

The role of teachers is to help them navigate and negotiate the journey, to encourage them to seek help from others as well as reading signs along the way and, as I've suggested in a previous post, attending to the 'trees' as and when they encounter them, taking responsibility along the way.

Embracing Uncertainty in Teaching, Learning and Life – a question of balance - jennymackness

Jenny talks about how every one is perceiving uncertainty in different ways which leads the conversation in different directions.

"Some are talking about uncertainty in relation to not knowing which path to follow or what is going to happen next, others in relation to teaching without having all the answers, and others in relation to the validity of knowledge and the question of what is truth?
For Dave – uncertainty means accepting that ‘not knowing is something we all share’ and lies at the heart of rhizomatic learning. Uncertainty is related to abundance of information. According to Dave, in the past ‘certainty’ was created through a scarcity of information. ‘We were supposed to get it all’. But now with so much information it is impossible for teachers to have all the answers. Teachers are now more uncertain, than in the past, about their ability to answer learners’ questions.

Uncertainty is also about not being able to predict what is going to happen in the future and therefore not being able to predict what we might learn. (This relates to my interest in emergent learning and environments that promote emergent learning.)"

FB and G+ Posts

Comfortable in the uncomfortable: Jess Huddart at TEDxSouthBankWomen

Felicia Sullivan's Network Graph

Mariana Funes

Journey to the rhizome

Filters

It’s time to engineer some filter failure

Bonnie Stewart
just saw this courtesy of Audrey Watters...thought the conversation about echo chambers and the value, in a sense, of uncertainty might resonate with this week's topic.
the author reminisces about the differences between the old days of Napster and today's Pandora, specially engineered to give you what you're already proven to like. makes me realizes experiences like this course tend to be among the few places in my life these days where there's active frisson: yes, i find much that i like here, but in the midst of so much focused discussion i tend to become acutely aware of significant gaps and dissonance too. and sometimes, new perspectives from which i can learn. it's true...there needs to be some lack of agreement for new doors to open.

Some FB discussion:

Diana Samson Jon states: "I stay connected to people with whom I profoundly disagree." All my life I have tried to do the same and I always took courses or whole programs because I hated the topic. I knew if I hated a topic I obviously was ignorant to it and definitely needed to study it.
January 29 at 5:41pm · Edited · Like · 4
Sarah Honeychurch Same here, Diana.
January 29 at 6:13pm · Unlike · 1
Apostolos Koutropoulos Just because you hate something doesn't mean you are ignorant. I hated early American authors, like Hawthorne. Sometimes you can appreciate something and still really dislike it.
January 29 at 6:57pm · Unlike · 2
Diana Samson Apostolos Koutropoulos I totally appreciate what you are saying and agree. What I meant was the psychology of dislike. If you dislike something you tend to avoid it and focus, like in the article on views that reflect your own or topics that you like. So I have my guard up just like the author and am well aware that if I dislike something or an opinion I must move toward it to make sure I am not avoiding its deeper side.
January 29 at 7:06pm · Edited · Like · 2
Carol Yeager Diana Samson ... and I have done the same with jobs, for the better part of my life ... always learned something too!
January 29 at 7:06pm · Unlike · 1
Diana Samson Carol Yeager that practices really reflects embracing uncertainty. It takes courage to accept a job that you may dislike or feel uncomfortable doing. What an amazing learning experience.
January 29 at 7:10pm · Like · 1
Carol Yeager @Diana ... indeed, I often decided that I still did not like the job ... but often, for a totally different reason ... yes, wonderful learning experiences. And, sometimes, I actually decided that I liked the experience/job/position; even when I thought I would not Amazing, how we can unlearn through experiences. and ... sorry for the long post ... I also found that some jobs/positions I thought I would love/like ... did not turn out that way ... back to the drawing board!
January 29 at 7:16pm · Edited · Unlike · 1

Fun Stuff

Enforcing Independence

noreply@blogger.com (Diana Samson (DJay)) — Sun, 26 Jan 2014 22:53:00 GMT

Week 2: Rhizomatic Learning

Simon Ensor: Be still.

Week 2 of Rhizo14 - Enforcing independence

Learning rhizomatically is the goal, but how do we get there? The position of teachers is based on whole set of power structures that create a reliance on the teacher for setting objectives, assessing progress and giving direction. How can we take people who've spent their whole lives believing that this is 'learning' and MAKE them independent?

Overview: Explanation and Contract

Life Of Brian (1979) - clip: "You're all individuals"

Blogs:

Aaron states: "Whenever we talk about student-centred learning this discussion usually revolves around creating authentic situations through which students can take responsibility of their actions. The very term 'enforce independence' seems antithetical, contradictory. Maybe the answer to enforcing independence is that students create and assess their own learning. In this scenario, the learner is facilitator and assessor. Where they create their own narratives, their own successes, their own continual feedback. What might be termed, 'education without teachers'."Reading Writing Responding

Yes, the word enforced seems to have bothered many of us. It does not pair well with independence or learning. He emphasizes that we cannot force independence but nurture it by providing the appropriate environment, experiences and support. An environment where self-directedness is valued. Learning needs to have purpose and inquiry.

Explore a model of enforced independence.

Chadi's tale of learning to be independent during his PhD
Why openness in education.. and the effect of a real learning moment

jennymackness gives a great summary of paths in which participants in the mooc are taking and asks if this represents a community? As we are in the spirit of community is the content. If participants go their own ways is this still a community. In a community does everyone need go in the same direction. I don't think so. I believe in diversity. My community is diverse and we can all learn from each other.
The Community is the Curriculum in #rhizo14

Here is a model to assist learners to become independent
Effective Use of the Gradual Release of Responsibility Model

As KEITH BRENNAN states in the article below:

To learn in a cMOOC you need to connect.

To connect in a cMOOC you need to learn.

Yes the community is the curriculum and in order to learn you must connect and vice versa connection and learning depend on each other.

In Connectivism, No One Can Hear You Scream: a Guide to Understanding the MOOC Novice

jennymackness reflects on the topic and the unfortunate title "enforced independence" stating that they are the wrong words. Quoting the Oxford dictionary, Independence is ‘capable of thinking or acting for oneself’.
To me, "enforced independence" are two opposing words. Enforced feels harsh and independence feels liberating and soft. You cannot force independence but you can invite learners to an environment that nurtures and promotes independence. Ed Brodebent (former Director of the International Centre for Human Rights and Democratic Development) once stated "You cannot drag someone kicking and screaming into freedom" And I would agree with the need for learner autonomy. I like how you differentiated autonomy and independence. Thus it is not about independence but about exercising autonomy. Yes, autonomy and interdependence.

Breaking out from ‘Enforced Independence’ – #rhizo14

Week 2 of Rhizo14 - Enforced independence

How do we become more independent as educators, and engender independence on the part of our learners? Yes, we need to become independent ourselves before expecting learners to be independent. Well I believe that environment will not only have a significant impact but will be the major deciding factor of the learning experience (motivation, participation and success). Doug talks about moving outside our comfort zone to become more independent. Expand our horizons then we will understand what our learners are going through. Well put "walk a mile in their shoes" So go out and challenge yourself...

(re)enforcing independence #rhizo14

In this post Ann states that it is the teacher's role to guide their learners to independence. Yes the guide on the side. We should not enforce independence but guide learners to independence and provide the environment the supports independence.Enforced independence: The responsibility, ethics, and architecture

Terry states

What do the adjectives ‘rhizomatic’ and ‘deep’ add to the abstract noun ‘learning’.

We need to start in a simple place and from there you let it grow

"The mob is deeply rooted in community values dontcha know. I just need to find the right mob."
I think we all are doing this dance of independence and interdependence all the time.

We already reinforce and remediate both our independent stance and interdependent connections.

I am participating, but I often don’t feel particularly ‘invited’. Generally invited, but not particularly. I am expectant but without the visitation. I feel the disconnections and gaps in the map and the anomie that comes from knowing not WTF.
I KNOW NOT WTF: SOME SHALLOW, ARBOREAL LEARNAGE

Here is a summary from jennymackness

The Messiness of Rhizomatic Learning – Words Steal My Intent

Jaap posted a nice article differentiating a community vs. a network
How is a community of practice different from an informal network in regard to social learning?

And a nice wrap up is Cathleen's post
10 Principles for the Future of Learning

Rhizo14 feedback results and explanation

Cheating as Learning

noreply@blogger.com (Diana Samson (DJay)) — Sun, 26 Jan 2014 22:43:00 GMT

Week 1: Rhizomatic Learning

Rhizomatic learning : community as curriculum (rhizo14) Week 1

Week 1 THINGS TO DO
Introduce yourself, follow one of the threads of discussion somewhere. Comment on someone's work. Get acclimated.
Week 1 Challenge - Use cheating as a weapon. How can you use the idea of cheating as a tool to take apart the structures that you work in? What does it say about learning? About power? About how you see teaching?
Bonus - Do lots of rhizomatic teaching? Tell us about it

Free Range Pen provided a nice activity: So, after a week are you any closer to understanding what Rhizomatic Learning is? Please drop a word into this AnswerGarden

Growing Rhizomes

A tags explorer was created of twitter connections:

TagsExplorer#rhizo14

Rhizo14 Week1 - Cheating as Learning

University of Alberta video help students get honest about cheating

Cite Your Sources!

Enforcing or Supporting Independence

noreply@blogger.com (Diana Samson (DJay)) — Sun, 26 Jan 2014 20:29:00 GMT

Rewriting the Rules to Support Independence

You can never do it better until you are allowed to do it differently ~ Earl Fray

I would like to honor two educational stories about independence.

The first I posted earlier: Enforcing Basic Independence

The second was presented at TED West Vancouver by Shelly Wright

Shelly States in her talk:

"Our students will often exceed our expectations of them if we only give them the opportunity."

The power of student-driven learning: Shelley Wright at TEDxWestVancouverED

In her classroom Shelly went from being the "master of the universe" to walking into class asking "so what are we doing today?"

Empowering for both her and her students. She quickly became comfortable with uncertainty.

She handed over the decision making to the learners and followed the path they carved out.

Like any great guide on the side, she stood back and supported there creativity, excitement, curiosity and drive which resulted in some powerful outcomes.

Her learners experienced autonomy, master and purpose. All drivers of their success.

Shelly was a true guide on the side by letting go of all power and rolling with the punches.

Enforcing Basic Independence

noreply@blogger.com (Diana Samson (DJay)) — Sun, 26 Jan 2014 16:23:00 GMT

Access to Education: A First Step to Independence

As we talk about enforcing independence this week lets not forget that many do not have basic educational independence - access to education.

A student from Morocco wrote this today and I thought it was fitting.

A Community Problem in Morocco

Anas Fassih

Dropping out of school, especially for girls, is a pervasive problem in all Moroccan rural areas. My community is not immune to such problems with which I have been bitterly dissatisfied for a long time. What saddens me most is when I see girls, who are cut off from school early in their life, pay the price for their parents’ blind tradition and lack of education.

I have grown up in a countryside located 9 kilometers away from a village called Ain Dorij in Sidi Kacem province of Morocco. Throughout my school life, I have witnessed many cases of girls forced to stop going to school. This has long incited a feeling inside me and pushed me to look for any solution that would bring back the rights of females to resume their education.

The living conditions are poor indeed and some fathers can hardly fulfill their family’s basic needs, let alone providing for education. They therefore feel compelled to make their daughters stay home and try to send only boys to school, and most of the time even boys are unable to attend school. I recall the large proportion of my classmates who reluctantly deserted schools earlier on in their life and went instead to do hard labor, learn a craft or perform any job in nearby cities, whereby they can send their parents money monthly.

It sickens me indeed when I see that my mother doesn’t know how to read or write. She became accustomed to her name only because the script became familiar to her. She is one among a zillion others who have never been inside a classroom. She married at the age of 15 years. Ironically, it is the age that girls are supposed to move from secondary to high schools. The patriarchal society is perpetrating a crime against woman. Uprooting them from school, sending them to perform hard labor in agricultural fields, and obliging them to marry at a very early age are morally and humanely unacceptable.

I am not against women who aid their parents for the simple reason that they are born and raised in a countryside and should therefore lend a hand to assist their parents. I am simply defending the rights of females to pursue their education. They must be educated to contribute to the welfare of the community and themselves so both can move forward.

If it were in my hands, I would encourage parents to send their children to school (both males and females), sensitize them to the paramount importance education plays in one’s life, and offer advantages for girls that would entice their parents to send them to school.

It is also incumbent on the government to solve this problem by looking for ways to grab parents’ attention to the importance of education. The government can, for example, provide a sum of money for female students in rural areas monthly, and make sure that all females who benefit from the fund are enrolled in school.

Rhizomatic Learning Introduction

noreply@blogger.com (Diana Samson (DJay)) — Wed, 22 Jan 2014 13:41:00 GMT

An Overview and Resources

Rhizome Poster

Success in a Mooc

Novelty, noise, and scaffolding

What problem does rhizomatic learning solve for me?

Unravelling -> a model for an open course?

Community as Curriculum and Open Learning

Your unguided tour of Rhizo14

A talk on Rhizomatic Learning for ETMOOC

The Cynefin Framework

Seeing rhizomatic learning and MOOCs through the lens of the Cynefin framework

Rhizomatic Learning Is A Metaphor For How We Learn

Explaining Rhizomatic Learning to my five year old
Learning pathways

Rhizome (philosophy)

Heutagogy

Blog Posts:

Rhizomatic Learning – does the metaphor stand up?

#RHIZO14: THE UNCOMFORTABLE FUTURE EMERGES, FLUX HAPPENS. PART ONE

Rhizomatic Learning – Motivation for Participating

Students Motivation!

Rhizomatic learning: metaphors

Rhizomatic Learning - An unLearning Camp

Rhizonmatic Learning - Learnist

Resources:

Mendeley Group : Rhizomatic Learning

#Rhizo14 learning places: #rhizo14: Digital Desk

Twitter: #rhizo14

Tweetchat

Facebook

Diigo

G+ : Rhizomatic Learning

Dave's Blog

Flickr

Listly

Gephi

Cheating or Collaboratively Rewriting the Rules

noreply@blogger.com (Diana Samson (DJay)) — Sun, 19 Jan 2014 19:07:00 GMT

Learning and Innovation

From: Neurolove

“They say that time changes things, but you actually have to change them yourself.” ~Andy Warhol

Now that scarcity of knowledge is not an issue we are free to be innovative, open and different. We are free to be ourselves, free to share and connect.

We have the capacity to create and maintain learning environments that are open, cooperative, connected, learner centered and embrace diversity without blame or shame.

We have the capacity but the rules need to be rewritten.

We need to get to a place where it is about the journey, what can be created on that journey and who you shared the journey with. A place where we promote connection, collaboration, thinking and creating through the aggregation of information, remixing and re-purposing of that information and then finally sharing of the re-purposed information.

This week we have been talking about "cheating as learning". I think we need "rewriting of rules for learning". I would rather fix the system then try to circumvent it.

I am not talking about rule breaking destructive style like the scenario in Pink Floyd's video:

Pink Floyd Another Brick In The Wall Music Video

I am talking more about collaboratively agreeing to rewrite them and then doing it:

Apple CEO Tim Cook on the Importance of Writing Your Own Rules

Remember “Chance favors the connected mind.” ~Steven Johnson

Where Good Ideas Come From: The Natural History of Innovation

2013 Reform Symposium Conference

noreply@blogger.com (Diana Samson (DJay)) — Sun, 13 Oct 2013 02:52:00 GMT

The Future of Education
RSCON4 Blog posts
RSCON3 Recordings, Days 1, 2 & 3

The Highlights

Session Highlights

Plenary: Dr. Sugata Mitra

The Future of Learning In this talk, Sugata Mitra will take us through the origins of schooling as we know it, to the dematerialisation of institutions as we know them. Thirteen years of experiments in children's education takes us through a series of startling results – children can self organise their own learning, they can achieve educational objectives on their own, can read by themselves. Finally, the most startling of them all: Groups of children with access to the Internet can learn anything by themselves. From the slums of India, to the villages of India and Cambodia, to poor schools in Chile, Argentina, Uruguay, the USA and Italy, to the schools of Gateshead and the rich international schools of Washington and Hong Kong, Sugata's experimental results show a strange new future for learning.

Jake Duncan

I believe that gamification has tremendous potential for education. My students have shown increased engagement, seem more motivated to participate in class, are more willing to take risks, and have become self-driven problem solvers since I began my attempts to gamify my classroom. I am excited to share simple ideas that I have used in my attempts at gamification with limited technology and no budget. Free web tools that include ClassDojo, Habit RPG, Plickers, Zondle, GameSalad, Edmodo, Captain Up, and ClassBadges have proven to me to be simple to integrate in class and have made a huge difference in my teaching this year. Our physical learning space has begun to resemble a video game and has become a welcoming environment for my students as a result. I’m excited to share how gamifying my classroom has had such a profound impact on me and how I relate to my students. Come power up with us!

Keynote: Chris Lehmann

Chris emphasized care for the learner and not for the technology. Focus on the learner. Collaboration, peer learning and personally attend to the learner by leveraging technology. Learning should be Inquiry based, Learning by talking and listening. We should work toward fully empowering the learner to be a "Citizen of the World"

Keynote: Steve Wheeler

Learning should include:
Connection, Context, Complexity and Connotation.

Learning 2.0:
The learner goes from:
Skills > Compentencies > Literacies > Mastery

Chadi's Nomination

Chadi and I met while participating in a MOOC: Designing a New Learning Environment (DNLE). Chadi was a University Professor in Aleppo, Syria. We never met in person, just on-line and not only did we share interests and built a friendship, but also planned and implemented educational projects for developing countries. We focused on Syria which was suffering the effects of a massive war, a war that did not beat the life out of the hearts of the people. Despite all the difficulties people continued to live and not only go to University but even try, and often happily, new educational strategies and methods.

Our goal in educational design was to assist learner engagement, encourage positive interactions and foster a learner centric environment using collaborative learning.

Chadi and his Syrian students are truly inspiring considering the obstacles his students and he faced. No matter the barriers they gladly forged ahead embracing new learning strategies. He is a passionate and devoted educator. His persistence and raw courage to overcome adversity is truly incredible.

Chadi's friend once said “It is one of the miracles of life that people living thousands of miles apart, can meet, share interests and build friendships”

Chadi and his family successfully fled Syria to France and he is presently teaching at Université Lille 1.

EdInspire Honorees:

On Sun., Oct.13th, 10amCDT/11amNYC (more time zones, http://bit.ly/15OPmC8 ), the Reform Symposium E-Conference will have a special ceremony celebrating EdInspire Honorees. The EdInspire award identifies extraordinary contributions to education by inspiring individuals. Educators nominated an individual and wrote 200 words describing that individual's contribution to education. Each honoree received the EdInspire badge and a personalized e-mail. During the ceremony, they will be honored and we will hear about their amazing work. See all honorees below. The ceremony will begin with a Keynote from Josh Stumpenhorst, 2012 Teacher of the Year and 2013 Pearson Foundation Global Learning Fellow. 5 of the honorees will receive year memberships to SimpleK12. We will announce them during this ceremony.

Diana Samson

October 7

Congratulations, you earned it!

Chadi Aljundi

#RSCON4 I have just got an EdInspire badge and it is on my blog!

Diana Samson shared a link.

October 10 · Edited

Congrats to my friend Chadi Aljundi who will be honored Sunday with the EdInspire award nomination and maybe the award (I am crossing my fingers). This award identifies extraordinary contributions to education by inspiring individuals.
Join us for the 2013 Reform Symposium E-Conference #RSCON4 October 11th - 13thhttp://www.futureofeducation.com/page/2013-reform-symposium

2013 EdInspire Awards Reform Symposium eConference

www.futureofeducation.com

On Sun., Oct.13th, 10amCDT/11amNYC (more time zones, http://bit.ly/15OPmC8 ), the Reform Symposium E-Conference will have a special ceremony celebrating EdInsp…

Top MOOCs

noreply@blogger.com (Diana Samson (DJay)) — Sat, 21 Sep 2013 14:08:00 GMT

Sept 2013 Top 9 Coursera Courses by Enrollment

The top moocs, rated on active participation, learning and growth I have taken are:

#etmooc - (Alec Couros)
Designing a New Learning Environment (DNLE) - Stanford (Paul Kim)
Learning Creative Learning - MIT (Mitch Resnick)
Rhizomatic Learning - U of PEI (Dave Cormier)
A Beginner's Guide to Irrational Behavior - Duke (Dan Ariely)
Moralities of Everyday Life - Yale University (Paul Bloom)
The Science of Happiness - UC BerkeleyX (Dacher Keltner & Emiliana Simon-Thomas)
Design Thinking Action Lab - Stanford (Leticia Britos Cavagnaro)
Social Media - (Maria H. Andersen)
Introduction to Openness in Education - (David Wiley)
Think Again - Duke (Walter Sinnott-Armstrong & Ram Neta)
Social Psychology - Wesleyan (Scott Plous)
A Brief History of Humankind - Hebrew University of Jerusalem (Dr. Yuval Noah Harari)
#moocmooc (Jesse Stommel)
Instructional Ideas and Technology Tools for Online Success - Indiana (Curtis Bonk)
Creating an Open Classroom - CEET (Verena Roberts)

Personal Knowledge Management

noreply@blogger.com (Diana Samson (DJay)) — Mon, 01 Jul 2013 17:16:00 GMT

Harold Jarche PKM
The PKM value-add
Personal Knowledge Management 2

Personal Knowledge Management Introduction

Harold Jarche: YouTube

PKM is the key to social learning. It is a social living contract.It focuses on managing your own knowledge to get things done. It is therefore active, experiential and connected.

Seek people for connection and constantly be curious. We seek using mechanical and human aggregators. We use tools to connect to individual experts or networks of experts.

Sense Making/Critical Thinking by actively observe, study, challenge and evaluate. Challenge our own thinking and assumptions. Find a tool to express yourself. One needs to do something with their knowledge.

Five ways to make sense: Filtering, Validation, Synthesis, Presentation and Customization.

Share by finding people to share knowledge with.

Process of narration done in a transparent way create serendipitous connections. Chance favors the connected mind.

Students should have the opportunity to learn how to manage their learning and knowledge. PKM supports learning literacy / fluency - and in order to obtain learning fluency today one needs to be digitally fluent,

The implementation of PKM as a metacognition strategy and also the use of motivation strategies will be crucial to the learner's growth and success.

PKM also promotes the employment of content creation moving learning away form content consumption. It emphasizes the importance of actively engaging, sense making/critical thinking, evaluating/challenging and reflecting.

Another critical aspect is using the right tools in the right way when seeking, sense making and sharing.

When one builds personal learning networks outside their institution walls then one builds lifelong learning opportunities and becomes a node in a network of distributed creativity and one has the ability to create sustained artifacts that can be re-used and re-purposed supporting openness.

Here are some other resources:

Another person's blog to read is Howard Rheingold: http://rheingold.com/author/hrheingold/

Knowledge Management blogs:

Lilia Effimova: http://blog.mathemagenic.com/

Nick Milton http://www.nickmilton.com/

Open World

noreply@blogger.com (Diana Samson (DJay)) — Mon, 01 Jul 2013 15:49:00 GMT

5 reasons to use Open Source Software

Mozilla, Open Badges, and a Learning Standard for Web Literacy from Doug Belshaw

Open & Collaborative Learning from Alec Couros

OLnet: Open Learning Network

Don Tapscott

Four principles for the open world - Ted Talk

Don Tapscott - How To Solve the World's Problems - SXSW Interactive 2013

Solving the World's Problems Differently

How Open Access Empowered a 16-Year-Old to Make Cancer Breakthrough

Creative Commons: Wanna Work Together?

Connectivism

noreply@blogger.com (Diana Samson (DJay)) — Mon, 01 Jul 2013 01:07:00 GMT

Connectivism

Knowledge is distributed across a network of connections thus learning consists of the ability to construct and traverse those networks.

Knowledge is not acquired, it is a set of connections formed by actions and experiences. These connections are formed naturally, through a process of association and not through some intentional act.

Therefore, in connectivism one does not transfer, make or build knowledge. Instead, connectivisim is focused on partaking in activities to learn by growing or developing ourselves and our society in connected ways.

Connectivism Pedagogy:

Seeks to describe thriving networks that are characterized as having diversity, autonomy, openness, and connectivity
Seeks to describe the practices that lead to such networks, both in the individual and in society in which teachers model and demonstrate and Learners practice and reflection.

George Siemen's Articulate presentation What is Connectivism provides an overview of the connectivists view of how we learn and what we need to be able to learn. This is fundamental to how and why we select, implement and support educational technology.

We need to assist learners to:

Connect, collaborate, think and create through the aggregation of information, remixing and re-purposing of that information and then finally sharing of the re-purposed information.

Thus we move away from content centered to creation centered. The learner themselves aggregate content (information) individually and within groups, remixing, re-purposing and sharing. The learner accomplishes this by connecting through the building of networks.

To summarize George's intro Video on Connectivism.

How we Learn:

The Learner:

Needs to externalize to make sense (language and artifacts)
Requires frameworks and structures for sense making (mediating new stimuli to compensate from overwhelming amount of info)
Needs to socialize and negotiate around knowledge (our exchange with others)
Uses patterning (note and recognize patterns)
Should have the opportunity to and acknowledge their role in extending humanity through technology (to compensate or overcome our limitations)

Knowledge is networked and distributed. Knowing is being in a particular manner of connectedness.

Learning is forming new networks that occur in new and chaotic spaces.

Learning is aided by technology.

Learners are nodes in a network of distributed creativity or knowledge.

To understand learning we need to understand how and why connections form.

Types of Networks:

Neural-Biological - Learning is the formation of new neuro connections
Conceptual - Relatedness, constructiveness and relations/associations of ideas and concepts. Depth of understanding is related to the conceptual network that they have formed and relationships they have formed (concept maps). Connections create meaning.
External-Social - How we are connected to other people and information itself. How information flows.

How learning occurs:

Depth/Diversity of connections
Frequency of exposure
Integration of existing ideas
Strong/Weak ties

Moving forward with the aid of Technology we should:

Create/Share Content
Dialogue with others
Simulate Experiences

Resources:

George Siemens:

What is Connectivism:

Stephen Downes:

'Connectivism' and Connective Knowledge:

Dave Cormier:

Rhizomatic Education : Community as Curriculum

Seeing rhizomatic learning and MOOCs through the lens of the Cynefin framework

The Cynefin Framework

Connectivism Resources:

Webpage: Connectivism

Glossary: Connectivism glossary

MOOC: CCK12

Canada, Atlantic Canada and Nova Scotia Videos

noreply@blogger.com (Diana Samson (DJay)) — Sun, 30 Jun 2013 21:44:00 GMT

Happy Canada Day 2013!

Happy Canada Day from Google Maps!

Canada Shared by Canadians - Keep Exploring 2012

Canada: The Month in Weather: February 2013

Atlantic Canada: The Atlantic Provinces of Eastern Canada2012

Nova Scotia Come To Life Video 2008

Nova Scotia, Canada - We've Got Momentum! 2013

Powerful Talks

noreply@blogger.com (Diana Samson (DJay)) — Sun, 30 Jun 2013 19:20:00 GMT

Chimamanda Adichie: The danger of a single story

Amy Cuddy: Your body language shapes who you are

Brene Brown: The power of vulnerability

Brené Brown: Listening to shame

TEDxKC - Brené Brown - The Price of Invulnerability

The Power of Vulnerability

Benjamin Zander - Work (How to give an A)

Ken Robinson: Do Schools Kill Creativity?

Ken Robinson: Bring on the learning revolution!

Ken Robinson: How to escape education's death valley

Shawn Achor: The happy secret to better work

David Kelley: How to build your creative confidence

Finding Community Through the Internet: Kina Grannis at TEDxHollywood

In Your Arms - Kina Grannis (Official Music Video) Stop Motion Animation

Eric Whitacre: A virtual choir 2,000 voices strong

Web Literacy

noreply@blogger.com (Diana Samson (DJay)) — Sat, 29 Jun 2013 23:37:00 GMT

Learning and Web Literacy

Code Club - The Interview

What most schools don't teach

Howard Rheingold

A Guide to Crap Detection Resources

Crap Detection 101

Mozilla's Web Literacy Standards

Learning/WebLiteracyStandard/CompetencyGrid

Learning/WebLiteracyStandard/CompetencyDescriptors

Learning/WebLiteracyStandard/Vision

Doug Belshaw Blog

Differences Between Literacies, Skills and Compencies

Doug Belshaw's (Mozilla) latest presentation at the Irish Learning Technology Association:

He gives an overview of what is happening now in Digital / Web Literacy

I have included all the resources mentioned in his presentation here:

http://journal.ilta.ie/2013/05/21/zen-and-the-art-of-digital-literacies/

Doug's Mozilla site: Learning/Web Literacy Standard

https://wiki.mozilla.org/Learning/WebLiteracyStandard

Doug's Never Ending Thesis Page: Digital Literacy

http://neverendingthesis.com/index.php?title=Main_Page

Michelle Levesque: Web Literacy Diagram

http://rwxweb.wordpress.com/2012/01/30/web-literacy-skills-now-in-diagram-form/

JISC: Digital Technologies for Education and Research
http://www.jisc.ac.uk/

JISC Design Studio is for Educational Technologists and Instructional Designers:

http://jiscdesignstudio.pbworks.com/w/page/12458422/Welcome%20to%20the%20Design%20Studio

Mozilla Webmakers

https://webmaker.org/en-US/

Mozilla Thimble: web making made easy

https://thimble.webmaker.org/en-US/

Mozilla Open Badges

http://www.openbadges.org/

Mozilla, Open Badges, and a Learning Standard for Web Literacy from Doug Belshaw

Educational Technology and Mobile Learning

7 Important Literacies of the 21st century education.

Information Literacy
Information literacy is the ability to search for, recognize, access, evaluate, synthesize, organize, apply and use information, from different sources and in different formats, to make enlightened choices in your personal, professional, and academic life.

Information Technology Literacy
ITL is about understanding the technology infrastructure that underpins today's world. More specifically, it is an understanding of the tools technology provides and how these tools interact with the overall infrastructure.

Digital Literacy
Digital literacy is the ability to find, evaluate, utilize, share, and create content using information technologies and the Internet.

Media literacy
Media literacy is the knowledge and skills necessary to understand and use the codes and conventions of a wide variety of media forms and genres appropriately, effectively and ethically.

Visual Literacy

The ability to recognize, read, interpret, negotiate, and make meaning from information and data presented in visual forms such as pictures, graphics, charts...etc

Cultural Literacy

Cultural literacy is about understanding the cultural cues embedded in the wildest sense of language ( verbal, non verbal and visual )

Critical Literacy

As Think Critically puts it , Critical literacy is the ability to actively read text in a manner that promotes a deeper understanding of socially constructed concepts; such as power, inequality, and injustice in human relationships. Critical literacy encourages individuals to understand and question the attitudes, values, and beliefs of written texts, visual applications, and spoken words.
The development of critical literacy pushes students to question issues of power; in essence, to become thoughtful, active citizens. Becoming critically literate means that students have developed and mastered the ability to read, analyze, critique, and question the messages inherently present within any form of text.

History of the Internet

noreply@blogger.com (Diana Samson (DJay)) — Sat, 29 Jun 2013 20:50:00 GMT

Going Digital

Leo Laporte's interview with Father of the Internet Vint Cerf

The front cover of the Sun May 20, 1991

The Internet: A Warning from History

Digital Life

If you give a kid the web...

Internet Access Is a Human Right, Says United Nations

Network

History of the Internet

Andrew Blum: What is the Internet, really?

KPCB's Mary Meeker on Internet Trends 2013

KPCB Internet Trends 2013

In this image provided by The European Organization for Nuclear Research, or CERN, a screenshot of the original NeXT web browser in 1993 is seen.

Chuck Severance

Internet History, Technology, and Security

"Collider" video clip by "Les Horribles Cernettes".

Great Timelines:

Politics and the Internet

The Evolution of the Web

Internet Archive

The Wayback Machine allows users to browse through a collection of archived sites all the way back to 1996, the Archive acts as a digital reference library. The multi-genre collection of over 117,000 recordings includes live shows, in addition to over 1.6 million audio files ranging from radio broadcasts to audiobooks. Nearly 1.3 million video clips and over 4.5 million texts are also available for fair use

McMaster University digital scholarship centre

Isaac Asimov predicted the Internet of today 20 years ago

Lego Antikythera Mechanism

Two thousand years ago, a Greek mechanic set out to build a machine that would model the workings of the known Universe. The result was a complex clockwork mechanism that displayed the motions of the Sun, Moon and planets on precisely marked dials. By turning a handle, the creator could watch his tiny celestial bodies trace their undulating paths through the sky.

Ancient Discoveries - The Antikythera Machine
Ancient astronomy: Mechanical inspiration

The History of the Internet

The Risks of Posting in Social Networks

Learning for Growth

noreply@blogger.com (Diana Samson (DJay)) — Sat, 29 Jun 2013 19:55:00 GMT

What Children Need from Us

Yes, I would agree with the video but it is missing the foundation of needs for learning and success in life.

Below are the 4 base needs (lifelong needs) that are required to be met in order for learning, growth, happiness and success to occur in life and in the classroom:

What to do as a Teacher:

Safety and Security: offer a safe and secure learning environment that incorporates routine especially at the beginning of the class - this will calm the brain down and prepare the learner to learn. The learner should walk into the class and know exactly what will happen at the beginning of the class. For example: tell a story at the beginning of each class that bridges to the learning outcome. Your learners need a teacher that is confident, compassionate, consistent, flexible and willing to learn.
Autonomy and Belonging: promote the ability to self govern and build a sense of belonging and promote participation
Gratitude: model and teach gratitude. Use kids strengths to fuel gratitude
Mastery: offer the opportunity to achieve attainable goals/outcomes. Give kids the opportunity to share what they are grateful for.
Purpose: offer an opportunity for learners to participate in meaningful activities that have a meaningful outcome and contribute to the class, community and society.

What to do as a Parent:

Safety and Security: your child needs to feel safe and secure with you as a parent and in the home environment. If they don't you will never know what is going on in their lives until it is too late. Listen to your children and provide feedback in a non threatening manner. Your child should be able to tell you anything without fear of repercussions. Instead of punishing your child give them advice on moving forward, don't solve their problem for them but stand by them and be supportive. Of course there may be consequences to their actions. But unlike punishment, consequences are directly related to the action which provides a great leaning experience if parental support is present. This is hard as a parent but you need to be a disciplined person in order for discipline to occur and discipline does not mean punishment. Punishment is an undisciplined act. This is your chance to be a role model of how to be a disciplined person for your child. Your child needs a parent that is confident, compassionate, consistent, flexible and willing to learn.
Autonomy and Belonging: build a sense of belonging and participation. Help your child find opportunities where they can achieve a sense of belonging. Welcome your child's friends into the home and family activities.
Gratitude: model and teach gratitude. Use kids strengths to fuel gratitude. Give kids the opportunity to share what they are grateful for.
Mastery: offer the opportunity to achieve attainable goals. Help your child find activities they enjoy and can master
Purpose: offer an opportunity for your child to participate in meaningful activities that have meaningful outcomes and contribute to the family, community and society.

Brené Brown - Engaged Feedback Checklist

5 Magic Questions: The Secret to Helping Your Teen Learn from Mistakes

The Neuroanatomical Transformation of the Teenage Brain: Jill Bolte Taylor at TEDxYouth@Indianapolis

Wow, I stumbled on this great Ted Talk from Vancouver about replacing punishment with a problem solving method called: Restorative Practices to Resolve Conflict/Build Relationships.

This is a great way for parents and teachers to move away from non effective punishment to powerful learning strategies for behaviour modification and motivation.

Restorative Practices to Resolve Conflict/Build Relationships:

Katy Hutchison at TEDxWestVancouverED

Colin Stokes: How movies teach manhood

5 Keys to Social and Emotional Learning Success

ETMOOC Archive Sessions

noreply@blogger.com (Diana Samson (DJay)) — Sat, 27 Apr 2013 00:31:00 GMT

Educational Technology MOOC

The following is a list of archived Blackboard Collaborate sessions. Click on the title of the session to view the recording. Sessions are listed in Eastern Time.

Topic 0: Welcome & Orientation to #etmooc:

T0S1 - Welcome & Orientation w/ Alec Couros (Jan 14, 7pm)
T0S1 - Welcome & Orientation REPEAT w/ Alec Couros (Jan 15, 1pm)
fast-forward this session to 46:21 – someone hit the record button early.
T0S2 - Introduction to Twitter w/ Michelle Franz (Jan 16, 1pm)
T0S3 - Introduction to Social Curation w/ Jeffery Heil (Jan 17, 12pm)
T0S4 – Introduction to Blogging w/ Sue Waters (Jan 17, 7pm)
see supporting materials here.

Topic 1: Connected Learning (Tools, Processes & Pedagogy)

T1S1 – Introduction to Connected Learning w/ Alec Couros (Jan 21, 7pm)
see supporting materials here.
T1S1 – Introduction to Connected Learning REPEAT w/ Alec Couros (Jan 22, 1pm)
see supporting materials here.This seems to be the better presentation of the two.
T1S2 – Sharing As Accountability w/ Dean Shareski (Jan 22, 7pm)
T1S2 – Sharing as Accountability REPEAT w/ Dean Shareski (Jan 23, 1pm)
T1S3 – Introduction to Blogging (Director’s Cut) w/ Sue Waters (Jan 23, 9pm)
see supporting materials here.
T1S4 – Advanced Blogging w/Sue Waters (January 24, 7pm)
T1S5 – Introduction to Rhizomatic Learning w/ Dave Cormier (Jan 28, 7pm)
T1S5 – Introduction to Rhizomatic Learning REPEAT w/ Dave Cormier (Jan 29, 1pm)
T1S6 – Becoming a Networked Educational Leader w/ George Couros (Jan 29, 7pm)
T1S7 – Blogging with Students w/ Sue Waters (Jan 31, 7pm)

Topic 2: Digital Storytelling (Multimedia, Remix & Mashups)

T2S1 – How We Stopped Worrying & Learned to Love the GIF w/ Jim Groom & Friends (Feb 5, 7pm) – see also, accompanying post.
T2S2 – Digital Storytelling in K12 w/ Darren Kuropatwa (Feb 8, 1pm)
see alsoslidedeck, session resources, video created by participants, and Wes Fryer’s summary of the session.
T2S3 – Web Storytelling: 50 Ways, 5-Card Flickr & Pecha Flickr w/ Alan Levine (Feb 11, 7pm) – see also session notes.
T2S4 – A Flurry of Cursors: Digital Collaborative Writing w/ Jesse Stommel (Feb 14, 1pm).

Topic 3: Digital Literacy (Information, Memes & Attention)

T3S1 – Introduction to Digital Literacies w/ Doug Belshaw (Feb 18, 3pm)
see alsoDoug Belshaw’s resources.
T3S2 – Literacies of Attention, Crap Detection, Participation, Collaboration & Network Know-How w/ Howard Rheingold (Feb 19, 7pm) – see also crowdsourced notes created during session.
T3S3 – The Challenges & Opportunities of Modern Learning w/ Will Richardson (Feb 21, 7pm).
T3S4 – Who Owns Your Education Data (And Why It Matters) w/ Audrey Watters (Feb 25, 8pm)

Topic 4: The Open Movement (Open Access, OERs & the Future of Ed.)

T4S1 – True Stories of Openness w/ Alan Levine (March 5, 7pm)
T4S2 – Open Educator’s Panel (March 7, 1pm)
T4S3 – Open Educator’s Panel – Student Edition (March 8, 12:15pm)

Topic 5: Digital Citizenship (Identity, Footprint & Social Activism)

T5S1 – Introduction to Digital Citizenship w/Alec Couros (March 18, 7pm)
T5S2 – When the Digital Interrupts! Leaders, Educators and Students in the Digital Era w/ Janet Corral (March 21, 9pm)
T5S3 – Digital Identities – Who Are We In Networked Publics? w/ Bonnie Stewart (March 25, 8pm)

LeadORS

noreply@blogger.com (Diana Samson (DJay)) — Sat, 06 Apr 2013 02:35:00 GMT

Thomas Power has developed an interaction/leadership style indicator called LeadORS that is comprised of 8 styles.

Thomas is interested in the difference between online and face to face behavior.

His LeadORS test can be taken at: http://www.leadors.co/

Why ORS in leadORS? Because the Internet needs leaders who are ORS people: Open, Random and Supportive

In an online environment we need to be Open, Random and Supportive

Institutions need CSC leaders: Closed, Selective and Controlling

Here are the 8 Styles:

ORS - a Friend
As someone who is Open, Random and Supportive (ORS), you are inclined to be free-flowing, accepting and supportive of those around you.

CRS – a Manager
Someone who is Closed, Random and Supportive, (CRS) tends to be reluctant to promote themselves online but that does not necessarily mean that they are closed to the Internet.

OSC – a Mentor
Someone who is Open, Selective and Controlling (OSC), tends to exhibit an open mind and are careful with what they choose to hear and share. They like to gain control of people and situations.

CRC – an Engineer
Someone who is Closed, Random and Controlling (CRC) tends to be reluctant to reveal themselves online but is accepting of new data insights. They may seek to control others and situations around them, using the information and knowledge you generate from the Internet. Often keen to game systems.

ORC – a Scout
Someone who is Open, Random and Controlling (ORC) tend to be free-flowing online and accepting of most things. They tend to seek control of others and situations, using the online data they receive.

CSC – a Chief
Someone who is Closed, Selective and Controlling (CSC) tends to be closed about themselves, choosy or skeptical about the data they absorb. These people are direct opposites of the behavior of the social web, which can lead them to control people and situations. They are often skeptical about the role or value, the internet can play in their own lives.

CSS – an Analyst
Someone who is Closed, Selective and Supportive (CSS) tends to keep themselves to themselves online and are selective of new data insights. They also have a natural desire to be helpful and supportive to those around them.

OSS – a Guide
Someone who is Open, Selective and Supportive (OSS) tends to present an open mind online but are selective about what they hear and share. These people have a desire to support others, regardless of their situation or predicament.Thomas states the following:

To create habits say to yourself in your mind's eye:

"Go Open"
whenever you feel yourself closing down

"Go Random"
whenever you feel yourself desperate to choose (select)

"Go Supportive"
whenever you feel the desire for control taking you over

Here is some more information: LeadORS with Thomas Power, interviewed by Martin Shervington

The Psychology of ‘Networked Thinking’ on Google Plus

There is another test for Face to Face interactions:

http://www.ima-power.com/

Styles include:

http://www.ima-high-green.com/
http://www.ima-high-blue.com/
http://www.ima-high-red.com/
http://www.ima-high-yellow.com/

Learning Analytics Part III

noreply@blogger.com (Diana Samson (DJay)) — Sat, 06 Apr 2013 02:05:00 GMT

Stephanie Teasley

Stephanie Teasley is a Research Professor in the School of Information and Director of the USE Lab at the University of Michigan. She received her PhD in cognitive psychology from the University of Pittsburgh in 1992. Throughout her career, her work has focused on issues of collaboration and learning, looking specifically at how sociotechnical systems can be used to support effective collaborative processes and successful outcomes. She is the co-editor of the volume, Perspectives on Socially Shared Cognition, and co-author of several highly cited book chapters on collaborative learning. Her work has also appeared in numerous scholarly journals including Science, Developmental Psychology, the International Journal of Computer Supported Collaborative Learning, and Computers and Education. Dr. Teasley’s research has been funded by the US National Science Foundation and the National Institutes of Health. She is on the Executive Committee of the Society for Learning Analytics Research (SoLAR).

Using Learning Analytics to support academic advising for at-risk STEM undergraduates - Stephanie Teasley

Abelardo Pardo

Abelardo Pardo is a Lecturer at the School of Electrical and Information Engineering, University of Sydney. He has a PhD in Computer Science by the University of Colorado at Boulder applied to formal verification of digital circuits. His research interest is in the application of software engineering techniques to improve all aspects of the well-being of humans and communities. He has experience in the use of mobile devices in areas such as behavioral analytics, social networks, computer supported collaboration, personalization, and technology enhanced learning, which he deploys in his teaching activities. He has participated in national and international projects funded by NSF (USA) and the European Union. Abelardo is author of more than 100 research publications in prestigious conferences and journals, member of the steering committee of the Society for Learning Analytics Research (www.solaresearch.org), and member of the editorial board of the Journal of Social Media and Interactive Learning Environments and the Journal for Learning Analytics.

Practical Privacy Issues Around Learning Analytics - Abelardo Pardo

Stefan Dietze

Stefan Dietze currently is research group leader at the L3S Research Center of the Leibniz University Hannover (Germany). His main research interests are in Semantic Web and Linked Data technologies and their application to Web data integration problems in domains such as education or Web archiving. He holds a Ph.D. (Dr. rer. nat.) in Applied Computer Science from Potsdam University and previously held research positions at the Knowledge Media Institute (KMI) of The Open University (UK) and the Fraunhofer Institute for Software and Systems Engineering (Berlin, Germany). Stefan currently is coordinator of the EU-funded project LinkedUp (http://linkedup-project.eu) and he has been involved in leading roles in numerous EU R&D projects, such as LUISA, NoTube, ARCOMEM or mEducator. Furthermore, he is co-founder of the Linked Learning workshop series and of the community platform http://linkededucation.org. Stefan’s work has been published throughout major conferences and journals in areas such as Semantic Web, Linked Data, Services-oriented Systems and Technology-enhanced Learning and he is reviewer, organiser and committee member for numerous scientific events and publications.

Linked Data as a new environment for Learning Analytics and education - Stefan Dietze

Marie Bienkowski

Marie Bienkowski is the Deputy Director of the Center for Technology in Learning, at the nonprofit research organization, SRI International. She works with educational researchers to develop and evaluate technology in K-12 education, and to contribute research data to education policy discussions. Many of her projects involve efforts to interest underrepresented groups in science, technology, engineering, and mathematics careers with a focus on computer science. She leads software projects in the areas of learning resource analytics and intelligent information management. Dr. Bienkowski co-authored the report “Enhancing Teaching and Learning through Educational Data Mining and Learning Analytics” for the U.S. Department of Education (released in October 2012). She is the co-PI of an NSF-funded grant on assessing computational thinking for high school students and is leading SRI’s contributions to the core infrastructure of the open-source learning-resource analytics project called the Learning Registry. She received her Ph.D. in computer science from the University of Connecticut.

The Learning Registry: Applying Social Metadata for Learning Resource Recommendations - Marie Bienkowski

A word from Rob

admin — Sat, 16 Feb 2013 15:26:38 GMT

In the process of rebuilding my blog with updated technologies. Thanks for your patience!

A Crowdsourced Essay on MOOCs and Higher Education Pedagogies

admin — Tue, 08 Jan 2013 13:54:50 GMT

Note: This essay was created as a crowdsourced Google Doc as part of the Monday assignment for #MOOCMOOC. I found it worthwhile reading. I also wanted to get an initial post up for #MOOCMOOC to test the RSS feed. Enjoy.

MOOCs and Higher Education Pedagogies

In Higher Education (HE), students develop abilities to think independently, critically, and creatively in ways which support lifelong learning. HE learning goals include building upon pre-existing information (such as that learned in grades K-12), developing knowledge and skills, and acquiring credentials.

HE has traditionally used a range of teaching and learning methods: classroom lectures, small-group tutorials, guided seminars, supervised lab or practice-based sessions, guided field trips, guided reading and private study for a formative assignment with comments and marking. Assessment methods include tests, essays, problem-solving, individual and group projects, and other discipline-specific approaches.

Since digital technologies have been available in HE, we have explored digital versions of some of these methods, mainly lectures / reading delivered as digital resources, videos, using digital media tools from web 2.0 and online discussion groups, mainly asynchronously.

These include related concepts of connection and engagement, including learner-to-learner interaction and the cooperative construction of new knowledge.

However, there is still the expectation that a person attending an institution of HE is mainly acquiring knowledge through attendance within a closed learning environment during a specified period of time. Even when students in HE (including taught graduate students) . These pedagogies use similar instructional strategies and have similar learning goals.

While it is as yet unclear to what extent MOOCs will affect traditional higher education structures, we can speculate about the ways they may influence approaches to teaching and learning. If layers of double loop learning can be developed, MOOCs could be beneficial for professional development in HE. As most HE professionals are self-directed learners, MOOCs can be used for field specific or general development.

Because knowledge is not scarce, MOOCs can help traditional student/teacher conversations focus on higher-level outcomes instead of facts. This is in line with newer learning ideas, such as “flipped classrooms” and provides the ability for more HE instructors to use a flipped style, letting the MOOC be the lecture, and spending class time on discussion and problem-solving, giving students skills that will be useful for them.

Connectivist MOOCs (cMOOCs) may alter pedagogy from instructor-as-director and even beyond peer-to-peer learning to a focus on learning within a personal learning network with input from within and beyond the MOOC participants. For example, Coursera MOOCs allow students to review their peers’ assignments . The issue then becomes avoiding “the blind leading the blind” while allowing students to determine their own learning methods and goals. As with Wikipedia and other social knowledge websites, someone should navigate the ship and ensure that information is accurate.

One change MOOCs provide is the openness and democratization of education. Students in traditional courses can use MOOCs to learn outside of the classroom, use that knowledge to improve their interactions with faculty, and strengthen a lifelong interest in learning.

MOOCs have emerged from components of online learning and social media revolutionizing connections and networks. They form a badly-needed response to CFCs – Small, Closed, Face-to-Face (F2F) Courses and for profit online learning as well. They represent the most radical undertaking to date of ‘flipping the classroom’. Since they are relatively new and untested, MOOCs will likely not replace any traditional classroom learning (in the near-future), but rather enhance and enrich it. F2F sessions still have much to offer, such as supervised practice, immediate feedback from both peers and teachers, and synchronous work.

A Massive Open Online Course (MOOC) allows people to explore some of the same knowledge and content available in formal HE institutions, while also generating and contributing knowledge within a network. MOOCs use the Internet to offer organized learning experiences that are asynchronous and free, thus bypassing limitations that can keep some people out of HE institutions. Everyone with an internet connection can have access to a MOOC. According to Savitz (2012), even if edX MOOCS cannot replace a Harvard Diploma, online courses can still provide significant value in filling gaps in skills to meet job market demands. Further, MOOCs can help such people expand their career options by providing quality content, openness, and flexibility producing new opportunities to acquire additional skills. A campus-based education prior to joining the workforce with paper-based “proof” of competence may be replaced by real time, permanent, secured, cloud-based digital records of accomplishment, freeing individuals to pursue knowledge that is relevant to their needs in the moment.

There are a number of approaches to learning associated with MOOCs, most of which are similar to existing HE pedagogies: (i) Knowledge is often acquired by lecture but is self-paced, meaning it can occur anytime and anywhere, and may be done without anyone else in the room. (ii) There is peer-to-peer learning, but it occurs through the internet, either in real-time or not. The main difference is that some MOOCs create a connectivist and networked, but decentralized flow of information and ideas between peer learners, where there is more potential for generating and sharing own understandings and knowledge instead of simply absorbing. Because they are not as teacher-directed, MOOCs aren’t currently thought of as being connected with a distinct curriculum. The learner decides what courses to take, depending on their own objectives, at their own time and pace. All of this requires an especially self-regulated, motivated, and autonomous learner because there is little personalised guidance and feedback. MOOCS often maintain rigid time schedules, but are less likely to maintain the planned curricula as more traditional courses do.

The instructional principles of the early MOOCs were based in pedagogies of openness and connection. They were designed to maximize four key digital learning affordances (Downes & Siemens, 2010):Aggregation, remixing, re-purposing, and feeding forward. MOOCs also have additional learning outcomes including enhancing digital skills and developing a Personal Learning Environment (PLE).

These instructional principles relate to a great degree to several pedagogic approaches that have found their way into (higher) education, e.g. challenge-based or problem-based learning socializing are worthwhile. However, MOOCs, despite their short existence, have made a significant impact upon HE practices and pedagogy.

The MOOCMOOC is described as “A mini-MOOC, a meta-MOOC, a MOOC about MOOCs” and can be found online at MOOCMOOC.com. You can follow along on Twitter following the hashtag #MOOCMOOC and can follow the project on Twitter at @MOOCMOOC, @HybridPed, and leading this MOOC is Jess Stommel, @jessifer along with a team of inspiring educators.

Test #moocmooc

Sun, 06 Jan 2013 06:12:30 GMT

Just a test :)

MOOC MOOC

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