Dictators: why heroes slide into villainy

What do Vladimir Putin, Fidel Castro, and Kim Il-sung have in common? All took power promising change for the better: a fairer distribution of wealth, an end to internal corruption, limited foreign influence, and future peace and prosperity. To an extent, they all achieved this, but then things began to go wrong. Corruption raged in Putin’s Russia; millions fled grinding poverty and political persecution in Castro’s Cuba; and Kim’s Korea launched a disastrous war that, to this day, makes reunification with the now-wealthy South a distant dream.

Rather than admitting failure and handing over power to the next generation, each autocrat hung on, long outliving their welcome. And we can add Joseph Stalin, Benito Mussolini, Hugo Chavez, Zine El Abidine Ben Ali, Muammar Gaddafi, and Daniel Ortega to the list.

So, why does this happen so often?

From triumph to tyrant

A new model, developed by Professor Kaushik Basu from Cornell University, and published in Oxford Open Economics, simulates the decisions national leaders face. It reveals positive feedback conditions that lead to escalating acts that serve the autocrat’s self-interest, and preservation of power, rather than the best interests of the country.

“My paper was provoked by a personal encounter with Daniel Ortega of Nicaragua,” Professor Basu, former chief economist of the World Bank, recalls. “When I met him in September 2013 he still had the aura of a progressive leader, but subsequently morphed into a kind of tyrant that I would not have predicted. I wrote up the algebra to explain this transformation, and realized I had hit upon an argument that explains the behaviour of a large number of authoritarian leaders around the world.”

It can take many years of struggle for a dictator to reach office and achieve their political ambitions. Reluctance to risk this work in progress with a popular vote after a mere four- or five-year term leads to actions such as intimidating, imprisoning, or assassinating political rivals; silencing the press, financial corruption, and tax evasion; influencing or corrupting the judiciary. Such tactics work—in the short term, at least —but opponents can’t be silenced forever: truth finds a way, often supported by domestic or foreign antagonists.

As these controlling behaviours escalate, it becomes clear that relinquishing power would lead to legal persecution or imprisonment: just ask Chilean autocrat General Pinochet, arrested after leaving office; or Uganda’s Idi Amin, forced into exile in Saudi Arabia.

Basu’s algebraic model shows that, after a threshold of bad behaviour is passed, no amount of do-gooding can undo the damage: the only choice left a dictator is to tighten his grip on power especially if, beyond national justice, lie international courts and tribunals. For example, Sudan’s former leader Omar al-Bashir has been in hiding since 2009, a fugitive of the International Criminal Court.

Democratic policy matters

The model explains why two-term limits have evolved as a popular way to curb such damage as they provide a limited time for unsavoury actions to accumulate, a single opportunity for re-election, and better options for leaving office peaceably. So, could this common system be more widely employed to prevent dictatorships?

“A globally-enforced term limit is an important step, but may not be enough,” says Basu, who half-jokingly suggests that creating an easy exit for dictators, such as offering them a castle on a Pacific island, could also be effective.

Joking aside, the paper has important implications for current US politics. Former US president Donald Trump faces 91 felony counts across four states. Is the barrage of legal indictments boxing him into the corner where he has no option but to regain office, and then corrupt power to save himself, just as the model predicts? 

At a time where authoritarian regimes are on the rise—the 2024 Economist Intelligence Unit report shows that 39.4% of the world’s population is under authoritarian rule, an increase from 36.9% in 2022—the need is greater than ever to promote policies that could halt the slide.

_{Feature image by Peterzikas, via Pixabay. Public domain.}

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State supported Covid-19 nudges only really worked on the young

Who says young people never listen? A study in Sweden examining responses to state-backed nudges to get Covid-19 vaccination appointments booked has found that 16- to 17-year-olds responded much more strongly to prompts by letter, text, or email than 50- to 59-year-olds.

At first glance, the result, published in an article in Oxford Open Economics, is paradoxical. Older people who are much more vulnerable to the virus should have signed up more than younger people whose lives are much less at stake, but the reverse was the case. In fact, the study is consistent with the theory that nudges are more effective for decisions that don’t really matter to the person subjected to the nudge.

Politics, populations, and pandemic policies

‘Nudge theory’ has interested researchers and politicians for decades as a means to influence population-level behaviour such as how to get people to sign up for a donor card, or take out a pension earlier in life. And it works. Just ask any marketeer or social media influencer what the impact of a well-timed and skilfully worded notification can be. But the nudge effect is limited and often misses the target population.

Niklas Jakobsson from Karlstad University, and colleagues, saw an opportunity to empirically test nudge theories using data collected by the 21 regions of Sweden during the Covid-19 pandemic. While 20 regions sent out letters directing people to book their jabs by phone or online, one—Uppsala—nudged its citizens by summoning them to pre-booked appointments.

The researchers matched up vaccination rates in each age group in Uppsala against a synthetic control devised from a weighted combination of all the other Swedish regions. The results showed that Uppsala’s nudged, pre-booked appointments had only a small (if any) effect on older people, but boosted vaccination uptake among younger people by 10%.

“It makes sense that people who benefit a lot from vaccinations will take them even without a nudge,” Jakobsson explains. “For younger people, vaccinations were not as important and thus they were more affected by the nudge.”

When should a nudge become a push?

What is the behavioural mechanism behind such a counterintuitive finding?  One line of reasoning suggests that you can’t really nudge people into doing something they don’t want to do when their volition—that is, the power of free will—is high. However, you can change behaviour when people don’t really care either way, that is, when volition is low.

According to Kahneman’s theory of Fast and Slow Thinking, once the fast, unconscious, ‘System 1’ cognitive processes are passed, nudges get held up in the slower, deliberative, ‘System 2’ thoughts, and are less effective.

“People do actually think through their decisions and do not act randomly,” says Jakobsson. “The nudges that are most likely to work are changes of defaults that clearly decrease the costs of making a decision.”

Policymakers could take a closer look at this study to work out what they are doing right, as well as wrong, when developing future nudges. “This nudge worked, but more so for younger people,” says Jakobsson. “So, it could be a way to somewhat increase vaccinations at a low cost.”

_{Feature image by tortensimon via Pixabay, public domain.}

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Unlocking the Moon’s secrets: from Galileo to giant impact

It is a curious fact that some of the most obvious questions about our planet have been the hardest for scientists to explain. Surely the most conspicuous mystery in paleontology was “what killed the dinosaurs.” Scores of hypotheses were proposed, ranging from the sublime to the ridiculous and including such delusions as “senescent overspecialization, lack of standing room in Noah’s Ark, and paleoweltschmerz.” Not until 1980 did a hypothesis catch on—the Alvarez Theory of dinosaur extinction by meteorite impact—and it is still not fully accepted today, 182 years after Richard Owen discovered the terrible lizards.

In the nineteenth century, scientists were preoccupied with explaining the ice ages, when vast sheets of ice marched from the poles, obliterating everything in their path, only to retreat—and repeat. Not until the 1970s did scientists discover that cyclical changes in Earth’s orientation in space and distance from the Sun caused the great glaciers to wax and wane. The first maps of the Atlantic Ocean in the sixteenth century showed that the facing coastlines of Africa and South America fit together like two pieces of a jigsaw puzzle. Geologists rejected Alfred Wegener’s 1912 explanation that continental drift had ripped a giant protocontinent asunder. It took until the mid-1960s and the discovery of plate tectonics to explain the jigsaw fit. This led to the realization that the continents and ocean basins do not stay in one place, but move constantly, albeit at the minuscule rate at which our fingernails grow. Or consider one of the most conspicuous features of our planet, America’s Grand Canyon, plainly visible from space. In the nineteenth century, its cause seemed obvious: the land underneath the Colorado River had risen, causing the muddy stream to incise itself ever deeper in its channel in order to keep up with the uplift. But by mid-twentieth century, newly discovered facts about the age of the canyon led scientists to finally reject the old hypothesis.

Every field of science has puzzles that despite being obvious, are exceedingly difficult to solve. Witness the Moon, the most viewed object in the sky, whose largest features we can see with the naked eye. Where did it come from and why does it hang there in space, the same face always turned toward us? The very first person to view the Moon through a telescope, the great Italian father of science, Galileo, saw that it’s surface was not smooth and regular, as the Greeks had supposed, but is marked by pits that came to be called craters. Another great scientist, Robert Hooke, unaware of the existence of meteorites, conducted experiments that indicated that the forces that had created the craters had come from below, from volcanism, a view that persisted for centuries.

Isaac Newton was able to answer the question of why the Moon neither crashes into Earth nor flies off into space: the Moon’s velocity in space gives it a momentum that exactly balances the gravitational pull of the Earth. Another great of the Enlightenment, the German philosopher Immanuel Kant, pointed out that the Moon’s gravity would slow the Moon’s rotation until eventually it kept the same face turned to the Earth.

Sometimes the answer to a scientific question cannot even be conceived because it depends on some fact yet to be discovered or some research method yet to be invented. Hooke realized that an object descending from above could have created lunar craters, but as far as he knew, the sky contained no such objects. Another reason scientists are slow to adopt a new idea is that they have become wedded to a particular dogma or theory and are unwilling to change their minds. Meteorite impact on the Moon was proposed in the 1870s, but it took nearly a century and spacecraft voyages to the Moon before scientists were willing to entertain it. Both meteorite impact and continental drift were resisted in part because they violated uniformitarianism: the belief that geological processes are gradual rather than catastrophic. Most geologists were unwilling to give up this fundamental principle, learned at their professor’s knee.

I have always been fascinated by how scientists have handled these great questions, both because I would like to know the answers myself, but also because they reveal so much about how science really works, as opposed to the idealized scientific method that we learned about in high school. I hope that readers will be interested in my latest effort to make science accessible: Unlocking the Moon’s Secrets: From Galileo to Giant Impact. You will learn how our seemingly placid and unchanging heavenly companion was born in the most colossal act of violence in the history of the solar system.

_{Feature image by Helen Field, via iStock.}

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Is humanity a passing phase in evolution of intelligence and civilisation?

“The History of every major Galactic Civilization tends to pass through three distinct and recognizable phases, those of Survival, Inquiry and Sophistication…”

Douglas Adams, The Hitchhiker’s Guide to The Galaxy (1979)

“I think it’s quite conceivable that humanity is just a passing phase in the evolution of intelligence.”

Geoffrey Hinton (2023)

In light of the recent spectacular developments in artificial intelligence (AI), questions are now being asked about whether AI could present a danger to humanity. Can AI take over from us? Is humanity a passing phase in the evolution of intelligence and civilisation? Let’s look at these questions from the long-term evolutionary perspective.

Life has existed on Earth for more than three billion years, humanity for less than 0.01% of this time, and civilisation for even less. A billion years from now, our Sun will start expanding and the Earth will soon become too hot for life. Thus, evolutionarily, life on our planet is already reaching old age, while human civilisation has just been born. Can AI help our civilisation to outlast the habitable Solar system and, possibly, life itself, as we know it presently?

Defining life is not easy, but few will disagree that an essential feature of life is its ability to process information. Every animal brain does this, every living cell does this, and even more fundamentally, evolution is continuously processing information residing in the entire collection of genomes on Earth, via the genetic algorithm of Darwin’s survival of the fittest. There is no life without information.

It can be argued that until very recently on the evolutionary timescale, i.e. until human language evolved, most information that existed on Earth and was durable enough to last for more than a generation, was recorded in DNA or in some other polymer molecules. The emergence of human language changed this; with language, information started accumulating in other media, such as clay tablets, paper, or computer memory chips. Most likely, information is now growing faster in the world’s libraries and computer clouds than in the DNA of all genomes of all species.

We can refer to this “new” information as cultural information as opposed to the genetic information of DNA. Cultural information is the basis of a civilisation; genetic information is the basis of life underpinning it. Thus, if genetic information got too damaged, life, cultural information, and civilisation itself would disappear soon. But could this change in the future? There is no civilisation without cultural information, but can there be a civilisation without genetic information? Can our civilisation outlast the Solar system in the form of AI? Or will genetic information always be needed to underpin any civilisation?

For now, AI exists only as information in computer hardware, built and maintained by humans. For AI to exist autonomously, it would need to “break out” of the “information world” of bits and bytes into the physical world of atoms and molecules. AI would need robots maintaining and repairing the hardware on which it is run, recycling the materials from which this hardware is built, and mining for replacement ones. Moreover, this artificial robot/computer “ecosystem” would not only have to maintain itself, but as the environment changes, would also have to change and adapt.

Life, as we know it, has been evolving for billions of years. It has evolved to process information and materials by zillions of nano-scale molecular “machines” all working in parallel, competing as well as backing each other up, maintaining themselves and the ecosystem supporting them. The total complexity of this machinery, also called the biosphere, is mindboggling. In DNA, one bit of information takes less than 50 atoms. Given the atomic nature of physical matter, every part in life’s machinery is as miniature as possible in principle. Can AI achieve such a complexity, robustness, and adaptability by alternative means and without DNA?

Although this is hard to imagine, cultural evolution has produced tools not known to biological evolution. We can now record information as electron density distribution in a silicon crystal at 3 nm scale. Information can be processed much faster in a computer chip than in a living cell. Human brains contain about 10¹¹ neurons each, which probably is close to the limit how many neurons a single biological brain can contain. Though this is more than computer hardware currently offers to AI, for future AI systems, this is not a limit. Moreover, humans have to communicate information among each other via the bottleneck of language; computers do not have such a limitation.

Where does this all leave us? Will the first two phases in the evolution of life—information mostly confined to DNA, and then information “breaking out” of the DNA harness but still underpinned by information in DNA, be followed by the third phase? Will information and its processing outside living organisms become robust enough to survive and thrive without the underpinning DNA? Will our civilisation be able to outlast the Solar system, and if so, will this happen with or without DNA?

To get to that point, our civilisation first needs to survive its infancy. For now, AI cannot exist without humans. For now, AI can only take over from us if we help it to do so. And indeed, among all the envisioned threats of AI, the most realistic one seems to be deception and spread of misinformation. In other words, corrupting information. Stopping this trend is our biggest near-term challenge.

_{Feature image by Daniel Falcão via Unsplash.}

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Five books to celebrate British Science Week 2023

British Science Week is a ten-day celebration of science, technology, engineering and math’s, taking place between 10-19 March 2023. To celebrate, join in the conversation, and keep abreast of the latest in science, delve into our reading list. It contains five of our latest books on plant forensics, the magic of mathematics, women in science, and more.

1. Planting Clues: How Plants Solve Crimes

Discover the extraordinary role of plants in modern forensics, from their use as evidence in the trials of high-profile murderers such as Ted Bundy to high value botanical trafficking and poaching.

In Planting Clues, David Gibson explores how plants can help to solve crimes, as well as how plant crimes are themselves solved. He discusses the botanical evidence that proved important in bringing a number of high-profile murderers such as Ian Huntley (the 2002 Soham Murders), and Bruno Hauptman (the 1932 Baby Lindbergh kidnapping) to trial, from leaf fragments and wood anatomy to pollen and spores. Throughout he traces the evolution of forensic botany, and shares the fascinating stories that advanced its progress.

Buy Planting Clues, How Plants Solve Crimes

Take a look at Gibson’s blog on Environmental DNA, as well as John Parrington’s (author of ‘Mind Shift’) blog on what neuroscience can tell us about the mind of a serial killer.

2. The Spirit of Mathematics: Algebra and all that

 What makes mathematics so special? Whether you have anxious memories of the subject from school, or solve quadratic equations for fun, David Acheson’s book will make you look at mathematics afresh.

Following on from his previous bestsellers, The Calculus Story and The Wonder Book of Geometry, here Acheson highlights the power of algebra, combining it with arithmetic and geometry to capture the spirit of mathematics. This short book encompasses an astonishing array of ideas and concepts, from number tricks and magic squares to infinite series and imaginary numbers. Acheson’s enthusiasm is infectious, and, as ever, a sense of quirkiness and fun pervades the book.

Buy The Spirit of Mathematics, Algebra and all that

To learn more, discover our Very Short Introductions series, including editions about Geometry, Algebra, Symmetry, and Numbers.

3. Not Just for the Boys: Why We Need More Women in Science

Why are girls discouraged from doing science? Why do so many promising women leave science in early and mid-career? Why do women not prosper in the scientific workforce?

Not Just For the Boys looks back at how society has historically excluded women from the scientific sphere and discourse, what progress has been made, and how more is still needed. Athene Donald, herself a distinguished physicist, explores societal expectations during both childhood and working life using evidence of the systemic disadvantages women operate under, from the developing science of how our brains are—and more importantly aren’t—gendered, to social science evidence around attitudes towards girls and women doing science.

Buy Not Just for the Boys, Why We Need More Women in Science

Make sure not to miss Athene Donald’s limited 4-part podcast series featuring Donald in conversation with fellow female scientists and allies about the issues women face in the scientific world. 

4. Distrust: Big Data, Data-Torturing, and the Assault on Science

Using a wide range of entertaining examples, this fascinating book examines the impacts of society’s growing distrust of science, and ultimately provides constructive suggestions for restoring the credibility of the scientific community.

This thought-provoking book argues that, ironically, science’s credibility is being undermined by tools created by scientists themselves. Scientific disinformation and damaging conspiracy theories are rife because of the internet that science created, the scientific demand for empirical evidence and statistical significance leads to data torturing and confirmation bias, and data mining is fueled by the technological advances in Big Data and the development of ever-increasingly powerful computers.

Buy Distrust, Big Data, Data-Torturing, and the Assault on Science

Check out Gary Smith’s previous titles, including: The Phantom Pattern Problem, The 9 Pitfalls of Data Science, and The AI Delusion.

5. Sentience: The Invention of Consciousness

What is consciousness and why has it evolved? Conscious sensations are essential to our idea of ourselves but is it only humans who feel this way? Do animals? Will future machines?

To answer these questions we need a scientific understanding of consciousness: what it is and why it has evolved. Nicholas Humphrey has been researching these issues for fifty years. In this extraordinary book, weaving together intellectual adventure, cutting-edge science, and his own breakthrough experiences, he tells the story of his quest to uncover the evolutionary history of consciousness: from his discovery of blindsight after brain damage in monkeys, to hanging out with mountain gorillas in Rwanda, to becoming a leading philosopher of mind. Out of this, he has come up with an explanation of conscious feeling—”phenomenal consciousness”—that he presents here in full for the first time.  

Buy Sentience, The Invention of Consciousness (UK Only)

As an added bonus, you can also read more on the topics of evolutionary biology, the magic of mathematics, and artificial intelligence with the Oxford Landmark Science series. Including “must-read” modern science and big ideas that have shaped the way we think, here are a selection of titles from the series to get your started.

You can also explore more titles via our extended reading list via Bookshop UK.

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Celebrating women in STEM [timeline]

March is Women’s History Month, an annual occurrence dedicated to commemorating and highlighting the contributions that women have made throughout history. Many of these contributions have gone unsung or undervalued, particularly in the fields of science, technology, engineering, and medicine, where women have historically been underrepresented. Celebrating and recognizing the work of women in these field remains a priority for Oxford University Press, and this month and every month we are proud to support diverse voices across our publishing. We seek to create an inclusive space to highlight the work of women in STEM, and celebrate the contributions of trans and cis women, and women of all races, ethnicities, and sexual orientations.

The timeline—first published in 2018, now updated in 2022—provides a curated selection of achievements, discoveries, and innovations made by women in STEM, from the foundation of modern nursing to critical contributions in the effort to fight the COVID-19 pandemic. This is just a small picture of the countless women making an impact in STEM. For more on this subject we also offer two collections featuring the work of women in STEM: women in medicine and women in science.

_{Featured image by Dmytro Zinkevych via Shutterstock.} 

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Five books to celebrate British Science Week

British Science Week is a ten-day celebration of science, technology, engineering and maths, taking place between 11-20 March 2022. To celebrate, join in the conversation, and keep abreast of the latest in science, delve into our reading list. It contains five of our latest books on evolutionary biology, the magic of mathematics, artificial intelligence, and more.

1. The Parrot in the Mirror: How evolving to be like birds made us human

How similar are your choices, behaviours, and lifestyle to those of a parrot?

Discover how many of our defining human traits are far more similar to birds than to our fellow mammals in The Parrot in the Mirror, by Antone Martinho-Truswell. From our lifespans to our intelligence, our relationships and our language, we can learn a great deal about ourselves by thinking of the human species as “the bird without feathers.” In this insightful read, learn more about how parrots, specifically, are our biological mirror image; an evolutionary parallel to ourselves. And how they are the only species to share one particular human trait: spite.

Read The Parrot in the Mirror: How evolving to be like birds made us human.

To learn more about how, much like humans, the senses of animals are key to their survival, discover Secret Worlds: The extraordinary senses of animals, by Martin Stevens.

2. Mind Shift: How culture transformed the human brain

The mental capacities of the human mind far outstrip those of other animals. Our imaginations and creativity have produced art, music, and literature; built bridges and cathedrals; enabled us to probe distant galaxies, and to ponder the meaning of our existence. What makes the human brain unique, and able to generate such a rich mental life? In this book, John Parrington draws on the latest research on the human brain to show how it differs strikingly from those of other animals in its structure and function at a molecular and cellular level. And he argues that this “shift,” was driven by tool use, but especially by the development of one remarkable tool—language.

Read Mind Shift: How culture transformed the human brain.

You can also read Parrington blog on what neuroscience can tell us about the mind of a serial killer, as well as listening to his podcast on culture and the human brain.

3. Colliding Worlds: How cosmic encounters shaped planets and life

In Colliding Worlds, Simone Marchi explores the key role that collisions in space have played in the formation and evolution of our solar system, the development of planets, and possibly even the origin of life on Earth. Analysing our latest understanding of the surfaces of Mars and Venus, gleaned from recent space missions, Marchi presents the dramatic story of cosmic collisions and their legacies. You can also read his blog’s on the Earth’s wild years and the creative destruction of cosmic encounters, as well as his response to Netflix’s “Don’t Look Up!” satire, Do Look Up! Could a comet really kill us all?

Read Colliding Worlds: How cosmic encounters shaped planets and life.

To learn more, discover our Very Short Introductions series, including Planetary Systems, Climate Change, Evolution, Human Evolution, and The Animal Kingdom.

4. The Wonderful Book of Geometry: A mathematical story

How can we be sure that Pythagoras’s theorem is really true? Why is the “angle in a semicircle” always 90 degrees? And how can tangents help determine the speed of a bullet?

David Acheson takes the reader on a highly illustrated tour through the history of geometry, from ancient Greece to the present day. He emphasizes throughout elegant deduction and practical applications, and argues that geometry can offer the quickest route to the whole spirit of mathematics at its best. Along the way, we encounter the quirky and the unexpected, meet the great personalities involved, and uncover some of the loveliest surprises in mathematics.

Read The Wonderful Book of Geometry: A mathematical story.

Take a sneak peek inside, and listen to Acheson explain the magic of geometry.

5. Human-centered AI

Focusing not on the risks of AI, but on the opportunities it presents and how to capitalize on them, Ben Shneiderman puts forward 15 recommendations about how programmers, business leaders, educators, professionals, and policy makers can implement human-centered AI. Bridging the gap between ethical considerations and practical realities to make successful, reliable systems, Schneiderman provides a range of human-centered AI design metaphors to show ways to get beyond current limitations and see new design possibilities that empower people, giving humans control.

Read Human-centered AI.

To learn more, discover our What Everyone Needs to Know® series, including titles on Artificial Intelligence (and a blog post on What is Artificial Intelligence?), and Evolution.

As an added bonus, you can also read more on the topics of evolutionary biology, the magic of mathematics, and artificial intelligence with the Oxford Landmark Science series. Including “must-read” modern science and big ideas that have shaped the way we think, browse the series here:

You can also explore more titles via our extended reading list via Bookshop UK.

OUPblog - Academic insights for the thinking world.

The top 10 science blog posts of 2021

From the evolution of consciousness to cosmic encounters, the Brain Health Gap to palliative medicine, 2021 has been a year filled with discovery across scientific disciplines. On the OUPblog, we have published blogs posts showcasing the very latest research and insights from our expert authors at the Press. Make sure you’re caught up with the best of science in 2021 with our top 10 blog posts of the year:

1. Why did evolution create conscious states of mind?

“When we open our eyes in the morning, we take for granted that we will consciously see the world in all of its dazzling variety. The immediacy of our conscious experiences does not, however, explain how we consciously see.”

Read the blog post from Stephen Grossberg, author of Conscious Mind, Resonant Brain: How Each Brain Makes a Mind, to learn how—and why—we have evolved to consciously see.

Read the blog post ->

2. The neuroscience of human consciousness

How can the study of the human brain help us unravel the mysteries of life? Going a step further, how can having a better understanding of the brain help us to combat debilitating diseases or treat mental illnesses?

On this episode of The Oxford Comment, we focused on human consciousness and how studying the neurological basis for human cognition can lead not only to better health but a better understanding of human culture, language, and society as well.

Listen to episode 63 on The Oxford Comment ->

3. 10 books on palliative medicine and end-of-life care

Each year an estimated 40 million people are in need of palliative care, 78% of whom live in low- and middle-income countries. This reading list of recent titles can help you to reflect on palliative medicine as a public health need.

Explore the reading list ->

4. Can what we eat have an effect on the brain?

Food plays an important role in brain performance and health. In general, the old saying, “a healthy mind in a healthy body,” is still very valid, and the overall positive results on cognitive ability of entire diets can be summarised with: “what is good for your heart, is also good for your brain.”

This blog post from review co-author Bo Ekstrand discusses the role of diet in key areas of brain development and health from the findings published in the journal Nutrition Reviews.

Read the blog post ->

5. What can neuroscience tell us about the mind of a serial killer?

Serial killers—people who repeatedly murder others—provoke revulsion but also a certain amount of fascination in the general public. But what can modern psychology and neuroscience tell us about what might be going on inside the head of such individuals?

Read the blog post from the John Parrington, author of Mind Shift: How Culture Transformed the Human Brain, to learn more about recent neuroscience studies investigating serial killers’ minds.

Read the blog post ->

6. Does “overeating” cause obesity? The evidence is less filling

The usual way of thinking considers obesity a problem of energy balance. Take in more calories than you expend—in other words, “overeat”—and weight gain will inevitably result. The simple solution, according to the prevailing Energy Balance Model (EBM), is to eat less and move more. New research shows that viewing body weight control as an energy balance problem is fundamentally wrong, or at least not helpful, for three reasons.

Discover the three reasons in this blog post from David S. Ludwig, co-author of new research published in The American Journal of Clinical Nutrition.

Read the blog post ->

7. Earth’s wild years: the creative destruction of cosmic encounters

Contrary to common sense, cosmic collisions are not all about destruction and death. It appears entirely possible that collisions could have been beneficial to the development of conditions suitable for the formation of first organisms—our distant relatives—on Earth. What do we know about these early cosmic catastrophes?

Learn about the innumerable challenges facing the research of early cosmic events by reading the blog post from Simone Marchi, author of Colliding Worlds: How Cosmic Encounters Shaped Planets and Life.

Read the blog post ->

8. What if COVID-19 emerged in 1719?

We’re often told that the situation created by the attack of the new coronavirus is “unique” and “unprecedented.” And yet, at the same time, scientists assure us that the emergence of new viruses is “natural”—that viruses are always mutating or picking up and losing bits of DNA. But if lethal new viruses have emerged again and again during human history, why has dealing with this one been such a struggle?

In this blog post, Lesley Newson and Peter Richerson, authors of A Story of Us: A New Look at Human Evolution, consider what makes our “cultural DNA” unique and how the story of COVID-19 would have been very different had it emerged 300 years ago.

Read the blog post ->

9. Closing the brain health gap: addressing women’s inequalities

There is a clear sex and gender gap in outcomes for brain health disorders across the lifespan, with strikingly negative outcomes for women. The “Brain Health Gap” highlights and frames inequalities in all areas across the translational spectrum from bench-to-bedside and from boardroom-to-policy and economics.

Read the blog post to learn how closing the Brain Health Gap will help economies create recovery and prepare our systems for future global shocks.

Read the blog post ->

10. The case for readdressing the three paradigms of basic astrophysics

A long-held misunderstanding of stellar brightness is being corrected, thanks to a new study published in the Monthly Notices of the Royal Astronomical Society based on International Astronomical Union (IAU) General Assembly Resolution B2.

Learn about the key findings in this blog post from Zeki Eker, lead author on the study published in Monthly Notices of the Royal Astronomical Society.

Read the blog post ->

OUPblog - Academic insights for the thinking world.

How research abstracts succeed and fail

The abstract of a research article has a simple remit: to faithfully summarize the reported research. After the title, it’s the most read section of the article. It’s freely available on the publisher’s website and in online databases. Crucially, it makes the case to the reader for reading the article in full. 

Alas, not all abstracts succeed. 

Some take the notion of abstraction to extremes. This example is from a physics article:

Unitarity and geometrical effects are discussed for photon-photon scattering.

It has just ten words. Fortunately, most abstracts say rather more, though it’s possible to say too much. The next example, from a geology article, has over 370 words. It starts:

Diagenesis of the Holocene-Pleistocene volcanogenic sediments of the Mexican Basin produced, in strata of gravel and sand, 1H₂O- and 2H₂O-smectite, kaolinite, R3-2H₂O-smectite (0.75)-kaolinite, R1-2H₂O-smectite (0.75)-kaolinite, R3-kaolinite (0.75)-2H₂O-smectite and R1-1H₂O-smectite (0.75)-kaolinite. Smectite platelets…

It continues in a similar vein for a further 350 words, accumulating more and more detail. The reason for the work is hinted at, but only becomes clear in the full article, at which point it’s too late.

Some abstracts introduce citations to previous research to provide background, contrary to the expectation that abstractions stand alone. In practice, citations can block the reader’s progress, as in this example from a remote-sensing article: 

The purpose of this paper is to extend the stationary stochastic model defined in [1] to a time evolving sea state and platform motion.

The reference pointed to by “[1]” isn’t attached to the abstract, and the source article is obviously elsewhere. Yet without it, the rest of the text is difficult to appreciate. Similar problems can occur with abbreviations explained only in the article.

Some abstracts confuse their remit by summarizing the paper rather than its content. The shift to meta-reporting can lead to uninformative boiler-plate text. This example is from a medical education article:

Implications of these results are discussed.

It’s uninformative because readers already know that most research articles contain a discussion section where, by definition, results and their implications are discussed.

Some abstracts expand their remit to include personal research plans. This example is from a clinical article:

We plan to investigate why general practitioners are not complying with the pathway.

It’s common to find research aspirations in internal reports and in research grant applications, where they have a specific function. But published in an abstract, they can present a reader working in the same area with a difficult choice.

Some abstracts expand their remit even further with a self-evaluation of the research. This example is from a finance article:

We believe this study will benefit academics, regulators, policymakers and investors.

The problem is that the reader may not see these pronouncements as truly impartial, with the result that the authority of the article is weakened, not strengthened.

Abstracts can of course fail in many other ways, for example, omitting caveats, adding new information, exaggerating certainty, or providing no more than an advertisement, a piece of puffery.

How to write a successful abstract

In the light of all this, what should go into a successful abstract? Some clinical journals settle the matter by imposing a structured format. But most journal and conference proceedings don’t and may offer little or no detailed guidance to the author, who may be left confused about what’s needed.

One starting point is to think of the abstract not as a condensed version of the paper that preserves the original structure and proportions, but as a mini- or micro-paper in its own right, with certain basic elements:

• the context or scope of the work
• the research question or other reason for the work, if relevant
• the approach or methods
• a key result or two
• a conclusion, if appropriate, or other implications of the work.

Naturally the weight given to each element depends on the research—whether it’s experimental, observational, or theoretical, and whether the expected audience is general or specialized. How much to write about each element is then a balance between including detail and retaining the reader’s interest. 

Within those constraints, it’s important to identify any critical assumptions, non-standard methods, and limitations on the findings so that the scope and potential application of the research is clear. The reader shouldn’t discover on reading the article that the abstract was misleading.

Here’s an example of a well-written abstract from a neuroscience article: 

An unresolved question in neuroscience and psychology is how the brain monitors performance to regulate behavior. It has been proposed that the anterior cingulate cortex (ACC), on the medial surface of the frontal lobe, contributes to performance monitoring by detecting errors. In this study, event-related functional magnetic resonance imaging was used to examine ACC function. Results confirm that this region shows activity during erroneous responses. However, activity was also observed in the same region during correct responses under conditions of increased response competition. This suggests that the ACC detects conditions under which errors are likely to occur rather than errors themselves.

From C. S. Carter et al., Science 1998, 280, 747-749. Reprinted with permission from AAAS.

Successive sentences describe the context, the reason for the work, the methods, some results, and an implication. According to Elsevier’s Scopus database, the article has been cited over 2,500 times.

Encapsulating a body of research so effectively usually takes repeated rewriting. The timing, though, can be a challenge, since the abstract is often prepared last, when the main sections of the paper have found a settled form. It then risks being rushed while material is assembled for submission for publication. 

Despite these pressures, the abstract needs as much attention as any other section of the paper. After all, if it doesn’t do its job, the reader may turn to other abstracts that do. And the published article may languish unretrieved and unseen, waiting in vain for the recognition it deserves.

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Gottfried Leibniz: the last universal genius

Gottfried Wilhelm Leibniz was a German seventeenth-century philosopher, an incredible logician, and one of the most important contributors to the philosophy of metaphysics, philosophical theology, mathematics, and ethics. His metaphysical career spanned over thirty years, and he was an inspiration to other contemporary philosophers from the Enlightenment period.

Born in 1646 in Leipzig, Germany, Leibniz’s theories in metaphysics changed philosophy. One of his signature doctrines and particularly prominent theory, which disputed many others at the time, is about substance, monads, and pre-established harmony. Monads are ‘soul-like’ immaterial entities which exist as a substance in themselves. Monads do not compose into anything. Therefore, Leibniz argued, mind and bodies are not made of substance.

His metaphysical work in philosophy dealt with significant issues, posing theories and explanations for the problem of evil, the problem of free will, and the nature of space and time. The problem of evil and metaphysics absorbed his attention throughout his career; the significance he attributed to the topic can be seen through the extent of his writing, which spanned the course of his lifetime. As well as having written many short pieces, he wrote two important books dedicated to the problem of evil: The Philosopher’s Confession (1672) and Theodicy (1709), the only full-length book that he wrote in his career, which he completed just seven years before his death in 1716.

In the period that Leibniz lived in, evil was not necessarily considered an argument for atheism, but rather an argument for a form of theism that was unorthodox. Thus, many contemporary philosophers argued, it was not the case that God could not exist because evil did, but that there could not be an omniscient God whilst evil existed. Leibniz’s philosophical stance on God was that possible worlds only existed in God’s mind, therefore possible worlds do not exist in the universe. He believed that only the actual world exists, one of harmonious order.

Leibniz was a monumental mathematician who changed the field of mathematics when he invented the first, early calculating machine. He also created the modern-day mathematical notation for the differential and integral calculus, and invented binary code which he explained in his 1703 essay, “Explanation of the Binary Arithmetic.” This two-point number system is used to write computer programmes and data processors, meaning that Leibniz’s invention was pivotal to how we live now, over 300 years later.

He was, and still is, credited as being the father of calculus. However, an argument once existed about whether it was Leibniz or Newton who invented it. In a drawn-out letter exchange between Newton and Leibniz, the German philosopher realised that he needed to describe his methods more clearly to prove his independent theory of calculus was original. Despite this, Newton made it clear that he believed Leibniz had stolen his methods. Now, there is no dispute that they each discovered the Fundamental Theorem independently of each other.

Leibniz was an exceptional polymath. His pivotal theories in metaphysical philosophy, logic, ethics, mathematics, as well as his philosophical writing on the problem of evil, truth, and free will and the nature of space and time, categorise him as the last ‘universal genius’.

Feature image by Maxime Valcarce via Unsplash

Editor’s note: updated on 22 September 2020

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There’s no vaccine for the sea level rising

We will get by the current pandemic. There will be a vaccine eventually. There will be other pandemics. Hopefully, we will be better organized next time.

Waiting in the wings are the emerging impacts of climate change, the next big challenge. There will be no vaccine to stem sea level rise. We will first lose Miami (highest elevation of 24 feet), London (36 feet), Barcelona (39 feet) and New Orleans (43 feet).  Paris and New York City will disappear a bit later.

There are estimates that the cost of saving every threatened global city may be $14 trillion per year. The US portion of this number will represent a significant portion of the federal budget all devoted to one problem for many years. This may seem outlandish, but consider the nature of the threat.

If the ice cap in Antarctica totally melts, sea levels will rise 300 feet. I was surprised at this number, but I did the calculations from scratch and got the same results. Assuming this worst-case scenario, a 350-foot seawall around Manhattan, at current construction costs, would cost $25 trillion. Of roughly 50,000 buildings in Manhattan, over 500 skyscrapers would still be visible above the seawall.

Of course, the rest of New York City and Long Island would be deeply under water. Many inland cities (e.g., Atlanta, Burlington, and Columbus in the US, and Birmingham and Newcastle in the UK) might then have beachfronts.

Sea level rise is just one of the major impacts of climate change. Consider the overall causal chain:

• Burning of fossil fuels increases CO2
• Deforestation increases CO2 in atmosphere
• Greenhouse warming increases
• Earth’s temperature rises
• Ice melting and sea level rise
• Salinization of groundwater and estuaries
• Decrease in freshwater availability
• Ocean acidification affects sea life
• Food supply and health suffer

In the end, we are drowned, starved and/or diseased. This is a challenge we should address sooner rather than later, although we are already a bit late.

We need an integrated approach to managing failures. This begins with failure prevention, e.g., building houses on elevated foundations or stockpiling personal protective equipment. But, it is impossible to prevent all failures, particularly those that you never imagined could happen. Managing failures involves detecting, diagnosing, and fixing problems.

These tasks can be integrated within a framework that involves predicting future challenges Organizations such as the United Nations, World Health Organization, Centers for Disease Prevention, and Control, and the Federal Reserve make such predictions regularly. Though often what occurs is different from what these organizations predicted.

When measured states differ significantly from predicted states, one then diagnoses these differences. Has something happened or are the models underlying the predictions in error? The first priority is to compensate for the consequences of these problems, e.g., dispatching teams to rescue flood victims.  Focus then shifts to remediating the causes of these problems – eliminating causes and/or fixing predictive models.

In order to make this happen it’s important to get support from stakeholders: including populations, industries, and governments. Gaining these stakeholders’ support for decisions to address climate change will depend upon the credibility of the predictions of behavior, at all levels in the system. Central to this support is the extent to which stakeholders interpret and pay attention to official predictions.

We cannot wait for the consequences of climate change to happen and then fix them– remember, there will be no vaccine for sea level rise. How will we afford to address this daunting future? Where will the money come from?

The answer is that we have to view climate change as an enormous economic opportunity. There is a wide range of possibilities for technological innovation, business creation, and millions of well-paying jobs. We should consider this to be the next Age of Exploration. It has already started. The difference between the current situation and the 15^th-17^th centuries is that we now need to explore in an integrated and coordinated manner. We must address climate change for everybody.

Featured image by Daniel Sorm on Unsplash

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Quantitative thinking during a pandemic

Today is not right. The weather is fine. My family and friends are healthy and waiting to hear from me, ready for ordinary things like coffee and conversation. Normally, I’d be taking my grandkids to daycare and checking up on grocery and laundry lists. Then, a bit of reading and some writing. But, instead of my usual activity I sit alone in a world of similars. No driving grandkids to school, no personal contact, no “I’ll-pick-it-up-when-next-I’m-at-the-store.” How peculiar this sudden-forced life. We live at the mercy of the coronavirus, specifically the COVID-19 strain. Now, our life is dictated by the statistical models for infection and survival rates, as calculated by mathematicians working with arcane algorithms. Our thoughts are about “flattening the curve” and “opening up,” terminology we have learned to express the theory of having fewer people get sick at once so that needed medical facilities do not become overwhelmed. We accept the restrictions to our daily activities because we believe them to be in our best interests; and, of course, they are enforced to some degree. Then, too, we each know we are not alone. Virtually all countries in the world—140 or so sovereign regions—have experienced coronavirus.

Soon, the statistical modelers tell us, some businesses and public places will be open, following a phased plan that progresses stepwise toward normality. But each day the modelers posit new predictions. We react by revising our expectations. Millions of people have lost jobs and income, savings are wiped out, many businesses have no expectation of restarting much less thriving. The psychological stress has been devastating. Depression, torn relationships, drug use, and alcoholism are rising to unprecedented levels.  But, for other millions, families and partners have grown closer. Couples and individuals spend time with their children—time together that otherwise would not have happened.

Our circumstances are led by the statistical modelers with their continuous flow of information to medical experts who hope that politicians will follow their recommendations. So . . . where are we?  Safer, or have we just endured the most colossal mistake in the history of mankind?  All because of statistical predictions. The statistical models are recalculated to currently reveal a less severe, but not brightening, future. The infection-and-survival models follow the available medical data and standard biometric risk-analyses assumptions. Technically, the information is processed by various types of regressions, odds-ratios, and correlational relationships. One presumes these algorithms are accurate and reliable.

Interestingly, we do not imagine that we are victims at the mercy of these statisticians—in fact, quite the opposite.  We look to them for truth and guidance. We believe their calculations, trusting that they get the math right. Even when their projections do not materialize with exactness, we realize they are working with a paucity of data and that their assumptions are presumed from an unprecedented and unknowable circumstance. We trust science as the most accurate route to truth that we have. We live with a quantitative perspective.  Apart from ages past, we do not perceive ourselves as beings who are at the whim of fate or providence. For us today, quantification is our moment-by-moment reality. With this viewpoint, we live with an internal sense of odds and probability.

There is a truly remarkable story of how these maths (and specifically, probability theory) used by today’s modelers came about. It was not through the slow progression of technical advances across the centuries. Rather, the formulas these statistical models employ were developed within a short period of time, and not too long ago: about 130 years or so, beginning during the last (but most significant) years of the Enlightenment through the first third of the twentieth century, roughly the 1790s through the 1920s. It can be traced that the period’s history drove many of the technical developments.

Even more astounding is the fact that the measurement of uncertainty—statistical modeling—stem from the work of a very few men and women (fewer than about fifty principals), each of almost unimaginably high intellect and most working in about the same time period. Many of the major characters knew one another personally or at least were aware of the others’ corresponding work in prob- ability theory or statistics, and more broadly in mathematics. Yet, aside from knowing one another and their reputation in intellectual circles, most of these individuals worked in relative obscurity and were not popularly known. Even today, most of these individuals are still not well known, except to biographers and scholars. But, as we shall see, their influence on us today is nothing short of astounding. In large part, this is what makes the story of quantification so interesting.

Our quantitative mindset—how we view things today—is what brings us hope and despair. The outcome is simultaneously both.

Image by clay-banks-U0-r0JMypE0 via Unsplash.

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Maths can help you thrive during the COVID-19 pandemic

When Isaac Newton practiced social distancing during the Great Plague that hit London in 1665, he was not expected to transition from face-to-face work with scientist colleagues to a patchwork of conference calls and email. With no children underfoot who needed care at home, he concentrated on developing early calculus ideas. With no exposure to a 24-7 news cycle of the escalating crisis, he had the mental space to develop a theory of optics. He even found a quiet moment in which to note an apple falling from a tree, which helped him unlock a fundamental law of physics.

Your efforts to focus on work while social distancing to help flatten the curve of the COVID-19 pandemic may present more challenges. As you adjust, consider the following mathematical metaphors for thriving with your personal and professional goals.

Abandon perfectionism, given the Hairy Ball Theorem

Mathematicians like to say that you can’t comb a hairy ball. The expression is a reference to a curious statement called the Hairy Ball Theorem. To understand this theorem, imagine a ball with an abundance of porcupine quills emanating from it. Now imagine trying to comb the quills so that all lie flat against the ball’s surface.

You might make good progress combing at first. You might even begin to believe that it’s possible to comb the quills so that all lie flat against the ball. However, the Hairy Ball Theorem assures you that you will never succeed. That is, any attempt to comb the hairy ball of quills will always leave at least one quill sticking out.

Working to make progress on personal and professional goals in the midst of a pandemic is a lot like trying to comb a hairy ball. Perfection is an unachievable goal. Put forth your best effort and celebrate your progress.

Resist comparison, given chaos theory 

Chaos theory asserts that a small change in an initial condition of an experiment or event may lead to a dramatic change in outcome. The idea is sometimes described as the Butterfly Effect, in which something as inconsequential as the flap of a butterfly’s wings in one part of the world may induce a sequence of events over time that causes a far-off tornado.

What does chaos have to do with this social distancing era? Your initial conditions at the start of the pandemic may have differed from that of a friend or colleague. Perhaps you were more or less farther along with your goals. Maybe you have different caretaking responsibilities for children or aging parents. Perhaps you have an underlying health condition that warrants extra attention. Resist comparing your path to others’ as the exercise is futile; A small difference in your starting place may imply a large difference in outcome.

Be okay with small steps, given the harmonic series

In good times, you may enjoy achieving 100% of what you aim to accomplish in a given day – a sentiment you might represent with an infinite sum of ones, where each “1” in the sum symbolizes the feeling of “achieving 100%” on a collection of sequential days moving forward:

This infinite sum approaches infinity, which might suggest that “the sky’s the limit” regarding your personal and professional goals.

However, in challenging times, some pursuits might leave you feeling like you’ve hit a ceiling. You might represent this scenario with an infinite sum in which each consecutive term gets noticeably smaller according to a pattern:

What does this sum equal? Note that you might rewrite each term in the sum as the areas of the increasingly smaller squares and rectangles in the following sketch:

So, this infinite sum must equal the area of the largest, outer rectangle whose length is two and width is one. Since the area of any rectangle is its length multiplied by its width, you obtain:

As this example illustrates, some infinite sums of increasingly smaller numbers are bounded. If the terms in this sum represented a fraction of what you accomplish in collection of consecutive days moving forward, you might be understandably discouraged.

However, when the challenges of social distancing leave you unable to achieve 100% of your daily goals, you might draw inspiration from a different infinite sum known as the harmonic series:

The harmonic series grows much more slowly than the infinite sum of ones – so slowly that the sum of the first 100 terms does not even reach six. Nonetheless, like the infinite sum of ones, the harmonic series also approaches infinity. It has no upper bound at all.

If pandemic-induced social distancing slows progress towards your goals, be okay with taking small steps. Like the harmonic series, the size of your steps does not limit you; the sky may still be the limit.

Mathematics is more than a tool for computation. The field offers an invitation for deep, delightful thinking, along with metaphors for fostering courage in challenging times. 

Featured image via Pixabay.

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The remarkable life of philosopher Frank Ramsey

Frank Ramsey, the great Cambridge philosopher, economist, and mathematician, was a superstar in all three disciplines, despite dying at the age of 26 in 1930. One way to glimpse the sheer genius of this extraordinary young man is by looking at some of the things that bear his name. My favourite was coined by Donald Davidson: the Ramsey Effect is the phenomenon of discovering that your exciting and apparently original philosophical discovery has been already presented, and presented more elegantly, by Frank Ramsey.

Ramsey published a grand total of eight pages in pure mathematics. He had been working the Entscheidungsproblem in the foundations of mathematics, which asked whether there is a way of deciding whether or not any particular sentence in a formal system is valid or true. He solved a special case of the problem, pushed its general expression to the limit, and saw that limit clearly. A theorem he proved along the way showed that in apparently disordered systems, there must be some order. The branch of mathematics that studies the conditions under which order must occur is now named Ramsey Theory, with more discrete parts of it called Ramsey’s Theorem and Ramsey Numbers.

In 1927-28, Ramsey published two papers in economics, with the encouragement of John Maynard Keynes. When the Economic Journal celebrated its 125th anniversary with a special edition in 2015, both papers were included. That is, looking back over a century and a quarter, one of the world’s best journals of economics decided that two of its 13 most important papers were written by Frank Ramsey when he was 25 years old. The editors explained themselves by saying that the papers initiated “entirely new fields”—optimal savings and optimal taxation theory. In addition, they produced the Ramsey-Cass-Koopmans model, Ramsey Pricing, Ramsey’s Problem, the Keynes-Ramsey Rule, and more.

Ramsey was also the first to set out the subjective conception of probability and expected utility theory that underpins much of contemporary economics. That is, he figured out how to measure degrees of belief and preferences and then showed how we might determine what a rational decision would be, given what someone believes and desires. He was a socialist and wouldn’t have been happy with what became of his idea—he didn’t think that all human action and decision should be crammed into the strictures of rational choice theory, as many economists and social scientists today seem to assume. Nonetheless, in this domain we find the Ramsey/de Finetti Theorem, the Ramsey-Good Result, Ramsey’s Procedure for measuring the intensity of preferences, and more.

In philosophy, he was just as impressive. When an undergraduate, he translated Ludwig Wittgenstein’s Tractatus and wrote a critical notice of it that still stands as one of its most important commentaries. He went on to have a profound influence on Wittgenstein, persuading him to drop the quest for certainty and purity, and turn to ordinary language and human practices. Ramsey was in search of a realistic philosophy and was leaning in the direction of American pragmatism when he died.

He also made major contributions to analytic philosophy. For instance, in a footnote in a draft paper published after his death, he suggested that when someone evaluates a conditional “if p, then q” they are hypothetically adding the antecedent p to their stock of knowledge and then seeing if q would also be there. Robert Stalnaker in 1968 proposed a theory of truth conditions for counterfactuals on the basis of that footnote. What is now known as the Ramsey Test for Conditionals is a method for determining whether we should believe a conditional.

In another draft paper, Ramsey devised a novel and important account of scientific theories and their unobservable entities. A theory is a long and complex sentence, starting with: “There are electrons, which . . .” and going on to tell a story about those electrons. We assume there are electrons for the sake of the theory, just as we assume there is a girl when we listen to a story that starts “Once upon a time there was a girl, who . . . .”  Unlike the story, we commit ourselves to the existence of the entities in our theory, knowing that if it gets overthrown, so will our commitment to its entities. In the meantime, we use and believe the theory. Any additions to it are made within the scope of the quantifier that says that there exists at least one electron—the theory can evolve while still being about the original entities. Such a sentence is now called a Ramsey Sentence and is employed by both philosophers of science and philosophers of mind.

But Ramsey was the antithesis of the kind of figure usually associated with great genius. He was not an enigmatic, cult-encouraging eccentric, like his friend Wittgenstein. He was genial, open, and modest. “Never a showman,” said I.A. Richards, one of the founders of the new Cambridge school of literary criticism. Frances Marshall, at the centre of the Bloomsbury circle, never heard anyone say a word against him—she didn’t think it would be possible. But the best one-liner is from Michael Ramsey (Archbishop of Canterbury), who said that his brother had “a total lack of uppishness.”

Featured Image Credit: Rights owned by Oxford University Press

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Robot rats are the future of recycling

I just watched WALL-E for the first time in five years or so. It’s the story of a plucky little robot tasked with cleaning up the world by compacting rubbish into blocks and building structures out of the blocks to minimize the amount of land they take up. Of course, he falls in love and saves the world along the way, but for a geek like me, that’s beside the point. It’s a great movie about robots and, laws of physics aside, shows quite a deep understanding of their potential. However, in their quest for a truly dystopian scenario, I think the storytellers missed the point about how such robots could really help us.

Imagine that, twenty years from now, we have got our production of plastic rubbish mostly under control. I saw Prof Mark Miodownik of University College London give a great talk on how he and his colleagues are working to make this happen for plastics. This would involve limiting our use of everyday plastics to those that easily be recycled, using enzymes to break them down into the hydrocarbons we need to make more stuff, and having a much clearer and more efficient recycling streams. To be really effective, this would have to happen not just for plastics but for everything: paper, organic matter, electronics. Though only a tiny fraction of the waste we produce is currently dealt with constructively, change is already happening: there are industrial machines designed to split out construction waste for re-use, disassemble iPhones, and sort in recycling plants.

Just stopping making the situation worse isn’t enough though. I think what haunts many of us is the idea that we have done too much damage to the planet for it to ever recover. So, after we stop (quite literally) trashing the planet, how do we deal with all those hundreds of thousands of acres of landfill across the world? Or, for that matter, the Texas-sized garbage patch floating in the ocean, made infamous by Sir David Attenborough two years ago?

To me, the obvious answer is autonomous robots. Not some magic-bullet-solves-the-whole-problem race of giant super-intelligent humanoid robots, but a swarm of mostly-small robots with dexterity and a specific expertise, but relatively limited native intelligence otherwise. I imagine hundreds of robot rats (probably looking more like cockroaches actually), scurrying up landfill with little sensors and dextrous arms, identifying particular types of material, and delivering the sorted rubbish to larger carrier robots that will take the sorted materials back to the recycling plant when they are full. Some robots will be bigger and stronger to pick up and lug old appliances back to base. Some will have specialist sensors to identify tricky materials. And all will have some ability to communicate with each other, even if only in the most rudimentary way.

This heterogeneity – the idea that there will be a diverse community of machines with different skills and of different generations – is one of several things that the makers of WALL-E gets very right. Another is the idea that robots don’t need to squander one of our precious resources: power. Apart from the obvious idea that small machines can use solar power to keep going, there are other possibilities too. Refueling robots (you’d want these stations to be able to move to where they’re most efficiently used) could also be powered by digesting some of the biological matter in the landfill. Two decades ago, researchers at the University of the West of England developed a system that could eat and get energy from slugs: surely a vegan digester must be possible by now. If that can’t work, then we could try to develop some kind of miniature version of the Klemetsrud waste-to-energy incinerator in Norway, which now includes carbon capture.

To make sure the electricity burden is minimized in the first place, we will want to use the most efficient hardware possible. For more than 30 years, we’ve been developing models of how to do vision, hearing, and other intelligent tasks using silicon circuits that work more like our brains do. This field, known as neuromorphic engineering, not only has the advantage of speed, but – when designed well – neuromorphic circuits can operate with several orders of magnitude less power than their conventional digital counterparts.

Of course, a vision like this has many challenges: not least ensuring that robot rats aren’t hurting biological wildlife (perhaps including real rats). In an effort like this we will undoubtedly encounter unexpected problems and unintended consequences. But scientists, engineers, and philosophers from across the world have been considering these issues for decades now and, apart from anything else, we are all now exquisitely aware that the biggest danger to human life is other humans.

Why don’t we have swarms of robots in our landfill yet, when most of the technological problems seem to be solvable? Perhaps it’s because we, as citizens, have never made it a priority. But it’s clear robotic technology can develop pretty fast when given the right resources. In this case, the research is funded by the military. Of course, they’re looking to replace pack animals, security guards, and people who have to decommission bombs, not sort garbage. The engineers work according to our priorities, so if we want to see progress, we need to put our money where rubbish is.

On the plus side, the kind of smaller, less-intelligent, less safety-critical robots that we need to sort trash should be massively cheaper than the machines that we need to accompany troops. And maybe a war on rubbish is one that we could eventually win.

Featured images from Unsplash and Pixabay.

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Why academics announce plans for research that might never happen

Why do academic writers announce their plans for further work at the end of their papers in peer reviewed journals? It happens in many disciplines, but here’s an example from an engineering article:

Additionally, in our future work, we will extend our model to incorporate more realistic physical effects . . . We will expand the detection procedure  . . . We will integrate our detection procedure . . . We will validate the performance of our proposed detector with real data.

There are several possible explanations for this practice. One is that it’s a continuation of a discussion between author and colleagues—participants in a society meeting, conference, or research network.  Another is that it’s a response to a reviewer’s critique of the submitted paper. To secure its publication, the author promises to remedy its defects in future work. Yet another is that it’s simply promotional—an advertisement of the scope of the research, its importance to the field.

To be clear, none of this refers to suggestions in an article to benefit research by the larger community. For example, an author might suggest new lines of investigation or potential confounds in experimental practice, here in a study of mental health problems: “Depression, too, can be heterogeneous . . . and future work would be wise to consider such variation.”

In contrast, an author’s announcement of his or her intentions can present readers with a dilemma. Should they restrict their own research to avoid duplication or, more drastically, turn to a different area of study? As Vernon Booth noted in his prize-winning booklet Writing a scientific paper, published in 1971, “An author who writes that an idea will be investigated may be warning you off [his or her] territory.”

Suppose, though, you’re committed to the territory. Should you accelerate your efforts to avoid being overtaken? Or continue at the same pace and assume that duplication is unlikely? Or should you exercise patience and wait for the follow-up publication to see what emerges? If you do wait, where should you look and for how long? Or should you simply ignore the announcement? After all, the author may never publish again. He or she may have promised too readily and not thought through what was entailed, or having done so, loses interest, or for other reasons disengages from the field, or from research more generally. And of course it’s possible that the announcement was meant only for effect, an empty version of the warning described by Booth. Parallels can be found in the software industry’s promotion of what became known as vapourware—advertised products that didn’t exist and were never likely to.

Curiously, the act of rounding off a paper with an announcement has a remarkably long history. Here’s an example from about 350 years ago: “And the next time, we hope to be more exact, especially in weighing the Emittent Animal before and after the Operation.”

And another from over 150 years ago: “We hope shortly to be able to prepare some pure cobalt and nickel by depositing galvanoplastically those metals in the form of foil from solutions of their pure salts.”

And another from about 50 years ago: “Quantitative results regarding these phenomena have been obtained and will be published in due course.”

In fact, this last example, which a colleague helpfully identified, is from one of my own publications, a note published during my thesis studies.

Nevertheless, it’s difficult to justify the inclusion of such material in papers now. In his booklet, subsequently revised and republished, Booth said that promises should be offered sparingly. The advice was probably too gentle. Whether they’re about future publications or research, it’s best to avoid statements of intent altogether. They can prove a challenge for the reader and, just occasionally, for the author too.

Featured image credit: “White Printer Paper Lot” by Pixabay. Public domain via Pexels.

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Standing in Galileo’s shadow: Why Thomas Harriot should take his place in the scientific hall of fame

The enigmatic Elizabethan Thomas Harriot never published his scientific work, so it’s no wonder that few people have heard of him. His manuscripts were lost for centuries, and it’s only in the past few decades that scholars have managed to trawl through the thousands of quill-penned pages he left behind. What they found is astonishing—a glimpse into one of the best scientific minds of his day, at a time when modern science was struggling to emerge from its medieval cocoon.

It was a time when many still believed in magic, and most of the methods of science and mathematics that we take for granted today had not yet been developed. For instance, calculus had not yet been discovered, and there was no formal understanding of infinite series. But Harriot was one of the best algebraists of the early seventeenth century, and he managed to make a number of pioneering mathematical discoveries—including some that anticipated calculus and limit theory. These discoveries enabled him not only to make advances in pure mathematics, they also enabled him to apply mathematics to various practical and theoretical problems.

There are many reasons that Harriot never got around to publishing his scientific work, not least the dramatic times in which he lived. He moved in the most glamorous of Elizabethan circles—the charismatic Sir Walter Ralegh was his patron; but stars often fall, and when Ralegh fell from grace, Harriot’s fortunes suffered, too.

But at the beginning of their association, Harriot and Ralegh’s lives were full of excitement and hope. After graduating from Oxford, Harriot’s first job was that of live-in navigational advisor during Ralegh’s preparations for the first English colony in America.

He began his new employment in 1583, when England was a tiny player in maritime commerce and geopolitics. If Ralegh’s planned American trading colony were to materialize, his mariners would have to sail safely across uncharted oceans with the sky as their only guide, and Harriot provided them with the best training then available. Which was just as well for Harriot, too, because in 1585 he sailed to Roanoke Island with the First Colonists.

He saw the Roanoke people as friendly and ingenious, and found their way of life appealing in many ways. Tragically, few of his fellow travellers were so open-minded. When food supplies eventually ran low, and illnesses unwittingly brought from England struck down many of the local people, tensions between the visitors and their hosts reached breaking point. The First Colonists abandoned their experiment and returned to England—having killed the chief who had earlier invited them to share his land.

The disaster at Roanoke seems to have weighed heavily on Harriot. Although he continued as navigational advisor to Ralegh’s follow-up ventures, he never sailed to America again.

Instead, he began to turn towards more detached scientific pursuits. Initially, these included original mathematical research with navigational applications, but later, he explored science and mathematics simply for their own sake. He explored almost every area of the mathematics and physics of his day, and he made a number of breakthroughs that today we link with others’ names.

He found the law of falling motion independently of his contemporary Galileo, and used a telescope to map the moon and the motion of sunspots, again independently of Galileo. He discovered the law of refraction before Snell, produced a fully symbolic algebra and fledgling analytic geometry before René Descartes, found the secret of colour and the nature of the rainbow before Isaac Newton, and the rules of binary arithmetic before Gottfried Leibniz—to name just a few of his many achievements.

If only he had published all this work! Unfortunately for early modern science, he had little interest in fame. But the vagaries of a life spent working for controversial patrons didn’t help. First Ralegh, and then Harriot’s second patron, the earl of Northumberland, ended up imprisoned in the Tower of London on false charges of treason. Harriot himself came under suspicion. For instance, his renown as an astronomer led to the false accusation that he had cast a horoscope to aid the Gunpowder Plotters.

Yet despite all the ups and downs his curiosity remained undimmed. Four centuries on, he deserves to be celebrated for the decades he devoted to scientific discovery. Although this means he should take his rightful place in the scientific hall of fame, it is refreshing, at this time when we are awash with celebrities, that Harriot did much of his work just for the love of it.

Featured image credit: Night sky in Hatchers Pass, Alaska. Photo by McKayla Crump. CC0 via Unsplash.

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Can plants help us avoid a climate catastrophe?

The amount of carbon dioxide released into the atmosphere by burning of fossil fuels is at a staggering all time high of 34 billion tonnes, having risen every decade since the 1960s. Not surprisingly, our carbon dioxide emissions from fossil fuels, and land-use changes, including deforestation, have raised the atmospheric concentration of this major greenhouse gas to a level unprecedented in human history.

What’s more, the concentration of atmospheric carbon dioxide has risen so rapidly over the past few decades that Earth’s temperature has yet to fully adjust to the new warmer climate it dictates. This means that even if we could magically stop our carbon dioxide emissions from fossil fuels overnight, we have already committed Earth to transition to a warmer climate. Global temperatures have risen by more than 1°C since the 1970s. How much more warming are we likely to experience? Another 0.5°C, 1.5°C, 2.5°C or worse? Scientists are working urgently to try and better constrain this number. Meantime, over 190 nations worldwide signed up to the 2015 Paris Agreement with the goal of limiting warming to less than 2°C and ideally less than 1.5°C. Given the current situation, even a lenient 2°C target now looks wildly optimistic, especially given 34+ billion tonnes of carbon dioxide are added every year we delay mitigation measures.

This is why, along with the United Nations and the National Academy of Sciences in the United States, the UK’s Royal Society acknowledges drastic phase-down of our carbon dioxide emissions from burning fossil fuels for energy will be insufficient to avoid seeding catastrophic human-caused climate change. We actually have to start removing carbon dioxide from the atmosphere, safely, affordably, and within the next 20 years.

Enter, the kingdom of plants.

Hundreds of millions of years ago, during the Devonian Period (393-383 millions of years ago), plants bioengineered a cooler climate as the spread of forests lowered atmospheric carbon dioxide levels. As their root systems evolved to become larger and more complex, trees generated soils and accelerated the breakdown of rocks and minerals into minute grains, forming dissolved bicarbonate in the process. Eventually, this bicarbonate washed into the oceans, where the carbon it carries was stored for hundreds of thousands of years or locked up on the sea floor.

We now think it may be possible to mimic those processes to remove carbon dioxide from the atmosphere. The method would be to dress the soils of agricultural landscapes with crushed rapidly weathering rocks, such as basalt. This biogeochemical soil improvement could also boost yields by adding plant-essential nutrients, helping reverse soil acidification, and helping restore degraded agricultural top soils that provide food security for billions of people. Although there are possible drawbacks and unintended consequences, the approach may be practicable. Humans have put over ten million square kilometres of land to the plough, and application of crushed rock to this farmland could be feasible by exploiting existing infrastructure.

However, at the very best, this approach might remove only about 1/10th of our current emissions.

We could also undertake reforestation of forested lands once cleared for agriculture and afforestation of new areas, again mimicking the ancient spread of forests across the continents. Planting millions of trees could help by storing carbon dioxide in forest biomass and soils. Undertaken across a sufficiently large area of the globe, these actions might sequester another few billion tonnes of carbon dioxide.

Even these sorts of radical measures will not represent a sufficient climate restoration plan, however. A wider portfolio of carbon removal techniques will be required to scrub sufficient amounts of carbon from the atmosphere each year. But the technologies need multibillion dollar investment to move them from the lab to pilot schemes and then to determine which can scale massively. At the same time, we will need to fundamentally transform our global energy systems to halt carbon emissions.

As Erik Solheim, until recently the executive director of the United Nations Environment Programme, has remarked, “if we invest in the right technologies, ensuring that the private sector is involved, we can still meet the promise we made to our children to protect their future. But we have to get on the case now.”

Right now, carbon dioxide removal looks like a prohibitively expensive option for helping slow the pace of climate change. Taking action places an enormous burden on young people and future generations. But taking no action asks them to face dire consequences including intensifying droughts, heat-waves, storms, ice-sheet melt and sea-level rise flooding coastal regions. This is the intergenerational injustice of our time.

Our current crisis is urgent and unfolding at a time when global food demand will need to more than double before the end of the century. Can we sustainably feed a crowded planet, preserve the wonderful diversity of life on Earth, and stabilize the climate? These are the daunting challenges facing humanity. Faced with the collective moral failure of world leaders to act, it is hardly surprising that young people worldwide are bravely striking for action on climate change supported by thousands of scientists. At stake is nothing less than the future of humanity.

Feature Image credit: “Green and white leaf plant” by Jackie DiLorenzo. Public Domain via Unsplash.

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The ethics of the climate emergency

During the last few days of February we experienced the warmest Winter day since records began, with a high of 20.6 degrees (Celsius) at Trawscoed in mid-Wales. As if that was not enough, the record was broken again the next day with 21.2 degrees at Kew Gardens. This unseasonable weather is one of many signs of climate change and global warming. Another has been the flowering of snowdrops this Winter which began in late December. So did the opening of daffodils, which in William Wordsworth’s day did not flower until April.

Recently, tens of thousands of school students stayed away from classrooms to demonstrate for action on climate change. They are recognising that there is a climate emergency, and that governments and corporations need to take emergency action.

Last October, an Intergovernmental Panel on Climate Change report explained why average temperature increases must be restricted to 1.5 degrees, one of the agreed goals of the Paris agreement of 2015.

Limiting average increases to 2 degrees, they explain, will be nowhere near enough to prevent the flooding of low-lying islands and coastal cities, and the loss of almost all coral reefs. Disappointingly, however, the national commitments made at Paris would spell a catastrophic increase of towards 3 degrees. Governments need to rachet up these commitments at coming review conferences, as a matter of urgency.

Antonio Guterres, the UN Secretary-General, is soon to hold the first of these review conferences. The UK government, which is hoping to host this conference, needs to commit now to more drastic cuts to set an example to the rest of the world.

Ethicists debate the grounds for taking such emergencies seriously. By now it is widely agreed that the people of the present matter, however distant, and wherever they live. But many of them are losing their livelihoods because of climate change, and they are usually people who have hardly at all contributed to it. And that is hardly fair.

Most people also agree that coming generations matter, and should be taken into account. Some suggest that the more distant in time future people are, the less they matter. Yet suffering in fifty or a hundred years is likely to be just as bad as suffering now. Many people already recognise this. As Hilary Graham and her fellow-researchers have shown, if you ask about future interests in an impersonal manner, you get answers that downplay these interests, but if you ask about what we should do to make life bearable for our grandchildren, you get much more affirmative answers, expressing deep concern about their well-being.

But this too means that we need to take action in the present to prevent rising sea-levels and freak weather events of greater intensity and frequency than the world has yet known both in the present and later in this century.

There is also a debate about whether other species matter. Everyone agrees that we need the ecosystems on which human beings depend to remain intact, and most hold that we need to preserve these and other ecosystems for the sake of their natural beauty. Many go on to hold that the needs of nonhuman species count ethically alongside our own, whether or not they count as much as our own.

When we get concerned about the bleaching of coral reefs and the disappearance of their polychrome communities, our concern expresses a blend of reasons of these kinds. But increasingly people (particularly young people) are worried about the wellbeing of animals and their habitats.

Just at the same time, there are alarming losses to populations of many wild species, and to biodiversity.  All governments need to make special efforts to preserve wild species, and the governments of developed countries should subsidise poorer countries (which are often the homes of biodiversity hot-spots) to enable them to do this.

But far from the biodiversity emergency being in competition for our attention with the climate crisis, they should be seen as a single emergency. This is because one of the main causes of threats to wildlife is nothing but climate change.

So we need urgent plans and policies to replace carbon-based energy generation with renewable energy. We need to eat less meat, thus increasing our life-expectancies and reducing emissions of methane. We need to replace vehicles with diesel and internal combustion engines with electric cars, lorries and (if possible) ships. And we need to cut down on our airline travel. Individuals, companies and governments all have a part to play.

While sunny days in February are welcome, an overheated, tempestuous and increasingly flooded future world is not. We need to support Antonio Guterres’ worldwide campaign to prevent it.

Featured image credit: Climate Change by TheDigitalArtist. Pixabay License via Pixabay.

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150 Years of the Periodic Table

2019 marks the 150th anniversary of the creation of the periodic table, and it has been declared the International Year of the Periodic Table by the United Nations Educational, Scientific, and Cultural Organization (UNESCO).

Here, we take a look at the fascinating history of the people behind the table, starting in 450 BCE and going through the present day, and the way the table and understanding of elements has evolved over time. Even today, many aspects of the periodic table remain unresolved—including a consensus on just how many elements remain undiscovered, leaving much room for discovery and further development in the future.

Featured age credit: “Beaker glass ware” by uncredited. Public Domain via Pxhere.

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Happy sesquicentennial to the periodic table of the elements

The periodic table turns 150 years old in the year 2019, which has been appropriately designated as the International Year of the Periodic Table by the UNESCO Organization. To many scientists the periodic table serves as an occasional point of reference, one that is generally considered to be something of a closed book. Of course they, and the general public, have become aware of the ever-growing list of new elements that need to be accommodated into the table, but surely the main structure and principles of the table must be fully understood by now?

Well it turns out that this is not the case. In this blog I will touch on just some of the loose ends in the study of the periodic table. The first has to do with the sheer number of periodic tables that have appeared, either in print or on the Internet, in the 150 years that have elapsed since the Russian chemist Dmitri Mendeleev first published a mature version of the table in 1869. There have been over 1000 such tables, although some of them are best referred to by the more general term periodic system, since they come in all shapes and sizes other than table forms, with some of them being 3-D representations.

Given that there are so many periodic systems on offer it is natural to ask whether there might be one ultimate periodic system that captures the relationship between the elements most accurately. This relationship that lies at the basis of the periodic system is a rather simple one. First the atoms of all the elements are arranged in a sequence according to how many protons are present in their nuclei. This gives a sequence of 118 different atoms for the 118 different elements, according to the present count. Secondly, one considers the properties of these elements as one moves through the sequence, to reveal a remarkable phenomenon known as chemical periodicity.

It is as though the properties of chemical elements recur periodically every so often, in much the same way that the notes on a keyboard recur periodically after each octave. In the case of musical notes the recurrence can easily be appreciated by most people, but it is quite difficult to explain in what way the notes represent a recurrence. In technical terms moving up an octave on a keyboard, or any other instrument for that matter, represents a doubling in the frequency of the sound.

Octaves in the case of elements, if we can call them so, are not quite like that. There is no single property which shows a doubling each time we encounter a recurrence. Nevertheless there are some intriguing patterns that emerge among the elements that are chemically ‘similar’. For example consider the number of protons in the nuclei of the atoms of lithium atom (3), sodium (11) and potassium (19). An atom of sodium has precisely the average number of protons among the two flanking elements (3 + 19)/2 = 11. This kind of triad relationship occurs all over the periodic table. In fact the discovery of such triads among groups of three similar elements predates the discovery of the mature periodic table by about 50 years.

Now most of the periodic tables that have been proposed display such triad relationships and so we must look elsewhere in order to find an optimal table, assuming that such an object actually exists. One possible course of action might be to consult the official international governing body of chemistry or IUPAC (International Union of Pure & Applied Chemistry) to see what their recommendation might be. The IUPAC organization has a rather odd policy when it comes to the periodic table of the elements. The official position is that they do not support any particular form of the periodic table. Nevertheless in the IUPAC literature one can find many instances of a version of the periodic table that is sometimes even labeled as “IUPAC periodic table”.

And if that’s not bad enough, the version that IUPAC frequently publishes, as shown in figure 1, is rather unsatisfactory for reasons that I will now explain.

Consider the third column from the left, or as it is aptly known, group 3 of the periodic table. Unlike all other columns of the table this group appears to contain just two elements, scandium and yttrium, shown by their symbols Sc and Y. If you look closely at the numbers in the two shaded spaces below these two elements you will see a range of values, such as 57-71 in the first case. This occurs because the elements numbered from 57 to 71 inclusive are assumed to fit in-between element 56 and 72, naturally enough. The reason why the two sequences of shaded elements are shown below the main body of the main table in a mysteriously detached manner is purely pragmatic.

It’s because doing otherwise, would produce a table that is perhaps too wide as shown in figure 2. But in a sense the far-too-wide table is more correct, since it avoids any breaks in the sequence of elements and avoids the impression that the shaded elements somehow have a different status from all others or that they represent something of an afterthought. But switching to such a wide table would not solve the problem even if IUPAC were to endorse doing so. This is because the table in figure 2 still shows only two elements in group 3 of the table and because it would imply that there are 15 so-called f-orbitals in each atom, whereas quantum mechanics, that provides the underlying explanation for the periodic table, suggests that there should be 14 of them.

OK, you might say, we can easily fix the problem by tweaking the periodic table slightly to produce figure 3. As far as I can see, from a lifetime of studying and writing about the periodic table, figure 3 is precisely the optimal periodic table that IUPAC should be publishing and even endorsing officially. This table restores the notion of 14 f-orbital elements as well as removing the anomaly whereby group 3 only contained 2 elements, since it now contains four, including lutetium and lawrencium.

Why will IUPAC not see things quite so simply? That’s a big and complicated question which I can only touch upon here. Like many organizations with rules and regulations, when push comes to shove, decisions are made by committees. As a result, the science takes second place while the various committee members vie with each other and ultimately take votes on what periodic table they should publish. Unfortunately, science is not like elections for presidents or prime ministers, where  voting is the appropriate channel for picking a winner. In science there is still something called the truth of the matter, which can be arrived at by weighing up all the evidence. The unfortunate situation is that IUPAC cannot yet be relied upon to inform us of the truth of the matter concerning the periodic table. In this respect there is indeed an analogy with the political realm and whether we can rely on what politicians tell us.

Featured image credit: Retro style chemical science by Geoffrey Whiteway. Public Domain via Stockvault.

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How Trump beat Ada’s big data

The Democratic Party’s 2008 presidential primary was supposed to be the coronation of Hillary Clinton. She was the most well-known candidate, had the most support from the party establishment, and had, by far, the most financial resources.

The coronation went off script. Barack Obama, a black man with an unhelpful name, won the Democratic nomination and, then, the presidential election against Republican John McCain because the Obama campaign had a lot more going for it than Obama’s eloquence and charisma: Big Data.

The Obama campaign put every potential voter into its database, along with hundreds of tidbits of personal information: age, gender, marital status, race, religion, address, occupation, income, car registrations, home value, donation history, magazine subscriptions, leisure activities, Facebook friends, and anything else they could find that seemed relevant.

Layered on top were weekly telephone surveys of thousands of potential voters that attempted to gauge each person’s likelihood of voting—and voting for Obama. These voter likelihoods were correlated statistically with personal characteristics and extrapolated to other potential voters so that the campaign’s computer software could predict how likely each person in its database was to vote and the probability that the vote would be for Obama.

This data-driven model allowed the campaign to micro-target individuals through e-mails, snail mail, personal visits, and television ads asking for donations and votes. In the crucial month of January 2008, Obama raised $36 million, a record for any politician, and nearly three times the amount raised by Clinton. After Obama secured the nomination, the fund-raising continued. For the full 2008 election campaign, Obama raised $780 million, more than twice the amount raised by his Republican opponent, John McCain. McCain didn’t have a realistic chance of winning, and he didn’t—with only 173 electoral votes to Obama’s 365.

Eight years later, Hillary Clinton made another presidential run, determined to have Big Data on her side.

This time, Big Data failed.

The Clinton campaign hired 60 mathematicians and statisticians—several from the Obama campaign—to create a software program that was named Ada in honor of a 19th-century female mathematician, Ada, Countess of Lovelace. After Clinton became the first female president, she would reveal Ada to be the secret behind her success. What a great story!

70% of the campaign budget went for television ads, and Ada determined virtually every dollar spent on these ads. The advice of experienced media advisors was neither sought or heeded.

No one really knew exactly how Ada made her decisions, but they did know that she was a powerful computer program analyzing an unimaginable amount of data. So, they trusted her. She was like an omniscient goddess. Don’t ask questions, just listen.

We do know that Ada took blue-collar voters for granted, figuring that they reliably voted Democratic, most recently for Obama, and they would do so again. With blue-collar votes as her unshakeable base, Clinton would coast to victory by persuading minorities and liberal elites to vote for her.

Ada is just a computer program and, like all computer programs, has no common sense or wisdom. Any human who had been paying the slightest bit of attention noticed Clinton’s vulnerability against Bernie Sanders, a virtually unknown 74-year-old Socialist senator from Vermont, who was not even a Democrat until he decided to challenge Clinton. A human would have tried to figure out why Sanders was doing so well; Ada didn’t.

When Clinton suffered a shock defeat to Sanders in the Michigan primary, it was obvious to seasoned political experts and campaign workers who were on the ground talking to real voters that Sanders’ populist message had tremendous appeal and that the blue-collar vote could not be taken for granted; Ada didn’t notice.

Ada did not compare the enthusiasm of the large crowds that turned out, first for Sanders, and later for Donald Trump, to the relatively subdued, small crowds that listened to Clinton. There were no enthusiasm numbers for Ada to crunch, so Ada ignored energy and passion, and Clinton’s data-driven campaign did, too. To a computer, if it can’t be measured, it isn’t important.

Most glaringly, the Clinton campaign’s data wonks shut out Bill Clinton, perhaps the best campaigner any of us have ever seen. The centerpiece of his successful 1992 election campaign against the incumbent president, George H. W. Bush, was “It’s the economy, stupid.” Bill instinctively knew what mattered to voters and how to persuade them that he cared.

In the 2016 election, Bill Clinton saw the excitement generated by Bernie Sanders and Donald Trump in their appeal to working-class voters and he counseled that “It’s the economy, stupid” should be the defining issue of Hillary’s campaign—particularly in the Midwestern rust-belt states of Ohio, Pennsylvania, Michigan, and Wisconsin, the so-called Blue Wall, the firewall of reliably blue states that Ada assumed would be the base for Clinton’s victory over Donald Trump.

Ada concluded that voters were more worried about Trump’s unpresidential behavior than they were about jobs, so Hillary focused her campaign on anti-Trump messages: “Hey, I may not be perfect, but Trump is worse.”

The Clinton campaign almost completely ignored Michigan and Wisconsin, even though her primary-campaign losses to Bernie Sanders in both states should have been a fire-alarm of a wake-up call. Instead, Clinton wasted time and resources campaigning in places like Arizona—states she probably would not win (and did not win)—because Ada decided that Clinton could secure a landslide victory with wins in marginally important states.

In the aftermath, a Democratic pollster said that “It’s nothing short of malpractice that her campaign didn’t look at the Electoral College and put substantial resources in states like Michigan and Wisconsin.”

After Trump’s victory, Bill pointed his middle finger at the data wonks who put all their faith in a computer program and ignored the millions of working-class voters who had either lost their jobs or feared they might lose their jobs. In one phone call with Hillary, Bill reportedly got so angry that he threw his phone out the window of his Arkansas penthouse.

Big Data is not a panacea—particularly when Big Data is hidden inside a computer and humans who know a lot about the real world do not know what the computer is doing with all that data.

Computers can do some things really, really well. We are empowered and enriched by them every single day of our lives. However, Hillary Clinton is not the only one who has been overawed by Big Data, and she will surely not be the last.

Featured image credit: American flag by DWilliams. CC0 via Pixabay.

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Modelling roasting coffee beans using mathematics: now full-bodied and robust

Coffee is one of the most traded commodities in the world, valued at more than $100 billion annually. Even if you’re not an entrepreneur looking for the next big coffee venture, you’ll probably still care about how to make the 2.25 billion cups of coffee globally consumed every day as delicious as possible. Fortunately, scientists and researchers have partnered with large coffee companies in the hopes of understanding some of the complexities behind making a good cup of coffee: specifically, how coffee beans are roasted.

During the roasting process, partially dried coffee beans turn from green to yellow to various shades of brown, depending on the length of the roast. Once the residual moisture content within the bean dries up in the yellowing phase, crucial aromas and flavours are developed. However, the associated chemical reactions that produce these desirable coffee traits are highly complex and not well understood. This is partially due to the fact that the browning reactions linked to aroma and flavour development, called the Maillard reactions, is comprised of a large network of individual chemical reactions, where only preliminary understanding of the network’s construction exists.

To tackle the challenges involved with creating mathematical models for the Maillard reactions, along with other chemical reaction groups in a roasting coffee bean, we use the concept of a Distributed Activation Energy Model (DAEM), originally developed to describe the pyrolysis of coal. Not dissimilar to the Maillard reactions, the pyrolysis of coal involves large numbers of parallel chemical reactions and, using the DAEM, can be simplified to a single global reaction rate that describes the overall process. Crucially, however, the DAEM relies on knowing the distribution of individual chemical reactions beforehand. While the overall distributions associated with the Maillard chemical reactions remain unknown, we can reasonably approximate the reaction kinetics of the majority of the Maillard chemical reaction group.

However, the DAEM approach to chemical reaction groups only works when each of the reactions is happening parallel of one another. Because of this, we examine a simplified pathway of reactions involving sugars (which are linked to the formation of Maillard products) and separate groups of reactions to follow a progression of reactants to products. Specifically, we examine how sucrose first hydrolyses into reducing sugars, which in turn become either Maillard products or products of caramelisation. This division of this sugar pathway network allows us not only to fit each reaction subgroup with different parameter values, but also to determine that the hydrolysis of sucrose creates a “bottleneck” in the sugar pathway and prevents Maillard products from forming too early in the roast.

To model the local moisture content and temperature of the bean, two variables that crucially change which chemical reactions can occur during the roast, we use multiphase physics to describe how the solid, liquid, and gas components within the coffee bean evolve. This is a crucial difference to what has previously been done to model coffee roasting, as existing models often treat the coffee bean as a single “bulk” material. Additionally, unlike in previous multiphase models for roasting coffee beans, we allow the porosity of the bean to change according to the consumption of products in the sugar pathway chemical reaction groups. We also incorporate a sorption isotherm, an equilibrium vapour pressure specific to the evaporation mechanisms present in coffee bean roasting, in our model. Finally, to reduce the system variables to functions of a single spatial variable and time, we model a whole coffee bean as a spherical “shell”, while modelling a chunk of a coffee bean as a solid sphere.  This is another improvement to previous multiphase models, which disagreed with recent experimental data describing the moisture content in both roasting coffee chunks and roasting whole beans.

Numerical simulations of this improved multiphase model (referred to as the Sugar Pathway Model) provide several key conclusions. Firstly, the use of spherical shells and solid spheres to describe whole and broken coffee beans, respectively, allows for good agreement with experimental data while simplifying the mathematical model’s structure. Secondly, due to the large number unknowns in the model, the Sugar Pathway Model can be fit to experimental data using a variety of parameter values. While this could be viewed as a drawback to the Sugar Pathway Model, we also show that small changes in parameter values do not drastically change the model’s predictions. Hence, the Sugar Pathway Model provides a reasonable qualitative understanding of how to model key chemical reactions that occur in the coffee bean, as well as how to model coffee bean chunks differently to whole coffee beans.

While largely theoretical, the Sugar Pathway Model provides a balance between the immensely complicated underlying physical processes occurring in a real-life coffee bean roast and its dominant qualitative features predicted by multiphase mathematical models. Additionally, industrial researchers can cheaply and efficiently use these multiphase mathematical models to determine the important features at play within a coffee bean under a variety of roasting configurations. While a basic framework for the roasting of a coffee bean is presented here, understanding the qualitative features of key chemical reaction groups allows us to get one step closer to that perfect cup of coffee.

Featured image credit: Coffee by fxxu. CC0 via Pixabay.

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The dilemma of ‘progress’ in science

Most practicing scientists scarcely harbor any doubts that science makes progress. For, what they see is that despite the many false alleys into which science has strayed across the centuries, despite the waxing and waning of theories and beliefs, the history of science, at least since the ‘early modern period’ (the 16th and 17th centuries) is one of steady accumulation of scientific knowledge. For most scientists this growth of knowledge is progress. Indeed, to deny either the possibility or actuality of progress in science is to deny its raison d’être. 

On the other hand careful examination by historians and philosophers of science has shown that identifying progress in science is in many ways a formidable and elusive problem. At the very least scholars such Karl Popper, Thomas Kuhn, Larry Laudan and Paul Thagard while not doubting that science makes progress, have debated on how science is progressive or what it is about science that makes it inherently progressive. Then there are the serious doubters and skeptics. In particular, those of a decidedly postmodernist cast of mind reject the very idea that science makes progress. They claim that science is just another ‘story’ constructed ‘socially’; by implication one cannot speak of science making objective progress.

A major source of the problem is the question of what we mean by the very idea of progress. The history of this idea is long and complicated as historian Robert Nisbet has shown. Even narrowing our concern to the realm of science we find at least two different views. There is the view espoused by most practicing scientists mentioned earlier, and stated quite explicitly by physicist-philosopher John Ziman that growth of knowledge is manifest evidence of progress in science. We may call this the ‘knowledge-centric’ view. Contrast this with what philosopher of science Larry Laudan suggested: progress in a science occurs if successive theories in that science demonstrate a growth in ‘problem–solving effectiveness’. We may call this the ‘problem-centric’ view.

The dilemma lies in that it is quite possible that while the knowledge-centric view may indicate progress in a given scientific field the problem-centric perspective may suggest quite the contrary.  An episode from the history of computer science illustrates this dilemma.

Around 1974, computer scientist Jack Dennis proposed a new style of computing he called data flow. This arose in response to a desire to exploit the ‘natural’ parallelism between computational operations constrained only by the availability of the data required by each operation. The image is that of computation as a network of operations, each operation being activated as and when its required input data is available to it as output of other operations: data ‘flows’ between operations and computation proceeds in a naturally parallel fashion.

The prospect of data flow computing evoked enormous excitement in the computer science community, for it was perceived as a means of liberating computing from the shackles of sequential processing inherent in the style of computing prevalent since the mid-1940s when a group of pioneers invented the so-called ‘von Neumann’ style (named after applied mathematician John von Neumann, who had authored the first report on this style). Dennis’s idea was seen as a revolutionary means of circumventing the ‘von-Neumann bottleneck’ which limited the ability of conventional (‘von Neumann’) computers from exploiting parallel processing. Almost immediately it prompted much research in all aspects of computing — computer design, programming techniques and programming languages — at universities, research centers and  corporations in Europe, the UK, North America and Asia. Arguably the most publicized and ambitious project inspired by data flow was the Japanese Fifth Generation Computer Project in the 1980s, involving the co-operative participation of several leading Japanese companies and universities.

There is no doubt that from a knowledge-centric perspective the history of data flow computing from mid-1970s to the late 1980s manifested progress — in the sense that both theoretical research and experimental machine building generated much new knowledge and understanding into the nature of data flow computing and, more generally, parallel computing. But from a problem-centric view it turned out to be unprogressive. The reasons are rather technical but in essence it rested on the failure to realize what had seemed the most subversive idea in the proposed style: the elimination of the central memory to store data in the von Neumann computer: the source of the ‘von Neumann bottleneck’. As research in practical data flow computing developed it eventually became apparent that the goal of computing without a central memory could not be realized. Memory was needed, after all, to hold large data objects (‘data structures’). The effectiveness of the data flow style as originally conceived was seriously undermined. Computer scientists gained knowledge about the limits of data flow, thus becoming wiser (if sadder) in the process. But insofar as effectively solving the problem of memory-less computing, the case for progress in this particular field in computer science was found to have no merit.

In fact, this episode reveals that the idea of the growth of knowledge as a marker of progress in science is trivially true since even failure — as in the case of the data flow movement — generates knowledge (of the path not to take). For this reason as a theory of progress knowledge-centrism can never be refuted: knowledge is always produced. In contrast the problem-centric theory of progress — that a science makes progress if successive theories or models demonstrate greater problem solving effectiveness — is at least falsifiable in any particular domain, as the data flow episode shows. A supporter of Karl Popper’s principle of falsifiability would no doubt espouse problem-centrism as a more promising empirical theory of progress than knowledge-centrism.

Featured image credit: ‘Colossus’ from The National Archives. Public Domain via Wikimedia Commons.

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Laudable mathematics – The Fields Medal

Kicking off the International Congress of Mathematicians 2018 in Rio de Janeiro was this year’s Fields Medal awards ceremony, celebrating the brightest young minds in mathematics. The prize is awarded every four years to up to four mathematicians under the age of 40, and is viewed as one of the highest honours a mathematician can receive.

This year’s recipients come from diverse mathematical backgrounds, spanning the fields of algebraic geometry, number theory, and optimal transport. Honourees in 2018 are:

Caucher Birkar

For the proof of the boundedness of Fano varieties and for contributions to the minimal model program.

Alessio Figalli

For contributions to the theory of optimal transport and its applications in partial differential equations, metric geometry and probability.

Peter Scholze

For transforming arithmetic algebraic geometry over p-adic fields through his introduction of perfectoid spaces, with application to Galois representations, and for the development of new cohomology theories.

Akshay Venkatesh

For his synthesis of analytic number theory, homogeneous dynamics, topology, and representation theory, which has resolved long-standing problems in areas such as the equidistribution of arithmetic objects.

To celebrate the achievements of all of the winners, we’ve put together a reading list of free materials relating to the work that contributed to this honour.

A Quantitative Analysis of Metrics on Rn with Almost Constant Positive Scalar Curvature, with Applications to Fast Diffusion Flows, by Giulio Ciraolo, Alessio Figalli, and Francesco Maggi, published in International Mathematics Research Notices

The authors prove quantitative structure theorem for metrics on Rn that are conformal to the flat metric, have almost constant positive scalar curvature, and cannot concentrate more than one bubble.

The Langlands–Kottwitz approach for the modular curve, by Peter Scholze, published in International Mathematics Research Notices

Scholze shows how the Langlands–Kottwitz method can be used to determine the local factors of the Hasse–Weil zeta-function of the modular curve at places of bad reduction.

The Behavior of Random Reduced Bases, by Seungki Kim and Akshay Venkatesh, published in International Mathematics Research Notices

Kim and Venkatesh prove that the number of Siegel-reduced bases for a randomly chosen n -dimensional lattice becomes, for n→∞ , tightly concentrated around its mean, while also showing that most reduced bases behave as in the worst-case analysis of lattice reduction.

A Note on Sphere Packings in High Dimension, by Akshay Venkatesh, published in International Mathematics Research Notices

An improvement on the lower bounds for the optimal density of sphere packings. In all sufficiently large dimensions, the improvement is by a factor of at least 10,000.

Featured image: Math concept. Shutterstock. 

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