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Originally Published in June 2015 Vol 32 (2) edition of Business Information Review

Revised 11 May 2026. All links checked and verified.

Abstract

A heuristic[1] is a word from the Greek meaning “to discover.” It is an approach to problem solving that takes one’s personal experience into account.  Heuristics provide strategies to scrutinise a limited number of signals and/or alternative choices in decision-making. Heuristics reduce the cognitive effort required to retrieve and store information in memory; they facilitate decision-making by limiting the volume of integrated information needed to make choices or judgments.  However, while heuristics can speed up our problem and decision-making process, they can introduce errors and biased judgments. This paper looks at commonly used heuristics and their human psychology origins. Understanding how heuristics work can give us better insight into our personal biases and influences, and (perhaps) lead to better problem solving and decision making.

Keywords

Heuristic, heuristics, social psychology, psychology, decision-making, problem-solving, information management, knowledge management, im, km,

Introduction

We make decisions and judgments every day – if we can trust someone, if we should do something (or not), which route to take, how to respond to someone’s question, the list is endless. If we carefully considered and analysed every possible outcome of these decisions and judgments, we would never get anything done!

Thankfully, our mind makes things easier for us by using efficient thinking strategies known as heuristics. A heuristic is a mental shortcut that helps us make decisions and judgments quickly without having to spend a lot of time researching and analysing information

Heuristics play important roles in both problem-solving[2] and decision-making[3].

When we are trying to solve a problem or make a decision, we often turn to these mental shortcuts when we need a quick solution.

For example, when walking down the street, you see a workman hauling up a pallet of bricks on a pulley. Without a break in stride, you would likely choose to walk around that area instead of directly underneath the bricks. Your intuition would tell you that walking under the bricks could be dangerous, so you make a snap judgment to walk around the danger zone. You would probably not stop and assess the entire situation or calculate the probability of the bricks falling on you or your chances of survival if that happened. You would use a heuristic to make the decision quickly and without using much mental effort

However, while heuristics can speed up our problem and decision-making process, they can introduce errors and biased judgments. Just because something has worked in the past does not mean that it will work again, and relying on an existing heuristic can make it difficult to see alternative solutions or come up with new ideas.

As a result of research and theorising, cognitive psychologists have outlined a host of heuristics people use in decision-making. Heuristics range from general to very specific and serve various functions. The “price heuristic”, in which people judge higher priced items to have higher quality than lower priced things, is specific to consumer patterns; while the “outrage heuristic”, in which people consider how contemptible a crime is when deciding on the punishment (Shah, & Oppenheimer, 2008). According to Shah and Oppenheimer three important heuristics are the representative, availability, and anchoring and adjustment heuristics. These and other heuristics are discussed in the next section.

Heuristics: The Psychology and Science

System 1 and System 2

“Dual process” theories of cognition (DPT) have been popularised by Daniel Kahneman[4], a Nobel Prize winning behavioural economist, who expounds the theory of “System 1” and “System 2”.  System 1 is fast, automatic, effortless, associative and often emotionally charged, and thus difficult to control or modify; and “System 2,” which is slower, serial, effortful, and deliberately controlled, and thus relatively flexible and potentially rule-governed.

System 1 is essentially an autopilot system in which we do things easier and through repetition. It filters out things in the environment that are irrelevant at that moment; it has high efficiency. It is based on the idea that neurons that fire together, wire together. It primes an idea so that one idea is more easily activated (wakening of associations). System 1 has a bias towards making thinking cheap and enables one to deal with information overload.

System 2 corrects and adjusts the perceptual blindness associated with system 1. It allows flexibility, giving nuance and precision more importance. Basically, system 1 is more associative and intuitive while system 2 is analytical and deliberative.

Availability Heuristic

This is the tendency to judge the frequency or likelihood of an event by the ease with which relevant instances come to mind. It operates on the assumption that if something can be recalled, it must be important or more important than alternative solutions that are not as readily recalled.  A decision maker relies upon knowledge that is readily available rather than examine other alternatives or procedures; as a result, individuals tend to weigh their judgments toward more recent information, making new opinions biased toward that latest news.

In other words, the easier it is to recall the consequences of something, the greater those consequences are often perceived to be.

This is inaccurate because our recall of facts and events is distorted by the vividness of information, the number of repetitions we are exposed to through advertisements on radio and television, and their subsequent familiarity.

Here is an example: a survey conducted in 2010 in the U.S. examined the most feared ways to die.  Six of the top ten results were: terrorist attacks, shark attacks, airplane crashes, murders, natural disasters, and falling.  What do these six results have in common?  Although they are the most “feared”, they are also some of the most unlikely causes of death in the United States.

1. Terrorist attack: Your chances of dying from such an attack is 1 in 9.3million, which is slightly greater than your risk of dying in an avalanche.  Front-page news of terrorist incidents worldwide exacerbates the availability heuristic.
2. Shark attacks: Even if you live near the ocean, your chances of being attacked by a shark are 1 in 11.5 million. In the last 500 years only 1,909confirmed shark attacks occurred worldwide; of the 737 that happened in the United States, only 38 people died. The chances of being killed by a shark are much slimmer than that of a shark attack: 1 in 264.1 million.
3. Airplane crashes: Your chances of being involved in a fatal airline accident are once every 19,000 years.
4. Being murdered: “Worldwide, one person is murdered every 60 seconds!” “Every year, ___ people are murdered.”  However, in the year 2000, about 520,000 people were murdered, compared with 6 million who died of cancer that same year.
5. Natural disaster: The chances of dying in a natural disaster (ex. hurricane, tornado, flood, etc.), is 1 in 3,357.
6. Falling: In 2001, 12,000 people aged 65+ died from a fall.  However, only 80 people die from falling from a tall height annually.

The actual annual leading causes of death in the U.S., the ones that should be feared, are:

• Tobacco usage: 435,000 deaths, 18.1% of total U.S. deaths
• Poor diet/physical inactivity: 400,000 deaths, 16.6%
• Alcohol consumption: 85,000 deaths, 3.5%
• Microbial agents: 75,000
• Toxic agents: 55,000
• Motor vehicle crashes: 43,000
• Incidents involving firearms: 29,000
• Sexual behaviours: 20,000
• Illicit use of drugs: 17,000

Statistically speaking, we will not die in an airplane crash, be eaten by a shark, or fall to our death.  Yet we are all much more scared of these unlikely events than we are of diabetes, heart disease, or traffic accidents.

When individuals are asked to assess the likelihood of an event, they often disregard statistical probabilities and instead consider how readily they can recall a relevant example.  this is how the frequency and probability of events become skewed in our minds: overestimation of subjective probabilities causing overreaction.  Dramatic deaths are more memorable and more exposed by the news, so examples from all different countries are constantly shown – intensifying the effects of this heuristic.

Availability provides a mechanism by which occurrences of extreme utility (or disutility) may appear more likely than they actually are.

Anchoring and Adjustment Heuristic

This is when people estimate an event’s frequency or probability by starting with an anchor and adjusting from it.  

When people make quantitative estimates, their estimates may be heavily influenced by previous values of the item. For example, it is not an accident that a used car salesman always starts negotiating with a high price and then works down. The salesman is trying to get the consumer anchored on the high price so that when he offers a lower price, the consumer will estimate that the lower price represents a good value.

This is also a commonly used heuristic in the property market. Sellers often overvalue their homes and set prices too high for buyers.  In these situations, the asking price is not a good anchor, but it may still play a role in helping buyers decide how much to offer. When the price is too high or too low, the buyer will, of course, offer something different than what is being asked. But the question here is, how much different?

Let’s say that there is a house worth something between £450K and £500K. In one case, the owners ask for £425K in the hope that they will get lots of interest and people will outbid each other. They may receive offers above asking, but how much higher will they go?  In the other case, the owners ask for £525K, perhaps because they bought it when the market was higher and can’t bear to sell for less. This asking price is clearly more than the house is worth, and buyers will offer below asking, but how much below? In both cases, people tend to base their offers on the original asking price and adjust from there.  In both cases, the house is worth the same and people should end up offering somewhere around £475K, but if people do apply the anchoring and adjustment heuristic, in the end, the higher priced house might ultimately go for more. 

The key point about the anchoring heuristic is that different starting points yield different estimates, which are biased toward the initial value or number.

Representativeness Heuristic

The Representative Heuristics is a mental shortcut for decision-making by matching information to our mental stereotypes.  It is the tendency to judge the frequency or likelihood of an event by the extent to which it resembles the typical case. Heavy reliance on this leads people to ignore other factors that heavily influence the actual frequencies and likelihoods, such as rules of chance, independence, and norms.

For an illustration of judgment by representativeness, consider an individual who has been described by a friend as follows: “Steve is very shy and withdrawn, invariably helpful, but with little interested in people, or in the world of reality. A meek and tidy soul, he has a need for order and structure, and a passion for detail.” How do people assess the probability that Steve in engaged in a particular occupation form a list of possibilities (for example, farmer, salesman, airline pilot, librarian, or physician)? … In the representativeness heuristic, the probability that Steve is a librarian, for example, is assessed by the degree to which his is representative of, or like, the stereotype of a librarian[5].

Another Example is the so-called gambler’s fallacy, the belief that runs in good and bad luck can occur. For example, if a coin toss turns up ‘heads’ multiple times in a row, many people think that ‘tails’ is a more likely occurrence in the next toss to “even things out”, even though each toss is an independent event not connected to the toss before or after it.

This heuristic, like others, saves us time and energy. We make a snap decision and assumption without thinking very much. Unfortunately, many examples of the representativeness heuristic involve succumbing to stereotypes or relying on information patterns, as in the sequence of heads and tails.  

Simulation Heuristic

This is the tendency to judge the frequency or likelihood of an event by the ease with which you can imagine (or mentally simulate) an event. You miss-predict the possibility of this happening because of the ease with which we can imagine it. It differs from the availability heuristic because of how previous experience is involved.

For example, would we be more upset at missing our train home by 5 minutes or by 45 minutes?

There is NO difference between the two outcomes, so we might statistically choose that roughly half the people would choose each option. The reality is different. People are much more likely to state they would be upset by missing the train by 5 minutes than by 45 minutes.

This is the simulation heuristic in action. We can visualise and imagine what it would be like to just miss the train. We have a mental script that shows an individual frantically running down the platform and barely missing the train. The scenario is easy to imagine. But we don’t have a mental script for what it is like to miss the train by 45 minutes. We can’t visualise this happening.

Another example is if someone missed winning the lottery by just one number. It is likely that they would be more upset than someone who didn’t have any of the winning numbers. The simulation heuristic explains this discrepancy by suggesting that it would be very easy for the person who “just missed” winning to imagine events being different based on the last number matching their number. As a result, the possibility of them winning seems much more likely and thus the fact that they lost much more disappointing.

As such we make choices based on the ease by which we can imagine examples of scenarios. This mental short cut is the basis of the simulation heuristic

Peak-and-end Heuristic

The peak end rule is a heuristic in which we judge our past experiences almost entirely on how they were at their peak (whether pleasant or unpleasant) and how they ended¹. By doing so, we eliminate nearly all additional information, such as the overall pleasantness or unpleasantness of the experience and its duration.  Individuals tend to recall select aspects of an experience, specifically its most intense point and its conclusion.  The user’s overall impression is not affected by whether most parts of the experience were acceptable.  

An experiment[6] in the application of pain was used to demonstrate this heuristic. The series 2-5-8 and 2-5-8-5 refer to periods of where pain has been applied every 5 minutes to volunteers. Rationally, adding 5 extra minutes of pain will only increase total discomfort, although the experiment showed the longer period of pain (20 minutes), but with a period of diminished discomfort in the end, was rated less discomforting than the shorter period of pain (15 minutes), but with an increased discomfort in the end.

Designing for the peak-end rule is another way of not focusing on what is less important, but about focusing on what brings the most value to the user’s experience. The peak-and-end heuristic is known for how we misremember the past.

Base-Rate Heuristic

The base-rate heuristic is a shortcut for making decisions using probability.  For an example, imagine you live in a big city and hear an animal howling around midnight. You would probably assume it was just a dog, as wolves aren’t likely to be found in the city. Statistically, a wolf howling in the city would be very improbable.

Base rate neglect occurs when people ignore relevant data and misjudge the probability of a situation.

Here is an example. Let’s say you have just met with your doctor who has informed you that you have tested positive for a typically fatal disease.  To make things worse, this test is accurate 95% of the time.  Most people would sadly conclude that there was a 95% chance that they have the disease, a virtual death sentence.  In fact, one would need to know the prevalence of the disease in the general population to determine the actual likelihood that the test was correct.  With a disease prevalence of 1 in 1,000, your chance of actually having the disease after testing is under 2%.   

When this exact problem was given to students at Harvard Medical School, almost half the students computed the likelihood that the patient had the disease was 95%.  The average response was 56%.  Similar studies with practicing physicians have had comparable findings; medical experts are prone to base rate neglect.

Base rate neglect is a fundamental flaw in human reasoning, resulting from our innate weakness in analysing complex probability problems.  It is an example of where our intuitive judgements or instincts can lead us astray.  Knowing base rate neglect and its math helps us make more accurate decisions.

Solution for Medical Test Problem[7]

Let’s assume a model where 100,000 people are tested for the diseased.  Since the disease affects 1 in 1000 people, we would expect 100 people to have the disease.  That means that 99,900 people would not have the disease.  Of those 99,900, 4,995 people would receive a false positive diagnosis.  That compares to only 95 people receiving a correct positive diagnosis.  Therefore, the probability upon receiving a positive test that one actually has the disease is 1.9% (95/4,995).  The table below illustrates the calculation:

Affect-as-information Heuristic.

Have you ever wondered why you feel inclined to go with your gut feelings when making a decision? This isn’t a glitch in your reasoning; in fact, it’s a phenomenon known as the affect heuristic, a mental shortcut that helps you to make decisions quickly by bringing your emotional response (or “affect”) into play. The affect heuristic is a swift, involuntary response to a stimulus that speeds up the time it takes to process information. Researchers have found that if we have pleasant feelings about something, we see the benefits as high and the risks as low, and vice versa. As such, the affect heuristic behaves as a first and fast response mechanism in decision-making

Heuristic processing assumes that affective processing, or emotional processing, occurs outside our awareness, with people simply making sense of their emotional reactions as they happen. As situations become more complicated and unanticipated, mood becomes more influential in driving evaluations and responses. This process is known as the “affect-as-information” (AIM) mechanism. A key assertion of AIM is that the effects of mood tend to be exacerbated in complex situations that demand substantial cognitive processing.

People in a positive mood will interpret the environment as benign. Hence, they process information globally and heuristically. In contrast, those in a negative mood will interpret the environment as problematic and they will process information locally and diagnostically. People tend to use Affect-as-Information heuristic when the evaluation objective is affective in nature, when information is too complex, or when there are time constraints (Clore et al. 1994).

In simple terms, the effect heuristic works as follows: imagine a child who lives with a cuddly collection of well-mannered dogs who comes across a strange dog. Also imagine a second child who was recently bitten severely by the neighbour’s cocker spaniel. The former child will associate dogs with pleasant feelings and will unconsciously judge the risk in saying hello to the new dog as low and the benefit as high. The latter child will associate dogs with fear and pain and will judge the risk in getting close to the strange dog as high and the benefit as low. Without thinking about it, the former will probably approach the dog in question, while the latter will not. Both children display the effect heuristic in action—an involuntary emotional response that influences decision-making[8].

Other Heuristics

The previous sections described the more commonly used heuristics. The following heuristics are also noted, but without detailed descriptions or examples. Included here for completeness.

• “Consistency heuristic” is a heuristic where a person responds to a situation in way that allows them to remain consistent.

• “Educated guess” is a heuristic that allows a person to reach a conclusion without exhaustive research. With an educated guess a person considers what they have observed in the past and applies that history to a situation where a more definite answer has not yet been decided.
• “Absurdity heuristic” is an approach to a situation that is very atypical and unlikely – in other words, a situation that is absurd. This heuristic is used when a claim appears unreasonable or contradicts common sense.
• “Common sense” is a heuristic that is applied to a problem based on an individual’s observation of a situation. It is a practical and prudent approach that is applied to a decision where the right and wrong answers seem relatively clear cut.
• “Contagion heuristic” causes an individual to avoid something that is thought to be bad or contaminated. For example, when eggs are recalled due to a salmonella outbreak, someone might apply this simple solution and decide to avoid eggs altogether to prevent sickness.
• “Working backward” allows a person to solve a problem by assuming that they have already solved it and working backward in their minds to see how such a solution might have been reached.
• “Familiarity heuristic” allows someone to approach an issue or problem based on the fact that the situation is one with which the individual is familiar, and so one should act the same way they acted in the same situation before.
• “Scarcity heuristic” is used when a particular object becomes rare or scarce. This approach suggests that if something is scarce, then it is more desirable to obtain.
• “Rule of thumb” applies a broad approach to problem solving. It is a simple heuristic that allows an individual to make an approximation without having to do exhaustive research.

Summary and Conclusion

We make hundreds, if not thousands of decisions each day, from what shirt to wear, to which train to catch to work, what to read, who to smile at, or who to ignore. Some decisions are limited to our own actions; some decisions may affect other people, either directly or implicitly. For example, we might make a decision about who to assign to a particular project – which will directly affect that person and their subsequent behaviour. Alternatively, the fact that we did not acknowledge a smile or greeting from someone – even though we saw it – may affect the subsequent behaviours and attitudes of that person (e.g. to stereotype you as indifferent and not to greet you the next time they see you).

Many of these decisions are made intuitively, or to be more precise, using one or more of the heuristics previously described. We don’t consciously choose a heuristic; it presents itself according to the context and environment we populate. The need to process large amounts of information in minimal time will draw upon those heuristics that we’ve previously relied upon. Remarkably, even without thorough cognitive analysis of every piece of data, we usually make the correct decision.  For example, there are masses of information reaching our senses before we cross the road. The distance and estimated speeds of cars and cyclists, the presence of other people who may or may not be obstructing our progress, etc. The fact that we got to the other side of the road safely justified our decision to cross when we did.

Most people rarely face life-altering decisions, but if we did, we would want to thoroughly analyse all available data and ensure our interpretations are accurate.  But what if that time is not available? For example, the surgeon undertaking a routine operation where the patient suddenly goes into cardiac arrest. Heuristics will enable the surgeon to instantly make – in all probability – the right decisions, e.g. the availability heuristic that enables recall of similar circumstances and the associated decisions. But at the same time, relying on precedent and recall will sometimes trip us up when we come across the unique.

This article merely touches on some of the social psychology research that underpins how we make decisions. Being aware of how we make decisions gives us some insight into the inherent biases we might use – often unconsciously – in making those decisions. In particular, the tendencies towards stereotyping, prejudice and discrimination.

The key point of this article is to raise awareness that while heuristics can speed up our problem and decision-making process, they can also introduce errors and biased judgments. Just because something has worked in the past does not mean that it will work again, and relying on heuristics can make it difficult for us to see alternative solutions or come up with new ideas.

We will always tend towards saving our brains from doing any extra work in cognitively analysing vast amounts of data if we think there is a shortcut to the answer. Heuristics usually make this process effective, but the risks of errors must be considered.  

Perhaps heuristics is best summed up by this quote from Daniel Kahneman[9]

“This is the essence of intuitive heuristics: when faced with a difficult question, we often answer an easier one instead, usually without noticing the substitution.”

Perhaps it would do us all good to ponder, now and again, how we came to a particular decision, and whether, on reflection it was the right one!

References and Further Reading

Cervone, D., & Peake, P. K. (1986). Anchoring, efficacy, and action: The influence of judgmental heuristics on self-efficacy judgments and behavior. Journal of Personality and Social Psychology, 50, 492-501

Chapman, G. B., & Bornstein, B. H. (1996). The more you ask for, the more you get: Anchoring in personal injury verdicts. Applied Cognitive Psychology, 10, 519-540

Chapman, L.J., 1967. “Illusory correlation in observational report”. Journal of Verbal Learning 6: 151–155.

Chapman, G. B., & Johnson, E. J. (1994). The limits of anchoring. Journal of Behavioral Decision Making, 7, 223-242

Carroll, J.S., 1978. … an event on expectations for the event: An interpretation in terms of the availability heuristic. Journal of Experimental Social Psychology.

Clore, G.L., Schwartz, N., and Conway, M. 1994. Affective causes and consequences of social information processing. Handbook of social cognition.

Epley, N., Keysar, B., Van Boven, L., & Gilovich, T. (2004). Perspective taking as egocentric anchoring and adjustment. Journal of Personality and Social Psychology, 87, 327-339.

Folkes, V.S., 1988. The Availability Heuristic and Perceived Risk. Journal of Consumer Research.

Gabrielcik, A. and Fazio, R.H., 1984. Priming and frequency estimation: A strict test of the availability heuristic. Personality and Social Psychology Bulletin.

Gilovich T., Griffin, D.W., Kahneman, D., 2002. Heuristics and Biases: The Psychology of Intuitive Judgment. Cambridge University Press

Jacowitz, K. E., & Kahneman, D. (1995). Measures of anchoring in estimation tasks. Personality and Social Psychology Bulletin, 21, 1161-1166.

Kahneman, D., 2003. Maps of Bounded Rationality: Psychology for Behavioral Economics.

Katsikopoulos, K.V., 2010. Psychological Heuristics for Making Inferences: Definition, Performance, and the Emerging Theory and Practice. https://eprints.soton.ac.uk/428882/

Mussweiler, T., & Strack, F. (1999). Hypothesis-consistent testing and semantic priming in the anchoring paradigm: A selective accessibility model. Journal of Experimental Social Psychology, 35, 136-164.

Mussweiler, T., & Strack, F. (2000). Numeric judgments under uncertainty: The role of knowledge in anchoring. Journal of Experimental Social Psychology, 36, 495-518.

Northcraft, G. B., & Neale, M. A. (1987). Experts, amateurs, and real estate: An anchoring-and-adjustment perspective on property pricing decisions. Organizational Behavior and Human Decision Processes, 39, 84-97

Quizlet – Social Psychology Heuristics https://quizlet.com/7346737/social-psychology-heuristics-flash-cards/

Schwarz, N., et al., 1991. Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology.

Schwarz, N. and Vaughn, L.A., 2002. The availability heuristic revisited: Ease of recall and content of recall as distinct sources of …. Heuristics and biases: The psychology of intuitive judgment.

Shedler, J. and Manis, M., 1986. Can the availability heuristic explain vividness effects. Journal of Personality and Social Psychology.

Strack, F., & Mussweiler, T. (1997). Explaining the enigmatic anchoring effect: Mechanisms of selective accessibility. Journal of Personality & Social Psychology, 73, 437-446.

Tversky, A. and Kahneman, D., 1973. Availability: A heuristic for judging frequency and probability. Cognitive Psychology.

Waenke, M., Schwarz N., and Bless, H., 1995. The availability heuristic revisited: Experienced ease of retrieval in mundane frequency estimates.Acta Psychologica.

Wansink, B., Kent, R. J., & Hoch, S. J. (1998). An anchoring and adjustment model of purchase quantity decisions. Journal of Marketing Research, 35, 71-81.

Wegener, D. T., Petty, R. E., Detweiler-Bedell, B. T., Jarvis, W., & Blair G. (2001). Implications of attitude change theories for numerical anchoring: Anchor plausibility and the limits of anchor effectiveness. Journal of Experimental Social Psychology, 37, 62-69.

Wilson, T. D., Houston, C. E., Etling, K. M., & Brekke, N. (1996). A new look at anchoring effects: Basic anchoring and its antecedents. Journal of Experimental Psychology: General, 125, 387-402.

Wright, W. F., & Anderson, U. (1989). Effects of situation familiarity and financial incentives on use of the anchoring and adjustment heuristic for probability assessment. Organizational Behavior and Human Decision Processes, 44, 68-82

Author Biography

Stephen Dale is a freelance community and collaboration ecologist with experience in creating off-line and on-line environments that foster conversations and engagement. He actively promotes and uses collaborative technologies and social media to enhance personal learning and growth. He also offers training sessions and masterclasses on leveraging social technologies and networks to boost digital literacy.

Profile: https://collabor8now.com/about-2/

Twitter: @stephendale

Notes

All web links checked on 11 May 2026

[1] Definition of heuristic: https://www.yourdictionary.com/heuristic

[2] Problem Solving: https://www.nu.edu.ye/wp-content/uploads/2014/05/Davidson-Sternberg-The-Psychology-of-Problem-Solving-2003.pdf

[3] Decision-making: https://sk.sagepub.com/book/mono/preview/the-psychology-of-decision-making-2e.pdfhttps://sk.sagepub.com/book/mono/preview/the-psychology-of-decision-making-2e.pdf

[4] Maps of Bounded Rationality: Psychology for Behavioral Economics. Kahneman Daniel, https://econweb.ucsd.edu/~jandreon/Econ264/papers/Kahneman%20AER%202003.pdf

[5] Amos Tversky & Daniel Kahneman, “Judgment Under Uncertainty: Heuristics and Biases,” https://www.science.org/doi/abs/10.1126/science.185.4157.1124

[6] Peak end rule experiment: https://ui-patterns.com/patterns/Peakend-rule

[7] Jumping to conclusions, Base Rate Neglect: https://www.simplypsychology.org/base-rate-fallacy.html

[8] The Affect heuristic: https://thedecisionlab.com/biases/affect-heuristic: https://thedecisionlab.com/biases/affect-heuristic

[9] Daniel Khaneman, Israeli-American psychologist: https://www.wikiwand.com/en/Daniel_Kahneman

I was asked by Managing Partners Forum (MPF) recently to give a brief overview of the current status and industry trends in Big Data and Data Analytics, topics I’ve been keeping an eye on for several years. The slides are available on Slideshare. The following is shortened abstract from the presentation.

One of the issues I have with with Big Data is just that — the term “Big Data”. It’s fairly abstract and defies a precise definition. I’m guessing the name began as a marketing invention, and we’ve been stuck with it ever since. I’m a registered user of IBM’s Watson Analytical Engine, and their free plan has a dataset limit of 500MByte. So is that ‘Big Data’? In reality it’s all relative. To a small accountancy firm of 20 staff, their payroll spreadsheet is probably big data, whereas the CERN research laboratory in Switzerland probably works in units of terabytes.

Eric Schmidt (Google) was famously quoted in 2010 as saying “There were 5 exabytes of information created between the dawn of civilisation through 2003, but that much information is now created in 2 days”. We probably don’t need to understand what an ‘exabyte’ is, but we can get a sense that it’s very big, and what’s more, we begin to get a sense of the velocity of information, since according to Schmidt it’s doubling every 2 days, and probably less than that since we’ve moved on by 6 years since his original statement.

It probably won’t come as a surprise to anyone that most organisations still don’t know what data they actually have, and what they’re creating and storing on a daily basis. Some are beginning to realise that these massive archives of data might hold some useful information that can be potentially deliver some business value. But it takes time to access, analyse, interpret and apply actions resulting from this analysis, and in the mean-time, the world has moved on.

According to the “Global Databerg Report” by Veritas Technologies, 55% of all information is considered to be ‘Dark’, or in other words, value unknown. The report goes on to say that where information has been analysed, 33% is considered to be “ROT” — redundant, obsolete or trivial. Hence the ‘credibility’ gap between the rate at which information is being created, and our abilities to process and extract value from this information before it becomes “ROT”.

But the good news is that more organisations are recognising that there is some potential value in the data and information that they create and store, with growing investment in people and systems that can make use of this information.

PwC Data Analytics emphasises the need for companies to establish a data-driven innovation culture — but there is still some way to go. Those using data and analytics are focused on the past, looking back with descriptive (27%) or diagnostic (28%) methods. The more sophisticated organisations (a minority at present) use a forward-looking predictive and prescriptive approach to data.

What is becoming increasingly apparent is that C-suite executives who have traditionally relied on instinct and experience to make decisions, now have the opportunity to use decision support systems driven by massive amounts of data. Sophisticated machine learning can complement experience and intuition. Today’s business environment is not just about automating business processes — it’s about automating thought processes. Decisions need to be made faster in order to keep pace with a rapidly changing business environment. So decision making based on a mix of mind and machine is now coming in to play.

One of the most interesting bi-products of this Big Data era is ‘Machine Learning’ — mentioned above. Machine learning’s ability to scale across the broad spectrum of contract management, customer service, finance, legal, sales, pricing and production is attributable to its ability to continually learn and improve. Machine learning algorithms are iterative in nature, constantly learning and seeking to optimise outcomes. Every time a miscalculation is made, machine learning algorithms correct the error and begin another iteration of the data analysis. These calculations happen in milliseconds which makes machine learning exceptionally efficient at optimising decisions and predicting outcomes.

So, where is all of this headed over the next few years? I can’t recall the provenance of the quote “never make predictions, especially about the future”, so treat these predictions with caution:

1. Power to business users: Driven by a shortage of big data talent and the ongoing gap between needing business information and unlocking it from the analysts and data scientists, there will be more tools and features that expose information directly to the people who use it. (Source: Information Week 2016)
2. Machine generated content: Content that is based on data and analytical information will be turned into natural language writing by technologies that can proactively assemble and deliver information through automated composition engines. Content currently written by people, such as shareholder reports, legal documents, market reports, press releases and white papers are prime candidates for these tools. (Source: Gartner 2016)
3. Embedding intelligence: On a mass scale, Gartner identifies “autonomous agents and things” as one of the up-and-coming trends, which is already marking the arrival of robots, autonomous vehicles, virtual personal assistants, and smart advisers. (Source: Gartner 2016)
4. Shortage of talent: Business consultancy A.T. Kearney reported that 72% of market-leading global companies reported that they had a hard time hiring data science talent. (Source: A.T Kearney 2016)
5. Machine learning: Gartner said that an advanced form of machine learning called deep neural nets will create systems that can autonomously learn to perceive the world on their own. (Source: Ovum 2016)
6. Data as a service: IBM’s acquisition of the Weather Company — with all its data, data streams, and predictive analytics — highlighted something that’s coming. (Source: Forrester 2016)
7. Real-time insights: The window for turning data into action is narrowing. The next 12 months will be about distributed, open source streaming alternatives built on open source projects like Kafka and Spark. (Source: Forrester 2016)
8. Roboboss: Some performance measurements can be consumed more swiftly by smart machine managers aka “robo-bosses,” who will perform supervisory duties and make decisions about staffing or management incentives. (Source: Gartner 2016)
9. Algorithm markets: Firms will recognize that many algorithms can be acquired rather than developed. “Just add data”. Examples of services available today, including Algorithma , and Kaggle (Source: Forrester 2016)

The one thing I have taken away from the various reports, papers and blogs I’ve read as party of this research is that you can’t think about Big Data in isolation. It has to be coupled with cognitive technologies — AI, machine learning or whatever label you want to give it. Information is being created at an ever-increasing velocity. The window is getting ever narrower for decision making. These demands can only be met by coupling Big Data and Data Analytics with AI.

Manufacturing has been at the forefront of using AI and robots to improve productivity. Robots don’t get tired, they don’t need meal-breaks or holidays and can work 24 x 7 if required. They also offer more accuracy and precision than human workers, but as yet, not agility. However, recent advances are freeing robots from their cages and enabling humans to work alongside them. For example, the robot providing the strength and precision to insert the dashboard console into the car, then moving away while the human does any fiddly bits requiring agility. Welcome to the world of ‘cobots‘.

Yes, manufacturing has come a long way in the past 60 or so years from where the first picture (below was) taken.

Can we say the same about office work? The left-hand picture below is a typical 1950’s office environment. What has changed when compared to the current office environment? Yes, we have computers on our desks now, but not much else has changed in almost 70 years.

Ah, I hear you say, what about email? Or desktop applications, such MSOffice and CRM? Harbingers of the paperless office! Have you seen one? No, nor me. Do these really represent the best office innovations of the past 70 years? Unfortunately it would appear so. The process for updating a credit & debit spreadsheet is the same now as it was 70 years ago, when accountants were manually filling in ledgers. Routine, repetitive and robotic.

There is much talk about ‘digital transformation‘, an IT term which means nothing to most office workers, and even less in terms of how this will change or optimise a business process. More recently, we hear about agile and flexible working, and the confusion about the two. At least these do offer some benefits in terms of process optimisation and the workplace environment (including working from home), but they do little in terms of changing the actual work.

So, what’s the answer? I think we’ve already seen it in the way that manufacturing has grasped and utilised AI and robotics over past 10-15 years. This doesn’t mean having a physical robot in the office, but rather looking at business processes in terms of what a machine is good at (speed, repetition, accuracy) and what a human is good at (decision making, handling exceptions, social interactions, collaboration, knowledge sharing) and intelligently combining the two.

In other words, don’t make a human do robotic tasks, and don’t expect a robot to act like a human!

I don’t want to invent a lexicon, but I call this ‘Intelligent Automation’, and is combination of Robotic Process Automation (RPA) and Artificial Intelligence (AI). I note vendors in this space refer to this as creating a ‘digital workforce’, which (IMHO) is as meaningless as ‘digital transformation’.

RPA is a software robot that mimics human actions; AI is the simulation of human intelligence by machines. Put another way, RPA is associated with doing whereas AI and machine learning (ML) are concerned with thinking and learning, respectively. According to Grand View Research, the RPA market is projected to be worth over $3 billion by 2025, and industries like health care, telecommunications and manufacturing have all invested in RPA technology.

Typically, processes that are ready for RPA/AI can be defined as:

• Having high volume transactions or keyboard activities
• Part of a defined workflow
• Being error-prone due to manual entry
• Being speed-sensitive with the possibility of causing delay to other processes
• Needing more than one system (e.g. requiring dual data entry)
• Requiring actions such as searching, collating, updating, matching or comparing
• Abstracting information from unstructured data (e.g. emails)
• Needing irregular labour resources (e.g. low and peak periods)

But what about the human? The office (knowledge) worker remains a fundamental part of the process, but is no longer doing repetitive or robotic tasks. Not everything in life is predictable, and the same goes for business processes. There will be issues and exceptions which require problem solving and interactions with customers or other employees. The difference now is humans will have more time to do these things, and to adapt and improve their skills and knowledge. Intelligent automation is the enabler for the new augmented workforce.

The office revolution is coming – it’s a shame that it’s taken almost 70 years to get here!

By its very definition, PKM is a personal journey of enlightenment. It requires the individual to recognise the benefits of KM processes and skills within the context of personal development. The journey starts by having an awareness of what KM is, and how it can be of personal benefit, a ‘what’s in it for me’ mindset. This is Level 1 on the PKM maturity model described below. The idea of the model is to both encourage and measure personal progress up the various tiers to Level 5.

I would advise against using this as a management or HR tool to use as stick to beat employees with as part of annual performance appraisals, though of course it could be used that way. In my opinion, it’s likely to be more effective if the knowledge worker embraces this as a their own personal development plan. I can almost guarantee that if/when they are at, or close to Level 5, they will be noticed by their colleagues and their managers and will be rewarded appropriately. How could they not be? They will be demonstrating all of the skills of the consummate knowledge management professional!

I would posit that the success of any organisational knowledge management strategy (OKM) will depend on a PKM component. In my experience this is quite often overlooked.

The Institute for the Future at the University of Phoenix Research Institute published  Future Work Skills 2020, a study designed to look at how we think about work, what constitutes work, and the skills we will need to be productive over the coming decade. The report was published in 2011, but is as relevant today as it was 10 years ago. The skills they identified include:

1. Sense-making: the ability to determine significance.
2. Social intelligence: the ability to connect with others in a deep way.
3. Adaptive thinking: the ability to come up with novel solutions.
4. Cross-cultural competency: the ability to operate in new contexts.
5. Computational thinking: the ability to think abstractly and make data-driven decisions.
6. New media literacy: the ability to assess new media critically and use it appropriately.
7. Transdisciplinarity: the ability to understand concepts across a wide range of disciplines.
8. Design mindset: the ability to understand how the physical environment impacts thinking and make conscious choices in using it.
9. Cognitive load management: the ability to turn information into knowledge.
10. Virtual collaboration: the ability to be a productive part of a virtual team.

Several of these – sense-making, new media literacy, transdisciplinarity, and cognitive load management – are particularly critical for successful knowledge curation.

Curation comes in many forms, from crowd-sourced lists and aggregated collections to authored stories that provide new and insightful perspectives on previously published material. Content curation can be used as a personal competency, to help us develop our understanding of this information rich, time poor world we live in. Relevant and decision-ready information is becoming a valuable commodity in its own right, and many organisations are beginning to appreciate the roles and skills of people who understand the specific information needs of their information consumers and can provide it in a timely and effective manner.

The role of the curator has been valued for centuries, but it has been traditionally associated with the professionals who practice their art in the confines of the world’s museums and galleries. To suggest that digital curators all bring the same depth and breadth of knowledge as a professional curator might be stretching a point. But there are more similarities than differences. Curation is all about creating value from collections – which can be physical things such as art exhibitions or museum artifacts; or digital content, such as music playlists, website reviews of best TV buys, or a collection of the best educational videos. Curators know that the sum of an experience can be greater than the individual parts. And you don’t always have to be an expert to tell a decent story.

If truth be told, most of us are digital curators, we just don’t know it. The difference is whether we’re doing it for the benefit of ourselves, or for others. The latter requires the skills listed above. This is ‘ the future of work‘ and the people who do this effectively are increasingly valued in today’s complex world.

Whether you are a Librarian, Information Professional or Knowledge Manager, titles which don’t really explain the role, make sure you tout the fact that your skills include knowledge curation, and maybe use the above list to help others understand the nature and value of the work you do.

Subsequent to writing this blog post, I’ve been made aware of an excellent article on this same topic, which goes into a lot more details than I have here. Check out “How to Overcome Information Overload: Complete Guide 101″ Well worth a read.

Artificial Intelligence (AI) has brought a true revolution in many aspects of human activities. Every day, we learn about a new industry niche that has successfully integrated this technology in its daily business operations. In this post, we will briefly explain why information management is not an exception to this rapidly growing trend. As you will see, implementing AI in this sector will soon become a necessity for successful business operations. Companies that fail to do this will undoubtedly lose out to competitors who will become much more efficient by adopting it.

What is AI

Simply put, AI includes processes such as learning, reasoning (using the rules to reach approximate or definitive conclusions), and self-correction. AI technology is focused on continuous improvement by learning from previous experiences on a trial-error basis.

Even though AI hasn’t evolved to the Terminator-movies level yet, it is helping us to automate as many daily tasks as possible. Smart devices such as Amazon Echo or smartphones with built-in smart assistants make our lives easier.

How Could AI Help with Information Management?

Traditionally, information management tasks used to be downright challenging. Checking, analyzing, and managing information manually can be time-consuming and tedious, especially big data. If you are a company that depends on such analyses, you probably agree that these things could be quite demanding.

However, thanks to this technology, these issues will become things of the past. First of all, it could automate a lot of complex tasks. For example, if you are into events management, you might have thousands of records you have to take into account to create an event calendar or agenda.

AI-powered systems are now able to go through all that information, sort it out according to the predefined criteria, delete what is outdated or irrelevant, and then create the agenda. Of course, the technology still might be in its early days and incapable of producing top-notch results, but at least it could save you some time by choosing the relevant information.

Secondly, the AI systems could perform much better than humans when it comes to pairing the relevant information with specific customers. Let’s say you run an email marketing campaign and want to send a promotional message on your latest product to a specific group of customers. Browsing manually through the database is not going to work, especially if you have thousands of subscribers. AI comes to the rescue once again. It will easily match your offer with the appropriate audience, without you having to interfere with the process.

As you can see, AI implementation in the information management has a bright future. For more information on the subject, check out the infographic that follows:https://techjury.net/blog/ai-stats/

1. Ensure you have community facilitator/community manager with strategic oversight for the platform and business goals. It takes a lot of time and effort to convert a new community member into an active community member. The community manager’s role is to build relationships with and between members, initiate discussions, prompt members to participate and slowly increase their interest. Relevant skills for a manager/facilitator are:

• Engagement & People Skills
• Content Development Skills
• Business & Strategic Skills
• Technical Skills

2. Focus on the people and not the technology. Keep the technology simple to begin with, e.g. threaded conversations, themed groups, polls and integration with corporate email (e.g. for activity updates). The most important aspect is making it easy for members to find and connect with each other.

3. Start slow and think small. The community needs to work small before it can work big. Community building is a slow process of stimulating interactions, building relationships and fostering social capital. It begins small and grows with time, at its own pace. It can take anything between 3 to 12 months to get the community to a stage where it’s generating its own discussions/content.

4. Consider having a low-key community launch workshop. Don’t make this a big publicity promotion – it may raise unrealistic expectations.

5. Be prepared to devote time and effort in attracting the first 50 members. Start with personal invitations to key contacts. Involve this “founder” group in strategic planning for the platform and communities and encourage them to be advocates, e.g. ask them for promotional ideas to help launch the community. Will they be willing to tell their friends about the community? Who do they know with an interest in the topic?

6. Start with as few themed groups as possible. Too many often means it will be hard to create a community feel; conversations will be diluted and stretched too thin. It’s difficult for members to get to know each other if you’ve got dozens of different groups with little or no activity.

7. Focus, encourage and nurture active members. Be aware that members are fickle beasts. They may check things out, but it takes a lot of personal attention to keep them interested. Otherwise they come, browse around for a few seconds and then vanish back into the corporate wilderness.

8. Welcome newcomers to the platform. Make them feel wanted.

9. Reach out consistently to new members; ask them what they want from their community rather than enforcing an environment that the business wants.

10. Don’t control the conversations. Let the users decide what is important and in their own voice.

11. Don’t set unrealistic or meaningless management targets. Getting people to register is relatively easy – if that’s a goal. Better to measure numbers and growth rates of active members.

12. Once the community is up and running, consider having regular community events, for example:

• Webinars on a key topic of interest.
• Themed and facilitated discussions. A community could join a live chat room to participate in a themed discussion. This discussion will have a set topic.
• Weekly Interview. Similar to the themed discussion, this is a weekly interview with
a VIP or “expert” on a particular topic.
• Competitions/Challenges. The community may host a regular competition or challenge for members to participate in.
• Newcomer orientation. Once a month, a community may host a day to welcome new arrivals in the community. This can involve regular members introducing themselves, setting up some basics threads for newcomers to participate in and help teach members about the culture of the community.
• Product-Launches. A community might celebrate the launch of a new product or service.
• Member achievements. The community may also celebrate the achievements of a member. This might be about a promotion, the birth of a child, a marriage, etc.

Next Steps

Collabor8now Ltd can support and assist community building projects  in the following areas:-

A facilitated community launch workshop, to include:

• Developing a community plan
• Communications and promotion
• Key roles and responsibilities
• Developing a community charter

Community Manager Training

A one-day event, tailored to specific customer requirements.

For more information, contact steve.dale@collabor8now.com

The debate about the various merits of taxonomies vs. folksonomies will probably continue long after I’ve departed this mortal life; it’s the lifeblood of dyed-in-the-wool information professionals. Based on experience I’ve had in implementing enterprise search solutions, users presented with either a taxonomic organisation of content vs. doing a keyword or free-text search for what they are seeking, the vast majority of users will choose a free-text search. The reason being that users don’t want to spend valuable time trying to understand the taxonomy, and particularly where the new breed of search engine is able to return relevant results AND cater for the serendipitous nature of some search queries. Interestingly, Verity (since bought by Autonomy, which was itself bought by Hewlett Packard….are you keeping up?!) had developed a collaborative taxonomy facility for their K2 search engine, where common terms could be identified for taxonomy labels. It sounds to me that they had recognised the limitations of the inflexible top-down taxonomy approach and were heading towards the realms of folksonomies. A strategy since adopted by nearly all of the current batch of enterprise search engines.

David Weinberger once said:

Folksonomies are not only frequently more useful than top-down taxonomies; they better reflect the bottom-up, messy, ambiguous, inconsistent, social nature of meaning, despite Aristotle and the tradition his genius spawned.

I couldn’t have put it better myself!

We probably don’t need to understand what an ‘exabyte’ is, but we can get a sense that it must be pretty big, and that it’s not just about size; there’s a component of velocity involved. If the rate of information was doubling every 2 days in 2010, we can be pretty sure it’s something less than 2 days now.

Most organisations still don’t know what data or information they actually have, and what they’re creating and storing on a daily basis, with one survey revealing that 52% of all information is considered to be ‘Dark’, where value is unknown, and 33% of information is considered to be redundant, obsolete or trivial (ROT). According to Veritas, left unchecked, hoarded data on this scale could mean that companies in Europe face $891bn of avoidable storage costs globally by 2020.

On a positive note, more and more organisations are beginning to realise these massive archives of data might actually hold some useful information that could potentially deliver new business opportunities. The problem is, without the right technology, it takes time to access, analyse, interpret and make decisions on these vast data repositories. In the mean-time, the world – and more agile business competitors – have moved on.

Paradoxically, there remains a fundamental scepticism about the practical use of data to drive the business. The explosion of data, new analytics techniques and machine learning have combined to create a degree of uncertainty about data driven decision making. Organisations are beginning to reflect on whether they are working with the right data; whether they can rely on algorithms to make decisions, or whether they are thinking the right way about using data to compete.

But the trend is clear. More and more organisations are changing, or plan to change the big decision making process because of big data, new analytics and use of AI, and specifically machine learning. Machine learning has the ability to scale across a broad spectrum of structured, semi-structured and unstructured data, sourced from, for example, contract management, customer service, finance, legal, sales, pricing and production. Machine learning algorithms are iterative in nature, constantly learning and seeking to optimise outcomes.  Every time a miscalculation is made, machine learning algorithms correct the error and begin another iteration of the data analysis. These calculations happen in milliseconds which makes machine learning exceptionally efficient at optimising decisions and predicting outcomes.

Executives who once relied firmly on their intuition and experience are now face-to-face with machines that can learn from massive amounts of data. It is changing people’s relationships with technology and opening the door to truly data-driven decision-making.

It is reasonable to assume that management intuition and experience will remain critical for interpreting the results, even though there is ample evidence to show that decisions made by humans can be inherently biased. For example, confirmation bias might lead executives to cherry-pick data that supports their viewpoint; or they might discard data that contradicts their gut feeling. They can’t see what lies within the data.

The challenge going forward is for C-suite executives and managers to integrate these two factors, to find a new mix of mind and machine. One thing is for certain, we’re going to have to start trusting the machines and the algorithms if we ever want to extract real value from big data and make decisions with the increasing velocity that competitive business requires!

I’ll be covering these points in more detail at the Ark Group Digital Workplace event on the 1st December 2016.

Steve Dale

Collabor8Now Ltd.

In his book “Knowledge Management for Teams and Projects”, Nick Milton says:

”Each project and each stage of each project, will need to import knowledge. Knowledge will be needed to deliver the project stage, and the tasks and activities within that stage. The project team will need to learn before they start, in order to access the knowledge they need. As the project stages continue, the project team will learn as they go. Some processes are needed for capturing that knowledge, and taking action to improve team performance based on the new knowledge. At the end of each project stage, the new knowledge that has been gained needs to identified and captured, for passing on to the next stage, or for passing onto other projects. At each stage therefore, the knowledge needs to be imported, gathered, applied, captured, documented and exported”.

Knowledge management (KM) encourages this learning at every opportunity. The concept is about learning before, during and after, and can be applied to any activity including projects, tasks and events.

This means that when you start a new piece of work, you should check if anyone has done something similar before and if there is anything you can learn from them. Then while you are working on that piece of work there should be a stage (or several) where you reflect on what you have done so far to help you decide how to continue forward. This brings you to the final stage of learning, when you have completed the piece of work. It is recommended that you review the work so that if you or someone else were to do a similar piece of work again, the approach and desired outcomes can be improved following your review.

The table below is a useful guide showing which KM tools and techniques can be applied before, during and after.

If you’re not too sure about the processes for each of the tools and techniques mentioned above, check out this free resource originally developed by the Improvement & Development Agency (long gone, but its heritage remains!)