Thinking, Fast and Slow by Kahneman

• Thinking, Fast and Slow is an internationally renowned book written for anyone interested in personal well-being and human development.
• The book gives a schematic explanation of how people can make better choices by describing how our thinking interprets past, future, and present situations in correct and incorrect ways.

‘Thinking, fast and slow’, a New York Times bestseller and multiple prize winner, is a highly praised book that provides us many insights into the human mind. It is seen as a guide to detecting and correcting our biased misunderstandings of the world. We see ourselves as rational thinkers and decision makers, but this book demonstrates how we are subject to many systematic errors, which we are not even aware of or tend to ignore.

The author, Daniel Kahneman, won the Nobel Prize in economics in 2002. Kahneman collaborated with psychologist Amos Tversky until his death in 1996, they together produced a fair amount of the research mentioned in this book. He brings in his own research, as well as that of other renowned experts (psychologists, economists, statisticians). Kahneman (1934) is Eugene Higgins Professor of Psychology Emeritus at Princeton University and Professor of Psychology and Public Affairs Emeritus at Princeton’s Woodrow Wilson School of Public and International Affairs. Kahneman is considered one of the greatest psychologists. He provides new insights into the understanding of risk, cognitive psychology, behavioral economics, the study of well-being and happiness and the analysis of reason and rationality. It is not only an important read for people who want to learn about human behaviour, the findings are valuable to investors, businesses, consumers, teachers, physicians, politicians, marketers and many others.

The book focuses mostly on biases of intuition. Kahneman’s aim is improving the ability to identify and understand errors of judgment and choice, particularly in ourselves, by presenting a view of how the mind works that draws on recent developments in social and cognitive psychology (as opposed to many authors who discuss earlier research). One of these developments is the better understanding of the flaws and wonders of intuitive thought.

The book is mainly about the distinction between fast and slow thinking, which Kahneman refers to as ‘System 1’ and ‘System 2. “Thinking, fast and slow’ is divided into five parts. Parts 1 discusses the basis elements of this two-system approach to judgment and choice. It demonstrates the distinction between System 1 (automatic operations) and System 2 (controlled operations) and how associative memory, the heart of System 1, consistently creates a coherent interpretation of what is happening around us. This automatic process underlies intuitive thinking and explains the heuristics of judgment. Part 2 is about new insights into judgment heuristics and why it is so hard to think statistically in contrary to thinking associatively. Part 3 demonstrates a significant limitation of our mind: the excessive confidence we have in what we believe we know and our inability to acknowledge

How do System 1 and System 2 deal with effortful tasks? - Chapter 2

System 2 is defined by its effortful operations, although it is also lazy: it puts in no more effort than needed. Some crucial tasks can only be performed by System 2, because they require self-control and effort to overcome the impulses and intuitions of System 1.

The ‘Add-1’ task puts our System 2 to work and demonstrates the limits of our cognitive abilities within seconds. If you truly want to challenge yourself, try Add-3. Your body will also react to the mental work. Eckhard Hess, psychologist, described pupils as windows to the soul. He found that the pupils indicate the level of mental effort: they dilate more if people have to solve more difficult problems. Kahneman set up an experiment to study the reaction of pupils while the participant performed paced tasks. The pupils got wider as the tasks got more demanding. The Add-1 task demonstrated how longer strings of numbers caused bigger pupils. When performing the Add-3 task, the pupils got 50% bigger and the heart rate increased. This is the maximum of mental effort, people give up if the task gets more demanding than Add-3 (the dilating of the pupils stopped). The pupils had a constant normal size when the participant was chatting to someone else during a break. Engaging in small talks and easy tasks are deemed effortless, while tasks as Add-1 and Add-3 are extremely effortful. This can be compared to walking in a natural pace and sprinting. When we are (mentally) sprinting, we could become effectively blind, like the Invisible Gorilla experiment demonstrated. 

System 2 has limited capacity. It responds to threatened (mental) overload by protecting the most important activity. That activity gets the attention it needs. Remaining capacity will be divided to other tasks. An experiment regarding the detection of the letter K as a ‘side task’ showed that the observes failed when the main task was highly demanding.

The allocation of attention has always played an important role in our evolution. The ability to orient and respond rapidly to sudden threats or great opportunities was needed to survive, which we also recognize in the animal world. Even now, System 1 is activated when an emergency occurs and fully focuses on self-protection. We respond to a sudden threat before we are fully aware of it.

Brain studies have demonstrated that the degree of activity needed for an action changes as we become more skilled. An increase in skills resulted into the involvement of fewer brain regions. The same goes for talent: the brain activity and pupil size of highly intelligent people show they need less effort to successfully complete the same task. Law of least effort: if we have several options for achieving a goal, we choose the least demanding one. It is human nature to be lazy.  

Only System 2 is

How does the 'associative machinery' of fast thinking work? - Chapter 4

Read the following words:

“Mango”                              “Puke”

In a few seconds you will experience appalling images and pull a disgusted face. You automatically responded to the word ‘puke’ like you would respond to the actual event. Our minds automatically assume causality between the words mango and puke, forming a scenario in which the mango caused nausea. This results into a short-term aversion to mangos. You are also extra ready to recognize and respond to concepts and objects associated with ‘puke’, such as vomit, sick, nausea and words associated with ‘mango’, such as exotic, fruit and red. Words associated with other causes of puking are also easier to recognize (food poisoning, hangover). You were a little surprised, as your System 1 noticed the uncommon juxtaposition of the words.

This wide range of responses occurred effortlessly, automatically, quickly and could not be stopped. This is an example of your System 1 at work. The visions and thoughts you experienced are the result of the process called ‘associative activation’: ideas that have been formed generate numerous other ideas. A word evokes memories, which triggers emotions, which evoke reactions like facial expressions and an avoidance tendency. These reactions intensify the feelings to which they are connected, and the feelings intensify compatible thoughts. This rapid process of physical, emotional and cognitive response is called ‘associatively coherent’. System 1 tries to make sense of the unusual situation (two random words) by linking them in a logical story. It starts with evaluating the current level of threat and then creates a context for the current situation and future events. System 1 treats the connection between two words as a representation of reality. Your body reacts as it would react to the real event and your emotional reaction is part of the interpretation of that event. As cognitive researchers recently emphasized: you do not merely think with your brain, but also with your body.

The process that causes mental events in sequences is called ‘the association of ideas’. Philosopher Hume identified three principles of association: causality, contiguity in place and time, and resemblance. An idea can be abstract or concrete and can be described as a noun, a verb, an adjective or in a physical way. A psychologist sees an idea as a node in a network, the associative memory, in which it is linked to numerous others. There are several types of links: cause-effect (drinking-hangover), things- properties (carrot - orange), things – categories (tulip – flower). According to the current view of the functioning of the associative memory, the mind goes through a sequence of ideas at once. One idea evokes many other ideas, of which only a few are conscious.

Psychologists discovered in the 1980’s that seeing or hearing words causes instant and measurable changes in the ease with which numerous related words can be evoked. If you have

The main function of System 1 is maintaining and updating a model of your personal world, which represents normality. This model is constructed by associations that connect ideas of events, circumstances, outcomes and actions that regularly occur. The formed connections become a pattern of associated ideas, which represents the structure of events in your life. It determines how you interpret the present and your future expectations.

Surprises are crucial elements of our mental life, they are the most sensitive indication of our understanding of the world and our expectations from it. Surprises can be divided into two varieties: conscious and active surprises, and passive surprises. Around dinner time, you expect your partner to return home from work. When you hear car doors slamming, you expect to see his face. It would be a surprise if someone else walks in. In case of a passive event you do not wait for it, but you are also not surprised when it happens. Although not actively expected, it is normal in that situation.

One event can make a recurrence less surprising. Imagine you run into your old friend Kim while holidaying far away from home. A few weeks later, you see her at a concert in Amsterdam. The second meeting is a more remarkable coincidence, but you are less surprised to meet her. The first one changed the idea of Kim in your mind. While System 2 knows it is an absurd idea, System 1 made it seem normal to meet Kim in unusual places. You would have been more surprised if you met another old friend at the concert, even though meeting Kim was statistically no more likely than any other old friend.

Passive expectations sometimes turn active. Imagine you are driving on the highway and spot a car with a flat tire. Exactly one week later, you see another person stranded with a flat tire, at the same location. You will be less surprised the second time, because you consider that location as the ‘place where people strand with flat tires’. Because the circumstances were similar, the second event evoked an active expectation: you will be reminded of stranded cars every time you pass that location for a long time and are prepared to spot another unlucky driver.

How does our mind assess normality?

How incidents come to be perceived as abnormal or normal can be explained by the ‘norm theory’. If you witness two abnormal events, the second event will retrieve the first one from memory and together they will make sense. An example is the ‘Moses illusion’: “How many animals of each kind did Moses take into the ark?” Very few people realize that it was Noah who took them into the ark. The thought of animals in an ark creates a biblical context, in which Moses is normal. Hearing or reading his name did not come as a surprise. The (unconscious) associative

System 2 deals with both questions from someone else (“Did you like the food?”) and from your own mind (“Do I really need to buy this?”). Both answers come from directing your attention and searching your memory. That is not how System 1 operates. It constantly monitors what is happening inside and outside our mind. It unintentionally and effortlessly assesses the elements of the situation. These ‘basic assessments’ affect our intuitive judgment, because they are easily replaced with harder questions. Two other characteristics of System 1 supporting the replacement of one judgment with another are the ability to translate values across dimensions (“If Chloe were as heavy as she is smart, how heavy would she be?”) and triggering other computations, like basic assessments.

An example of a ‘basic assessment’ is the ability to distinguish between an enemy and a friend in the blink of an eye. System 1 rapidly provides the judgment whether it is safe or not to interact with a stranger. In one glance at someone’s face, we can evaluate how trustworthy and dominant (thus threatening) that person is and whether we expect his/her intentions to be hostile or friendly. Dominance is assessed by looking at the shape of the face (square chin) and intentions are predicted through facial expressions. Face reading is not 100% reliable, a stranger with a round chin and a (fake) smile can still have bad intentions. In today’s society, this evolutionary ability is used to influence the voting behavior of people. Participants were shown campaign portraits of politicians and asked to rate their competence and likability based on their faces. The winner of the election turned out to be the person with the highest competence rating. Ratings of likability were less predictive of the voting result. Competence was judged by combining trustworthiness and strength. However, facial features are not predictive of how well someone will perform. Rejecting a candidate is based on the lack of attributes we consider important. Studies of the brain show that losing politicians evoked a greater negative emotional response, which is an example of a ‘judgment heuristic’.

The influence of System 1 on voting varies among people. Research shows that politically uninformed and television-prone voters are more likely to fall back on the automatic and quickly formed preferences of System 1. The effect of facial competence on their voting behavior is three times greater in comparison to informed voters who watch less television.

What is intensity matching?

Questions about one’s popularity, happiness or suitable punishments have one thing in common: they refer to an underlying dimension of amount or intensity. It is linked to using the word ‘more’ (more popular, more happy, more severe). This regards another ability of System 1: matching across various dimensions. An example is the following. “Richard read fluently when he was five years old. What is your prediction of his future GPA?” You will answer by translating from one scale

A study of cancer diagnoses in the United States showed a pattern: the number was lowest in sparsely populated, rural and Republican areas. What you make of this information, through searching memory and formulating hypotheses, is an operation of System 2. System 1 was also involved: System 2 depends on the suggestions and facts retrieved from associative memory. You probably focused on the rural-fact and did not link the lowest number to Republican policies. It makes sense to attribute it to the rural environment, with fresher foods and cleaner air. The number of diagnosis was highest in also rural and sparsely populated areas. You might link this information to poverty, access to good healthcare or smoking. However, living in rural areas cannot explain both numbers. The main factor was not the areas being Republican or rural, but having small populations. This example shows the complex relation between statistics and our mind.

System 1 excels in one form of thinking: it effortlessly and automatically detects causal links between events. System 1 fails to deal with merely statistical information, which affects the probability of the outcome but not the cause of the event.

Imagine a jar filled with balls: half of them are black, half are yellow. Without looking, you draw 4 balls from the jar, record the number of black balls and throw them back. You repeat this several times. If you summarize the outcomes, you will find that ‘2 black, 2 yellow’ occurs six times as often as the result ‘4 black’ or ‘4 yellow’. This is a mathematical fact. Now imagine the US-population as balls in a jar. Some balls are marked CD (cancer diagnosis). You draw samples of balls and populate each area. Rural samples are the smallest. Extreme outcomes (many or few diagnoses) will be found in sparsely populated areas. Fact: number of cancer diagnoses varies across areas. Explanation: extreme outcomes are more likely to be found in smaller samples. This is a statistical explanation, it is not causal. The small population of an area does not prevent or cause cancer. There is nothing to explain, the number of diagnoses is actually not higher or lower than normal, it just looks that way due to a sampling accident. The differences in sample sizes resulted into ‘artifacts’: observations that are produced exclusively by an aspect of the research method. Outcomes of large samples are more trustworthy, also known as the law of large numbers. The ‘sparsely populated’ part probably did not seem relevant to you and it took some effort to realize that large samples are more precise extreme outcomes are found more often in small samples. Even researchers have a poor understanding of sampling effects.

Research psychologists see sampling variation as an unpleasant obstacle in their research project. If the hypothesis to be tested is “The vocabulary of seven-year-old girls is greater than the vocabulary of seven-year-old boys”, you must use a large enough sample

What do people do when they want to estimate the frequency of certain categories (for example the divorce rate among professors, poisonous snakes)? The reliance on the ease of memory search (instances coming to mind) is called the ‘availability heuristic’. This heuristic is both an automatic operation (System 1) as a deliberate problem-solving strategy (System 2).

The availability heuristic substitutes questions, which results into biases (systematic errors).  Examples of factors that are potential sources of bias are:

• Conspicuous events attract attention and are easy to retrieve from memory. Instances of divorces among famous actors will come easily to mind, making you prone to exaggerate the frequency of divorces among famous actors.

• Dramatic events temporarily increase the availability of the concerning category. Hearing about a fatal accident with a bike on the news temporarily influences your opinion about the safety of bikes.

• A personal experience is more available than an incident that happened to someone else. Pictures are more available than words and vivid examples are more available than statistics.

It requires a fair amount of effort to resist so many potential availability biases. It takes reconsidering our intuitions and impressions by asking yourself questions. Examples are “Is my impression that house robbery is a major national problem due to my neighbor’s houses recently being robbed?” or “Is it possible that I feel no need to eat healthy because none of my friends got sick last year?”

A well-known study of availability indicates that being aware of our own biases contributes to peaceful marriage, and potentially other joint projects. Surveys among spouses about their own contributions to housekeeping and causing arguments demonstrated that they remember their own contributions more clearly. The same goes for people working in group projects: most members feel they had put in more effort than others.

An experiment carried out by psychologist Schwarz helped us gain a better understanding of the availability heuristic. He wanted to assess how our impressions of the frequency of a category will be influenced by the task to list a certain number of instances. The experiment showed that the listing instances task enhances the judgment by the ease with which they come to mind and the number of instances retrieved. The first instances will come easily to mind, but the fluency of the last instances will be low. People who list eight instances of indecisive behavior will rate themselves as less indecisive than people who list only three. People who are asked to list eight instances of decisive behavior will think of themselves as rather indecisive. Self-rating is dominated by the ease with which instances come to mind. The fluency of the retrieval counts more than the amount of retrieved instances. Numerous experiments have yielded paradoxical results. Other examples are: people who are asked to report more

Imagine drawing one ball from a jar. To determine whether the ball is more likely to be black or yellow, you need to know how many balls of each color there are in the jar. The proportion of balls of a specific color is called a ‘base rate’.

If you have to guess the graduate specialization of university student Patrick by ranking a list of fields in order of likelihood, you quickly realize that the relative size of enrollment in these field is important. Due to the absence of more information about Patrick, you will go by the base rates and predict that Patrick is more likely to be enrolled in law, than in medicine or art history, because there are more students overall in law. We use base-rate information when there is no further information.

Now you are asked to read a description about Patrick’s personality. “He is smart, socially skilled, a great listener and he cares for others. He is very capable of taking decisions under pressure and working in a team. He is responsible, disciplined and committed. His handwriting is terrible.” Again, you are asked to guess the specialization of Patrick from a list of fields. This question requires retrieving a stereotype of graduate students in the fields mentioned. The description fits the stereotype of a medical student.

The task of ranking the fields is hard and requires organization and discipline, which are operations of System 2. The clues used in the description activated an association with a stereotype, which is an automatic operation of System 1. The accuracy of the description and knowing the base rates of the fields are irrelevant. Focusing exclusively on the similarity of someone’s description to stereotypes is called ‘representativeness’.

The description of Patrick causes a clash between base rates and representativeness. When a statistic was asked to carry out similar tasks and guess Patrick’s profession, he answered with ‘medical doctor’. He neglected the relevance of the base rates after reading the description. An experiment among psychology students also resulted into substitution: the easier question about similarity (judgment of representativeness) substituted the difficult question about probability. Ignoring base rates and not paying attention to the accuracy of evidence in probability tasks will certainly lead to serious mistakes.

Statisticians and logicians disagree about the meaning of ‘probability’. Many consider it a measure of subjective degree of belief. Both have formulated a competing, precise definition of ‘probability’. For non-experts, it is a vague notion, also known as ‘likelihood’. It is associated with plausibility, surprise and uncertainty. We more or less know what we mean when we use the word, so it is not troublesome. Questions about likelihood or probability activate a mental shotgun: evoking answers to less difficult questions. An example of an easy answer is the automatic assessment of representativeness. System 1 unintentionally produces an impression of similarity.

Other examples of the representativeness

Read the following problem, what is your intuitive answer?

“Last night, a bus was involved in a traffic accident. Two bus companies, the Yellow and the Brown, operate in the area. 80% of the busses are Yellow and 20% are Brown. A witness declared the bus being Brown. The reliability of the witness in similar circumstances was tested: the witness correctly identified each color 75% of the time and failed 25% of the time. What is the probability that the bus was Brown?”

There are two pieces of information: a base rate and the not fully reliable testimony of the witness. Without the witness, the probability of the bus being Brown is 20%: the base rate. If the bus companies had been equally large, the base rate would be useless (50/50) and merely the reliability of the witness would be considered. Most people ignore the base rate and answer 75%. Both sources of information should have been combined by the probability rule of Bayes.

Now consider the following story, in which the base rate is differently presented. “The two bus companies operate the same number of busses, but Yellow busses are involved in 80% of traffic accidents”. The versions are psychologically different, although they are mathematically the same. People who read the first story tend to ignore the base rate, because they don’t know what to do with it. The base rate regards the number of Yellow and Brown busses in the area, which does not explain the bus driver getting into an accident. People who read the second version give more weight to the base rate, their judgments are closer to the right answer. Drivers of Brown busses cause four times as many accidents, which leads to the instant conclusion that Brown drivers are dangerous drivers. You have formed a stereotype of Brown drivers, which fits easily into a causal story.

The bus examples demonstrates two types of base rates: statistical base rates (irrelevant facts about a population) and causal base rates. Statistical base rate are often underweighted or even neglected when specific information about the individual case is available. Causal base rates are used as information about a concrete case and are easily combined with other relevant facts. The causal version of the bus problem was formulated as a stereotype. Stereotypes: statements about a group that are accepted as facts about individual members. System 1 is known for representing categories as prototypical exemplars and norms, our memory holds a representation of one or more regular members of a category (cats, blenders). A representation is called a stereotype when the category is social. In the bus problem, stereotyping the Brown drivers improved the accuracy of judgment. In other cases, like profiling or hiring, stereotyping is seen as morally (and lawfully) wrong and causal base rates get rejected. However, rejecting valid stereotypes results in judgments that are not optimal. It might be politically correct, but it is

Forecasting is a major part of our professional and private lives. A number of predictive judgments are based on analyses or computations, but most involve System 1 and intuition. Some intuitions draw on expertise and skill, gained through experience. The automatic and quick judgments and decisions of physicians, chess masters and fire chiefs are examples of skilled intuitions. They quickly come with solutions, because they recognize familiar cues. Other intuitions are the result of (substitution) heuristics. Numerous judgments arise from a combination of intuition and analysis. 

What are nonregressive intuitions?

A question regarding a current situation and a prediction activates System 1. “Mark is currently a bachelor student. He could count to 30 when he was two years old. What is his GPA?” People who have knowledge about the educational system provide quick answers thanks to the operations of System 1:

• Seeking a causal connection between the evidence (Mark’s counting) and the target of the prediction (his GPA), which in this case is academic talent. The associative memory then automatically and rapidly forms the best possible story from the available information (WYSIATI).

• Evaluating the evidence in relation to the relevant norm. How precocious is a child who counts to 30 at the age of two?

• Substitution and intensity matching. The evaluation of the evidence of cognitive ability at an early age is substituted as an answer to the question about his GPA in college. Mark will be assigned the same percentile score for his GPA and for his achievements as an early counter.

• Intensity matching: from the general impression of Mark’s academic achievements to a GPA score that matches the evidence for his talent. This impression needs to be translated into a corresponding GPA score.

The task required evaluating the evidence and predicting an outcome. This example clearly shows the role of substitution: people substitute an evaluation about the evidence when a prediction is asked, without being aware of the fact that the question they answer is not the question they were asked. This will lead to systematically biased predictions, as regression to the mean is fully ignored.

How can intuitive predictions be corrected?

The right way to predict Mark’s GPA is by using a formula for the factors that determine college grades and counting age:

GPA = factors specific to GPA + shared factors = 100%

Counting age = factors specific to counting age + shared factors = 100%

The shared factors are the degree to which family supports academic interests, genetically determined aptitude and other factors that would cause similar people to be precocious counters as minors and academical talents as adults. The correlation between both measures (GPA and counting age) equals the proportion of shared factors among their determinants. Assume the proportion being 30%.

System 1 is known for jumping to conclusions from limited evidence (WYSIATI). The coherence of the story created by System 1 and System 2 makes us confident about our opinions. The quality and amount of the evidence are less important, because poor evidence created a good story. We even have beliefs without any evidence for them, we just believe something because our partner or friend does. It is ridiculous how confident we are in our beliefs when we know so little.

The illusion of validity can be explained by the following experiment. Two psychology students watched soldiers in officer-training trying to solve a problem. They decided to evaluate them: who would make a great army leader? The exercise was exhausting and required several attempts. Some soldiers seemed arrogant, persistent, submissive, hot-tempered, stubborn, patient or quitters. Some were expected to become great leaders, others were ruled out for the officer rank. The students were convinced the exercise revealed the true nature of the soldiers and were very confident in their evaluations. Their impressions were coherent and did not conflict. After some time, they learned how the soldiers were doing and compared their evaluations against the reports by their commanders. This showed that the predictions about their performance at the officer-training were hardly any better than blind guesses. The students decided to repeat the experiment with a new group of soldiers. The shocking truth about the quality of their previous predictions had no effect on how they evaluated the soldiers and very little effect on how confident they felt in their judgments and predictions. The evidence of their prior failure did not damage their confidence and did not cause a moderation of the predictions, which it should have. They still felt like their predictions were valid. Another example of the illusion of validity is the Müller-Lyer illusion.

The predictions of the future performances of the soldiers are instances of the representativeness heuristic and of substitution. The predictions were based on 60 minutes of the soldiers behavior in artificial circumstances. The predictions were totally nonregressive and supported by weak evidence (WYSIATI). The experiment demonstrates that confidence reflects the coherence of the information and the cognitive ease of processing the information. Remember that a very confident person has formed a coherent story in his mind, which does not necessarily mean it’s the truth.

What is the illusion of stock-picking skill?

Each day, billions of shares are traded, which involves many buyers and sellers. They have the same information, their exchange is based on a difference in opinions. A buyer thinks the price will rise, while the seller expects it to drop. Something makes them believe they know more about the future price than others, but for many, that belief is merely an illusion. The price of stock is based on the available information about the company value and the best prediction about the future of the stock. If someone believes

Gary Klein is the intellectual leader of students of Naturalistic Decision Making (NDM), who study real people in natural situations. He rejects the focus on biases in heuristics, doing artificial experiments and is highly skeptical about choosing algorithms over human judgments. Klein is known for studies of expertise in firefighters and the development of intuitive skills in experienced experts. Despite their differences, Kahneman worked together with Gary Klein on a joint project in order to answer the question “When can you trust an experienced professional who claims to have an intuition?” They both agreed about Gladwell’s bestselling book ‘Blink’ about art experts that had the gut feeling that the object was a fake, but could not tell what it exactly was that made them think it was not the real deal. They knew it was a fake without knowing how they knew: a perfect example of intuition.

While Kahneman’s views of intuition were shaped by observing the illusion of validity in himself and reading Meehl’s review about clinical predictions, Klein’s thinking was shaped by his studies of fire ground commanders. He observed and interviewed them. He introduced the ‘recognition-primed decision (RPD) model, which applies to several experts (from fire commanders to chess masters). System 1 and System 2 are both involved in this process. A tentative plan automatically comes to mind (System 1) and then gets mentally tested (System 2). The model of making intuitive decisions involves recognition: the situation provides a cue, the cue retrieves information from memory, which provides the solution. Intuition is merely recognition.

Information gets stored in memory by learning emotions, like fear. A scary experience stays with you for a long time. Fear can be learned by experience and by words. Soldiers get trained to identify situations and firefighters discussed all types of fires with others. Emotional learning is quick, developing expertise takes a long time. Chess masters need more than 10.000 hours of practice to reach the top. During these hours, players become familiar with all the possible moves and able to quickly read the situation.

Kahneman and Klein agreed that the confidence of people in their intuitions is not a reliable source of validity. But if subjective confidence cannot be trusted, how do we know when judgments reflect true expertise? The answer lies in the two conditions for acquiring a skill:

• The environment must be sufficiently regular so its predictable

• There must be an opportunity to learn the regularities through prolonged practice.

An intuition is normally skilled when both conditions are met. Chess players, nurses, physicians, firefighters and sportsmen are active in regular, orderly situations. Political scientists and stock pickers are not, they operate in a less regular (non-validity) environment.

The planning fallacy is one of many manifestations of the optimistic bias. Many people view their attributes as more favorable than they probably are and consider their goals as more achievable than they probably are. Optimistic bias can be a risk as well as a blessing, which is why you should be cautious when you feel optimistic.

Some people are more optimistic than others. They are usually happy, popular and resilient. Optimists play a disproportionate role in shaping society. Their decisions have an impact on others: they are leaders, inventors, entrepreneurs. They seek challenges and take risks, are talented and lucky. Their successes and the admiration by others makes them even more confident. This description results into the following hypothesis: the most influential people are likely to be overconfident and optimistic, and take more risks than they are aware of. The evidence indicates that an optimistic bias causes institutions or people to take on risks.

The chances that a small company will survive for five years in the US are slightly over 33%. Someone who starts a company believes that these statistics do not apply to him/her. Research shows that American entrepreneurs are prone to believe that their company is something else: their estimated chance of success was almost twice as high: 60%. Would they still have invested time and money if they knew the odds? They never thought of the outside view.

One of the benefits of being an optimist is considered persistence when faced with obstacles. However, being persistent can be costly. Studies show how almost half of the people continue their project after being told it would not succeed. Their initial losses doubled.

According to psychologist, the majority of people genuinely believes that they are better than others, they would even bet money on it. This belief has significant consequences in the market. Misguided acquisitions by large businesses in the stock market are explained by the ‘hubris hypothesis’: leaders of acquiring firms are less competent than they think they are. The optimistic risk taking of entrepreneurs contributes to the economic dynamism of our capitalistic society, but also evoke policy issues. Should founders of small companies be financially supported by the government, when they are very likely to fail? There is no satisfying answer to this question.

Entrepreneurial optimism is not merely explained by wishful thinking, emotions and cognitive biases also play a significant role, especially the WYSIATI-rule of System 1. Focusing on the goal and neglecting relevant base rates can result into the planning fallacy. Focusing on the causal role of skill and neglecting the role of luck can result into the illusion of control. Focusing on what is known and neglect what is not known leads to overconfidence.

Many founders believe that the success of their company depends to a great extent on their effort. They think their fate is almost completely in their own hands.

What is the prospect theory? – Chapter 26

In utility theory, the utility of a gain is determined by comparing the utilities of two states of wealth. The utility of receiving an extra € 400 when your wealth is € 2 million is the difference between the utility of € 2.000.400 and the utility of 2 million. If you lose € 400, the disutility is again the difference between the utilities of both states of wealth. It was assumed that the distinction between losses and gains did not matter and was not examined due to the theory-induced blindness.

Kahneman and Amos had focused on differences between gambles with low or high probabilities of winning, until Amos casually mentioned the losses. The risk aversion turned out to be replaced by going for the risk. Consider the following problems:

1. Which do you prefer? Get € 800 for sure or a 90% chance to get € 900.

2. Which do you prefer? Lose € 800 for sure or 90% to lose € 900.

The first problem induces risk aversion, you would go for gaining € 800. In case of the second problem, you will probably go for the gamble (the risk). When all options are bad, people tend to become risk seeking.

The comparison between the problems emphasize the importance of the reference point: the previous state relative to which losses and gains are evaluated. Reference points usually get ignored by people and Bernoulli’s theory lacks them. The prospect theory takes reference points into account. The prospect theory involves three cognitive features (associated with System 1), which play a crucial role in the evaluation of financial outcomes and are common to a lot of automatic processes of emotion, judgment and perception:

• Evaluation is relative to a neutral reference point (‘adaptation level’).

• A principle of diminishing sensitivity applies to the evaluation of changes of wealth and to sensory dimensions.

• Loss aversion.

In an experiment, people lying in a brain scanner were shown several images. Among them were pictures of the eyes of a happy person and of a terrified person. They were shown for a fraction a second: the participants never consciously knew they had seen the pictures. One part of their brain did know: the amygdala, the ‘treat center’. Brain images showed an intense reaction to the threatening picture. The same process makes us process angry faces (a possible threat) more efficiently and faster than happy faces. An angry person in a happy crowd gets noticed faster than the opposite situation. Our brains are equipped with a mechanism that gives priority to bad news.

Our brains also respond faster to merely symbolic threats. Bad words (war, murder),  emotionally loaded words and opinions with which you strongly disagree attract attention quicker than their opposites. Loss aversion is another manifestation of negativity dominance. Bad feedback and bad parenting proved to have more impact, and bad impressions and stereotypes are formed faster. As Gottman argues: long-term success of marriages depends more on the avoidance of negatives than on looking for positives. One bad action can ruin a long-term relationship. The boundary between good and bad is a reference point that changes over time and depends on the current situation.

People are driven more strongly to avoiding a loss than to achieving a gain. A reference point can be a future goal or a the status quo. These two motives have different strengths: loss aversion (not reaching the goal) is a lot stronger than the wish to exceed it. This explains why many people set short-term goals.

The different intensities of the motives to achieve gains and avoid losses show up in many situations. It is often detected in negotiations, in particular the renegotiations of existing contracts. Reference point: existing terms. Any proposed change is considered a concession (loss) by one of the parties. Loss aversion makes reaching an agreement difficult. 

A study on what the public considers unfair behavior by employers, landlords and merchants showed that the opprobrium linked to unfairness imposes constraints on profit seeking.

Reference point: the existing rent, wage or price. The participants deemed it unfair for stores to impose losses on customers, while the stores behaved according to the standard economic model: increased demand leads to a raised price. The latter is seen as a loss. Exploiting market power to impose losses on others is considered unfair. On the other hand, companies are entitled to retain current profit if it faces a loss by transferring the loss to customers or workers. Research shows that merchants who set unfair prices are likely to lose sales and that employers who are considered unfair have to deal with reduced productivity.

Try remembering a time in which terrorist attacks in public transport were relatively common. The attacks were fairly rare in absolute numbers and the risks for travellers very small, but that is not how they felt about it. People tried to avoid public transport or were very cautious. Kahneman also disliked being near buses, even though he knew the risk of a terrorist attack happening was negligible. People assigned an absurdly high decision weight to a very small probability due to the experience of the moment: being near a bus made them have unpleasant thoughts, so they avoided buses. Terrorism is effective because it evokes an availability cascade. Very vivid images of victims, constantly mentioned by media and the topic of many conversations, become highly accessible, especially if it related to a specific situation (seeing a bus). This emotional response is automatic, uncontrolled, associative and it generates an impulse for protective behavior. System 2 knows about the low probability, but System 1 cannot be switched off.

The same goes for big lotteries. The exciting possibility of winning the jackpot is shared by the community and reinforced by interactions with others. Buying a ticket instantly results into appealing fantasies, just like avoiding public transport was an immediate response to fear. Merely the possibility matters, not the actual probability. According to the prospect theory, highly unlikely events get overweighted or ignored. Kahneman’s current view of weighting decisions has been shaped by research on the role of vividness in decision making and of emotions. Vividness and emotion influence judgments of probability, availability and fluency and therefore explain disproportionate responses to rare events.

Consider the following questions:

1. What is your judgment of the probability that the national football team of Saudi Arabia wins the next world cup?

2. How much will you pay for a bet in which you receive € 500 if the next world cup winner is Saudi Arabia, and no money otherwise?

The first question regards the assessment of the probability of an unlikely event. The second question requires assigning a decision weight to the same event. People tend to overestimate the probability of an unlikely event and overweight the unlikely event. Overweighting and overestimation are different notions, but the psychological mechanism behind them are the same: cognitive ease, confirmation bias and focused attention. The associative machinery of System 1 is triggered by specific descriptions. When thinking about the unlikely win of Saudi Arabia, your associative machinery starts selectively retrieving evidence, images and instances that would make the statement true. The judgment of probability was determined by the cognitive ease with which a credible scenario come to mind. The probability of a rare event will be overestimated in case of a not fully specified alternative.

Research demonstrates that the valuation is a gamble is much less sensitive to probability when the outcomes are emotional (kissing, getting electric shocks) than when the outcomes

For most people, gaining money reflects achievement and self-regard. We keep score in our mind when we lose or gain money and consider them punishments and rewards, threats and promises. The ‘scores’ motivate our actions and influence our preferences. Cutting our losses feels like a failure, so we refuse doing it.

We hold money in both physical and mental accounts. We have general savings, special savings for our kids or emergencies and money to spend. We are more willing to draw on some of these accounts compared to other accounts to cover current needs. Self-control also plays a role: some accounts can be used only for household expenses, they limit have much we can spend. Mental accounts are a form of narrow framing: they keep things manageable and under control. Mental accounts are used for keeping score. For instance, successful golfers have a separate account for each hole, not just one for their overall score.

Consider the following example of mental accounting. Two Rihanna fans have to travel 50 miles to visit her concert. One of them bought the ticket, the other one got it as a gift. A heavy snowstorm is announced for the day of the concert. Which of the two fans is more likely to brave the storm to see Rihanna? The fan who paid for the ticket. Both of them set up a mental account for the concert they hoped to visit. Missing the concert will result into a negative balance. Both will be sad, but the closing balance is more negative for the fan who paid for the ticket. System 1 performs the calculations of emotional balance. For System 2 to respond rationally, it would have to be aware of the counterfactual possibility: “Would I still travel in this blizzard if I had gotten the ticket as a gift?” This requires a disciplined and active mind.

Imagine an individual investor having to sell stock because he is in need of money. He still remembers the price at which he bought each stock and can identify it as a loser or a winner. Fafsung is a winner: if he sells it now, he gains € 8.000. He holds an equal investment in Fapple, which is now worth € 8.000 less than he paid for it. In the last month, the value of both stocks has been stable. Which is he more likely to sell? Closing the Fafsung account results into an positive score. Closing the Fapple account would lead to a negative score. He has to choose between pleasure and pain, so he will likely sell Fafsung. Financial research shows that there is a major preference for selling winners. This bias is called the ‘disposition effect’, which is an example of narrow framing. The state of the mental account was considered a valid consideration for selling. If you care more about your wealth, you would sell the loser.

Imagine a firm

France and Argentina competed in the 2022 World Cup final. The following sentences both describe the outcome: “Argentina won.” “France lost.” Whether these statements have the same meaning or not depends on your idea of ‘meaning’.

The truth conditions of the two interchangeable descriptions are identical: one is true, so the other is true as well. Economics consider the preferences and beliefs of people as reality-bound, which are not influenced by the wording of their descriptions. There is another sense of ‘meaning’, in which both sentences have different meanings. The two sentences induce different associations (System 1). “Argentina won” induces thought of the actions of their national team and “France lost" induces thought of what they did that made them lose. In terms of induced associations, the sentences mean something else.

Most people do not have reality-bound preferences as System 1 is not reality-bound. Many are influenced by the formulating of a problem. For instance, a negative outcome is more acceptable if it is framed as the cost of a lottery ticket instead of lost gamble. Losses evoke stronger negative feelings than costs. The same goes for discounts and surcharges: they are economically the same thing, but emotionally not.

Neuroscientists performed an experiment in which they studied framing effects by recording the activity of several brain areas. Participants were asked to imagine receiving 50 pound. They then had to choose between a sure outcome and a gamble on a wheel. Wheels stops on white: they receive the full amount. Wheels stops on black: they get nothing. The sure outcome is a gain of 20 pound (expected value of the gamble). The same sure outcome can be framed as a ‘lose’ of 30 pound and a ‘keep’ of 20 pound. These words evoke tendencies to avoid or approach and System 1 tends to be biased in favor of the option framed as ‘keep’. All participants demonstrated framing effect: they choose the sure thing in the keep frame and accepted the gamble in the lose frame. There was a distinction among the participants though: some were very susceptible to the framing, others were reality-bound. The participants were ranked by a ‘rationality index’.

The study resulted into three significant findings:

• The amygdala (region related to emotional arousal) was most likely to be active when participants’ choices conformed to the frame. This region is accessed very quickly by emotional stimuli (System 1).

• The anterior cingulate (region related to self-control and conflict) was more active when participants did not act naturally (choosing the sure thing despite the ‘lose’-label). Resisting the suggestion by System 1 appears to cause conflict.

• The most rational participants showed enhanced activity in the frontal area that is known for combining reasoning and emotion.

This study illustrates how words that induce emotion influence our decision making.

When people are about to die, their loved ones rush to them for one last moment, before death sets in. Why do we care so much about that final moment? Why is it so important that a movie ends with people arriving in time, while the length of the life was not taken in consideration? It hardly matters learning that they actually had several days together instead of 5 minutes, but someone being too late would be a big deal to you. Stories are about memorable moments and important events, not about the passing of time. In a story, duration neglect is normal and the ends often defines whether it’s a good or bad story.

Caring for someone usually means being concerned for the quality of his/her story, not for his/her feelings. We also deeply care for the narrative of our own life story.

Psychologist Diener examined whether the peak-end rule and duration neglect govern the evaluation of an entire life. The results demonstrated that both did. Doubling the duration of the life of the fictitious woman had no effect on the judgments of her total happiness nor on the desirability of her life. In addition, a less-more effect was found: adding ‘slightly happy’ years to a really happy life caused a drop in evaluations of total happiness: they made the whole life worse.

Consider you are making vacation plans. Would you go for the beautiful place you enjoyed with your family last summer or visit a whole new location, enriching your memory store? The tourism industry helps people collecting memories and constructing stories. The goal of storing memories shapes the vacation plans and the experience of it. The word ‘memorable’ is frequently used to describe the highlights of the vacation. The remembering self is the one that chooses vacations. A study shows that the final evaluation of a vacation entirely determines the intentions for future breaks, although that did not accurately reflect the quality of the whole experience (as described in a diary). We choose by memory when we decide whether we repeat an experience or not. Eliminating memories is likely to significantly reduce the value of the experience.

The decision to marry someone reflects a huge error of ‘affection forecasting’. On their big day, the groom and bride know that the divorce rate is high, but they believe that these numbers do not apply to them.

A study on the level of life satisfaction from the day people get married shows a gradual drop. It is argued that the honey moon phase fades and married life becomes a routine. Another example is plausible: heuristics of judgment. A mood heuristic is one way of answering questions about life-satisfaction. In addition to the current mood, people are likely to think about significant events in the recent past. Only a few relevant ideas come to mind, but most do not. The rating of life-satisfaction is heavily influenced by a small amount of highly available ideas, not by carefully weighting all life domains. People who recently got married will retrieve that happy event when asked a general question about life. As time passes, the salience of the thought will diminish. This explains the remarkably high level of life satisfaction in the first years after marriage. On average, experienced well-being is not affected by marriage, not because marriage does not makes us happy, but because it changes some aspects of life for the worse and others for the better.

A reason for the low correlations between life-satisfaction and the circumstances of individuals, is that life-satisfaction and experienced happiness are significantly determined by the genetics of temperament. A disposition for well-being is heritable. In other cases, like marriage, the correlations with well-being are low due to balancing effects. Setting (financial) goals also proved to have lifelong effects.

People tend to respond fairly quick to life questions. This speed of answering and the effects of current mood on the answers demonstrate that they skip a careful assessment. They probably use heuristics, which are examples of WYSIATI and substitution. When attention is directed to a specific aspect of life, it greatly affects the overall evaluation. This is known as the ‘focusing illusion’. The most important thing in life seems the thing you are thinking about. The essence of this illusion is WYSIATI. The focusing illusion results into a bias in favor of experiences and goods that are initially appealing, but will eventually lose their charm.