Behavioral Science S5 PDF

S5. Judgement by Heuristics Chapter 12 The Science of Availability One of Kahneman and Tversky’s projects was the study of what that called the availability heuristic. They thought of that heuristic when they asked themselves what people actually do when they wish to estimate the frequency of...

S5. Judgement by Heuristics Chapter 12 The Science of Availability One of Kahneman and Tversky’s projects was the study of what that called the availability heuristic. They thought of that heuristic when they asked themselves what people actually do when they wish to estimate the frequency of a category, such as “people who divorce after the age of 60” or “dangerous plants.” The answer was straightforward: instances of the class will be retrieved from memory, and if retrieval is easy and fluent, the category will be judged to be large. The availability heuristic, like other heuristics of judgment, substitutes one question for another: you wish to estimate the size se ost c d of a category or the frequency of an event, but you report an impression of the ease with which instances come to mind. Substitution of questions inevitably produces systematic errors. You can discover how the heuristic leads to biases by following a simple procedure: list factors other than frequency that make it easy to come up with instances. Each factor in your list will be a potential source of bias. Examples: -A salient event that attracts your attention will be easily retrieved from memory like Divorces among Hollywood celebrities. You are therefore likely to exaggerate the frequency of both Hollywood divorces and political sex scandals. -A dramatic event temporarily increases the availability of its category. A plane crash that attracts media coverage will temporarily alter your feelings about the safety of flying. Accidents are on your mind, for a while, after you see a car burning at the side of the road, and the world is for a while a more dangerous place. -Personal experiences, pictures, and vivid examples are more available than incidents that happened to others, or mere words, or statistics. A judicial error that affects you will undermine your faith in the justice system more than a similar incident you read about in a newspaper. The Psychology of Availability The ease with which instances come to mind is a System 1 heuristic, which is replaced by a focus on content when System 2 is more engaged. Multiple lines of evidence converge on the conclusion that people who let themselves be guided by System 1 are more strongly susceptible to availability biases than others who are in a state of higher vigilance. The following are some conditions in which people “go with the flow” and are affected more strongly by ease of retrieval than by the content they retrieved: -when they are engaged in another effortful task at the same time -when they are in a good mood because they just thought of a happy episode in their life -if they score low on a depression scale -if they are knowledgeable novices on the topic of the task, in contrast to true experts -when they score high on a scale of faith in intuition -if they are (or are made to feel) powerful Chapter 14 Tom W’s Specialty It was an experiences about base rate. It requires to rank the following nine fields of graduate specialization in order of the likelihood that Tom W is now a student in each of these fields: business administration computer science engineering humanities and education law medicine library science physical and life sciences social science and social work The relative size of enrollment in the different fields is the key to a solution. So far as you know, Tom W was picked at random from the graduate students at the university, like a single marble drawn from an urn. To decide whether a marble is more likely to be red or green, you need to know how many marbles of each color there are in the urn. The proportion of marbles of a particular kind is called a base rate. Similarly, the base rate of humanities and education in this problem is the proportion of students of that field among all the graduate students. In the absence of specific information about Tom W, you will go by the base rates and guess that he is more likely to be enrolled in humanities and education than in computer science or library science, because there are more students overall in the humanities and education than in the other two fields. Using base-rate information is the obvious move when no other information is provided. The second task that has nothing to do with base rates. Need to define Tom’s speciality based on personality sketch of Tom W written during Tom’s senior year in high school by a psychologist, on the basis of psychological tests of uncertain validity: Tom W is of high intelligence, although lacking in true creativity. He has a need for order and clarity, and for neat and tidy systems in which every detail finds its appropriate place. His writing is rather dull and mechanical, occasionally enlivened by somewhat corny puns and flashes of imagination of the sci-fi type. He has a strong drive for competence. He seems to have little feel and little sympathy for other people, and does not enjoy interacting with others. Self-centered, he nonetheless has a deep moral sense. It is necessary to rank the nine fields of specialization listed below by how similar the description of Tom W is to the typical graduate student in each of the following fields. For example, hints of nerdiness (“corny puns”) -> computer science “neat and tidy systems” -> engineering  If you examine Tom W again, you will see that he is a good fit to stereotypes of some small groups of students (computer scientists, librarians, engineers) and a much poorer fit to the largest groups (humanities and education, social science and social work). Predicting by Representativeness The third task in the sequence was administered to graduate students in psychology, and it is the critical one: rank the fields of specialization in order of the likelihood that Tom W is now a graduate student in each of these fields. The members of this prediction group knew the relevant statistical facts: they were familiar with the base rates of the different fields, and they knew that the source of Tom W’s description was not highly trustworthy. However, it is expected them to focus exclusively on the similarity of the description to the stereotypes—that they called it representativeness—ignoring both the base rates and the doubts about the veracity of the description. They would then rank the small specialty— computer science—as highly probable, because that outcome gets the highest representativeness score. Although it is common, prediction by representativeness is not statistically optimal. The Sins of Representativeness Judging probability byals representativeness has important virtues: the intuitive impressions that it produces are often—indeed, usually—more accurate than chance guesses would be. -On most occasions, people who act friendly are in fact friendly. -A professional athlete who is very tall and thin is much more likely to play basketball than football. -People with a PhD are more likely to subscribe to The New York Times than people who ended their education after high school. -Young men are more likely than elderly women to drive aggressively. One sin of representativeness is an excessive willingness to predict the occurrence of unlikely (low base-rate) events. Here is an example: you see a person reading The New York Times on the New York subway. Which of the following is a better bet about the reading stranger? -She has a PhD. -She does not have a college degree. Representativeness would tell you to bet on the PhD, but this is not necessarily wise. You should seriously consider the second alternative, because many more nongraduates than PhDs ride in New York subways. People without training in statistics are quite capable of using base rates in predictions under some conditions. As a result of experiment with student of Harvard university laziness seems to be the proper explanation of base-rate neglect. The second sin of representativeness is insensitivity to the quality of evidence. Recall the rule of System 1: WYSIATI. In the Tom W example, what activates your associative machinery is a description of Tom, which may or may not be an accurate portrayal. The statement that Tom W “has little feel and little sympathy for people” was probably enough to convince you (and most other readers) that he is very unlikely to be a student of social science or social work. But you were explicitly told that the description should not be trusted! So, the little evidence you have is not trustworthy, so the base rates should dominate your estimates. How to Discipline Intuition To be useful, your beliefs should be constrained by the logic of probability. The relevant “rules” for cases such as the Tom W problem are provided by Bayesian statistics. Bayes’s rule specifies how prior beliefs (in the examples of this chapter, base rates) should be combined with the diagnosticity of the evidence, the degree to which it favors the hypothesis over the alternative. There are two ideas to keep in mind about Bayesian reasoning and how we tend to mess it up. The first is that base rates matter, even in the presence of evidence about the case at hand. This is often not intuitively obvious. The second is that intuitive impressions of the diagnosticity of evidence are often exaggerated. The combination of WY SIATI and associative coherence tends to make us believe in the stories we spin for ourselves. The essential keys to disciplined Bayesian reasoning can be simply summarized: Anchor your judgment of the probability of an outcome on a plausible base rate. Question the diagnosticity of your evidence. Speaking of Representativeness “The lawn is well trimmed, the receptionist looks competent, and the furniture is attractive, but this doesn’t mean it is a well-managed company. I hope the board does not go by representativeness.” “This start-up looks as if it could not fail, but the base rate of success in the industry is extremely low. How do we know this case is different?” “They keep making the same mistake: predicting rare events from weak evidence. When the evidence is weak, one should stick with the base rates.” “I know this report is absolutely damning, and it may be based on solid evidence, but how sure are we? We must allow for that uncertainty in our thinking.” Kahneman Appendix A Representativeness The subjects used prior probabilities correctly when there is no other information. When no specific evidence is given, prior probabilities are properly utilized; when worthless evidence is given, prior probabilities are ignored. Availability  There are situations in which people assess the frequency of a class or the probability of an event by the ease with which instances or occurrences can be brought to mind. Availability is a useful clue for assessing frequency or probability, because instances of large classes are usually recalled better and faster than instances of less frequent classes. Lifelong experience has taught that, in general, instances of large classes are recalled better and faster than instances of less frequent classes; that likely occurrences are easier to imagine than unlikely ones; and that the associative connections between events are strengthened when the events frequently co-occur. As a result, man has at his disposal a procedure (the availability heuristic) for estimating the numerosity of a class, the likelihood of an event, or the frequency of co-occurrences, by the ease with which the relevant mental operations of retrieval, construction, or association can be performed. However, as the preceding examples have demonstrated, this valuable estimation procedure results in systematic errors.