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COPYRIGHT NOTICE: Edited by Richard H. Thaler: Advances in Behavioral Finance, Volume II - page 14 / 23

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A SURVEY OF BEHAVIORAL FINANCE

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Optimism and Wishful Thinking. Most people display unrealistically rosy views of their abilities and prospects (Weinstein 1980). Typically, over 90 percent of those surveyed think they are above average in such domains as driving skill, ability to get along with people, and sense of humor. They also display a systematic planning fallacy: they predict that tasks (such as writing survey papers) will be completed much sooner than they actually are (Buehler, Griffin, and Ross 1994).

Representativeness. Kahneman and Tversky (1974) show that when peo- ple try to determine the probability that a data set A was generated by a model B, or that an object A belongs to a class B, they often use the repre- sentativeness heuristic. This means that they evaluate the probability by the degree to which A reflects the essential characteristics of B.

Much of the time, representativeness is a helpful heuristic, but it can gen- erate some severe biases. The first is base rate neglect. To illustrate, Kahne- man and Tversky present this description of a person named Linda:

Linda is thirty-one years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with is- sues of discrimination and social justice, and also participated in anti- nuclear demonstrations.

When asked which of “Linda is a bank teller” (statement A) and “Linda is a bank teller and is active in the feminist movement” (statement B) is more likely, subjects typically assign greater probability to B. This is, of course, impossible. Representativeness provides a simple explanation. The description of Linda sounds like the description of a feminist—it is repre- sentative of a feminist—leading subjects to pick B. Put differently, while Bayes’s law says that

p(statement Bdescription)

p(descriptionstatement B) p(statement B)

p (description)

,

people apply the law incorrectly, putting too much weight on p (description statement B), which captures representativeness, and too little weight on the base rate, p (statement B).

Representativeness also leads to another bias, sample-size neglect. When judging the likelihood that a data set was generated by a particular model,

ineptitude. Doing this repeatedly will lead people to the pleasing but erroneous conclusion that they are very talented. For example, investors might become overconfident after several quarters of investing success (Gervais and Odean 2001). Hindsight bias is the tendency of peo- ple to believe, after an event has occurred, that they predicted it before it happened. If people think they predicted the past better than they actually did, they may also believe that they can predict the future better than they actually can.

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