Incentives and Behavior Prof. Dr. Heiner Schumacher KU Leuven 10. - - PowerPoint PPT Presentation

Incentives and Behavior

• Prof. Dr. Heiner Schumacher

KU Leuven

• 10. Behavioral Biases II
• Prof. Dr. Heiner Schumacher (KU Leuven)

Incentives and Behavior

• 10. Behavioral Biases II

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Introduction

Overview The Conjunction Fallacy Stereotypes Regression to the Mean The Halo E¤ect

• Prof. Dr. Heiner Schumacher (KU Leuven)

Incentives and Behavior

• 10. Behavioral Biases II

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The Conjunction Fallacy

Consider the following description (apologies for the somewhat

• utdated example):

Linda is thirty-one old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in antinuclear demonstrations. Now rank the following scenarios for Linda by probability.

• Prof. Dr. Heiner Schumacher (KU Leuven)

Incentives and Behavior

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The Conjunction Fallacy

Linda is a teacher in elementary school. Linda works in a bookstore and takes yoga classes. Linda is active in the feminist movement. Linda is a psychiatric social worker. Linda is a member of the League of Women Voters. Linda is a bank teller. Linda is an insurance salesperson. Linda is a bank teller and is active in the feminist movement.

• Prof. Dr. Heiner Schumacher (KU Leuven)

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The Conjunction Fallacy

Obviously, Linda …ts the idea of a “feminist bank teller”. Adding the detail “feminist” to the scenario makes for a more coherent story. However, by the rules of pure logic, the probability that Linda is a feminist bank teller must be smaller than the probability that Linda is a bank teller. Nevertheless, a large majority of subjects (e.g. students from Stanford Graduate School of Business), between 80% and 90%, ranked “feminist bank teller” as more likely than “bank teller”. The systematic violation of the conjunction rule is called conjunction fallacy.

• Prof. Dr. Heiner Schumacher (KU Leuven)

Incentives and Behavior

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The Conjunction Fallacy

The conjunction fallacy is quite robust. It does not disappear if one reduces the set of scenarios to the relevant two ones: Linda is a bank teller. Linda is a bank teller and is active in the feminist movement. Now 35% of the student subjects fall prey to the conjunction fallacy. In order to appreciate the role of plausibility, consider the following question: Which alternative is more probable? Mark has hair. Mark has blond hair. This question does not cause a fallacy, because the more detailed

• utcome is not more plausible.
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The Conjunction Fallacy

Estimate the value of these two sets of dinnerware: Set A: 40 pieces Set B: 24 pieces Dinner plates 8, all in good condition 8, all in good condition Salad bowls 8, all in good condition 8, all in good condition Dessert plates 8, all in good condition 8, all in good condition Cups 8, 2 of them are broken Saucers 8, 7 of them are broken Set A must be valued more. If subjects can compare the two sets (within-subject), they are indeed willing to pay a little more for A than for B (32 USD and 30 USD, respectively). But what happens if each subject only evaluates one of the two sets (between-subjects)?

• Prof. Dr. Heiner Schumacher (KU Leuven)

Incentives and Behavior

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The Conjunction Fallacy

Under single evaluation, set B was priced much higher than set A: 33 USD versus 23 USD. It seems that subjects price the sets according to the average value of the dishes (which of course is lower for set A). Sets are represented by norms and prototypes. From an economic perspective, this result is troubling for theory: the economic value of a collection of items is a sum-like variable. Adding a positively valued item to the set can only increase its value.

• Prof. Dr. Heiner Schumacher (KU Leuven)

Incentives and Behavior

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The Conjunction Fallacy

In order to avoid the objection that the conjunction fallacy is caused by a misinterpretation of probability, the following experiment has been conducted. There is a six-sided dice with four green and two red faces, which would be rolled 20 times. Subjects were shown three sequences of greens and reds, and were asked to choose one:

• 1. RGRRR
• 2. GRGRRR
• 3. GRRRRR

They would win 25 USD if their chosen sequence showed up. What sequence would/should subjects choose?

• Prof. Dr. Heiner Schumacher (KU Leuven)

Incentives and Behavior

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The Conjunction Fallacy

The second sequence is constructed from the …rst by adding a G. So the second sequence is less likely to show up than the …rst one. However, the second sequence looks more representative than the …rst, because it contains more Gs. Indeed, two-thirds of respondents choose the second sequence. Hence, the conjunction fallacy is not due to a misinterpretation of the term probability.

• Prof. Dr. Heiner Schumacher (KU Leuven)

Incentives and Behavior

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Stereotypes

Consider the following scenario. A cab was involved in a hit-and-run accident at night. Two cab companies, the Green and the Blue, operate in the city. You are given the following data:

• 1. 85% of the cabs in the city are Green and 15% are Blue.
• 2. A witness identi…ed the cab as Blue. The court tested the

reliability of the witness under the circumstances that existed

• n the night of the accident and concluded that the witness

correctly identi…ed each one of the two colors 80% of the time and failed 20% of the time. What is the probability that the cab involved in the accident was Blue rather than Green?

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Stereotypes

Obviously, this is a Bayesian Inference problem and the correct answer is P(color = b j witness = b) = 0, 15 0, 80 0, 85 0, 20 + 0, 15 0, 80 0, 41. Perhaps not surprisingly, people mostly ignore the base rate and go with the witness. The common answer is 80%. Now consider the same question except that the information 85% of the cabs in the city are Green and 15% are Blue. is substituted by The two companies operate the same number of cabs, but Green cabs are involved in 85% of accidents.

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Stereotypes

The two versions of the problem are mathematically identical. However, while people often ignore the statistical information in the …rst version, they give considerable weight to the base rate in the second version. The second version of the problem creates a stereotype: Drivers of the green company are reckless madmen! The stereotype is easily …tted into a causal story. The inferences of the two stories are contradictory and approximately cancel each other. Hence, subjects come up with a much better estimate under the second version of the problem (around 50%).

• Prof. Dr. Heiner Schumacher (KU Leuven)

Incentives and Behavior

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Stereotypes

Many experiments con…rm the following …ndings. Statistical base rates are generally underweighted, and sometimes neglected altogether, when speci…c information about the case at hand is available. Causal base rates (created by stereotypes) are treated as information about the individual case and are easily combined with other case-speci…c information.

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Stereotypes

Stereotypes, both correct and false, are how we think of categories. We consider it morally desirable for base rates to be treated as statistical fact about the group rather than as presumptive facts about individuals. In other words: There is a strong social norm against stereotyping. However, as we just saw, causal base rates/stereotypes may improve probability judgments!

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Regression to the Mean

Outcomes in many domains such as business and sports are determined by two factors, talent and luck. This causes regression to the mean: if a variable (pro…t, scores) is extreme on its …rst measurement, it will tend to be closer to the average on its second measurement (and vice versa). Whenever the correlation between two scores is imperfect, there will be regression to the mean. However, our mind is strongly biased toward causal explanations and does not deal well with statistics. So the phenomenon of regression is quite strange to the human mind.

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Regression to the Mean

Consider the following proposition: Highly intelligent women tend to marry men who are less intelligent than they are. Most people will spontaneously interpret the statement in causal terms (i.e., they come up with a story that explains this phenomenon). The following statement is algebraically equivalent, but far less interesting for most people. The correlation between the intelligence scores

• f spouses is less than perfect.

If the correlation between the intelligence of spouses is less than perfect, it is a mathematical inevitability that highly intelligent women will be married to husbands who are on average less intelligent than they are.

• Prof. Dr. Heiner Schumacher (KU Leuven)

Incentives and Behavior

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Regression to the Mean

Regression to the mean matters for the design of experiments. Suppose that a study shows that “depressed children treated with an energy drink improve signi…cantly over a three-month period.” A causal interpretation would be completely unjusti…ed. The correlation between depression scores on successive occasions of testing is less than perfect, so there will regression to the mean. Depressed children will get somewhat better over time. Although this now seems obvious to us, there were many researchers who have made the mistake of confusing correlation with causation.

• Prof. Dr. Heiner Schumacher (KU Leuven)

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Regression to the Mean

The following example is taken from Max Bazerman’s “Judgment in Managerial Decision Making”. You are the sales forecaster for a department store chain. All stores are similar in size and merchandise selection, but their sales di¤er because of location, competition, and random factors. You are given the results for 2012 and asked to forecast sales for 2013. You have been instructed to accept the overall forecast of economists that sales will increase overall by 10%. How would you complete the following table?

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Regression to the Mean

Store 2011 2012 1 USD 11.000.000 ? 2 USD 23.000.000 ? 3 USD 18.000.000 ? 4 USD 29.000.000 ? Total USD 81.000.000 USD 89.100.000

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The Halo E¤ect

In many situations, evidence accumulates gradually and the interpretation is shaped by the emotion attached to the …rst impression. Consider the following descriptions of two people on their personality. What do you think of Alan and Ben? Alan: intelligent - industrious - impulsive -critical - stubborn - envious Ben: envious - stubborn - critical - impulsive - industrious - intelligent Most subjects view Alan much more favorably than Ben, because of …rst impressions. The tendency to like (or dislike) everything about a person is known as the halo e¤ect.

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The Halo E¤ect

Even Nobel Prize Winners may fall prey of the halo e¤ect. Daniel Kahneman reports that when grading exams the evaluations of the answers to di¤erent questions were quite homogeneous. In particular, this was the case when he evaluated a student’s essays in immediate succession. He changed the procedure to scoring all students’ answers to a question, and then going on to the next question. A student’s grades on several essays now were much more heterogeneous.

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Incentives and Behavior

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The Halo E¤ect

Clearly, the halo e¤ect can be exploited to in‡uence people.1 The following factors contribute to positive …rst impressions. A person’s attractiveness produces an halo e¤ect: attractive persons are assumed to have a more socially desirable personality (i.e., a higher level of altruism, stability, trustworthiness, extraversion, etc.) We also seem to like people who are similar to us (opinions, personality traits, background, lifestyle). Compliments can be used to create a halo e¤ect.

1See, for example, Cialdini, Robert (2009): In‡uence – Science and Practice,

Pearson, 5th Edition, Chapter 5 (“Liking - The Friendly Thief”).

• Prof. Dr. Heiner Schumacher (KU Leuven)

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