The Representativeness Heuristic & Framing Effects in the Psych of Choice

1. The Representativeness Heuristic& Framing Effects in the Psych of Choice Psychology 355: Cognitive PsychologyInstructor: John Miyamoto06/04/2014: Lecture 10-3 This Powerpoint presentation may contain macros that were used to create the slides. The macros aren’t needed to view the slides. If necessary, you can disable the macros without any change to the presentation.

2. Outline • Representativeness heuristic – a heuristic for judging probability • Risk aversion and risk seeking – the technical definition and some examples • Framing effects in preferential choice Main Claims of the Heuristics and Biases Program Psych 355, Miyamoto, Spr '14

3. Main Claims of the Heuristics & Biases (H&B) Movement • Human cognitive processes do not follow the pattern of a rational model. • (Rational model = expected utility theory & Bayesian decision model) • Human decision making uses heuristic reasoning strategies – reasoning strategies that are useful because they are easy and generally effective, even though they can lead to errors. Heuristic reasoning strategies .... • .... are often fast and effective, • .... place low demands on cognitive resources. • .... but they can lead to errors in particular situations. Representativeness Heuristics - Intro Psych 355, Miyamoto, Spr '14

4. Representativeness Heuristic Event A is more representativethan Event B Event A is more probable than Event B Representativeness Heuristic: Events that are more representative are regarded as more probable. "more representative" means "more similar to a stereotype or to a typical member of a class." Example of Jim: An Atheletic, Muscular & Competitive Guy Psych 355, Miyamoto, Spr '14

5. Representativeness Heuristic – An Example This is the better bet. Question: Jim is tall and very muscular. He's also very competitive. He drives an expensive car and wears flashy clothing. Which is more probable? • Jim is a professional athlete. • Jim is a lawyer or financial analyst. • People predict that Jim is a professional athlete because Jim is similar to a stereotype of a professional athlete. • It is a better bet that Jim is a lawyer or financial analyst because there are many more lawyers and financial analysts than professional athletes. This response is predicted by theRepresentativeness Heuristic Return to Slide with Diagram of Representativeness Heuristic Psych 355, Miyamoto, Spr '14

6. Representativeness Heuristic a professional athlete? a lawyer or financial analyst? Event A is more representativethan Event B Event A is more probable than Event B Is he ..... Representativeness Heuristic: Events that are more representative are regarded as more probable. • Example: Jim is muscular/athletic/competitive. Jim is more similar to the stereotype. Jim is less similar to the stereotype. Lawyer/Engineer Problem Psych 355, Miyamoto, Spr '14

7. Lawyer/Engineer Problem (K&T, 1973) DESCRIPTION OF JACK: Jack is a 45-year-old man. He is married and has four children. He is generally conservative, careful, and ambitious. He shows no interest in political and social issues. • 30:70 Condition: High Base Rate for EngineerIf Jack's description were drawn at random from a set of 30 descriptions of lawyers and 70 descriptions of engineers, what would be the probability that Jack is one of the engineers? • 70:30 Condition: Low Base Rate for EngineerIf Jack's description were drawn at random from a set of 70 descriptions of lawyers and 30 descriptions of engineers, what would be the probability that Jack is one of the engineers? Findings re Lawyer/Engineer Problem Psych 355, Miyamoto, Spr '14

8. Findings re Lawyer/Engineer Problem • Result: Subjects rate “engineer” as equally probable when the base rate is low and when it is high. • High base rate condition = 30 lawyers:70 engineers Low base rate condition = 70 lawyers:30 engineers • Probability theory implies that Jack is much more likely to be an engineer in the high base rate condition than in the low base rate condition. • This behavior is called “insensitivity to base rate” • Why do people ignore base rates? See next slide Why Do People Ignore Base Rates? The Representativeness Explanation Psych 355, Miyamoto, Spr '14

9. Why Do People Often Ignore Base Rates? The representativeness heuristic emphasizes the points: • Jack is equally representative of a typical engineer in the low and high base rate conditions. • The base rate is irrelevant to the similarity of Jack to the stereotype of an engineer. • If people base the probability judgment on the similarity of Jack to the stereotypic engineer, they will ignore the base ratebecause it is irrelevant to the similarity judgment. Probability theory shows that the base rate is very relevant to judging the probability that Jack is an engineer, butcognitive theory shows that it is often not psychologically relevant to judging the probability that Jack is an engineer. When Does It Matter Whether People Ignore Base Rates? Psych 355, Miyamoto, Spr '14

10. When Does It Matter Whether People Ignore Base Rates? • Evidence shows that physicians sometimes overlook base rates when attempting to diagnose a disease. • Evidence suggests that investors are overly influenced by short-term information regarding the value of stocks. • Business decisions tend to be overly influenced by short-term trends. Criticism of Goldstein’s Description of the Lawyer/Engineer Problem Psych 355, Miyamoto, Spr '14

11. Criticism of GoldsteinTextbook Description of the Lawyer/Engineer Problem • The Goldstein description of this study is inadequate because it does not contrast the 30:70 condition with the 70:30 condition. It only mentions the 70:30 condition. • The important finding is that subjects in the 30:70 and 70:30 conditions are equally confident that Jack is an engineer (subjects in the two conditions overlook the difference in the base rate). • Knowing only the result for the 70:30 condition does not establish that subjects ignore base rates. • See p. 372. Ignorance of Regression Effects Psych 355, Miyamoto, Spr '14

12. Statistical Theory Implies that Regression Effects Will Occur • Very tall fathers tend to have sons who are not quite as tall. • Very short fathers tend to have sons who are somewhat taller. • Why? very short father very tall father fairly short son fairly tall son Psych 355, Miyamoto, Spr '14

13. Statistical Theory Implies that Regression Effects Will Occur • Statistical reason for regression effects: A predicted value will be closer to the mean than is the variable on which the prediction is based. Zpredicted Y =   ZX ZpredictedY = predicted z-score for Y  = the population correlation between X and Y ZX= z-score for the predictor X • Implication: If X and Y are not perfectly correlated, then the predicted value of Y is always closer to its mean than the value of X. very short father very tall father fairly short son fairly tall son Psych 355, Miyamoto, Spr '14

14. People's Tendency to Ignore Regression Effects • Sophomore Slump: A baseball player who does exceptionally well during his rookie season often does noticeably worse during his sophomore (second) season. Why does this happen? • Reason: A player’s batting average during the rookie season is not perfectly correlated with his batting average during the sophomore season. Therefore, on the average, a regression effect is inevitable. • Same holds for any other statistic, like number of home runs, stolen bases, earned run average (for a pitcher), etc. Misconceptions of Regressions – Other Examples Psych 355, Miyamoto, Spr '14

15. Misconceptions of Regression – Other Examples • Israeli flight instructors and the effects of praise and punishment. • Evaluating medical treatments or psychotherapies that select patients who are already in extreme difficulty. Why Do People Overlook Regression Effects? Psych 355, Miyamoto, Spr '14

16. Why Does the Representativeness Heuristic Cause People to Overlook Regression Effects? • Suppose a pilot just made a terrible landing. What should you predict for the next landing? • Another terrible landing (most similar outcome) A bad landing that is not as bad as the first landing (less similar to the previous landing, but it is more probable because of regression to the mean). • People predict another terrible landing because it is the most similar outcome, while ignoring a factor, regression to the mean, that is statistically relevant, but not related to similarity. Conjunction Fallacies Psych 355, Miyamoto, Spr '14

17. Conjunction Fallacies – The Famous Linda Problem Linda is 31 years 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 anti-nuclear demonstrations. F: Judge the probability that Linda is a feminist. T: Judge the probability that Linda is a bank teller. F & T: Judge the probability that Linda is a feminist and a bank teller. • Probability Theory: P(F) ≥ P(F & T), P(T) ≥ P(F & T) • Typical Judgment: P(F) > P(F & T) > P(T) Why Are Conjunction Fallacies Psychologically Interesting? Psych 355, Miyamoto, Spr '14

18. Why Conjunction Fallacies Are Psychologically Interesting? • Conjunction fallacies strongly support the claim: Human reasoning with uncertainty is different from probability theory. • Human reasoning with uncertainty is based on a various heuristics – the conjunction fallacy is caused by the use of a representativeness heuristic. Two Question Regarding Conjunction Fallacies: • What is wrong with the judgment pattern: P(F) > P(F & BT) > P(BT)? • Why do people's judgments have this pattern? Probability & the Set Inclusion Principle Psych 355, Miyamoto, Spr '14

19. Probability and the Set Inclusion Principle Sample Space (set of all possibilities) B A • If set B is a subset of set A, then the probability of B must be equal or less than the probability of A. B  A  P(B) < P(A) Rationale: When B occurs, A also occurs, so the probability of B cannot exceed the probability of A. Interpretation of Linda Problem in terms of Set Inclusion Psych 355, Miyamoto, Spr '14

20. Conjunction Fallacy Sample Space T F & T F F: Judge the probability that Linda is a feminist. T: Judge the probability that Linda is a bank teller. F & T: Judge the probability that Linda is a feminist and a bank teller. • Probability Theory: P(F) ≥ P(F & T), P(T) ≥ P(F & T) • Typical Judgment: P(F) > P(F & T) > P(T) Linda Problem: Linda is 31 years 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 anti-nuclear demonstrations. Why Do People Make Conjunction Errors? Psych 355, Miyamoto, Spr '14

21. Why Do People Make Conjunction Errors? • Remember: The representativeness heuristic predicts that people judge the probability based on how similar the individual case is to a typical member (stereotype) of a group. • The description of Linda sounds more similar to someone who is a feminist and a bank teller, than to someone who is only a bank teller. Bank TellerPrototype weaker similarity Descriptionof Linda stronger similarity Feminist Bank TellerPrototype Criticisms of the Representativeness Explanation of Conjunction Fallacies Psych 355, Miyamoto, Spr '14

22. Criticisms of This Interpretation • Criticism: The Linda problem is just one problem.Reply: Same pattern is found with many similar problems. • Criticism: Maybe people think “bank teller” means someone who is a bank teller and not a feminist. • Criticism: Conjunction errors can be eliminated by statingthe question in terms of frequencies instead of probabilities. Summary re Representativeness Heuristic Psych 355, Miyamoto, Spr '14

23. Summaryre Representativeness Heuristic • There is nothing wrong with using similarity as a relevant factor in judging a probability. • The problem is that attention to similarity causes people to ignore other factors, like base rates, regression effects and set inclusion, that are also relevant to judging probability. Consequences of the Use of the Representativeness Heuristic • Base rate neglect • Conjunction errors • Overlooking the importance of sample size • Overlooking regression effects Two Major Issues in the Psychology of Decision Making Psych 355, Miyamoto, Spr '14

24. Two Major Issues in Psychology of Decision Making • Judgments of likelihood • What outcomes are likely? Which are unlikely? • How likely? Slightly possible? Almost certain? Etc. • Judgments of preference & making choices • How strongly do you like or dislike differentpossible outcomes? • How risky are difference choices? • What risks are worth taking for potential gains? We’ve been talking briefly about this topic. Next topic. Basic Questions of Psych of Preference Psych 355, Miyamoto, Spr '14

25. Wednesday, June 04, 2014: The Lecture Ended Here Psych 355, Miyamoto, Spr '14