Heuristics & Biases

# Heuristics & Biases

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## Heuristics & Biases

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1. Heuristics & Biases MAR 3053 February 28, 2012

2. The use and misuse of affect, availability, representative-ness, and anchors Part 1: Heuristics & intuitive judgment

3. Two systems of reasoning System 1 System 2 “Reflective” Controlled Effortful Slow & often serial May be abstract Rule-based • “Intuitive” • Automatic • Effortless • Rapid & parallel • Concrete • Associative

4. Which bet would you choose? 1 in 10 9 in 100

5. Who chooses the large box? Percentage of participants choosing the box with greater # of total balls (odds with small box = 10%; odds with large box = value shown on x-axis)

6. What is a heuristic? • “Mental shortcut” used in judgment and decision making • Essential for living in an uncertain world • But they can lead to faulty beliefs and suboptimal decisions • By looking at errors and biases, we can learn how people are reasoning under uncertainty

7. Two types of heuristics • Special purpose heuristics – use restricted to specific domains • Height as a guide for ability as basketball player • # of publications as guide for quality as an academic • General use heuristics • Affect • Availability • Representativeness (similarity)

8. The affect heuristic • ## migrating birds die each year by drowning in uncovered oil ponds, which the birds mistake for bodies of water. Covering the ponds with nets could prevent these deaths. How much money would you be willing to pay to provide the needed nets? • 2,000 birds -- \$80 • 20,000 birds -- \$78 • 200,000 birds -- \$88

9. The identifiable victim effect • “A death of a single Russian solder is a tragedy. A million deaths is a statistic.” – Joseph Stalin

10. Affect • Judgments of life happiness: • People asked 2 questions: • 1) How satisfied are you with your life these days? • 2) How many dates have you had in the last month? • Correlation = -.12 • Another group asked in opposite order – 2), then 1) • Correlation = .66 Strack et al., 1993

11. The availability heuristic • Making judgments about the frequency or likelihood of an event based on the ease with which evidence or examples come to mind • Example: Category size

12. Availability • Egocentric allocations of responsibility: “Overclaiming” • People claim more responsibility for collective endeavors than is logically possible • Self-allocations sum to more than 100% • Why? Because one’s own contributions are more available than those of others

13. Availability • Experimental evidence • Married couples asked to allocate responsibility for: • Positive events: Making breakfast, planning activities, shopping for family, making important decisions • Negative events: Causing arguments, causing messes, irritating spouse • Results: • Overclaiming occurred for 16 of 20 activities • Equivalent overclaiming for positive and negative events Ross & Sicoly, 1979; Kruger & Gilovich, 1999

14. Availability • What is availability? Two possibilities: • 1. Number – amount of information generated • 2. Ease – the ease with which information can be generated • Iconic study teased them apart: • Participants were asked to evaluate their own assertiveness… • By generating either 6 (easy) or 12 (hard) examples of assertiveness or unassertiveness

15. Availability: number versus ease Moral: Ease influences judgments sometimes in spite of number Schwarz et al., 1991

16. Representativeness • Determining class inclusion or likelihood by similarity: • A member ought to resemble the overall category • An effect ought to resemble or be similar to the cause • An outcome ought to resemble the process that produced it • Like goes with like • Often easier to assess similarity than probability • Does he look like an engineer? • Does it look like it could cause a clogged artery? • Does it look like a random sequence?

17. Representativeness • Leads to several classic judgment errors • Conjunction fallacy • Misperceiving randomness • Regression fallacy

18. The 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 criminal justice, and also participated in anti-nuclear demonstrations. • Rank likelihood that Linda is: • A teacher in elementary school • Active in the feminist movement • A member of the League of Women Voters • A bank teller • An insurance salesperson • A bank teller and active in the feminist movement

19. The Linda problem • Class data (rankings—lower numbers mean more likely): • Active in the feminist movement: • A bank teller: • Active in feminist movement a bank teller:

20. Representativeness: Conjunction fallacy • Judging the conjunction of two events to be more probable than one of the constituent elements Feminists Bank tellers P(A & B) > P(A) or P(B) /

21. Conjunction fallacy • How much would you be willing to pay for a new insurance policy that would cover hospitalization for: • 1. Any disease or accident • Mean = \$89.10 • 2. Any reason • Mean = \$41.53 Johnson et al., 1993

22. Conjunction fallacy • How much would you be willing to pay for flight insurance (1 flight to London) that covers death due to: • 1. Any act of terrorism • Mean = \$14.12 • 2. Any reason • Mean = \$12.03 Johnson et al., 1993

23. Representativeness: Randomness • Effects should resemble the process that produced them • If something is random, it should look random • What does random look like? • HTHHHTTTTHTHHTTTHHHTH • HTHTHTTTHHTHTHTTHHHTH

24. The hot hand • “If I’m on, I find that confidence just builds…you feel nobody can stop you. It’s important to hit that first one, especially if it’s a swish. Then you hit another, and…you feel like you can do anything.” • --Lloyd Free (a.k.a. World B. Free)

25. The hot hand • The belief that success breeds success, and failure breeds failure • 100 basketball fans… • 91% thought player has a better chance of making a shot after having just made his last two or three shots than he does after having just missed his last two or three shots • Given a player who makes 50% of his shots, subjects thought that shooting percentage would be… • 61% after having just made a shot • 42% after having just missed a shot • 84% thought that it’s important to pass the ball to someone who has just made several shots in a row Gilovich, Vallone, & Tversky, 1985

26. The hot hand • Calculate probability of making a shot after missing previous 1, 2, or 3 shots and after making previous 1, 2, or 3 shots Gilovich, Vallone, & Tversky, 1985

27. What the hot hand results mean • “The independence between successive shots, of course, does not mean that basketball is a game of chance rather than skill, nor should it render the game less exciting to play, watch, or analyze. It merely indicates that the probability of a hit is largely independent of the outcome of previous shots, although it surely depends on other parameters such as skill, distance to the basket, and defensive pressure…The availability of plausible explanations may contribute to the erroneous belief that the probability of a hit is greater following a hit than following a miss.” • –Gilovich et al., 1985, pp.312-313

28. Regression to the mean

29. The SI jinx

30. The SI jinx • In sports (the SI jinx, the sophomore slump, rehiring the interim manager, etc.) • In education (the illusory superiority of punishment over reward) • In medicine (why it’s so easy to believe that a worthless “remedy” really works) • In politics (be careful about taking office during an economic boom or a drop in crime)

31. Overconfidence and its causes Part 2: Biases

32. Overconfidence in social predictions • Would the target person… • Prefer to subscribe to Playboy or the New York Review of Books? • Describe his/her lecture notes as neat or messy? • Say s/he would pocket or turn in \$5 found on the ground? • Object when the experimenter referred to him/her by the wrong name? • Comb his/her hair before posing for a photograph in the lab? • How confident are you in your answer (50-100%)? • Mean confidence: 75.7% • Mean accuracy: 60.8% • When 100% confident, accuracy = 78.5%! Dunning et al., 1990

33. Overconfidence in self predictions • Will you… • Visit San Francisco more than 3 times this year? • Participate in the dorm play? • Drop a course? • Question your decision to attend Stanford? • Become best friends with your roommate? • Visit a friend more than 100 miles away? • Get a new boy/girlfriend? • Overall confidence: 82.3% • Overall accuracy: 68.2% • When participants were 100% confident, they were correct only 77.4% of the time! Vallone et al., 1990

34. Causes of overconfidence • Hindsight bias • Motivated and non-motivated confirmatory thinking • Confirmation bias • Wishful thinking • Naïve realism

35. Naïve realism • You drive up to San Francisco with friends to celebrate the end of the quarter. The plans include dinner and then some entertainment afterward. • How much money will you personally spend on the dinner? • You receive a telephone call from a survey firm. You initially agree to answer some questions. There is a long series of questions • How many minutes will you spend answering questions before you end the call? Griffin, Dunning, & Ross, 1990

36. Naïve realism • Three conditions: • Control condition: Confidence intervals simply given a second time • “Assumers” condition: Asked to assume that their image of the situation was, in fact, correct in all details • Multiple construal condition: Asked to describe several alternative ways the situation they would be in could turn out Griffin, Dunning, & Ross, 1990

37. Naïve realism Griffin, Dunning, & Ross, 1990

38. Summary • Engage “System 2” • Learn the common errors that people make in our uncertain world • They rely too much on affect, availability and representativeness • They’re overconfident in their decisions • Take a skeptical mindset even when you like an initial judgment • Don’t be an “assumer” • Invoke an audience to which you need to justify your thinking • Next time: What is construal?