Simple Heuristics That Make Us Smart

1. Simple Heuristics That Make Us Smart Gerd Gigerenzer Max Planck Institute for Human Development Berlin

2. Which US city has more inhabitants, San Diego or San Antonio? Americans: 62% correct Germans: ? correct Germans: 100% correct Goldstein & Gigerenzer, 2002, Psychological Review

3. Recognition Heuristic If one of two objects is recognized and the other is not, then infer that the recognized object has the higher value. Ecological Rationality The heuristic is successful when ignorance is systematic rather than random, that is, when lack of recognition correlates with the criterion.

5. Wimbledon 2003 Correct Predictions 69% 70% 68% 66% 60% 50% ATP Entry Ranking ATP Champions Race Seedings Recognition Laypeople Recognition Amateurs Frings & Serwe (2004)

6. Wimbledon 2003 Correct Predictions 72% 69% 70% 68% 66% 66% 60% 50% ATP Entry Ranking ATP Champions Race Seedings Recognition Laypeople Recognition Amateurs Frings & Serwe (2004)

7. The Less-is-More Effect The expected proportion of correct inferences c is where is the number of recognized objects is the total number of objects is the recognition validity, and is the knowledge validity n N a b A less-is-more effect occurs when a>b

8. 80 b=.8 75 b =.7 70 Percentage of Correct Inferences (%) 65 b =.6 60 55 b =.5 50 0 50 100 Number of Objects Recognized (n)

9. Ignorance-based Decision Making:Recognition heuristic • kin recognition in animals  food choice & failures of aversion learning in rats (Galef et al. 1990) • overnight fame (Jacoby et al., 1989) • less-is-more effect (Goldstein & Gigerenzer, 2002) • advertisement without product information (Toscani, 1997) • consumer choice based on brand name • picking a portfolio of stocks (Borges et al.,1999; Boyd, 2001; Ortmann et al., in press) • Institutions competing for the public’s recognition memory

10. Gaze heuristic

11. Gaze heuristic

12. Gaze heuristic

13. Gaze heuristic

14. Gaze heuristic:One-reason Decision Making • Predation and pursuit: bats, birds, dragonflies, hoverflies, teleost fish, houseflies • Avoiding collisions: sailors, aircraft pilots • Sports: baseball outfielders, cricket, dogs catching Frisbees NOTE: Gaze heuristic ignores all causal relevant variables Shaffer et al., 2004, Psychological Science; McLeod et al., 2003, Nature

15. People don’t, deviations indicate reasoning fallacies Cognitive limitations People act like econometricians As-if optimization under constraints Three Visions of Bounded Rationality There is a world of rationality beyond optimizationFast and frugal heuristics:Ecological rationality Gigerenzer & Selten (Eds.) (2001). Bounded Rationality: The Adaptive Toolbox. MIT Press.

16. When Is Optimization Not An Option? Well-defined problems: • NP-hard problems (e.g., chess, traveling salesman, Tetris, minesweeper) • Criterion lacks sufficient precision (e.g., Mill’s greatest happiness of all; acoustics of concert hall) • Multiple goals or criteria (e.g.,shortest, fastest, and most scenic route) • Problem is unfamiliar and time is scarce (Selten, 2001) • In domains like mate choice and friendship, “calculated” optimization can be morally unacceptable. All ill-defined problems

17. Four Mistaken Beliefs People use heuristics because of their cognitive limits. Real-world problems can always be solved by optimization. Heuristics are always second-best solutions. More information is always better.

18. The Science of Heuristics Descriptive Goal: The Adaptive Toolbox What are the heuristics, their building blocks, and the abilities they exploit? Normative Goal: Ecological rationality What class of problems can a given heuristic solve? Engineering Goal: Design Design heuristics that solve given problems. Design environments that fit given heuristics. Gigerenzer et al. (1999). Simple heuristics that make us smart. Oxford University Press.

19. The Adaptive Toolbox: Heuristics, Building Blocks, Evolved Abilities Gaze heuristic: “fixate ball, start running, keep angle constant” Building block: “fixate ball” Evolved ability: object tracking Tit-For-Tat: “cooperate first, keep memory of size one, then imitate.” Building block: “cooperate first” Evolved ability: reciprocal altruism

20. Take The Best Search rule: Look up the cue with highest validity vi Stopping rule: If cue values discriminate (+/-), stop search. Otherwise go back to search rule. Decision rule: Predict that the alternative with the positive cue value has the higher criterion value. Tallying Search rule: Look up a cue randomly. Stopping rule: After m (1<m≤M) cues stop search. Decision rule: Predict that the alternative with the higher number of positive cue values has the higher criterion value. Sequential Search Heuristics Don’t add Don’t weight

21. Class Ignorance-based decisions One-reason decisions Tallying Elimination Satisficing Motion pattern Cooperation Heuristic Recognition heuristic, fluency heuristic Take The Best, Take The Last, QuickEst, fast & frugal tree Tally-3 Elimination-by-aspect, categorization-by-elimination Sequential mate search, fixed or adjustable aspiration levels Gaze heuristic, motion-to-intention heuristics Tit-for-tat, behavior-copying The Adaptive Toolbox: Heuristics

22. Heuristic Take The Best Take The Last Minimalist Fast and frugal tree Tally-3 QuickEst Satisficing Building Blocks Search rules: Ordered search Recency search Random search Stopping rules One-reason stopping Tally-n stopping (n>1) Elimination Aspiration level Decision rules Tally-n One-reason decision making The Adaptive Toolbox: Building Blocks

23. Heuristic Take The Best Take The Last Minimalist Fast and frugal tree Tally-3 QuickEst Satisficing Building Blocks Search rules: Ordered search Recency search Random search Stopping rules One-reason stopping Tally-n stopping (n>1) Elimination Aspiration level Decision rules Tally-n One-reason decision making The Adaptive Toolbox: Building Blocks

24. Heuristic Take The Best Take The Last Minimalist Fast and frugal tree Tally-3 QuickEst Satisficing Building Blocks Search rules: Ordered search Recency search Random search Stopping rules One-reason stopping Tally-n stopping (n>1) Elimination Aspiration level Decision rules Tally-n One-reason decision making The Adaptive Toolbox: Building Blocks

25. Heuristic Take The Best Take The Last Minimalist Fast and frugal tree Tally-3 QuickEst Satisficing Building Blocks Search rules: Ordered search Recency search Random search Stopping rules One-reason stopping Tally-n stopping (n>1) Elimination Aspiration level Decision rules Tally-n One-reason decision making The Adaptive Toolbox: Building Blocks

26. Heuristic Take The Best Take The Last Minimalist Fast and frugal tree Tally-3 QuickEst Satisficing Building Blocks Search rules: Ordered search Recency search Random search Stopping rules One-reason stopping Tally-n stopping (n>1) Elimination Aspiration level Decision rules Tally-n One-reason decision making The Adaptive Toolbox: Building Blocks

27. Heuristic Take The Best Take The Last Minimalist Fast and frugal tree Tally-3 QuickEst Satisficing Building Blocks Search rules: Ordered search Recency search Random search Stopping rules One-reason stopping Tally-n stopping (n>1) Elimination Aspiration level Decision rules Tally-n One-reason decision making The Adaptive Toolbox: Building Blocks

28. Heuristic Take The Best Take The Last Minimalist Fast and frugal tree Tally-3 QuickEst Satisficing Building Blocks Search rules: Ordered search Recency search Random search Stopping rules One-reason stopping Tally-n stopping (n>1) Elimination Aspiration level Decision rules Tally-n One-reason decision making The Adaptive Toolbox: Building Blocks

29. Ecological Rationality

30. Take The Best Search rule: Look up the cue with highest validity vi Stopping rule: If cue values discriminate (+/-), stop search. Otherwise go back to search rule. Decision rule: Predict that the alternative with the positive cue value has the higher criterion value. Tallying Search rule: Look up a cue randomly. Stopping rule: After m (1<m≤M) cues stop search. Decision rule: Predict that the alternative with the higher number of positive cue values has the higher criterion value. Sequential Search Heuristics Don’t add Don’t weight

31. Ecological Rationality Take The Best Tallying non-compensatory compensatory Weight 1 2 3 4 5 1 2 3 4 5 Cue Cue Martignon & Hoffrage (1999), In: Gigerenzer et al., Simple heuristics that make us smart. Oxford University Press

32. Reinforcement learning: Which heuristics to use? 100 Non-compensatoryFeedback 90 80 70 60 Choices predicted by Take The Best (%) 50 40 CompensatoryFeedback 30 20 10 0 0-24 25-48 49-72 73-96 97-120 121-144 145-168 Feedback Trials Rieskamp & Otto (2004)

33. Heuristic Recognition heuristic Take The Best; Fast & frugal tree Tallying QuickEst Imitation Environment alpha > .5 Noncompensatory information: Scarce information: M<log2N Compensatory information, abundant information J-shaped distribution of objects on the criterion Stable environments, reliable information wj > ∑ wk k>j Ecological Rationality Boyd & Richerson (2001); Goldstein et al. (2001); Hogarth & Karalaia (in press); Martignon & Hoffrage (1999, 2002).;

34. Robustness

35. How accurate are fast and frugal heuristics? • Homelessness rates (50 cities in the United States) • Attractiveness judgments of famous men and women • Average motor fuel consumption per person (all states in the United States) • Rent per acre paid (58 counties in Minnesota) • House prices (Erie, Pennsylvania) • Professors' salaries (a midwestern college) • Car accident rate per million vehicle miles (Minnesota highways) • High school drop-out rates (all high schools in Chicago) Czerlinski, Gigerenzer, & Goldstein (1999), In: Gigerenzer et al.,Simple heuristics that make us smart. OUP

36. Robustness 75 70 Take The Best Tallying Accuracy (% correct) Multiple Regression 65 Minimalist 60 55 Czerlinski, Gigerenzer, & Goldstein (1999) Fitting Prediction

37. Robustness: What if predictors are quantitative? Czerlinski et al. (1999): Multiple regression (MR) improves performance (76%) but so does Take The Best (76%)Hogarth & Karelaia (2004): Take The Best is more accurate than MR if: - variability in cue validities is high - average intercorrelation between cues ≥ .5 - ratio of cues to observations is highAkaike’s Theorem: Assume two models belong to a nested family where one has fewer adjustable parameters than the other. If both have, on average, the same number of correct inferences on the training set, then the simpler model (i.e., the one with fewer adjustable parameters) will have greater (or at least the same) predictive accuracy on the test set.

38. Design

39. The heart disease predictive instrument (HDPI) Chest Pain = Chief Complaint EKG (ST, T wave ∆'s) History ST&T Ø ST T ST ST&T ST&T No MI& No NTG 19% 35% 42% 54% 62% 78% MI or NTG 27% 46% 53% 64% 73% 85% MI and NTG 37% 58% 65% 75% 80% 90% Chest Pain, NOT Chief Complaint EKG (ST, T wave ∆'s) History ST&T Ø ST T ST ST&T ST&T No MI& No NTG 10% 21% 26% 36% 45% 64% MI or NTG 16% 29% 36% 48% 56% 74% MI and NTG 22% 40% 47% 59% 67% 82% No Chest Pain EKG (ST, T wave ∆'s) History ST&T Ø ST T ST ST&T ST&T No MI& No NTG 4% 9% 12% 17% 23% 39% MI or NTG 6% 14% 17% 25% 32% 51% MI and NTG 10% 20% 25% 35% 43% 62% See reverse for definitions and instructions

40. Fast and frugal classification: Heart disease ST segment changes? no yes Coronary Care Unit chief complaint of chest pain? yes no regular nursing bed any one other factor? (NTG, MI,ST,ST,T) no yes regular nursing bed Coronary Care Unit Green & Mehr (1997)

41. Emergency Room Decisions: Admit to the Coronary Care Unit? 1 .9 .8 .7 .6 SensitivityProportion correctly assigned Physicians .5 Heart DiseasePredictive Instrument .4 Fast and Frugal Tree .3 .2 .1 .0 .0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 False positive rateProportion of patients incorrectly assigned

42. Sequential Search Heuristics:One-reason decision making Where? • “Rules of thumb” in non-verbal animals • Bail decisions in London courts (Dhami, 2003)  Patient allocation to coronary care unit (Green & Mehr, 1997) • Prescription of antibiotics to young children (Fischer et al. 2002) • Parent’s choice of doctor when child is seriously ill (Scott, 2002) • Physician’s prescription of lipid-lowering drugs (Dhami & Harris, 2001) • Voting and evaluating political parties (Gigerenzer, 1982) Why? • Fisher’s “runaway” theory of sexual selection • Zahavi’s handicap principle • Environmental structures • Robustness • Speed, frugality, and transparency

43. The Science of Heuristics The Adaptive Toolbox Ecological rationality Design Gigerenzer et al. (1999). Simple Heuristics That Make Us Smart. Oxford University Press. Gigerenzer & Selten (Eds.) (2001). Bounded Rationality: The Adaptive Toolbox. MIT Press.

44. Notes • Start with “if you open a textbook”, then “the term heuristic of of Greek origin” • Collective wisdom arising from individual ignorance: - Researchers from Oxford University and from the Georgia Institute of Technology developed computer programs mimicking honey bee heuristics to solve the problem of allocate computers to different applications when internet traffic is highly unpredictable. Economist, April17, 2004, p. 78-9. • Selten, in his Nobel Laureate speech, used the term “repair program” • A widely shared conclusion among decision theorists: neglecting attributes means neglecting information, thereby violating a central principle of good decision making. • Duration: 45-50 minutes