The document discusses how simple heuristics can make people smart. It describes research showing that heuristics like recognition heuristic and gaze heuristic allow people to make accurate inferences and decisions using very little time and information. These heuristics are composed of basic building blocks like search rules, stopping rules, and decision rules. The use of heuristics is described as ecologically rational because it can perform well in environments with scarce information, unstable conditions, or multiple goals.

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

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

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

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

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

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. Three Visions of Bounded Rationality People don’t, deviations indicate reasoning fallacies Cognitive limitations People act like econometricians As-if optimization under constraints There is a world of rationality beyond optimization Fast 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. Sequential Search Heuristics Take The Best Search rule: Look up the cue with highest validity v i 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. Don’t add Don’t weight

21. The Adaptive Toolbox: Heuristics 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

22. The Adaptive Toolbox: Building Blocks 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

23. The Adaptive Toolbox: Building Blocks 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

24. The Adaptive Toolbox: Building Blocks 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

25. The Adaptive Toolbox: Building Blocks 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

26. The Adaptive Toolbox: Building Blocks 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

27. The Adaptive Toolbox: Building Blocks 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

28. The Adaptive Toolbox: Building Blocks 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

29. Ecological Rationality

30. Sequential Search Heuristics Take The Best Search rule: Look up the cue with highest validity v i 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. Don’t add Don’t weight

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

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

33. Ecological Rationality Heuristic Recognition heuristic Take The Best; Fast & frugal tree Tallying QuickEst Imitation Environment alpha > .5 Noncompensatory information: Scarce information: M<log 2 N Compensatory information, abundant information J-shaped distribution of objects on the criterion Stable environments, reliable information Boyd & Richerson (2001); Goldstein et al. (2001); Hogarth & Karalaia (in press); Martignon & Hoffrage (1999, 2002). ; w j > ∑ w k k>j

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

37. 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 high Akaike’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. Robustness: What if predictors are quantitative?

38. Design

39. 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% The heart disease predictive instrument (HDPI) See reverse for definitions and instructions

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

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

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