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Choice by Heuristics

Choice by Heuristics. Eduard Brandstätter Johannes Kepler University of Linz Austria Conference of the Economic Science Association, Rome, June 30, 2007. Overview. Expectancy-value theories Problems Priority Heuristic Conclusion. Expectancy-Value Theories.

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Choice by Heuristics

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  1. Choice by Heuristics Eduard Brandstätter Johannes Kepler University of Linz Austria Conference of the Economic Science Association, Rome, June 30, 2007

  2. Overview • Expectancy-value theories • Problems • Priority Heuristic • Conclusion

  3. Expectancy-Value Theories Utility = ∑Probability x Value • Expected-value theory • Expected-utility theory • Prospect theory • Cumulative prospect theory • Security-potential/aspiration theory • Transfer of attention exchange model • Disappointment theory • Regret theory • Decision affect theory

  4. Heuristics!

  5. Three Steps • Check for dominance • Check for easy choice • Employ the priority heuristic • Brandstätter, E., Gigerenzer, G., & Hertwig, R. (2006). The priority heuristic: Making choices without trade-offs. Psychological Review, 113, 409-432.

  6. Problem A 80% chance to win $5,000 20% chance to win $0 B 2% chance to win $4,010 98% chance to win $4,000 What would you choose? A or B? O A O B

  7. Priority Heuristic A 80% chance to win $5,000 20% chance to win $0 B 2% chance to win $4,010 98% chance to win $4,000 • Three Reasons • Minimum gains • Chances of the minimum gains • Maximum gains

  8. Priority Heuristic A 80% chance to win $5,000 20% chance to win $0 B 2% chance to win $4,010 98% chance to win $4,000 STOP • Priority Rule • Do the minimum gains differ?

  9. Problem C 40% chance to win $5,000 60% chance to win $0 D 80% chance to win $2,500 20% chance to win $0 What would you choose? C or D? O C O D

  10. Priority Heuristic C 40% chance to win $5,000 60% chance to win $0 D 80% chance to win $2,500 20% chance to win $0 STOP • Priority Rule • Do the minimum gains differ? • Do the chances of the minimum gains differ?

  11. Problem E 0.001% chance to win $5,000 99.999% chance to win $0 F 0.002% chance to win $2,500 99.998% chance to win $0 What would you choose? E or F? O E O F

  12. Priority Heuristic E 0.001% chance to win $5,000 99.999% chance to win $0 F 0.002% chance to win $2,500 99.998% chance to win $0 Choose E! • Priority Rule • Do the minimum gains differ? • Do the chances of the minimum gains differ? • Choose the gamble with thehigher maximum gain!

  13. Questions When do the minimum gains differ? When do the chances differ?

  14. Aspiration Levels Minimum Gains10% of the highest gain of the decision problem Chances 10% E 0.001% chance to win $5,000 99.999% chance to win $0 F 0.002% chance to win $2,500 99.998% chance to win $0 Aspiration Levels: $500, 10%

  15. 100 1 0 0 9 0 90 8 0 80 7 0 70 ) % ( 6 0 60 s n o i t c i d 5 0 50 e r P t c e 40 4 0 r r o C 3 0 30 2 0 20 10 1 0 0 0 E q u i - E q u a l - M i n i - M a x i - B e t t e r T a l l y i n g M o s t L e x i c o - L e a s t P r o b a b l e p r o b a b l e w e i g h t m a x m a x t h a n l i k e l y g r a p h i c l i k e l y a v e r a g e Results (Kahneman & Tversky, 1979)

  16. 100 1 0 0 9 0 90 8 0 80 7 0 70 ) % ( 6 0 60 s n o i t c i d 5 0 50 e r P t c e 40 4 0 r r o C 3 0 30 2 0 20 10 1 0 0 0 T A X E q u i - E q u a l - M i n i - M a x i - B e t t e r T a l l y i n g M o s t L e x i c o - L e a s t P r o b a b l e p r o b a b l e w e i g h t m a x m a x t h a n l i k e l y g r a p h i c l i k e l y a v e r a g e Results (Kahneman & Tversky, 1979)

  17. 100 1 0 0 9 0 90 8 0 80 7 0 70 ) % ( 6 0 60 s n o i t c i d 5 0 50 e r P t c e 40 4 0 r r o C 3 0 30 2 0 20 10 1 0 0 0 S P A T A X E q u i - E q u a l - M i n i - M a x i - B e t t e r T a l l y i n g M o s t L e x i c o - L e a s t P r o b a b l e p r o b a b l e w e i g h t m a x m a x t h a n l i k e l y g r a p h i c l i k e l y a v e r a g e Results (Kahneman & Tversky, 1979)

  18. 100 1 0 0 9 0 90 8 0 80 7 0 70 ) % ( 6 0 60 s n o i t c i d 5 0 50 e r P t c e 40 4 0 r r o C 3 0 30 2 0 20 10 1 0 0 0 C P T S P A T A X E q u i - E q u a l - M i n i - M a x i - B e t t e r T a l l y i n g M o s t L e x i c o - L e a s t P r o b a b l e E r e v e t a l . p r o b a b l e w e i g h t m a x m a x t h a n l i k e l y g r a p h i c l i k e l y ( 2 0 0 2 ) a v e r a g e Results (Kahneman & Tversky, 1979)

  19. 100 1 0 0 9 0 90 8 0 80 7 0 70 ) % ( 6 0 60 s n o i t c i d 5 0 50 e r P t c e 40 4 0 r r o C 3 0 30 2 0 20 10 1 0 0 0 C P T C P T S P A T A X E q u i - E q u a l - M i n i - M a x i - B e t t e r T a l l y i n g M o s t L e x i c o - L e a s t P r o b a b l e T & K E r e v e t a l . p r o b a b l e w e i g h t m a x m a x t h a n l i k e l y g r a p h i c l i k e l y ( 1 9 9 2 ) ( 2 0 0 2 ) a v e r a g e Results (Kahneman & Tversky, 1979)

  20. 100 1 0 0 9 0 90 8 0 80 7 0 70 ) % ( 6 0 60 s n o i t c i d 5 0 50 e r P t c e 40 4 0 r r o C 3 0 30 2 0 20 10 1 0 0 0 C P T C P T C P T S P A T A X E q u i - E q u a l - M i n i - M a x i - B e t t e r T a l l y i n g M o s t L e x i c o - L e a s t P r o b a b l e L & O T & K E r e v e t a l . p r o b a b l e w e i g h t m a x m a x t h a n l i k e l y g r a p h i c l i k e l y ( 1 9 9 9 ) ( 1 9 9 2 ) ( 2 0 0 2 ) a v e r a g e Results (Kahneman & Tversky, 1979)

  21. Results (Kahneman & Tversky, 1979)

  22. Results 100 90 80 ) 70 % ( s n 60 o LL i t c i BTA d PROB MINI e 50 GUESS r ML P EQUI LEX t c MAXI 40 e r r o EQW C 30 20 10 0 0 10 20 30 40 50 60 70 80 90 100 Information Ignored (%)

  23. 100 90 80 SPA CPT ) 70 % TAX ( s TALL n 60 o LL i t c i BTA d PROB MINI e 50 GUESS r ML P EQUI LEX t c MAXI 40 e r r o EQW C 30 20 10 0 0 10 20 30 40 50 60 70 80 90 100 Information Ignored (%) Results

  24. 100 90 PRIORITY 80 SPA CPT ) 70 % TAX ( s TALL n 60 o LL i t c i BTA d PROB MINI e 50 GUESS r ML P EQUI LEX t c MAXI 40 e r r o EQW C 30 20 10 0 0 10 20 30 40 50 60 70 80 90 100 Information Ignored (%) Results

  25. Conclusion • Expectancy-value theories rest on untested assumptions • Priority Heuristic Minimum gain, chances of minimum gain, maximum gain • New way to think about risky choice in the future Eduard Brandstätter Johannes Kepler University of Linz, Austria

  26. Choice by Heuristics Eduard Brandstätter Johannes Kepler University of Linz Austria Conference of the Economic Science Association, Rome, June 30, 2007

  27. Computer Experiment Choices between 2 gambles Dependent variable Decision time Independent variables • Number of conse-quences (2 or 5) • Number of reasons (1 or 3) 3 Reasons Decision time (sec) 1 Reason 2 Consequences 5

  28. Range of Application

  29. ResultsMellers et al. (1992)

  30. ResultsGambles with five consequences (Lopes & Oden, 1999)

  31. ResultsChoices between a gamble and a sure amount(Tversky & Kahneman, 1992)

  32. ResultsRandomly generated gambles (Erev et al., 2002)

  33. ResultsPriority Heuristic

  34. Priority Heuristic For Losses? • Gains • Do the minimum gains differ? • Do the probabilities of the minimum gains differ? • Choose the gamble with the higher maximum gain! • Losses • Do the minimum losses differ? • Do the probabilities of the minimum losses differ? • Choose the gamble with the lower maximum loss! AL: 10% of highest gain/loss, 10%

  35. Transitivity? Transitivity: If A > B and B > C then A > C

  36. Transitivity? A 29% chance to win $5.00 71% chance to win $0 B 38% chance to win $4.50 62% chance to win $0 Choose A! A > B

  37. Transitivity? A 29% chance to win $5.00 71% chance to win $0 B 38% chance to win $4.50 62% chance to win $0 C 46% chance to win $4.00 54% chance to win $0 Choose B! A > B, B > C

  38. Transitivity? A 29% chance to win $5.00 71% chance to win $0 B 38% chance to win $4.50 62% chance to win $0 C 46% chance to win $4.00 54% chance to win $0 STOP A > B, B > C, but C > A

  39. Transitivity? Empirical Pattern A-B: 68% A B-C: 65% B A-C 37% A Prioirty heuristic predicts intransitivies

  40. Going to Court? A plaintiff can either accept a €200,000 settlement or face a trial with a 50% chance of winning €420,000, otherwise nothing. • A defendant can either pay for a €200,000 settlement or • face a trial with a 50% chance of losing €420,000, • otherwise nothing.

  41. Example • A defendant can either pay for a $200,000 settlement or • face a trial with a 50% chance of losing $420,000, • or a 50% chanceof losing nothing. STOP • Losses • Do the minimum losses differ? AL: $42,000

  42. Decision Making • In real life, many risky choice situations. Whether to • approach an attractive boy/girl or not • operate one’s knee or not • take job offer A or B • invade a country or not • put sanctions on a country or not • go to court or not

  43. Outcome-Heuristics • Maximax Select the gamble with the highest maximum outcome. • Better-than-average Calculate the grand mean of all out- • comes of all gambles. For each • gamble calculate the number of out- • comes equal or above the grand mean. • Choose the gamble with the highest • number of such outcomes. A 80% chance 4 000 20% chance 0 B For sure 3 000

  44. Dual-Heuristics • Least-LikelyIdentify each gamble‘s worst payoff. Select the • gamble with the lowest probability of the worst • payoff. • ProbableCategorize probabilities as probable (i.e. p ≥ .5 • for two-outcome gambles) and improbable. Cancel improbable outcomes. Calculate the mean of all probable outcomes for each gamble. Select the gamble with the highest mean. A 80% chance 4 000 20% chance 0 B For sure 3 000

  45. Dual-Heuristics • Most-likelyDetermine the most likely outcome of each • gamble and their respective payoffs. Then • select the gamble with the highest, most likely • payoff. • LexikographicLike most-likely.If two outcomes are equal, determine the second most likely outcome of • each gamble and select the gamble with the • (second most likely) payoff. Proceed, until a decision is reached. A 80% chance 4 000 20% chance 0 B For sure 3 000

  46. Computerexperiment: Decision Time C 25% chance 4,000 75% chance 3,000 D 20% chance 5,000 80% chance 2,800 A 20% chance 5,000 80% chance 2,000 B 50% chance 4,000 50% chance 1,200 AL € = 500 p = 10% AL € = 500 p = 10% Prediction People need less time for choice between A and B than between C and D

  47. Zentrale Fragen: • Wie gut schneidet die Prioritäts-Heuristik im Vergleich zu … • einfachen Entscheidungs-Heuristiken, und • komplexen Entscheidungstheorien • Kumulative Prospekt-Theorie (CPT) • Security-Potential/Aspiration Theorie (SPA) ab • Transfer of attention exchange model?

  48. Datensatz Klassische Entscheidungsprobleme(14) (Kahneman & Tversky, 1979)

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