 Download Download Presentation Science of JDM as an Efficient Game of Mastermind

# Science of JDM as an Efficient Game of Mastermind

Download Presentation ## Science of JDM as an Efficient Game of Mastermind

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
##### Presentation Transcript

1. Science of JDM as an EfficientGame of Mastermind Michael H. Birnbaum California State University, Fullerton Bonn, July 26, 2013

2. Mastermind Game- Basic Game

3. Auf Deutsch => “SuperHirn”

4. Mastermind Game • Goal to find secret code of colors in positions. In “basic” game, there are 4 positions and 6 colors, making 64 = 1296 hypotheses. • Each “play” of the game is an experiment that yields feedback as to the accuracy of an hypothesis. • For each “play”, feedback = 1 black peg for each color in correct position and 1 white peg for each correct color in wrong position.

5. Play Mastermind Online • http://www.web-games-online.com/mastermind/index.php • (Mastermind is a variant of “Bulls and Cows”, an earlier code-finding game.)

6. A Game of Mastermind- 8,096 = 84

7. Analogies • EXPERIMENTS yield results, from which we revise our theories. • RECORD of experiments and results is preserved. • Experiments REDUCE THE SPACE of compatible with evidence. • Hypotheses can be PARTITIONED with respect to components.

8. Science vs. Mastermind • In Mastermind, feedback is 100% accurate; in science, feedback contains “error” and “bias.” Repeat/revise the “same” experiment, different results. • In Mastermind, we can specify the space of hypotheses exactly, but in science, the set of theories under contention expands as people construct new theories. • In Mastermind, we know when we are done; science is never done.

9. Analogies • EFFICIENT Mastermind is the goal: Find the secret code with fewest experiments. • If FEEDBACK IS NOT PERFECT, results are fallible, and it would be a mistake to build theory on such fallible results. • REPLICATION is needed, despite the seeming loss of efficiency.

10. Hypothesis Testing vs. Mastermind • Suppose we simply tested hypotheses, one at a time and a significance tests says “reject” or “retain”? • With 1296 hypotheses, we get closer to truth with each rejection--BARELY. • Now suppose that 50% of the time we fail to reject false theories and 5% of the time we reject a true theory. • Clearly, significance testing this way is not efficient. More INFORMATIVE FEEDBACK needed.

11. Experiments that Divide the Space of Hypotheses in Half • Basic game = 1296 Hypotheses • Suppose each experiment cuts space in half: 1296, 648, 324, 162, 81, 40.5, 20.25, 10.1, 5.1, 2.5, 1.3, done. 11 moves. • But typical game with 1296 ends after 4 or 5 moves, infrequently 6. • So, Mastermind is more efficient than “halving” of the space.

12. Index of Fit Informative? • Suppose we assign numbers to each color, R = 1, G = 2, B = 3, etc. and calculate a correlation coefficient between the code and the experimental results? • This index could be highly misleading, it depends on the coding and experiment. • Fit could be higher for “worse” theories. (Devil rides again 1970s).

13. Psychology vs. Mastermind • Mastermind: only ONE secret code. • In Psychology, we allow that different people might have different individual difference parameters. • Even more complicated: Perhaps different people have different models. • As if, different experiments in the game have DIFFERENT secret codes.

14. Partitions of Hypotheses

15. Testing Critical Properties • Test properties that do not depend on parameters. • Such properties partition the space of hypotheses, like the test of all REDs. • For example: CPT (including EU) implies STOCHASTIC DOMINANCE. This follows for any set of personal parameters (any utility/value function and any prob. weighting function).

16. Critical Tests are Theorems of One Model that are Violated by Another Model • This approach has advantages over tests or comparisons of fit. • It is not the same as “axiom testing.” • Use model-fitting to rival model to predict where to find violations of theorems deduced from model tested.

17. Outline • I will discuss critical properties that test between nonnested theories: CPT and TAX. • Lexicographic Semiorders vs. family of transitive, integrative models (including CPT and TAX). • Integrative Contrast Models (e.g., Regret, Majority Rule) vs. transitive, integrative models.

18. Cumulative Prospect Theory/ Rank-Dependent Utility (RDU)

19. TAX Model

20. “Prior” TAX Model Assumptions:

21. TAX Parameters For 0 < x < \$150 u(x) = x Gives a decent approximation. Risk aversion produced by d. d = 1 .

22. TAX and CPT nearly identical for binary (two-branch) gambles • CE (x, p; y) is an inverse-S function of p according to both TAX and CPT, given their typical parameters. • Therefore, there is little point trying to distinguish these models with binary gambles.

23. Non-nested Models

24. CPT and TAX nearly identical inside the M&M prob. simplex

25. Testing CPT TAX:Violations of: • Coalescing • Stochastic Dominance • Lower Cum. Independence • Upper Cumulative Independence • Upper Tail Independence • Gain-Loss Separability

26. Testing TAX Model CPT: Violations of: • 4-Distribution Independence (RS’) • 3-Lower Distribution Independence • 3-2 Lower Distribution Independence • 3-Upper Distribution Independence (RS’) • Res. Branch Indep (RS’)

27. Stochastic Dominance • A test between CPT and TAX: G = (x, p; y, q; z) vs. F = (x, p – s; y’, s; z) Note that this recipe uses 4 distinct consequences: x > y’ > y > z > 0; outside the probability simplex defined on three consequences. CPT  choose G, TAX  choose F Test if violations due to “error.”

28. Error Model Assumptions • Each choice pattern in an experiment has a true probability, p, and each choice has an error rate, e. • The error rate is estimated from inconsistency of response to the same choice by same person in a block of trials. The “true”p is then estimated from consistent (repeated) responses to same question.

29. Violations of Stochastic Dominance 122 Undergrads: 59% TWO violations (BB) 28% Pref Reversals (AB or BA) Estimates: e = 0.19; p = 0.85 170 Experts: 35% repeated violations 31% Reversals Estimates: e = 0.20; p = 0.50

30. 42 Studies of Stochastic Dominance, n = 12,152* • Large effects of splitting vs. coalescing of branches • Small effects of education, gender, study of decision science • Very small effects of 15 probability formats and request to justify choices. • Miniscule effects of event framing (framed vs unframed) * (as of 2010)

31. Summary: Prospect Theories not Descriptive • Violations of Coalescing • Violations of Stochastic Dominance • Violations of Gain-Loss Separability • Dissection of Allais Paradoxes: viols of coalescing and restricted branch independence; RBI violations opposite of Allais paradox; opposite CPT.

32. Results: CPT makes wrong predictions for all 12 tests • Can CPT be saved by using different formats for presentation? • Violations of coalescing, stochastic dominance, lower and upper cumulative independence replicated with 14 different formats and ten-thousands of participants. • Psych Review 2008 & JDM 2008 “new tests” of CPT and PH.

33. Lexicographic Semiorders • Intransitive Preference. • Priority heuristic of Brandstaetter, Gigerenzer & Hertwig is a variant of LS, plus some additional features. • In this class of models, people do not integrate information or have interactions such as the probability X prize interaction in family of integrative, transitive models (CPT, TAX, GDU, EU and others)

34. LPH LS: G = (x, p; y) F = (x’, q; y’) • If (y –y’ > D) choose G • Else if (y ’- y > D) choose F • Else if (p – q > d) choose G • Else if (q – p > d) choose F • Else if (x – x’ > 0) choose G • Else if (x’ – x > 0) choose F • Else choose randomly

35. Family of LS • In two-branch gambles, G = (x, p; y), there are three dimensions: L = lowest outcome (y), P = probability (p), and H = highest outcome (x). • There are 6 orders in which one might consider the dimensions: LPH, LHP, PLH, PHL, HPL, HLP. • In addition, there are two threshold parameters (for the first two dimensions).

36. Testing Lexicographic Semiorder Models Violations of Transitivity Violations of Priority Dominance Integrative Independence Interactive Independence LS Allais Paradoxes CPT TAX EU

37. New Tests of Independence • Dimension Interaction: Decision should be independent of any dimension that has the same value in both alternatives. • Dimension Integration: indecisive differences cannot add up to be decisive. • Priority Dominance: if a difference is decisive, no effect of other dimensions.

38. Taxonomy of choice models

39. Dimension Interaction

40. Family of LS • 6 Orders: LPH, LHP, PLH, PHL, HPL, HLP. • There are 3 ranges for each of two parameters, making 9 combinations of parameter ranges. • There are 6 X 9 = 54 LS models. • But all models predict SS, RR, or ??.

41. Results: Interaction n = 153

42. Analysis of Interaction • Estimated probabilities: • P(SS) = 0 (prior PH) • P(SR) = 0.75 (prior TAX) • P(RS) = 0 • P(RR) = 0.25 • Priority Heuristic: Predicts SS

43. Probability Mixture Model • Suppose each person uses a LS on any trial, but randomly switches from one order to another and one set of parameters to another. • But any mixture of LS is a mix of SS, RR, and ??. So no LS mixture model explains SR or RS.

44. Results: Dimension Integration • Data strongly violate independence property of LS family • Data are consistent instead with dimension integration. Two small, indecisive effects can combine to reverse preferences. • Observed with all pairs of 2 dims. • Birnbaum, in J. math Psych, 2010.

45. New Studies of Transitivity • LS models violate transitivity: A > B and B > C implies A > C. • Birnbaum & Gutierrez (2007) tested transitivity using Tversky’s gambles, using typical methods for display of choices. • Text displays and pie charts with and without numerical probabilities. Similar results with all 3 procedures.

46. Replication of Tversky (‘69) with Roman Gutierrez • 3 Studies used Tversky’s 5 gambles, formatted with tickets and pie charts. • Exp 1, n = 251, tested via computers.

47. Three of Tversky’s (1969) Gambles • A = (\$5.00, 0.29; \$0) • C = (\$4.50, 0.38; \$0) • E = (\$4.00, 0.46; \$0) Priority Heurisitc Predicts: A preferred to C; C preferred to E, But E preferred to A. Intransitive. TAX (prior): E > C > A

48. Response Combinations

49. Results-ACE

50. Comments • Results were surprisingly transitive. • Differences: no pre-test, selection; • Probability represented by # of tickets (100 per urn); similar results with pies. • Birnbaum & Gutierrez, 2007, OBHDP • Regenwetter, Dana, & Davis-Stober also conclude that evidence against transitivity is weak., Psych Review, 2011. • Birnbaum & Bahra: most Ss transitive. JDM, 2012.