1. This Pump Sucks: Testing Transitivity with Individual Data Michael H. Birnbaum and Jeffrey P. Bahra California State University, Fullerton

2. Transitivity of Preference If A > B and B > C then A > C. Satisfy it or become a money pump. But transitivity may not hold if data contain “error.” And different people might have different “true” preferences.

3. Tversky (1969) Tversky (1969) reported that selected subjects showed a pattern of intransitive data consistent with a lexicographic semi-order. Tversky tested Weak Stochastic Transitivity: If P(A>B) > 1/2 and P(B>C) > 1/2 then P(A>C) > 1/2.

4. Issues Iverson & Falmagne (1985) argued that Tversky’s statistical analysis was incorrect of WST. Tversky went on to publish transitive theories of preference (e.g., CPT).

5. Renewed Interest in Intransitive Preference New analytical methods for analysis of transitivity (Iverson, Myung, & Karabatsos; Regenwetter & Stober, et al); Error models (Sopher & Gigliotti, ‘93; Birnbaum, ‘04; others). Priority Heuristic (Brandstaetter, et al., 2006); stochastic difference model (González-Vallejo, 2002; similarity judgments, Leland, 1994; majority rule, Zhang, Hsee, Xiao, 2006). Renewed interest in Fishburn, as well as in Regret Theory.

6. Lexicographic Semi-order G = (x, p; y, 1 - p). F = (x’, q; y’, 1 - q). If y - y’ ≥  L choose G (  L = $10) If y’ - y ≥  L choose F If p - q ≥  P choose G (  P = 0.1) If q - p ≥  P choose F If x > x’ choose G; if x’ > x choose F; Otherwise, choose randomly.

7. Priority Heuristic “Aspiration level” is 10% of largest prize, rounded to nearest prominent number. Compare gambles by lowest consequences. If difference exceeds the aspiration level, choose by lowest consequence. If not, compare probabilities; choose by probability if difference ≥ 0.1 Compare largest consequences; choose by largest consequences.

8. New Studies of Transitivity Work currently under way testing transitivity using same procedures as used in other decision research. Participants view choices via the WWW, click button beside the gamble they would prefer to play. Today’s talk: Single-S data.

9. Studies with Roman Gutierez Four studies used Tversky’s 5 gambles, formatted with tickets or with pie charts. Studies with n = 417 and n = 327 with small or large prizes ($4.50 or $450) No pre-selection of participants. Participants served in other risky DM studies, prior to testing (~1 hr).

10. Three of Tversky’s (1969) Gambles A = ($5.00, 0.29; $0, 0.79) C = ($4.50, 0.38; $0, 0.62) E = ($4.00, 0.46; $0, 0.54) Priority Heurisitc Predicts: A preferred to C; C preferred to E, and E preferred to A.

11. Findings Results were surprisingly transitive, unlike Tversky’s data (est. 95% transitive). Of those 115 who were perfectly reliable, 93 perfectly consistent with EV (p), 8 with opposite ($), and only 1 intransitive. Differences: no pre-test; Probability represented by # of tickets (100 per urn), rather than by pies; Participants have practice with variety of gambles, & choices;Tested via Computer.

12. Pie Chart Format

13. Pies: with or without Numerical probabilities 321 participants randomly assigned conditions with probabilities displayed as pies (spinner), either with numerical probabilities displayed or without. Of 105 who were perfectly reliable, 84 were perfectly consistent with EV (prob), 13 with the opposite order ($); 1 consistent with LS.

14. Findings Priority Heuristic predicted violations of transitivity were rare and rarely repeated when probability and prize information presented numerically. Violations of transitivity are still rare but more frequent when probability information presented only graphically. Evidence of Dimension Interaction violates PH and additive Difference models.

15. Response to Birnbaum- Gutierrez Perhaps the intransitivity only develops in longer studies. Tversky used 20 replications of each choice. Perhaps consequences of Tversky’s gambles diminished since 1969 due to inflation. Perhaps transitivity occurs because those prizes are too small.

16. Birnbaum & Bahra Collected up to 40 choices/pair per person. (20 reps). 2 Sessions, 1.5 hrs, 1 week apart. Cash prizes up to $100. 51 participants, of whom 10 to win the prize of one of their chosen gambles. 3 5 x 5 Designs to test transitivity vs. Priority heuristic predictions

17. Notation-Two-branch Gambles G = (x, p; y, 1 - p); x > y ≥ 0 L = Lower Consequence P = Probability to win higher prize H = Higher consequence

18. LH Design A = ($84,.50; $24) B = ($88,.50; $20) C = ($92,.50; $16) D = ($96,.50; $12) E = ($100,.50; $8)

19. LP Design A = ($100,.50; $24) B = ($100,.54; $20) C = ($100,.58; $16) D = ($100,.62; $12) E = ($100,.66; $8)

20. PH Design A = ($100,.50; $0) B = ($96,.54; $0) C = ($92,.58; $0) D = ($88,.62; $0) E = ($84,.66; $0)

21. Priority Heuristic Predictions LH Design: E > D > C > B > A, but A > E LP Design: A ~ B ~ C ~ D ~ E, but A > E PH Design: A > B > C > D > E but E > A

22. One Rep = 2 choices/pair

23. Analysis Each replication of each design has 20 choices; hence 1,048,576 possible data patterns (2 20 ) per rep. There are 1024 possible consistent patterns (R ij = 2 iff R ji = 1, all i, j). There are 120 (5!) possible transitive patterns.

24. Within-Rep Consistency Count the number of consistent choices in a replicate of 20 choices (10 x 2). If a person always chose the same button, consistency = 0. If a person was perfectly consistent, consistency = 10. Randomly choosing between 1 and 2 produces expected consistency of 5.

25. Intransitive and Consistent

26. Within-Replicate Consistency The average rate of agreement was 8.63 (86% self-agreement). 46.4% of all replicates were scored 10; an additional 19.9% were scored 9.

27. LH Design: Overall Proportions Choosing Second Gamble

28. LP Design: Overall Proportions Choosing Second Gamble

29. PH Design: Overall Proportions Choosing Second Gamble

30. Majority Data WST LH Design A>B>C>D>E LP Design A>B>C>D>E PH Design E>D>C>B>A Patterns consistent with special TAX with “prior” parameters. But this analysis hides individual diffs

31. Individual Data Choice proportions calculated for each individual in each design. These were further broken down within each person by replication.

32. S# 8328 C = 9.6 Rep = 20

33. S# 8328 C = 9.8 Rep = 20

34. S# 8328 C = 9.9 Rep = 20

35. S# 6176 C = 9.8 Rep = 20; started with this pattern, then switched to perfectly consistent with the opposite pattern for 4 replicates at the end of the first day; back to this pattern for 10 reps on day 2.

36. S# 684 C = 8.1 Rep = 14; an intransitive pattern opposite that predicted by priority heuristic.

37. S# 7663 C = 6.3 Rep = 10; an intransitive pattern consistent with priority heuristic,  P = 0.05. Few reps and low self-consistency in this case.

38. Data Summary For n = 51, there are 153 matrices. Of these, 90% were perfectly consistent with WST: P(A,B) ≥ 1/2 & P(B,C) ≥ 1/2 then P(A,C) ≥ 1/2. 29 people had all three arrays fitting WST; no one had all three arrays with intransitive patterns.

39. Summary of WST Individuals

40. 29 People with 3 Perfectly WST Patterns

41. Within-Person Changes in Preference Pattern Criterion: Person must show perfect consistency (10 out of 10) to one pattern in one replication, and perfect consistency to another pattern on another replication. 15 Such cases were found (10%). There may be other cases where the data are less consistent.

43. Summary Recent studies fail to confirm systematic violations of transitivity predicted by priority heuristic. Adds to growing case against this descriptive model. Individual data are mostly transitive. Next Q: From individual data, can we predict, for example, from these data to other kinds of choices by same person, e. g., tests of SD?