New Paradoxes of Risky Decision Making that Refute Prospect Theories

Slides:



Advertisements
Similar presentations
New Paradoxes of Risky Decision Making that Refute Prospect Theories Michael H. Birnbaum Fullerton, California, USA.
Advertisements

Among those who cycle most have no regrets Michael H. Birnbaum Decision Research Center, Fullerton.
Science of JDM as an Efficient Game of Mastermind Michael H. Birnbaum California State University, Fullerton Bonn, July 26, 2013.
This Pump Sucks: Testing Transitivity with Individual Data Michael H. Birnbaum and Jeffrey P. Bahra California State University, Fullerton.
1 Upper Cumulative Independence Michael H. Birnbaum California State University, Fullerton.
Components of Source Credibility Michael H. Birnbaum Fullerton, California, USA.
1 Lower Distribution Independence Michael H. Birnbaum California State University, Fullerton.
True and Error Models of Response Variation in Judgment and Decision Tasks Michael H. Birnbaum.
Evaluating Non-EU Models Michael H. Birnbaum Fullerton, California, USA.
Who are these People Who Violate Stochastic Dominance, Anyway? What, if anything, are they thinking? Michael H. Birnbaum California State University, Fullerton.
1 st lecture Probabilities and Prospect Theory. Probabilities In a text over 10 standard novel-pages, how many 7-letter words are of the form: 1._ _ _.
Decision making and economics. Economic theories Economic theories provide normative standards Expected value Expected utility Specialized branches like.
Testing Lexicographic Semi- Order Models: Generalizing the Priority Heuristic Michael H. Birnbaum California State University, Fullerton.
Testing Heuristic Models of Risky Decision Making Michael H. Birnbaum California State University, Fullerton.
1 A Brief History of Descriptive Theories of Decision Making Kiel, June 9, 2005 Michael H. Birnbaum California State University, Fullerton.
Some New Approaches to Old Problems: Behavioral Models of Preference Michael H. Birnbaum California State University, Fullerton.
Chapter Seventeen HYPOTHESIS TESTING
1 Distribution Independence Michael H. Birnbaum California State University, Fullerton.
1 Upper Tail Independence Michael H. Birnbaum California State University, Fullerton.
Testing Models of Stochastic Dominance Violations Michael H. Birnbaum Decision Research Center California State University, Fullerton.
1 Upper Distribution Independence Michael H. Birnbaum California State University, Fullerton.
Ten “New Paradoxes” Refute Cumulative Prospect Theory of Risky Decision Making Michael H. Birnbaum Decision Research Center California State University,
Violations of Stochastic Dominance Michael H. Birnbaum California State University, Fullerton.
Testing Critical Properties of Models of Risky Decision Making Michael H. Birnbaum Fullerton, California, USA Sept. 13, 2007 Luxembourg.
Ten “New Paradoxes” Refute Cumulative Prospect Theory of Risky Decision Making Michael H. Birnbaum Decision Research Center California State University,
New Paradoxes of Risky Decision Making that Refute Prospect Theories Michael H. Birnbaum Fullerton, California, USA.
1 The Case Against Prospect Theories of Risky Decision Making Michael H. Birnbaum California State University, Fullerton.
Testing Transitivity (and other Properties) Using a True and Error Model Michael H. Birnbaum.
Web-Based Program of Research on Risky Decision Making Michael H. Birnbaum California State University, Fullerton.
Web-Based Program of Research on Risky Decision Making Michael H. Birnbaum California State University, Fullerton.
1 A Brief History of Descriptive Theories of Decision Making: Lecture 2: SWU and PT Kiel, June 10, 2005 Michael H. Birnbaum California State University,
1 Gain-Loss Separability and Reflection In memory of Ward Edwards Michael H. Birnbaum California State University, Fullerton.
I’m not overweight It just needs redistribution Michael H. Birnbaum California State University, Fullerton.
1 Ten “New Paradoxes” of Risky Decision Making Michael H. Birnbaum Decision Research Center California State University, Fullerton.
1 Gain-Loss Separability Michael H. Birnbaum California State University, Fullerton.
Is there Some Format in Which CPT Violations are Attenuated? Michael H. Birnbaum Decision Research Center California State University, Fullerton.
1 Lower Cumulative Independence Michael H. Birnbaum California State University, Fullerton.
Statistics 800: Quantitative Business Analysis for Decision Making Measures of Locations and Variability.
Stochastic Dominance Michael H. Birnbaum Decision Research Center California State University, Fullerton.
Web-Based Program of Research on Risky Decision Making Michael H. Birnbaum California State University, Fullerton.
Testing Transitivity with Individual Data Michael H. Birnbaum and Jeffrey P. Bahra California State University, Fullerton.
1 Restricted Branch Independence Michael H. Birnbaum California State University, Fullerton.
Presidential Address: A Program of Web-Based Research on Decision Making Michael H. Birnbaum SCiP, St. Louis, MO November 18, 2010.
Behavior in the loss domain : an experiment using the probability trade-off consistency condition Olivier L’Haridon GRID, ESTP-ENSAM.
Population All members of a set which have a given characteristic. Population Data Data associated with a certain population. Population Parameter A measure.
A Heuristic Solution To The Allais Paradox And Its Implications Seán Muller, University of Cape Town.
Stochastic choice under risk Pavlo Blavatskyy June 24, 2006.
Introduction to Inferential Statistics Statistical analyses are initially divided into: Descriptive Statistics or Inferential Statistics. Descriptive Statistics.
A Stochastic Expected Utility Theory Pavlo R. Blavatskyy June 2007.
Review Hints for Final. Descriptive Statistics: Describing a data set.
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 11-1 Chapter 11 Chi-Square Tests and Nonparametric Tests Statistics for.
Testing Transitivity with a True and Error Model Michael H. Birnbaum California State University, Fullerton.
Axiomatic Theory of Probabilistic Decision Making under Risk Pavlo R. Blavatskyy University of Zurich April 21st, 2007.
Can a Dominatrix Make My Pump Work? Michael H. Birnbaum CSUF Decision Research Center.
Chapter 3 Normal Curve, Probability, and Population Versus Sample Part 2 Aug. 28, 2014.
Lec. 19 – Hypothesis Testing: The Null and Types of Error.
Logic of Hypothesis Testing
Chapter 12 Chi-Square Tests and Nonparametric Tests
Part Four ANALYSIS AND PRESENTATION OF DATA
Inference and Tests of Hypotheses
Chapter 9: Inferences Involving One Population
Bivariate Testing (ANOVA)
Mohan Pandey 56th Edwards Bayesian Research Conference March 1-3, 2018
Introduction to Inferential Statistics
9 Tests of Hypotheses for a Single Sample CHAPTER OUTLINE
Behavioral Finance Economics 437.
Introduction to Hypothesis Testing
DIS 280 Social Science Research Methodology: Problem Framing
Hypothesis Testing.
Introduction to Hypothesis Testing
Presentation transcript:

New Paradoxes of Risky Decision Making that Refute Prospect Theories Michael H. Birnbaum Fullerton, California, USA

Outline I will review tests between Cumulative Prospect Theory (CPT) and Transfer of Attention eXchange (TAX) model. Emphasis will be on critical properties that test between these two non-nested theories.

Cumulative Prospect Theory/ Rank-Dependent Utility (RDU)

“Prior” TAX Model Assumptions:

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

Non-nested Models

CPT and TAX nearly identical inside the prob. simplex

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

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’)

Stochastic Dominance A test between CPT and TAX: G = (x, p; y, q; z) vs. F = (x, p – q; y’, q; z) Note that this recipe uses 4 distinct values of consequences. It falls outside the probability simplex defined on three consequences. CPT  G, TAX  F We can test if violations due to “error”

Violations of Stochastic Dominance 122 Undergrads: 59% repeat the violation (BB) 28% Pref Reversals (AB or BA) Estimates: e = 0.19; p = 0.85 170 Experts: 35% repeat violations 31% Reversals Estimates: e = 0.20; p = 0.50 Chi-Squared test reject H0: p < 0.4

Pie Charts

Aligned Table: Coalesced

Summary: 23 Studies of SD, 8653 participants Large effects of splitting vs. coalescing of branches Small effects of education, gender, study of decision science Very small effects of probability format, request to justify choice. Miniscule effects of event framing (framed vs unframed)

Lower Cumulative Independence R: 39% S: 61% .90 to win $3 .90 to win $3 .05 to win $12 .05 to win $48 .05 to win $96 .05 to win $52 R'': 69% S'': 31% .95 to win $12 .90 to win $12 .05 to win $96 .10 to win $52 Results shown here are for Birnbaum (1999b) student sample tested in lab. 124 x 2 reps.

Upper Cumulative Independence R': 72% S': 28% .10 to win $10 .10 to win $40 .10 to win $98 .10 to win $44 .80 to win $110 .80 to win $110 R''': 34% S''': 66% .10 to win $10 .20 to win $40 .90 to win $98 .80 to win $98 These results are for the student sample of Birnbaum (1999b), lab sample.

Summary: UCI & LCI 22 studies with 33 Variations of the Choices, 6543 Participants, & a variety of display formats and procedures. Significant Violations found in all studies.

Restricted Branch Indep. S’: .1 to win $40 .1 to win $44 .8 to win $100 S: .8 to win $2 .1 to win $40 R’: .1 to win $10 .1 to win $98 .8 to win $100 R: .8 to win $2 .1 to win $10 Significantly more have the preference pattern SR’ than the reverse RS’. “Median heuristic”-- The median describes this choice, but not details of the study. Choice depends on the values of the consequences. Inverse-S implies RS’ pattern.

3-Upper Distribution Ind. S’: .10 to win $40 .10 to win $44 .80 to win $100 S2’: .45 to win $40 .45 to win $44 .10 to win $100 R’: .10 to win $4 .10 to win $96 .80 to win $100 R2’: .45 to win $4 .45 to win $96 .10 to win $100 Here, TAX predicts a reversal: R’S2’. Indeed, of 1075 participants, more show this switch Than the opposite switch, predicted by CPT. Here RAM allows no switch. 56% (sig more than half) prefer R’ and 67% (sig more than half) prefer S2’.

3-Lower Distribution Ind. S’: .80 to win $2 .10 to win $40 .10 to win $44 S2’: .10 to win $2 .45 to win $40 .45 to win $44 R’: .80 to win $2 .10 to win $4 .10 to win $96 R2’: .10 to win $2 .45 to win $4 .45 to win $96 Here, TAX predicts no reversal. Indeed, of 1075 participants, more show this switch Than the opposite switch, predicted by CPT. Here RAM allows no switch. 56% (sig more than half) prefer R’ and 67% (sig more than half) prefer S2’.

Gain-Loss Separability

Notation

Wu and Markle Result

Birnbaum & Bahra--% F

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.

Summary-2 Property CPT RAM TAX LCI No Viols Viols UCI UTI R’S1Viols LDI RS2 Viols 3-2 LDI

Summary-3 Property CPT RAM TAX 4-DI RS’Viols No Viols SR’ Viols UDI RBI RS’ Viols

Results: CPT makes wrong predictions for all 12 tests Can CPT be saved by using different formats for presentation? More than a dozen formats have been tested. Violations of coalescing, stochastic dominance, lower and upper cumulative independence replicated with 14 different formats and thousands of participants.

Implications Results indicate that neither PT nor CPT are descriptive of risky decision making. TAX correctly predicts the violations of CPT. CPT implies violations of TAX that either fail or show the opposite pattern from predicted by CPT.