Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach

Collecting data to discriminate between models of risky choice requires careful selection of decision stimuli. Models of decision making aim to predict decisions across a wide range of possible stimuli, but practical limitations force experimenters to select only a handful of them for actual testing...

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Bibliographic Details
Published in:Management science 2013-02, Vol.59 (2), p.358-375
Main Authors: Cavagnaro, Daniel R., Gonzalez, Richard, Myung, Jay I., Pitt, Mark A.
Format: Article
Language:English
Subjects:
Active learning
Analysis
choice under risk
Data collection
Decision making
Decision making models
Design optimization
Economic discrimination
Economic models
Economic theory
Expected utility
Experiment design
experimental design
Experiments
Indifference curves
Management science
model discrimination
Modeling
Parametric models
Risk assessment
Sensory discrimination
Simulations
Studies
Utility functions
Validity
Violations
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Summary:Collecting data to discriminate between models of risky choice requires careful selection of decision stimuli. Models of decision making aim to predict decisions across a wide range of possible stimuli, but practical limitations force experimenters to select only a handful of them for actual testing. Some stimuli are more diagnostic between models than others, so the choice of stimuli is critical. This paper provides the theoretical background and a methodological framework for adaptive selection of optimal stimuli for discriminating among models of risky choice. The approach, called adaptive design optimization, adapts the stimulus in each experimental trial based on the results of the preceding trials. We demonstrate the validity of the approach with simulation studies aiming to discriminate expected utility, weighted expected utility, original prospect theory, and cumulative prospect theory models. This paper was accepted by Teck Ho, decision analysis.
ISSN:0025-1909
1526-5501
DOI:10.1287/mnsc.1120.1558