Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach

Authors:
Daniel R. Cavagnaro;Richard Gonzalez;Jay I. Myung;Mark A. Pitt
Affiliations:
Mihaylo College of Business and Economics, California State University, Fullerton, Fullerton, California 92834;Department of Psychology, University of Michigan, Ann Arbor, Michigan 48109;Department of Psychology, The Ohio State University, Columbus, Ohio 43210;Department of Psychology, The Ohio State University, Columbus, Ohio 43210
Venue:
Management Science
Year:
2013

Citing 7
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Abstract

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.