Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach

Daniel R. Cavagnaro (), Richard Gonzalez (), Jay I. Myung () and Mark A. Pitt ()
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Daniel R. Cavagnaro: Mihaylo College of Business and Economics, California State University, Fullerton, Fullerton, California 92834
Richard Gonzalez: Department of Psychology, University of Michigan, Ann Arbor, Michigan 48109
Jay I. Myung: Department of Psychology, The Ohio State University, Columbus, Ohio 43210
Mark A. Pitt: Department of Psychology, The Ohio State University, Columbus, Ohio 43210

Management Science, 2013, vol. 59, issue 2, 358-375

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.

Keywords: experimental design; active learning; choice under risk; model discrimination (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:59:y:2013:i:2:p:358-375

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