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- Daniel R. Cavagnaro
- Mihaylo College of Business and Economics, California State University, Fullerton, Fullerton, California 92834
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- Richard Gonzalez
- Department of Psychology, University of Michigan, Ann Arbor, Michigan 48109
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- Jay I. Myung
- Department of Psychology, The Ohio State University, Columbus, Ohio 43210
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- Mark A. Pitt
- Department of Psychology, The Ohio State University, Columbus, Ohio 43210
抄録
<jats:p>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.</jats:p><jats:p>This paper was accepted by Teck Ho, decision analysis.</jats:p>
収録刊行物
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- Management Science
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Management Science 59 (2), 358-375, 2013-02
Institute for Operations Research and the Management Sciences (INFORMS)