Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach

• 成果类型: Article
• 署名作者: Cavagnaro, Daniel R.; Gonzalez, Richard; Myung, Jay I.; Pitt, Mark A.
• 署名单位: California State University System; California State University Fullerton; University of Michigan System; University of Michigan; University System of Ohio; Ohio State University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.1120.1558
• 发表日期: 2013
• 页码: 358-375
• 关键词: Experimental design; Active learning; Choice Under Risk; model discrimination

摘要

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.