Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach
提出自适应设计优化方法，在风险选择实验中根据前序试验结果动态调整刺激，以更有效地区分期望效用、前景理论等决策模型，对实验经济学家和决策研究者有参考价值。
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.