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Prospect Theory; Time Discounting; Bayesian Statistics; Adaptive Experimental Design; Revealed Preference
The authors present a method that dynamically designs elicitation questions for estimating risk and time preference parameters. Typically these parameters are elicited by presenting decision makers with a series of static choices between alternatives, gambles or delayed payments. The proposed method dynamically (i.e., adaptively) designs such choices to optimize the information provided by each choice, while leveraging the distribution of the parameters across decision makers (heterogeneity) and capturing response error.The authors explore the convergence and the validity of our approach using simulations. The simulations suggest that the proposed method recovers true parameter values well under various circumstances.The authors then use an online experiment to compare our approach to a standard one used in the literature that requires comparable task completion time. The authors assess predictive accuracy in an out-of-sample task and completion time for both methods.For risk preferences, our results indicate that the proposed method predicts subjects’ willingness to pay for a set of out-of-sample gambles significantly more accurately, while taking respondents about the same time to complete. For time preferences, both methods predict out-of-sample preferences equally well while the proposed method takes significantly less completion time. For risk and for time preferences, average completion time for our approach is approximately three minutes.Finally, the authors briefly review three applications that used the proposed methodology with various populations, and discuss the potential benefits of the proposed methodology for research and practice.