Curvature of the probability weighting function
Management Science
Bayesian Statistics and Marketing
Marketing Science
Fast Polyhedral Adaptive Conjoint Estimation
Marketing Science
Eliciting Consumer Preferences Using Robust Adaptive Choice Questionnaires
IEEE Transactions on Knowledge and Data Engineering
Loss Aversion Under Prospect Theory: A Parameter-Free Measurement
Management Science
The Silver Lining Effect: Formal Analysis and Experiments
Management Science
Understanding the Two Components of Risk Attitudes: An Experimental Analysis
Management Science
Active Machine Learning for Consideration Heuristics
Marketing Science
Probability and Time Trade-Off
Management Science
Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach
Management Science
Networked individuals predict a community wide outcome from their local information
Decision Support Systems
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We 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. We 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. We then use an online experiment to compare our approach to a standard one used in the literature that requires comparable task completion time. We 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 time preferences, average completion time for our approach is approximately three minutes. Finally, we briefly review three applications that used the proposed methodology with various populations, and we discuss the potential benefits of the proposed methodology for research and practice. This paper was accepted by Teck Ho, decision analysis.