DEEP is an adaptive 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. DEEP 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. It also recovers true parameter values well under various circumstances.
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Authors
DEEP is an inter-university collaboration between researchers from Columbia Business School, INSEAD, and George Washington University. Below you’ll find a list of all the authors by alphabetical order.