Dynamically Optimized Sequential Experimentation (DOSE) for Estimating Economic Preference Parameters

Jonathan Chapman, Erik Snowberg, Stephanie W. Wang and Colin Camerer ()

No 33013, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We introduce DOSE—Dynamically Optimized Sequential Experimentation—to elicit preference parameters. DOSE starts with a model of preferences and a prior over the parameters of that model, then dynamically chooses a customized question sequence for each participant according to an experimenter-selected information criterion. After each question, the prior is updated, and the posterior is used to select the next, informationally-optimal, question. Simulations show that DOSE produces parameter estimates that are approximately twice as accurate as those from established elicitation methods. DOSE estimates of individual-level risk and time preferences are also more accurate, more stable over time, and faster to administer in a large representative, incentivized survey of the U.S. population (N = 2,000). By reducing measurement error, DOSE identifies a stronger relationship between risk aversion and cognitive ability than other elicitation techniques. DOSE thus provides a flexible procedure that facilitates the collection of incentivized preference measures in the field.

JEL-codes: C81 C9 D03 D81 D9 (search for similar items in EconPapers)
Date: 2024-10
New Economics Papers: this item is included in nep-upt
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