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

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

No 11361, CESifo Working Paper Series from CESifo

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.

Keywords: preference elicitation; risk preferences; time preferences; dynamic experiments; cognitive ability; preference stability (search for similar items in EconPapers)
JEL-codes: C81 C90 D03 D81 D90 (search for similar items in EconPapers)
Date: 2024
New Economics Papers: this item is included in nep-cbe, nep-dcm, nep-exp, nep-mac and nep-upt
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Working Paper: Dynamically Optimized Sequential Experimentation (DOSE) for Estimating Economic Preference Parameters (2024)
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