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

Author

Listed:
  • Jonathan Chapman
  • Erik Snowberg
  • Stephanie W. Wang
  • Colin Camerer

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.

Suggested Citation

  • Jonathan Chapman & Erik Snowberg & Stephanie W. Wang & Colin Camerer, 2024. "Dynamically Optimized Sequential Experimentation (DOSE) for Estimating Economic Preference Parameters," CESifo Working Paper Series 11361, CESifo.
    • Handle: RePEc:ces:ceswps:_11361
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    References listed on IDEAS

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    1. Steffen Andersen & Glenn W. Harrison & Morten I. Lau & E. Elisabet Rutström, 2008. "Eliciting Risk and Time Preferences," Econometrica, Econometric Society, vol. 76(3), pages 583-618, May.
    2. Chuang, Yating & Schechter, Laura, 2015. "Stability of experimental and survey measures of risk, time, and social preferences: A review and some new results," Journal of Development Economics, Elsevier, vol. 117(C), pages 151-170.
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    Cited by:

    1. Yohei Mitani & Nobuyuki Hanaki, 2025. "Pay a lot to a few instead of a bit to all! Evidence from online donation experiments," ISER Discussion Paper 1273, Institute of Social and Economic Research, The University of Osaka.
    2. Thomas Henning & Siddhartha M. Ojha & Ross Spoon & Jiatong Han & Colin F. Camerer, 2025. "LLM Trading: Analysis of LLM Agent Behavior in Experimental Asset Markets," Papers 2502.15800, arXiv.org, revised Apr 2025.

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    More about this item

    Keywords

    preference elicitation; risk preferences; time preferences; dynamic experiments; cognitive ability; preference stability;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D90 - Microeconomics - - Micro-Based Behavioral Economics - - - General

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