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Approximate Expected Utility Rationalization

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  • Echenique, Federico
  • Imai, Taisuke
  • Saito, Kota

Abstract

We propose a new measure of deviations from expected utility theory. For any positive number e, we give a characterization of the datasets with a rationalization that is within e (in beliefs, utility, or perceived prices) of expected utility (EU) theory, under the assumption of risk aversion. The number e can then be used as a measure of how far the data is to EU theory. We apply our methodology to data from three large-scale experiments. Many subjects in these experiments are consistent with utility maximization, but not with EU maximization. Our measure of distance to expected utility is correlated with the subjects’ demographic characteristics.

Suggested Citation

  • Echenique, Federico & Imai, Taisuke & Saito, Kota, 2023. "Approximate Expected Utility Rationalization," Department of Economics, Working Paper Series qt8pt4287c, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
  • Handle: RePEc:cdl:econwp:qt8pt4287c
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    Cited by:

    1. Yiting Chen & Tracy Xiao Liu & You Shan & Songfa Zhong, 2023. "The emergence of economic rationality of GPT," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 120(51), pages 2316205120-, December.
    2. Joshua Lanier & John K. -H. Quah, 2024. "Goodness-of-fit and utility estimation: what's possible and what's not," Papers 2405.08464, arXiv.org.
    3. Thomas Demuynck & John Rehbeck, 2023. "Computing revealed preference goodness-of-fit measures with integer programming," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 76(4), pages 1175-1195, November.
    4. Roy Allen & John Rehbeck, 2021. "Measuring rationality: percentages vs expenditures," Theory and Decision, Springer, vol. 91(2), pages 265-277, September.
    5. Pawel Dziewulski, 2021. "A comprehensive revealed preference approach to approximate utility maximisation," Working Paper Series 0621, Department of Economics, University of Sussex Business School.
    6. Federico Echenique, 2020. "New Developments in Revealed Preference Theory: Decisions Under Risk, Uncertainty, and Intertemporal Choice," Annual Review of Economics, Annual Reviews, vol. 12(1), pages 299-316, August.
    7. Thomas Demuynck & Tom Potoms, 2022. "Testing revealed preference models with unobserved randomness: a column generation approach," Working Papers ECARES 2022-42, ULB -- Universite Libre de Bruxelles.
    8. Jeongbin Kim & Matthew Kovach & Kyu-Min Lee & Euncheol Shin & Hector Tzavellas, 2024. "Learning to be Homo Economicus: Can an LLM Learn Preferences from Choice," Papers 2401.07345, arXiv.org.
    9. Federico Echenique & Taisuke Imai & Kota Saito, 2019. "Decision Making under Uncertainty: An Experimental Study in Market Settings," Papers 1911.00946, arXiv.org, revised May 2021.
    10. Mia Lu & Nick Netzer, 2022. "The swaps index for consumer choice," ECON - Working Papers 418, Department of Economics - University of Zurich, revised May 2023.
    11. Ashesh Rambachan, 2022. "Identifying Prediction Mistakes in Observational Data," NBER Chapters, in: Economics of Artificial Intelligence, National Bureau of Economic Research, Inc.

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    More about this item

    Keywords

    Economics; Applied economics; Econometrics; Economic theory;
    All these keywords.

    JEL classification:

    • D01 - Microeconomics - - General - - - Microeconomic Behavior: Underlying Principles
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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