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Dynamic Random Utility

Author

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  • Mira Frick
  • Ryota Iijima
  • Tomasz Strzalecki

Abstract

We provide an axiomatic analysis of dynamic random utility, characterizing the stochastic choice behavior of agents who solve dynamic decision problems by maximizing some stochastic process (Ut) of utilities. We show first that even when (Ut) is arbitrary, dynamic random utility imposes new testable across‐period restrictions on behavior, over and above period‐by‐period analogs of the static random utility axioms. An important feature of dynamic random utility is that behavior may appear history‐dependent, because period‐t choices reveal information about Ut, which may be serially correlated; however, our key new axioms highlight that the model entails specific limits on the form of history dependence that can arise. Second, we show that imposing natural Bayesian rationality axioms restricts the form of randomness that (Ut) can display. By contrast, a specification of utility shocks that is widely used in empirical work violates these restrictions, leading to behavior that may display a negative option value and can produce biased parameter estimates. Finally, dynamic stochastic choice data allow us to characterize important special cases of random utility—in particular, learning and taste persistence—that on static domains are indistinguishable from the general model.

Suggested Citation

  • Mira Frick & Ryota Iijima & Tomasz Strzalecki, 2019. "Dynamic Random Utility," Econometrica, Econometric Society, vol. 87(6), pages 1941-2002, November.
    • Handle: RePEc:wly:emetrp:v:87:y:2019:i:6:p:1941-2002
    DOI: 10.3982/ECTA15456
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    Cited by:

    1. Shadrack Kipkogei & Ruth Karoney & John Kipkorir Tanui, 2024. "Impact of Cooperative Membership on Tea Marketing Strategies and Farmers’ Income in Kericho, Kenya: Use of Endogenous Switching Approach," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(8), pages 3156-3173, August.
    2. Turansick, Christopher, 2022. "Identification in the random utility model," Journal of Economic Theory, Elsevier, vol. 203(C).
    3. Carlos Alós-Ferrer & Georg D. Granic, 2023. "Does choice change preferences? An incentivized test of the mere choice effect," Experimental Economics, Springer;Economic Science Association, vol. 26(3), pages 499-521, July.
    4. Xie, Erhao, 2021. "Empirical properties and identification of adaptive learning models in behavioral game theory," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 798-821.
    5. Yang, Erya & Kopylov, Igor, 2023. "Random quasi-linear utility," Journal of Economic Theory, Elsevier, vol. 209(C).
    6. Edi Karni, 2024. "Irresolute choice behavior," International Journal of Economic Theory, The International Society for Economic Theory, vol. 20(1), pages 70-87, March.
    7. Wilfried Youmbi, 2024. "Nonparametric Analysis of Random Utility Models Robust to Nontransitive Preferences," Papers 2406.13969, arXiv.org.
    8. Fedor Sandomirskiy & Po Hyun Sung & Omer Tamuz & Ben Wincelberg, 2023. "Independence of Irrelevant Decisions in Stochastic Choice," Papers 2312.04827, arXiv.org, revised May 2025.
    9. Paul H. Y. Cheung & Yusufcan Masatlioglu, 2025. "Frame-dependent Random Utility," Papers 2502.00209, arXiv.org.
    10. S. Cerreia-Vioglio & F. Maccheroni & M. Marinacci & A. Rustichini, 2017. "Multinomial logit processes and preference discovery: inside and outside the black box," Working Papers 615, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    11. Pennesi, Daniele, 2021. "Intertemporal discrete choice," Journal of Economic Behavior & Organization, Elsevier, vol. 186(C), pages 690-706.
    12. Jetlir Duraj & Yi-Hsuan Lin, 2022. "Identification and welfare evaluation in sequential sampling models," Theory and Decision, Springer, vol. 92(2), pages 407-431, March.
    13. Piermont, Evan, 2022. "Disentangling strict and weak choice in random expected utility models," Journal of Economic Theory, Elsevier, vol. 202(C).
    14. Simone Cerreia-Vioglio & Fabio Maccheroni & Massimo Marinacci, 2020. "Multinomial logit processes and preference discovery: outside and inside the black box," Working Papers 663, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    15. Paramahansa Pramanik & Alan M. Polansky, 2023. "Scoring a Goal Optimally in a Soccer Game Under Liouville-Like Quantum Gravity Action," SN Operations Research Forum, Springer, vol. 4(3), pages 1-39, September.

    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D90 - Microeconomics - - Micro-Based Behavioral Economics - - - General

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