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Dynamic Random Utility

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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 (U_t) of utilities. We show "rst that even when (U_t) is arbitrary, dynamic random utility imposes new testable restrictions on how behavior across periods is related, 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 past choices reveal information about agents� past utilities and (U_t) may be serially correlated; however, our key new axioms highlight that the model entails speci"c limits on the form of history dependence that can arise. Second, we show that when agents� choices today influence their menu tomorrow (e.g., in consumption-savings or stopping problems), imposing natural Bayesian rationality axioms restricts the form of randomness that (U_t) can display. By contrast, a speci"cation 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 allows 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.

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  • Mira Frick & Ryota Iijima & Tomasz Strzalecki, 2017. "Dynamic Random Utility," Cowles Foundation Discussion Papers 2092R, Cowles Foundation for Research in Economics, Yale University, revised Nov 2018.
    • Handle: RePEc:cwl:cwldpp:2092r
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    Cited by:

    1. 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.
    2. 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.
    3. Turansick, Christopher, 2022. "Identification in the random utility model," Journal of Economic Theory, Elsevier, vol. 203(C).
    4. Pennesi, Daniele, 2021. "Intertemporal discrete choice," Journal of Economic Behavior & Organization, Elsevier, vol. 186(C), pages 690-706.
    5. 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.
    6. 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.
    7. Yang, Erya & Kopylov, Igor, 2023. "Random quasi-linear utility," Journal of Economic Theory, Elsevier, vol. 209(C).
    8. 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.
    9. Edi Karni, 2024. "Irresolute choice behavior," International Journal of Economic Theory, The International Society for Economic Theory, vol. 20(1), pages 70-87, March.
    10. Wilfried Youmbi, 2024. "Nonparametric Analysis of Random Utility Models Robust to Nontransitive Preferences," Papers 2406.13969, arXiv.org.
    11. Fedor Sandomirskiy & Omer Tamuz, 2023. "Decomposable Stochastic Choice," Papers 2312.04827, arXiv.org, revised May 2024.
    12. Piermont, Evan, 2022. "Disentangling strict and weak choice in random expected utility models," Journal of Economic Theory, Elsevier, vol. 202(C).
    13. 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.
    14. 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.

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    More about this item

    Keywords

    Dynamic stochastic choice; Random utility; History dependence; Serially correlated utilities; Consumption persistence; Learning;
    All these keywords.

    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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