IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2608.06152.html

Stochastic Choice with Distribution-Dependent Preferences

Author

Listed:
  • Paramahansa Pramanik

Abstract

We develop a continuous-time stochastic choice theory with endogenous preference evolution. Unlike dynamic random utility, observed behavior affects future preferences through the conditional distribution of latent preference states, generating endogenous distributional feedback. We show that this feedback has observable behavioral implications and characterize stochastic choice by a behavioral representation consisting of contemporaneous choice and continuation behavior. This representation is identified from stochastic choice, yields a rigidity result linking structural preference dynamics to observable behavior, and characterizes exactly when distribution dependent utility is behaviorally reducible to dynamic random utility. We further prove a behavioral impossibility theorem: stochastic choice arrays exhibiting behavioral distributional feedback admit no dynamic random utility representation. On the probabilistic side, we establish existence and weak uniqueness for the underlying conditional McKean-Vlasov system with conditional law feedback. The structure unifies endogenous information, latent preference dynamics, behavioral identification, and stochastic choice within a single continuous-time model.

Suggested Citation

  • Paramahansa Pramanik, 2026. "Stochastic Choice with Distribution-Dependent Preferences," Papers 2608.06152, arXiv.org.
    • Handle: RePEc:arx:papers:2608.06152
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2608.06152
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
    2. Andrew Caplin & Mark Dean & John Leahy, 2022. "Rationally Inattentive Behavior: Characterizing and Generalizing Shannon Entropy," Journal of Political Economy, University of Chicago Press, vol. 130(6), pages 1676-1715.
    3. Jose Apesteguia & Miguel A. Ballester & Jay Lu, 2017. "Single‐Crossing Random Utility Models," Econometrica, Econometric Society, vol. 85, pages 661-674, March.
    4. Peter Arcidiacono & Paul B. Ellickson, 2011. "Practical Methods for Estimation of Dynamic Discrete Choice Models," Annual Review of Economics, Annual Reviews, vol. 3(1), pages 363-394, September.
    5. Mira Frick & Ryota Iijima & Tomasz Strzalecki, 2019. "Dynamic Random Utility," Econometrica, Econometric Society, vol. 87(6), pages 1941-2002, November.
    6. Yuichi Kitamura & Jörg Stoye, 2018. "Nonparametric Analysis of Random Utility Models," Econometrica, Econometric Society, vol. 86(6), pages 1883-1909, November.
    7. Aguirregabiria, Victor & Mira, Pedro, 2010. "Dynamic discrete choice structural models: A survey," Journal of Econometrics, Elsevier, vol. 156(1), pages 38-67, May.
    8. William A. Brock & Steven N. Durlauf, 2001. "Discrete Choice with Social Interactions," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 68(2), pages 235-260.
    9. Matias D. Cattaneo & Xinwei Ma & Yusufcan Masatlioglu & Elchin Suleymanov, 2020. "A Random Attention Model," Journal of Political Economy, University of Chicago Press, vol. 128(7), pages 2796-2836.
    10. Drew Fudenberg & Ryota Iijima & Tomasz Strzalecki, 2015. "Stochastic Choice and Revealed Perturbed Utility," Econometrica, Econometric Society, vol. 83, pages 2371-2409, November.
    11. Faruk Gul & Wolfgang Pesendorfer, 2006. "Random Expected Utility," Econometrica, Econometric Society, vol. 74(1), pages 121-146, January.
    12. Simone Cerreia-Vioglio & David Dillenberger & Pietro Ortoleva & Gil Riella, 2019. "Deliberately Stochastic," American Economic Review, American Economic Association, vol. 109(7), pages 2425-2445, July.
      • Simone Cerreia-Vioglio & David Dillenberger & Pietro Ortoleva & Gil Riella, 2012. "Deliberately Stochastic," PIER Working Paper Archive 17-013, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 25 May 2017.
    13. Paramahansa Pramanik, 2025. "Construction of an Optimal Strategy: An Analytic Insight Through Path Integral Control Driven by a McKean–Vlasov Opinion Dynamics," Mathematics, MDPI, vol. 13(17), pages 1-43, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Paramahansa Pramanik & Michael Bowdin, 2026. "Dynamic Physical Hedging amid Jump Losses, Reconstruction-Price Uncertainty, Population Interactions," Papers 2608.13745, arXiv.org.
    2. Mustapha Nyenye Issah & Paramahansa Pramanik, 2026. "Bayesian Signaling and Entry Decisions under Uncertain Market Conditions," Papers 2608.17273, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sean Duffy & John Smith, 2025. "An economist and a psychologist form a line: What can imperfect perception of length tell us about stochastic choice?," Theory and Decision, Springer, vol. 99(3), pages 701-734, November.
    2. Duffy, Sean & Gussman, Steven & Smith, John, 2021. "Visual judgments of length in the economics laboratory: Are there brains in stochastic choice?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 93(C).
    3. Pennesi, Daniele, 2021. "Intertemporal discrete choice," Journal of Economic Behavior & Organization, Elsevier, vol. 186(C), pages 690-706.
    4. Daniele Caliari & Henrik Petri, 2025. "The Luce Model, Regularity, and Choice Overload," Papers 2502.21063, arXiv.org.
    5. Roy Allen & Paweł Dziewulski & John Rehbeck, 2024. "Revealed statistical consumer theory," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 77(3), pages 823-847, May.
    6. Yang, Erya & Kopylov, Igor, 2023. "Random quasi-linear utility," Journal of Economic Theory, Elsevier, vol. 209(C).
    7. Turansick, Christopher, 2022. "Identification in the random utility model," Journal of Economic Theory, Elsevier, vol. 203(C).
    8. Chew, Soo Hong & Miao, Bin & Shen, Qiang & Zhong, Songfa, 2022. "Multiple-switching behavior in choice-list elicitation of risk preference," Journal of Economic Theory, Elsevier, vol. 204(C).
    9. Andreas Lanz & Gregor Reich & Ole Wilms, 2022. "Adaptive grids for the estimation of dynamic models," Quantitative Marketing and Economics (QME), Springer, vol. 20(2), pages 179-238, June.
    10. Paul H. Y. Cheung & Yusufcan Masatlioglu, 2025. "Frame-dependent Random Utility," Papers 2502.00209, arXiv.org.
    11. Kashaev, Nail & Aguiar, Victor H., 2022. "A random attention and utility model," Journal of Economic Theory, Elsevier, vol. 204(C).
    12. Jose Apesteguia & Miguel A. Ballester, 2016. "Stochastic representatitve agent," Economics Working Papers 1536, Department of Economics and Business, Universitat Pompeu Fabra.
    13. Molinari, Francesca, 2020. "Microeconometrics with partial identification," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 355-486, Elsevier.
    14. Arcidiacono, Peter & Miller, Robert A., 2020. "Identifying dynamic discrete choice models off short panels," Journal of Econometrics, Elsevier, vol. 215(2), pages 473-485.
    15. Kai Wang, 2026. "Random Utility Model with Endogenously Assigned Menus," Papers 2607.08218, arXiv.org.
    16. Wei Ma, 2023. "Random dual expected utility," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 75(2), pages 293-315, February.
    17. Guo, Liang, 2021. "Contextual deliberation and the choice-valuation preference reversal," Journal of Economic Theory, Elsevier, vol. 195(C).
    18. Yujung Hwang, 2026. "Conditional choice probability estimation with an imperfectly measured latent state," Quantitative Economics, Econometric Society, vol. 17(2), pages 497-540, May.
    19. Victor H. Aguiar & Nail Kashaev, 2025. "Identification and Estimation of Discrete Choice Models with Unobserved Choice Sets," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 43(1), pages 204-215, January.
    20. Liu Shi & Jianying Qiu & Jiangyan Li & Frank Bohn, 2024. "Consciously stochastic in preference reversals," Journal of Risk and Uncertainty, Springer, vol. 68(3), pages 255-297, June.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2608.06152. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: https://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.