IDEAS home Printed from https://ideas.repec.org/p/bon/boncrc/crctr224_2023_460.html
   My bibliography  Save this paper

Timing Decisions Under Model Uncertainty

Author

Listed:
  • Sarah Auster
  • Christian Kellner

Abstract

We study the effect of ambiguity on timing decisions. An agent faces a stopping problem with an uncertain stopping payoff and a stochastic time limit. The agent is unsure about the correct model quantifying the uncertainty and seeks to maximize her payoff guarantee over a set of plausible models. As time passes and the agent updates, the worst-case model used to evaluate a given strategy can change, creating a problem of dynamic inconsistency. We characterize the stopping behavior in this environment and show that, while the agent’s myopic incentives are fragile to small changes in the set of considered models, the best consistent plan from which no future self has incentives to deviate is robust.

Suggested Citation

  • Sarah Auster & Christian Kellner, 2023. "Timing Decisions Under Model Uncertainty," CRC TR 224 Discussion Paper Series crctr224_2023_460, University of Bonn and University of Mannheim, Germany.
    • Handle: RePEc:bon:boncrc:crctr224_2023_460
    as

    Download full text from publisher

    File URL: https://www.crctr224.de/research/discussion-papers/archive/dp460
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gilboa Itzhak & Schmeidler David, 1993. "Updating Ambiguous Beliefs," Journal of Economic Theory, Elsevier, vol. 59(1), pages 33-49, February.
    2. Frank Riedel, 2009. "Optimal Stopping With Multiple Priors," Econometrica, Econometric Society, vol. 77(3), pages 857-908, May.
    3. Paul Milgrom & Ilya Segal, 2002. "Envelope Theorems for Arbitrary Choice Sets," Econometrica, Econometric Society, vol. 70(2), pages 583-601, March.
    4. Sarah Auster & Yeon-Koo Che & Konrad Mierendorff, 2024. "Prolonged Learning and Hasty Stopping: The Wald Problem with Ambiguity," American Economic Review, American Economic Association, vol. 114(2), pages 426-461, February.
    5. Kellner, Christian & Le Quement, Mark T., 2018. "Endogenous ambiguity in cheap talk," Journal of Economic Theory, Elsevier, vol. 173(C), pages 1-17.
    6. Beauchêne, Dorian & Li, Jian & Li, Ming, 2019. "Ambiguous persuasion," Journal of Economic Theory, Elsevier, vol. 179(C), pages 312-365.
    7. David K. Levine & Drew Fudenberg, 2006. "A Dual-Self Model of Impulse Control," American Economic Review, American Economic Association, vol. 96(5), pages 1449-1476, December.
    8. Jianjun Miao, 2008. "Option exercise with temptation," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 34(3), pages 473-501, March.
    9. Bose, Subir & Daripa, Arup, 2009. "A dynamic mechanism and surplus extraction under ambiguity," Journal of Economic Theory, Elsevier, vol. 144(5), pages 2084-2114, September.
    10. Auster, Sarah & Kellner, Christian, 2022. "Robust bidding and revenue in descending price auctions," Journal of Economic Theory, Elsevier, vol. 199(C).
    11. Zuo Quan Xu & Xun Yu Zhou, 2011. "Optimal stopping under probability distortion," Papers 1103.1755, arXiv.org, revised Feb 2013.
    12. Larry G. Epstein & Martin Schneider, 2007. "Learning Under Ambiguity," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 74(4), pages 1275-1303.
    13. Shaowei Ke & Qi Zhang, 2020. "Randomization and Ambiguity Aversion," Econometrica, Econometric Society, vol. 88(3), pages 1159-1195, May.
    14. Subir Bose & Ludovic Renou, 2014. "Mechanism Design With Ambiguous Communication Devices," Econometrica, Econometric Society, vol. 82, pages 1853-1872, September.
    15. Ghosh, Gagan & Liu, Heng, 2021. "Sequential auctions with ambiguity," Journal of Economic Theory, Elsevier, vol. 197(C).
    16. Henderson, Vicky & Hobson, David & Tse, Alex S.L., 2017. "Randomized strategies and prospect theory in a dynamic context," Journal of Economic Theory, Elsevier, vol. 168(C), pages 287-300.
    17. Kota Saito, 2015. "Preferences for Flexibility and Randomization under Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1246-1271, March.
    18. Nicholas Barberis, 2012. "A Model of Casino Gambling," Management Science, INFORMS, vol. 58(1), pages 35-51, January.
    19. Gilboa, Itzhak & Schmeidler, David, 1989. "Maxmin expected utility with non-unique prior," Journal of Mathematical Economics, Elsevier, vol. 18(2), pages 141-153, April.
    20. Sebastian Ebert & Philipp Strack, 2015. "Until the Bitter End: On Prospect Theory in a Dynamic Context," American Economic Review, American Economic Association, vol. 105(4), pages 1618-1633, April.
    Full references (including those not matched with items on IDEAS)

    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. Sarah Auster & Christian Kellner, 2023. "Timing Decisions under Model Uncertainty," ECONtribute Discussion Papers Series 252, University of Bonn and University of Cologne, Germany.
    2. Auster, Sarah & Kellner, Christian, 2022. "Robust bidding and revenue in descending price auctions," Journal of Economic Theory, Elsevier, vol. 199(C).
    3. Cheng, Xiaoyu, 2022. "Relative Maximum Likelihood updating of ambiguous beliefs," Journal of Mathematical Economics, Elsevier, vol. 99(C).
    4. Xiaoyu Cheng, 2019. "Relative Maximum Likelihood Updating of Ambiguous Beliefs," Papers 1911.02678, arXiv.org, revised Oct 2021.
    5. Bade, Sophie, 2022. "Dynamic semi-consistency," Games and Economic Behavior, Elsevier, vol. 134(C), pages 117-126.
    6. Larry G Epstein & Yoram Halevy, 2024. "Hard-to-Interpret Signals," Journal of the European Economic Association, European Economic Association, vol. 22(1), pages 393-427.
    7. Tang, Rui & Zhang, Mu, 2021. "Maxmin implementation," Journal of Economic Theory, Elsevier, vol. 194(C).
    8. Song, Yangwei, 2022. "Approximate Bayesian Implementation and Exact Maxmin Implementation: An Equivalence," Rationality and Competition Discussion Paper Series 362, CRC TRR 190 Rationality and Competition.
    9. Grant, Simon & Stauber, Ronald, 2022. "Delegation and ambiguity in correlated equilibrium," Games and Economic Behavior, Elsevier, vol. 132(C), pages 487-509.
    10. Beauchêne, Dorian & Li, Jian & Li, Ming, 2019. "Ambiguous persuasion," Journal of Economic Theory, Elsevier, vol. 179(C), pages 312-365.
    11. Shishkin, Denis & Ortoleva, Pietro, 2023. "Ambiguous information and dilation: An experiment," Journal of Economic Theory, Elsevier, vol. 208(C).
    12. Song, Yangwei, 2023. "Approximate Bayesian implementation and exact maxmin implementation: An equivalence," Games and Economic Behavior, Elsevier, vol. 139(C), pages 56-87.
    13. Konstantinos Georgalos, 2019. "An experimental test of the predictive power of dynamic ambiguity models," Journal of Risk and Uncertainty, Springer, vol. 59(1), pages 51-83, August.
    14. Markus Dertwinkel-Kalt & Jonas Frey, 2020. "Optimal Stopping in a Dynamic Salience Model," CESifo Working Paper Series 8496, CESifo.
    15. Song, Yangwei, 2018. "Efficient Implementation with Interdependent Valuations and Maxmin Agents," Rationality and Competition Discussion Paper Series 92, CRC TRR 190 Rationality and Competition.
    16. Frank Riedel, 2017. "Uncertain Acts in Games," Homo Oeconomicus: Journal of Behavioral and Institutional Economics, Springer, vol. 34(4), pages 275-292, December.
    17. Evren, Özgür, 2019. "Recursive non-expected utility: Connecting ambiguity attitudes to risk preferences and the level of ambiguity," Games and Economic Behavior, Elsevier, vol. 114(C), pages 285-307.
    18. Matthew Kovach, 2021. "Ambiguity and Partial Bayesian Updating," Papers 2102.11429, arXiv.org, revised Mar 2023.
    19. Guo, Huiyi, 2024. "Collusion-proof mechanisms for full surplus extraction," Games and Economic Behavior, Elsevier, vol. 145(C), pages 263-284.
    20. Sosung Baik & Sung-Ha Hwang, 2022. "Revenue Comparisons of Auctions with Ambiguity Averse Sellers," Papers 2211.12669, arXiv.org.

    More about this item

    Keywords

    Stopping problem; ambiguity; consistent planning;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • 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

    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:bon:boncrc:crctr224_2023_460. 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: CRC Office (email available below). General contact details of provider: https://www.crctr224.de .

    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.