IDEAS home Printed from https://ideas.repec.org/p/hit/hiasdp/hias-e-101.html
   My bibliography  Save this paper

Response time and revealed information structure

Author

Listed:
  • Aoyama, Tomohito

Abstract

Consider a decision-maker who has an opportunity to wait for information before making a choice. He can obtain more information by waiting more, but this is costly. As a result, he endogenously determines the length of time to choose an alternative, which is called the response time. The present study models such a decision-maker as if he solves an optimal stopping problem. The model incorporates a dynamic information structure formalized as an evolving information partition, which is called filtration. I axiomatically characterize the model using behavioral data consisting of choices and response times that depend on choice situations and states. That is, from the data, we can identify filtration that governs the decision-maker's learning process as well as other model parameters. This result implies that using response time helps us understand the human cognitive process.

Suggested Citation

  • Aoyama, Tomohito, 2020. "Response time and revealed information structure," Discussion paper series HIAS-E-101, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    • Handle: RePEc:hit:hiasdp:hias-e-101
    Note: November 30, 2020
    as

    Download full text from publisher

    File URL: https://hermes-ir.lib.hit-u.ac.jp/hermes/ir/re/70499/070_hiasDP-E-101.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Youichiro Higashi & Kazuya Hyogo & Norio Takeoka, 2020. "Costly Subjective Learning," KIER Working Papers 1040, Kyoto University, Institute of Economic Research.
    2. Nobuo Koida, 2017. "A multiattribute decision time theory," Theory and Decision, Springer, vol. 83(3), pages 407-430, October.
    3. Chambers, Christopher P. & Liu, Ce & Rehbeck, John, 2020. "Costly information acquisition," Journal of Economic Theory, Elsevier, vol. 186(C).
    4. Andrew Caplin & Mark Dean, 2015. "Revealed Preference, Rational Inattention, and Costly Information Acquisition," American Economic Review, American Economic Association, vol. 105(7), pages 2183-2203, July.
    5. de Oliveira, Henrique & Denti, Tommaso & Mihm, Maximilian & Ozbek, Kemal, 2017. "Rationally inattentive preferences and hidden information costs," Theoretical Economics, Econometric Society, vol. 12(2), May.
    6. Sims, Christopher A., 2003. "Implications of rational inattention," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 665-690, April.
    7. Hyogo, Kazuya, 2007. "A subjective model of experimentation," Journal of Economic Theory, Elsevier, vol. 133(1), pages 316-330, March.
    8. Echenique, Federico & Saito, Kota, 2017. "Response time and utility," Journal of Economic Behavior & Organization, Elsevier, vol. 139(C), pages 49-59.
    9. Ellis, Andrew, 2018. "Foundations for optimal inattention," Journal of Economic Theory, Elsevier, vol. 173(C), pages 56-94.
    10. Takeoka, Norio, 2007. "Subjective probability over a subjective decision tree," Journal of Economic Theory, Elsevier, vol. 136(1), pages 536-571, September.
    11. Charalambos D. Aliprantis & Kim C. Border, 2006. "Infinite Dimensional Analysis," Springer Books, Springer, edition 0, number 978-3-540-29587-7, September.
    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. Youichiro Higashi & Kazuya Hyogo & Norio Takeoka, 2020. "Costly Subjective Learning," KIER Working Papers 1040, Kyoto University, Institute of Economic Research.
    2. Luciano Pomatto & Philipp Strack & Omer Tamuz, 2018. "The Cost of Information: The Case of Constant Marginal Costs," Papers 1812.04211, arXiv.org, revised Feb 2023.
    3. Lin, Yi-Hsuan, 2022. "Stochastic choice and rational inattention," Journal of Economic Theory, Elsevier, vol. 202(C).
    4. Tsakas, Elias, 2020. "Robust scoring rules," Theoretical Economics, Econometric Society, vol. 15(3), July.
    5. Bartosz Maćkowiak & Filip Matějka & Mirko Wiederholt, 2023. "Rational Inattention: A Review," Journal of Economic Literature, American Economic Association, vol. 61(1), pages 226-273, March.
    6. Daniele Pennesi, 2020. "Identity and information acquisition," Carlo Alberto Notebooks 610, Collegio Carlo Alberto, revised 2021.
    7. Nobuo Koida, 2021. "Intransitive indifference with direction-dependent sensitivity," KIER Working Papers 1061, Kyoto University, Institute of Economic Research.
    8. Monte, Daniel & Linhares, Luis Henrique, 2023. "Stealth Startups, Clauses, and Add-ons: A Model of Strategic Obfuscation," MPRA Paper 115926, University Library of Munich, Germany.
    9. Mogens Fosgerau & Emerson Melo & André de Palma & Matthew Shum, 2020. "Discrete Choice And Rational Inattention: A General Equivalence Result," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 61(4), pages 1569-1589, November.
    10. Tsakas, Elias, 2018. "Robust scoring rules," Research Memorandum 023, Maastricht University, Graduate School of Business and Economics (GSBE).
    11. Chambers, Christopher P. & Liu, Ce & Rehbeck, John, 2020. "Costly information acquisition," Journal of Economic Theory, Elsevier, vol. 186(C).
    12. Ellis, Andrew, 2018. "Foundations for optimal inattention," Journal of Economic Theory, Elsevier, vol. 173(C), pages 56-94.
    13. Philippe Jehiel & Jakub Steiner, 2020. "Selective Sampling with Information-Storage Constraints [On interim rationality, belief formation and learning in decision problems with bounded memory]," The Economic Journal, Royal Economic Society, vol. 130(630), pages 1753-1781.
    14. Valentino Dardanoni & Paola Manzini & Marco Mariotti & Christopher J. Tyson, 2020. "Inferring Cognitive Heterogeneity From Aggregate Choices," Econometrica, Econometric Society, vol. 88(3), pages 1269-1296, May.
    15. Valentino Dardanoni & Paola Manzini & Marco Mariotti & Christopher J. Tyson, 2020. "Inferring Cognitive Heterogeneity From Aggregate Choices," Econometrica, Econometric Society, vol. 88(3), pages 1269-1296, May.
    16. Dewan, Ambuj & Neligh, Nathaniel, 2020. "Estimating information cost functions in models of rational inattention," Journal of Economic Theory, Elsevier, vol. 187(C).
    17. Isaac Baley & Laura Veldkamp, 2021. "Bayesian learning," Economics Working Papers 1797, Department of Economics and Business, Universitat Pompeu Fabra.
    18. Erin T. Bronchetti & Judd B. Kessler & Ellen B. Magenheim & Dmitry Taubinsky & Eric Zwick, 2023. "Is Attention Produced Optimally? Theory and Evidence From Experiments With Bandwidth Enhancements," Econometrica, Econometric Society, vol. 91(2), pages 669-707, March.
    19. 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.
    20. Mensch, Jeffrey, 2021. "Rational inattention and the monotone likelihood ratio property," Journal of Economic Theory, Elsevier, vol. 196(C).

    More about this item

    Keywords

    Response time; Subjective learning; Information acquisition;
    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
    • 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:hit:hiasdp:hias-e-101. 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: Digital Resources Section, Hitotsubashi University Library (email available below). General contact details of provider: https://edirc.repec.org/data/ashitjp.html .

    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.