IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/2653.html
   My bibliography  Save this paper

Non-Probabilistic Decision Making with Memory Constraints

Author

Listed:
  • Vostroknutov, Alexander

Abstract

In the model of choice, studied in this paper, the decision maker chooses the actions non-probabilistically in each period (Sarin and Vahid, 1999; Sarin, 2000). The action is chosen if it yields the biggest payoff according to the decision maker’s subjective assessment. Decision maker knows nothing about the process that generates the payoffs. If the decision maker remembers only recent payoffs, she converges to the maximin action. If she remembers all past payoffs, the maximal expected payoff action is chosen. These results hold for any possible dynamics of weights and are robust against the mistakes. The estimates of the rate of convergence reveal that in some important cases the convergence to the asymptotic behavior can take extremely long time. The model suggests simple experimental test of the way people memorize past experiences: if any weighted procedure is actually involved, it can possibly generate only two distinct modes of behavior.

Suggested Citation

  • Vostroknutov, Alexander, 2005. "Non-Probabilistic Decision Making with Memory Constraints," MPRA Paper 2653, University Library of Munich, Germany, revised Jul 2007.
    • Handle: RePEc:pra:mprapa:2653
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/2653/1/MPRA_paper_2653.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Huck Steffen & Sarin Rajiv, 2004. "Players With Limited Memory," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 4(1), pages 1-27, September.
    2. Sarin, Rajiv, 2000. "Decision Rules with Bounded Memory," Journal of Economic Theory, Elsevier, vol. 90(1), pages 151-160, January.
    3. Sarin, Rajiv & Vahid, Farshid, 1999. "Payoff Assessments without Probabilities: A Simple Dynamic Model of Choice," Games and Economic Behavior, Elsevier, vol. 28(2), pages 294-309, August.
    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. Vostroknutov, Alexander, 2012. "Non-probabilistic decision making with memory constraints," Economics Letters, Elsevier, vol. 117(1), pages 303-305.
    2. Yan Chen & Laura Razzolini & Theodore Turocy, 2007. "Congestion allocation for distributed networks: an experimental study," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 33(1), pages 121-143, October.
    3. Philippe Jehiel & Aviman Satpathy, 2024. "Learning to be Indifferent in Complex Decisions: A Coarse Payoff-Assessment Model," Papers 2412.09321, arXiv.org, revised Dec 2024.
    4. Erhao Xie, 2019. "Monetary Payoff and Utility Function in Adaptive Learning Models," Staff Working Papers 19-50, Bank of Canada.
    5. Aaron Foote & Maryam Gooyabadi & Nikhil Addleman, 2023. "Factors in Learning Dynamics Influencing Relative Strengths of Strategies in Poker Simulation," Games, MDPI, vol. 14(6), pages 1-16, November.
    6. Brit Grosskopf & Ido Erev & Eldad Yechiam, 2006. "Foregone with the Wind: Indirect Payoff Information and its Implications for Choice," International Journal of Game Theory, Springer;Game Theory Society, vol. 34(2), pages 285-302, August.
    7. Sarin, Rajiv, 1999. "Simple play in the Prisoner's Dilemma," Journal of Economic Behavior & Organization, Elsevier, vol. 40(1), pages 105-113, September.
    8. 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.
    9. Funai Naoki, 2014. "An Adaptive Learning Model with Foregone Payoff Information," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 14(1), pages 149-176, January.
    10. Sobel, Joel, 2000. "Economists' Models of Learning," Journal of Economic Theory, Elsevier, vol. 94(2), pages 241-261, October.
    11. Funai, Naoki, 2022. "Reinforcement learning with foregone payoff information in normal form games," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 638-660.
    12. Hopkins, Ed, 2007. "Adaptive learning models of consumer behavior," Journal of Economic Behavior & Organization, Elsevier, vol. 64(3-4), pages 348-368.
    13. Ralph-C. Bayer & Hang Wu, 2013. "Do We Learn from Our Own Experience or from Observing Others?," School of Economics and Public Policy Working Papers 2013-21, University of Adelaide, School of Economics and Public Policy.
    14. Brit Grosskopf & Rajiv Sarin & Elizabeth Watson, 2015. "An experiment on case-based decision making," Theory and Decision, Springer, vol. 79(4), pages 639-666, December.
    15. Ralph-C Bayer & Elke Renner & Rupert Sausgruber, 2009. "Confusion and Reinforcement Learning in Experimental Public Goods Games," NRN working papers 2009-22, The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria.
    16. Naoki Funai, 2019. "Convergence results on stochastic adaptive learning," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 68(4), pages 907-934, November.
    17. Chen, Yan, 2003. "An experimental study of serial and average cost pricing mechanisms," Journal of Public Economics, Elsevier, vol. 87(9-10), pages 2305-2335, September.
    18. Iris Lorscheid & Bernd-Oliver Heine & Matthias Meyer, 2012. "Opening the ‘black box’ of simulations: increased transparency and effective communication through the systematic design of experiments," Computational and Mathematical Organization Theory, Springer, vol. 18(1), pages 22-62, March.
    19. Maxwell Pak & Bing Xu, 2016. "Generalized reinforcement learning in perfect-information games," International Journal of Game Theory, Springer;Game Theory Society, vol. 45(4), pages 985-1011, November.
    20. Alanyali, Murat, 2010. "A note on adjusted replicator dynamics in iterated games," Journal of Mathematical Economics, Elsevier, vol. 46(1), pages 86-98, January.

    More about this item

    Keywords

    Adaptive learning; constrained memory; bandit problem; non-probabilistic choice;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

    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:pra:mprapa:2653. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.