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Decision-making with distorted memory: Escaping the trap of past experience

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  • Mitsokapas, Evangelos
  • Harris, Rosemary J.

Abstract

Snapshots of “best” (or “worst”) experience are known to dominate human memory and may thus also have a significant effect on future behaviour. We consider here a model of repeated decision-making where, at every time step, an agent takes one of two choices with probabilities which are functions of the maximum utilities previously experienced. Depending on the utility distributions and the level of noise in the decision process, it is possible for an agent to become “trapped” in one of the choices on the basis of their early experiences. If the utility distributions for the two choices are different, then the agent may even become trapped in the choice which is objectively worse in the sense of expected long-term returns; crucially we extend earlier work to address this case. Using tools from statistical physics and extreme-value theory, we show that for exponential utilities there is an optimal value of noise which maximizes the expected returns in the long run. We also briefly discuss the behaviour for other utility distributions.

Suggested Citation

  • Mitsokapas, Evangelos & Harris, Rosemary J., 2022. "Decision-making with distorted memory: Escaping the trap of past experience," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    • Handle: RePEc:eee:phsmap:v:593:y:2022:i:c:s0378437121009535
    DOI: 10.1016/j.physa.2021.126762
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    References listed on IDEAS

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    1. Jos'e Moran & Antoine Fosset & Davide Luzzati & Jean-Philippe Bouchaud & Michael Benzaquen, 2020. "By Force of Habit: Self-Trapping in a Dynamical Utility Landscape," Papers 2003.13660, arXiv.org.
    2. Alan Kirman, 1993. "Ants, Rationality, and Recruitment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 108(1), pages 137-156.
    3. Jean-Philippe Bouchaud & Roger E. A. Farmer, 2023. "Self-Fulfilling Prophecies, Quasi Nonergodicity, and Wealth Inequality," Journal of Political Economy, University of Chicago Press, vol. 131(4), pages 947-993.
    4. Jędrzejewski, Arkadiusz & Sznajd-Weron, Katarzyna, 2018. "Impact of memory on opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 306-315.
    5. Janson, Svante, 2004. "Functional limit theorems for multitype branching processes and generalized Pólya urns," Stochastic Processes and their Applications, Elsevier, vol. 110(2), pages 177-245, April.
    6. Jos'e Moran & Antoine Fosset & Michael Benzaquen & Jean-Philippe Bouchaud, 2020. "Schr\"odinger's ants: A continuous description of Kirman's recruitment model," Papers 2004.06667, arXiv.org.
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