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Machine-Learning to Trust

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  • Ran Spiegler

Abstract

Can players sustain long-run trust when their equilibrium beliefs are shaped by machine-learning methods that penalize complexity? I study a game in which an infinite sequence of agents with one-period recall decides whether to place trust in their immediate successor. The cost of trusting is state-dependent. Each player's best response is based on a belief about others' behavior, which is a coarse fit of the true population strategy with respect to a partition of relevant contingencies. In equilibrium, this partition minimizes the sum of the mean squared prediction error and a complexity penalty proportional to its size. Relative to symmetric mixed-strategy Nash equilibrium, this solution concept significantly narrows the scope for trust.

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  • Ran Spiegler, 2025. "Machine-Learning to Trust," Papers 2507.10363, arXiv.org.
    • Handle: RePEc:arx:papers:2507.10363
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    References listed on IDEAS

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    1. Mohlin, Erik, 2014. "Optimal categorization," Journal of Economic Theory, Elsevier, vol. 152(C), pages 356-381.
    2. Spiegler, Ran, 2005. "Testing threats in repeated games," Journal of Economic Theory, Elsevier, vol. 121(2), pages 214-235, April.
    3. Zach Y. Brown & Alexander MacKay, 2023. "Competition in Pricing Algorithms," American Economic Journal: Microeconomics, American Economic Association, vol. 15(2), pages 109-156, May.
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    6. Spiegler, Ran, 2004. "Simplicity of beliefs and delay tactics in a concession game," Games and Economic Behavior, Elsevier, vol. 47(1), pages 200-220, April.
    7. Eliaz, Kfir, 2003. "Nash equilibrium when players account for the complexity of their forecasts," Games and Economic Behavior, Elsevier, vol. 44(2), pages 286-310, August.
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    10. Bendor Jonathan & Mookherjee Dilip & Ray Debraj, 2001. "Reinforcement Learning in Repeated Interaction Games," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 1(1), pages 1-44, March.
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