Machine-Learning to Trust

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ID: 329693
2026
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Abstract
Abstract Can society sustain long-run mutual trust when agents’ equilibrium beliefs are shaped by machine-learning predictive methods? I study an infinite-horizon game with state-dependent payoffs, in which each player in his turn decides whether to place trust in his immediate successor. Players best-reply to a probabilistic tit-for-tat belief, which is a coarse fit of the true population strategy with respect to a partition of relevant contingencies. In equilibrium, this partition minimises 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 can significantly narrow the scope for mutual trust.
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openalex_W4414740153 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ran Spiegler
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Journal the economic journal
Year 2026
DOI
10.1093/ej/ueag120
URL
https://doi.org/10.1093/ej/ueag120
Keywords Keywords not found

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