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This is a Quantitative Study study.

October 3, 2025Open Access

Machine-Learning to Trust

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Trust sustainability is affected by machine learning methods that penalize complexity in decision-making.
The study reveals that agents' beliefs about trust are informed by a coarse fit of true population strategies.
In equilibrium, a partition minimizes mean squared prediction error while considering complexity penalties.
This solution concept alters the landscape for trust relative to symmetric mixed-strategy Nash equilibrium.

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Analysis shows how machine learning shapes equilibrium beliefs, reducing potential for trust among agents.

Ran Spiegler (2025) studied this question.

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