machine-learning to trust: new paper

[Spiegler, Ran]

Dear colleagues,

I would like to share a new paper, called "Machine-Learning to Trust":

https://www.ranspiegler.sites.tau.ac.il/_files/ugd/4871e3_67544443707f4c969cf59872ffa9c76c.pdf

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 decide 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.

I hope you enjoy it. Recommended wine pairing: Non-alcoholic sangria.

Feedback is welcome.

Thank you,

Rani

Ran Spiegler

School of Economics, Tel Aviv University

&

Department of Economics, University College London

URL: https://www.ranspiegler.sites.tau.ac.il/ 

New book: The Curious Culture of Economic Theory (MIT Press)

Social media book pages: X @rani_spiegler, Bluesky @rani-spielger.bsky.social