EconCS Seminar

 

Lecturer: 

Prof. Ran Spiegler (TAU and UCL)

Title: 

Machine-Learning to Trust

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.

Link to paper: https://www.ranspiegler.sites.tau.ac.il/_files/ugd/4871e3_8e6291b05db14b2fb93f18198761e36c.pdf

Location: 

Room 130, Feldman Building, Edmond J. Safra Campus.

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