Technology Category
Multiagent Systems: Mechanism DesignGame Theory and Economic Paradigms: Mechanism DesignKnowledge Representation and Reasoning: Reasoning with Beliefs
Machine-Learning to Trust
🤖 AI Summary
This paper investigates how boundedly rational agents sustain trust in infinitely repeated interactions under a machine learning–inspired belief formation mechanism that penalizes model complexity. We develop an infinite-horizon dynamic game in which agents possess only one-period memory and decide whether to trust successors under state-dependent trust costs. Departing from standard equilibrium expectations, we introduce a “coarse-fitting” belief strategy that balances predictive accuracy against model simplicity—formalizing cognitive constraints via a trade-off between prediction error minimization and complexity regularization. Results show that this mechanism substantially shrinks the equilibrium set supporting trust and markedly reduces cooperation sustainability compared to symmetric mixed-strategy Nash equilibria. The key contribution is identifying cognitive simplicity—not mere bounded rationality—as a structural impediment to long-term trust, thereby establishing a novel machine learning–based paradigm for modeling belief formation in behavioral game theory.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Study trust sustainability with machine-learning shaped beliefs
Analyze equilibrium behavior in sequential trust games
Examine complexity-penalized partitions' impact on trust scope
Innovation
Methods, ideas, or system contributions that make the work stand out.
Machine-learning penalizes complexity in beliefs
Coarse fit partitions minimize prediction error
Equilibrium narrows trust scope significantly
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