Microeconomic Foundations of Multi-Agent Learning

• URL: http://arxiv.org/abs/2601.03451v1
• Date: Tue, 06 Jan 2026 22:37:47 GMT
• Title: Microeconomic Foundations of Multi-Agent Learning
• Authors: Nassim Helou,
• Abstract summary: Modern AI systems operate inside markets and institutions where data, behavior, and incentives are endogenous.<n>This paper develops an economic foundation for multi-agent learning by studying a principal-agent interaction in a Markov decision process with strategic externalities.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Modern AI systems increasingly operate inside markets and institutions where data, behavior, and incentives are endogenous. This paper develops an economic foundation for multi-agent learning by studying a principal-agent interaction in a Markov decision process with strategic externalities, where both the principal and the agent learn over time. We propose a two-phase incentive mechanism that first estimates implementable transfers and then uses them to steer long-run dynamics; under mild regret-based rationality and exploration conditions, the mechanism achieves sublinear social-welfare regret and thus asymptotically optimal welfare. Simulations illustrate how even coarse incentives can correct inefficient learning under stateful externalities, highlighting the necessity of incentive-aware design for safe and welfare-aligned AI in markets and insurance.

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