Incentive-Aware Learning in Stateful Environments: Internalizing Externalities in Principal-Agent MDPs and Welfare-Maximizing Diffusion Mechanisms

Modern AI systems increasingly operate in economic environments such as markets and insurance, where data, behavior, and incentives are endogenous and mutually reinforcing. This paper develops a microeconomic foundation for multi-agent learning by studying a repeated principal-agent interaction in a finite-horizon Markov decision process with strategic externalities, where both the principal and the agent learn over time and the agent’s actions affect payoffs and the environment’s dynamics. We design incentive schemes that align decentralized learning with social welfare via a two-phase mechanism: in Phase 1, the principal estimates the minimal transfers required to implement targeted actions by identifying how incentives shift the agent’s effective preferences; in Phase 2, the principal uses these estimates to steer long-run state-action visitation toward welfare-optimal behavior. We evaluate performance using social-welfare regret relative to the best achievable welfare benchmark, and we show that the mechanism achieves sublinear social-welfare regret under mild conditions (sublinear agent regret and sufficient exploration/coverage), implying asymptotically optimal welfare despite endogenous externalities and simultaneous learning. Simulations in a simple pollution-control environment illustrate that even coarse incentives can correct inefficient learning outcomes and substantially improve welfare. These results underscore that incentive-aware design, grounded in contract theory and mechanism design, is essential for safe, welfare-aligned AI deployed in strategic economic systems.