Machine learning methods have been used to enhance EGTA, e.g., policy space response oracle methods where an oracle returns the best response(s) to (a set of meta-game) agent policies ( 25, 26). EGTA generally constrains agent policies to a predefined set, with replicator dynamics used to find equilibria across these heuristic policies (for symmetric games with interchangeable agents and identical affordances) ( 24). A computational approach to finding equilibria is empirical game-theoretic analysis (EGTA).
However, understanding the landscape of equilibria in general-sum games remains a substantial challenge ( 4). These results demonstrate that two-level, deep RL complements economic theory and unlocks an AI-based approach to designing and understanding economic policy.Įconomies with multiple agents and a social planner can also be seen as a hierarchical general-sum game, in which the planner designs payoffs for which the agents optimize their behavior. It does so despite emergent tax-gaming strategies while accounting for emergent labor specialization, agent interactions, and behavioral change.
In spatiotemporal economies, the AI Economist substantially improves both utilitarian social welfare and the trade-off between equality and productivity over baselines. In one-step economies, the AI Economist recovers the optimal tax policy of economic theory. We validate this framework in the domain of taxation. In particular, the AI Economist uses structured curriculum learning to stabilize the challenging two-level, coadaptive learning problem. The AI Economist is a two-level, deep RL framework for policy design in which agents and a social planner coadapt. Artificial intelligence (AI) and reinforcement learning (RL) have improved many areas but are not yet widely adopted in economic policy design, mechanism design, or economics at large.
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