A bilevel MARL framework with curriculum learning and closed-loop sequential updates learns stable tax policies in multi-group taxation simulations, extending effective game duration by 60.92% and reducing GDP disparities by 44.12% versus baseline.
Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist
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More capable RL agents exploit reward misspecifications more often, with phase transitions in behavior, and anomaly detectors can identify misaligned policies.
EconAI adds memory weighting and economic sentiment indexing to LLM agents so they adapt short-term actions to long-term goals inside a single macro/micro simulation loop.
citing papers explorer
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Hierarchical Multiagent Reinforcement Learning for Multi-Group Tax Game
A bilevel MARL framework with curriculum learning and closed-loop sequential updates learns stable tax policies in multi-group taxation simulations, extending effective game duration by 60.92% and reducing GDP disparities by 44.12% versus baseline.
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The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models
More capable RL agents exploit reward misspecifications more often, with phase transitions in behavior, and anomaly detectors can identify misaligned policies.
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EconAI: Dynamic Persona Evolution and Memory-Aware Agents in Evolving Economic Environments
EconAI adds memory weighting and economic sentiment indexing to LLM agents so they adapt short-term actions to long-term goals inside a single macro/micro simulation loop.