mlsanityData to 14 Oct 2026

Empirical Equilibria in Agent-based Economic systems with Learning agents

arXiv 2408.12038 ↗First posted 21 Aug 2024cs.MA cs.GT econ.GNcited by 1

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

Abstract (arXiv)

We present an agent-based simulator for economic systems with heterogeneous households, firms, central bank, and government agents. These agents interact to define production, consumption, and monetary flow. Each agent type has distinct objectives, such as households seeking utility from consumption and the central bank targeting inflation and production. We define this multi-agent economic system using an OpenAI Gym-style environment, enabling agents to optimize their objectives through reinforcement learning. Standard multi-agent reinforcement learning (MARL) schemes, like independent learning, enable agents to learn concurrently but do not address whether the resulting strategies are at equilibrium. This study integrates the Policy Space Response Oracle (PSRO) algorithm, which has shown superior performance over independent MARL in games with homogeneous agents, with economic agent-based modeling. We use PSRO to develop agent policies approximating Nash equilibria of the empirical economic game, thereby linking to economic equilibria. Our results demonstrate that PSRO strategies achieve lower regret values than independent MARL strategies in our economic system with four agent types. This work aims to bridge artificial intelligence, economics, and empirical game theory towards future research.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 11 of its 66 references that are in the map. First posted on arXiv 21 Aug 2024.

Builds on

11 of 66 references.

References · 11

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  3. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  4. 2017A Unified Game-Theoretic Approach to Multiagent Reinforcement Learningcited by 268
  5. 2021ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Marketscited by 36
  6. 2019ABIDES: Towards High-Fidelity Market Simulation for AI Researchcited by 32
  7. 2021Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economistcited by 23
  8. 2021Iterative Empirical Game Solving via Single Policy Best Responsecited by 12
  9. 2021Deep Reinforcement Learning in a Monetary Modelcited by 11
  10. 2021Solving Heterogeneous General Equilibrium Economic Models with Deep Reinforcement Learningcited by 10
  11. 2023Analyzing the Impact of Tax Credits on Households in Simulated Economic Systems with Learning Agentscited by 2

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025From Individual Learning to Market Equilibrium: Correcting Structural and Parametric Biases in RL Simulations of Economic Modelscited by 0

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  1. 2024Learning and Calibrating Heterogeneous Bounded Rational Market Behaviour with Multi-Agent Reinforcement Learning
  2. 2025Heterogeneous RBCs via Deep Multi-Agent Reinforcement Learning
  3. 2024Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based Modelling
  4. 2025Structural Reinforcement Learning for Heterogeneous Agent Macroeconomics
  5. 2024Equilibrium Selection for Multi-agent Reinforcement Learning: A Unified Framework
  6. 2026A Survey of Reinforcement Learning For Economics
  7. 2025Policy Abstraction and Nash Refinement in Tree-Exploiting PSRO
  8. 2024Game Theory and Multi-Agent Reinforcement Learning : From Nash Equilibria to Evolutionary Dynamics