# Empirical Equilibria in Agent-based Economic systems with Learning agents

arXiv: https://arxiv.org/abs/2408.12038 · HTML: https://mlsanity.com/paper/2408.12038/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Multi-agent reinforcement learning, 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.

## Placed in

- Deep reinforcement learning methods / [Multi-agent reinforcement learning](https://mlsanity.com/deeprl/multi-agent-reinforcement-learning/)

## Builds on (most cited first, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning](https://mlsanity.com/paper/1711.00832/) (2017)
- [ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Markets](https://mlsanity.com/paper/2110.14771/) (2021)
- [ABIDES: Towards High-Fidelity Market Simulation for AI Research](https://mlsanity.com/paper/1904.12066/) (2019)
- [Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist](https://mlsanity.com/paper/2108.02904/) (2021)
- [Iterative Empirical Game Solving via Single Policy Best Response](https://mlsanity.com/paper/2106.01901/) (2021)
- [Deep Reinforcement Learning in a Monetary Model](https://mlsanity.com/paper/2104.09368/) (2021)
- [Solving Heterogeneous General Equilibrium Economic Models with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.16977/) (2021)
- [Analyzing the Impact of Tax Credits on Households in Simulated Economic Systems with Learning Agents](https://mlsanity.com/paper/2311.17252/) (2023)

## Built on by (most cited first, 1 of 1)

- [From Individual Learning to Market Equilibrium: Correcting Structural and Parametric Biases in RL Simulations of Economic Models](https://mlsanity.com/paper/2507.18229/) (2025)

## 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.