We introduce a multi-agent simulator for economic systems comprised of heterogeneous Households, heterogeneous Firms, Central Bank and Government agents, that could be subjected to exogenous, stochastic shocks. The interaction between agents defines the production and consumption of goods in the economy alongside the flow of money. Each agent can be designed to act according to fixed, rule-based strategies or learn their strategies using interactions with others in the simulator. We ground our simulator by choosing agent heterogeneity parameters based on economic literature, while designing their action spaces in accordance with real data in the United States. Our simulator facilitates the use of reinforcement learning strategies for the agents via an OpenAI Gym style environment definition for the economic system. We demonstrate the utility of our simulator by simulating and analyzing two hypothetical (yet interesting) economic scenarios. The first scenario investigates the impact of heterogeneous household skills on their learned preferences to work at different firms. The second scenario examines the impact of a positive production shock to one of two firms on its pricing strategy in comparison to the second firm. We aspire that our platform sets a stage for subsequent research at the intersection of artificial intelligence and economics.

翻译：我们提出一个由异质性家庭、异质性企业、中央银行和政府智能体构成的经济系统多智能体仿真器，该仿真器可受到外生随机冲击的影响。智能体间的互动定义了经济中商品的生产与消费，以及货币流动。每个智能体既可按照固定规则策略行动，也可通过与仿真器中其他智能体的交互学习自身策略。我们依据经济学文献选择智能体异质性参数，并根据美国实际数据设计其行动空间，从而夯实仿真基础。该仿真器通过为经济系统定义类似OpenAI Gym的环境接口，支持智能体使用强化学习策略。为展示仿真器的实用性，我们模拟并分析了两个假设性（但有趣）的经济场景：第一个场景探讨异质性家庭技能对其在不同企业工作偏好学习的影响；第二个场景分析某企业相对于另一企业的正向生产冲击对其定价策略的影响。我们期望该平台能为人工智能与经济学交叉领域的后续研究奠定基础。