ABIDES-Economist: Agent-Based Simulation of Economic Systems with Learning Agents
CoRR(2024)
摘要
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.
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