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In economic modeling, there has been an increasing investigation intomulti-agent simulators. Nevertheless, state-of-the-art studies establish themodel based on reinforcement learning (RL) exclusively for specific agentcategories, e.g., households, firms, or the government. It lacks concerns overthe resulting adaptation of other pivotal agents, thereby disregarding thecomplex interactions within a real-world economic system. Furthermore, we payattention to the vital role of the government policy in distributing taxcredits. Instead of uniform distribution considered in state-of-the-art, itrequires a well-designed strategy to reduce disparities among households andimprove social welfare. To address these limitations, we propose an expansivemulti-agent economic model comprising reinforcement learning agents of numeroustypes. Additionally, our research comprehensively explores the impact of taxcredit allocation on household behavior and captures the spectrum of spendingpatterns that can be observed across diverse households. Further, we propose aninnovative government policy to distribute tax credits, strategicallyleveraging insights from tax credit spending patterns. Simulation resultsillustrate the efficacy of the proposed government strategy in amelioratinginequalities across households.

Analyzing the Impact of Tax Credits on Households in Simulated Economic Systems with Learning Agents