In economic modeling, there has been an increasing investigation into multi-agent simulators. Nevertheless, state-of-the-art studies establish the model based on reinforcement learning (RL) exclusively for specific agent categories, e.g., households, firms, or the government. It lacks concerns over the resulting adaptation of other pivotal agents, thereby disregarding the complex interactions within a real-world economic system. Furthermore, we pay attention to the vital role of the government policy in distributing tax credits. Instead of uniform distribution considered in state-of-the-art, it requires a well-designed strategy to reduce disparities among households and improve social welfare. To address these limitations, we propose an expansive multi-agent economic model comprising reinforcement learning agents of numerous types. Additionally, our research comprehensively explores the impact of tax credit allocation on household behavior and captures the spectrum of spending patterns that can be observed across diverse households. Further, we propose an innovative government policy to distribute tax credits, strategically leveraging insights from tax credit spending patterns. Simulation results illustrate the efficacy of the proposed government strategy in ameliorating inequalities across households.

翻译：在经济建模领域，对多智能体模拟器的研究日益增多。然而，现有研究仅针对特定智能体类别（如家庭、企业或政府）建立基于强化学习的模型，忽视了其他关键智能体的适应性调整，从而忽略了真实经济系统中的复杂交互关系。此外，我们关注政府在税收抵免分配中的关键作用——不同于现有研究中采用的统一分配方式，需要精心设计的策略来缩小家庭间差异并提升社会福利。为突破这些局限，我们提出一个包含多种类型强化学习智能体的扩展多智能体经济模型。本研究全面探讨了税收抵免分配对家庭行为的影响，并捕捉了不同家庭可观测的消费模式谱系。进而，我们提出一项创新的政府政策，通过战略性地运用税收抵免消费模式洞察来分配税收抵免。仿真结果表明，该政府策略在改善家庭间不平等问题上具有显著成效。