{"title":"Analyzing the Impact of Tax Credits on Households in Simulated Economic Systems with Learning Agents","authors":"Jialin Dong, Kshama Dwarakanath, Svitlana Vyetrenko","doi":"arxiv-2311.17252","DOIUrl":null,"url":null,"abstract":"In economic modeling, there has been an increasing investigation into\nmulti-agent simulators. Nevertheless, state-of-the-art studies establish the\nmodel based on reinforcement learning (RL) exclusively for specific agent\ncategories, e.g., households, firms, or the government. It lacks concerns over\nthe resulting adaptation of other pivotal agents, thereby disregarding the\ncomplex interactions within a real-world economic system. Furthermore, we pay\nattention to the vital role of the government policy in distributing tax\ncredits. Instead of uniform distribution considered in state-of-the-art, it\nrequires a well-designed strategy to reduce disparities among households and\nimprove social welfare. To address these limitations, we propose an expansive\nmulti-agent economic model comprising reinforcement learning agents of numerous\ntypes. Additionally, our research comprehensively explores the impact of tax\ncredit allocation on household behavior and captures the spectrum of spending\npatterns that can be observed across diverse households. Further, we propose an\ninnovative government policy to distribute tax credits, strategically\nleveraging insights from tax credit spending patterns. Simulation results\nillustrate the efficacy of the proposed government strategy in ameliorating\ninequalities across households.","PeriodicalId":501487,"journal":{"name":"arXiv - QuantFin - Economics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Economics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2311.17252","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.