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ORIGINAL RESEARCH article
Front. Phys.
Sec. Social Physics
Volume 12 - 2024 |
doi: 10.3389/fphy.2024.1451423
This article is part of the Research Topic Real-World Applications of Game Theory and Optimization, Volume II View all 3 articles
HMAE: A high-fidelity multi-agent simulator for economic phenomenon emergence
Provisionally accepted- 1 Research Center for Data Hub and Security, Zhejiang Lab, Hangzhou, China
- 2 School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China
Economic models based on multi-agents are increasingly attracting attention and can provide a new perspective for exploring the causes behind social phenomena at the individual level. Existing research usually adopts society-level learning methods and needs more research on micro-level heterogeneity among individuals. For this, we propose a High-fidelity Multi-Agent Economy (HMAE) model based on evolutionary game theory, including three types of agents: workers, firms, and the government. In particular, we characterize worker heterogeneity regarding laziness factors, work endowments, and commuting distances. These agents continuously and iteratively update their strategies by randomly exploring and imitating their neighbors to maximize their utility value. We simulated the evolution process of agents' behavioral decisions through experiments and found that individual heterogeneity can significantly affect the decisions of workers and firms. These phenomena are consistent with some economic evolution trends in real life, and our research can provide an analytical tool for analyzing the causes of emerging economic phenomena.
Keywords: agent-based model, evolutionary game theory, individual heterogeneity, Economy model, Multi-Agent (MA)
Received: 19 Jun 2024; Accepted: 29 Aug 2024.
Copyright: © 2024 Wang, Ma, Zeng, Jin and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xing Jin, School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China
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