Network Analysis on the Symmetric Coordination in a Reinforcement-Learning-Based Minority Game
The Minority Game (MG) is a paradigmatic model in econophysics, widely used to study inductive reasoning and self-organization in multi-agent systems. Traditionally, coordinated phases in the MG are associated with spontaneous symmetry breaking, where agents differentiate into polarized roles. Recen...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/27/7/676 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849418563127869440 |
|---|---|
| author | Chunqiang Shao Wenjia Rao Wangfang Xu Longbao Wei |
| author_facet | Chunqiang Shao Wenjia Rao Wangfang Xu Longbao Wei |
| author_sort | Chunqiang Shao |
| collection | DOAJ |
| description | The Minority Game (MG) is a paradigmatic model in econophysics, widely used to study inductive reasoning and self-organization in multi-agent systems. Traditionally, coordinated phases in the MG are associated with spontaneous symmetry breaking, where agents differentiate into polarized roles. Recent work shows that policy-based reinforcement-learning can give rise to a new form of symmetric coordination—one achieved without role segregation or strategy specialization. In this study, we thoroughly analyze this novel coordination using tools from complex networks. By constructing the correlation networks among agents, we carry out a structural, functional, and temporal analysis of the emergent symmetric coordination. Our results confirm the preservation of symmetry at the collective level, and reveal a consistent and robust form of distributed coordination, demonstrating the power of network-based approaches in understanding the emergent order in adaptive multi-agent systems. |
| format | Article |
| id | doaj-art-afa7e25e5f214b06a496dfb4eef086ca |
| institution | Kabale University |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-afa7e25e5f214b06a496dfb4eef086ca2025-08-20T03:32:26ZengMDPI AGEntropy1099-43002025-06-0127767610.3390/e27070676Network Analysis on the Symmetric Coordination in a Reinforcement-Learning-Based Minority GameChunqiang Shao0Wenjia Rao1Wangfang Xu2Longbao Wei3School of Sciences, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Sciences, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Accounting, Zhejiang University of Finance and Economics, Hangzhou 310018, ChinaChina Academy for Rural Development, School of Public Affairs, Zhejiang University, Hangzhou 310058, ChinaThe Minority Game (MG) is a paradigmatic model in econophysics, widely used to study inductive reasoning and self-organization in multi-agent systems. Traditionally, coordinated phases in the MG are associated with spontaneous symmetry breaking, where agents differentiate into polarized roles. Recent work shows that policy-based reinforcement-learning can give rise to a new form of symmetric coordination—one achieved without role segregation or strategy specialization. In this study, we thoroughly analyze this novel coordination using tools from complex networks. By constructing the correlation networks among agents, we carry out a structural, functional, and temporal analysis of the emergent symmetric coordination. Our results confirm the preservation of symmetry at the collective level, and reveal a consistent and robust form of distributed coordination, demonstrating the power of network-based approaches in understanding the emergent order in adaptive multi-agent systems.https://www.mdpi.com/1099-4300/27/7/676minority gamereinforcement learningcomplex network |
| spellingShingle | Chunqiang Shao Wenjia Rao Wangfang Xu Longbao Wei Network Analysis on the Symmetric Coordination in a Reinforcement-Learning-Based Minority Game Entropy minority game reinforcement learning complex network |
| title | Network Analysis on the Symmetric Coordination in a Reinforcement-Learning-Based Minority Game |
| title_full | Network Analysis on the Symmetric Coordination in a Reinforcement-Learning-Based Minority Game |
| title_fullStr | Network Analysis on the Symmetric Coordination in a Reinforcement-Learning-Based Minority Game |
| title_full_unstemmed | Network Analysis on the Symmetric Coordination in a Reinforcement-Learning-Based Minority Game |
| title_short | Network Analysis on the Symmetric Coordination in a Reinforcement-Learning-Based Minority Game |
| title_sort | network analysis on the symmetric coordination in a reinforcement learning based minority game |
| topic | minority game reinforcement learning complex network |
| url | https://www.mdpi.com/1099-4300/27/7/676 |
| work_keys_str_mv | AT chunqiangshao networkanalysisonthesymmetriccoordinationinareinforcementlearningbasedminoritygame AT wenjiarao networkanalysisonthesymmetriccoordinationinareinforcementlearningbasedminoritygame AT wangfangxu networkanalysisonthesymmetriccoordinationinareinforcementlearningbasedminoritygame AT longbaowei networkanalysisonthesymmetriccoordinationinareinforcementlearningbasedminoritygame |