Modelling the Dynamics of Regret Minimization in Large Agent Populations: a Master Equation Approach

Wang, Zhen; Mu, Chunjiang; Hu, Shuyue; Chu, Chen; Li, Xuelong

doi:10.24963/ijcai.2022/76

Modelling the Dynamics of Regret Minimization in Large Agent Populations: a Master Equation Approach

Zhen Wang, Chunjiang Mu, Shuyue Hu, Chen Chu, Xuelong Li

Video #1 Video #2 Download BibTeX Cite

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. Main Track. Pages 534-540.

https://doi.org/10.24963/ijcai.2022/76

Keywords

Agent-based and Multi-agent Systems: Multi-agent Learning
Agent-based and Multi-agent Systems: Agent-Based Simulation and Emergence

Abstract

Understanding the learning dynamics in multiagent systems is an important and challenging task. Past research on multi-agent learning mostly focuses on two-agent settings. In this paper, we consider the scenario in which a population of infinitely many agents apply regret minimization in repeated symmetric games. We propose a new formal model based on the master equation approach in statistical physics to describe the evolutionary dynamics in the agent population. Our model takes the form of a partial differential equation, which describes how the probability distribution of regret evolves over time. Through experiments, we show that our theoretical results are consistent with the agent-based simulation results.

Zhen Wang, Chunjiang Mu, Shuyue Hu, Chen Chu, and Xuelong Li. 2022. Modelling the Dynamics of Regret Minimization in Large Agent Populations: a Master Equation Approach. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22. IJCAI, 534–540. https://doi.org/10.24963/ijcai.2022/76

@inproceedings{ijcai2022p76,
  title     = {Modelling the Dynamics of Regret Minimization in Large Agent Populations: a Master Equation Approach},
  author    = {Wang, Zhen and Mu, Chunjiang and Hu, Shuyue and Chu, Chen and Li, Xuelong},
  booktitle = {Proceedings of the Thirty-First International Joint Conference on
               Artificial Intelligence, {IJCAI-22}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Luc De Raedt},
  pages     = {534--540},
  year      = {2022},
  month     = {7},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2022/76},
  url       = {https://doi.org/10.24963/ijcai.2022/76},
}

TY  - CPAPER
TI  - Modelling the Dynamics of Regret Minimization in Large Agent Populations: a Master Equation Approach
AU  - Wang, Zhen
AU  - Mu, Chunjiang
AU  - Hu, Shuyue
AU  - Chu, Chen
AU  - Li, Xuelong
T2  - Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2022
SP  - 534
EP  - 540
DO  - 10.24963/ijcai.2022/76
UR  - https://doi.org/10.24963/ijcai.2022/76
ER  -