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Updated: Jul 11, 2025
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The HoneyComb Paradigm for Research on Collective Human Behavior
Published on: January 19, 2019
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纳什平衡实现基于社会学习过程的人口游戏
Zhiyan Xing1, Yanlong Yang1, Zuopeng Hu1
1School of Mathematics and Statistics, Guizhou University, Guiyang 550025, China.
Mathematical biosciences and engineering : MBE
|November 3, 2023
概括
这项研究介绍了一种用于游戏理论的新型群体智能算法,模拟模仿学习. 该算法有效地趋于纯策略纳什平衡,并表现出混合策略纳什平衡的周期性趋同.
科学领域:
- 游戏理论 游戏理论
- 计算智能是一种计算智能.
- 人工智能的人工智能
背景情况:
- 传统的游戏理论模型经常假设理性玩家,忽视学习和适应.
- 在多代理系统中模拟复杂的学习行为是一个重大挑战.
研究的目的:
- 开发一种新的群体智能算法,用于模拟在两个人群游戏中的仿真学习.
- 在具有不同纳什平衡类型的游戏中分析这个算法的收性质.
- 将算法的性能与现有的混合策略纳什平衡实现方法进行比较.
主要方法:
- 提出了一种新的群体智能算法,集成粒子群体优化 (PSO),玩家被视为粒子.
- 在三个不同的游戏类型上进行了模拟:囚犯困境,币游戏和协调游戏.
- 分析了算法的融合行为,涉及纯策略和混合策略的纳什平衡.
主要成果:
- 当存在时,算法成功地汇聚到纯策略纳什平衡.
- 对于缺乏纯策略纳什平衡的游戏,该算法证明了周期性趋同到独特的混合策略纳什平衡.
- 周期性收的大小与内观率成反比例.
- 拟议的算法在实现混合策略纳什平衡方面表现优异,与Meta Equilibrium Q学习算法相比.
结论:
- 开发的群体智能算法有效地模拟了游戏理论中的模仿学习.
- 它强大地汇聚到不同类型的纳什平衡,展示了它的多功能性.
- 该算法提供了一种改进的方法,用于在计算游戏理论中找到混合策略纳什平衡.
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