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合作的复杂途径源于异质人群中的不对称性
Hao Guo1, Chen Shen2, Rongcheng Zou3
1Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China.
Chaos (Woodbury, N.Y.)
|February 28, 2024
概括
在不对称的群体中,合作动态取决于玩家的力量和游戏类型. 最佳的合作出现在网格结构中,中间有强有力的参与者和适度的不对称性,弱势参与者保护合作者.
科学领域:
- 进化游戏理论 进化游戏理论
- 人口动态 人口动态
- 行为经济学是一种行为经济学.
背景情况:
- 在社会系统中,合作至关重要,但在不对称的人口中,合作的演变仍然很复杂.
- 不对称的群体具有不同优势和优势的参与者,影响战略互动.
- 了解这些动态是解释不同生物和社会背景下的合作的关键.
研究的目的:
- 研究不同弱势和强势参与者的群体中合作的演变.
- 分析不同程度的不对称性和游戏结构 (囚犯困境,雪地游戏) 如何影响合作策略.
- 确定有利于在混合良好的和空间结构良好的群体中合作的条件.
主要方法:
- 开发一种具有不对称玩家 (弱者和强者) 的进化游戏模型.
- 分析不同参数设置 (不对称性,成本,收益) 下的合作和叛逃策略.
- 在无限的混合群体和方格格子结构中对结果的比较.
主要成果:
- 在混合群体中,如果强有力的参与者扮演囚犯困境,叛逃会占主导地位.
- 全球合作在雪地游戏中增加了强大的玩家比例,强大的合作者具有复杂的动态.
- 在正方形格子中,最优的合作发生在中间的强有力的参与者比例和适度的不对称性,弱势参与者保护合作集群.
结论:
- 在不对称的人口中,合作的演变对人口结构和玩家类型非常敏感.
- 在特定的不对称条件下,空间结构 (格子) 可以比混合群体更有效地促进合作.
- 弱势玩家对合作行为起着保护作用,防止被强大的叛逃者剥削.
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