人与人之间的信任:随机协调游戏的异面分析与多代理学习
Benedikt V Meylahn1, Arnoud V den Boer1, Michel Mandjes1,2
1Korteweg-de Vries Institute for Mathematics, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands.
Chaos (Woodbury, N.Y.)
|June 7, 2024
概括
人与人之间对人口的信任可以随机出现,代理商通过指数移动平均线学习. 增加的代理记忆力使动态更接近预期,但机会仍然可以决定社会信任的高低.
科学领域:
- 计算社会科学 计算社会科学
- 游戏理论 游戏理论
- 基于代理的建模模型
背景情况:
- 人际信任对于社会运作至关重要.
- 随机协调游戏模拟社会互动和信任的形成.
- 代理学习机制影响人口层面的结果.
研究的目的:
- 调查随机机会是否可以确定人口中人际信任的高低.
- 分析代理学习规则,特别是指数移动平均线在信任动态中的作用.
- 检查代理记忆和回报矩阵分布对信任趋同的影响.
主要方法:
- 开发了一个离散时间,随机协调游戏模型,随机对对互动.
- 实施了一个基于过去交互的指数移动平均值 (EMA) 的代理学习规则.
- 采用分析方法来证明长期的人口信任趋同.
- 利用模拟来研究回报矩阵分布和代理记忆的影响.
主要成果:
- 分析证据表明,人口总是趋于普遍的信任或普遍的不信任 (概率一).
- 这种趋同甚至在理论预期相反时也是如此.
- 模拟显示,增加的代理记忆 (对最近数据的权重较小) 将人口动态与理论预期更紧密地对齐.
- 支付矩阵分布影响信任趋同的速度和稳定性.
结论:
- 随机的机会确实可以导致一个人群汇聚在高或低的人际信任上.
- 代理记忆充当调节者,影响观察到的动态如何与理论预期相匹配.
- 虽然机会起着重要作用,但潜在的游戏参数 (社会背景) 提供了对潜在信任水平的重要见解.
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