解码公平:一个强化学习的观点
Guozhong Zheng1, Jiqiang Zhang2, Xin Ou1
1Shaanxi Normal University, School of Physics and Information Technology, Xi'an 710061, People's Republic of China.
Physical review. E
|August 1, 2025
概括
在最后通游戏 (UG) 中的人类行为显示出对公平的偏好,挑战传统的经济模式. 这项研究表明,内部动机,而不是外部因素,通过强化学习推动公平的决策.
科学领域:
- 行为经济学是一种行为经济学.
- 计算神经科学是一种神经科学.
- 游戏理论 游戏理论
背景情况:
- 关于最后通游戏 (UG) 的行为实验表明人类更喜欢公平,这与正统的经济预测相矛盾.
- 现有的解释往往依赖于模仿学习框架内的外部 (外源) 因素.
研究的目的:
- 通过强化学习 (RL) 范式,调查最后通游戏中公平的出现.
- 确定内生激励单独是否可以解释没有外部因素的公平性.
主要方法:
- 应用Q学习到最后通游戏中,为每个玩家分配两个Q表,用于提出者和响应者的角色.
- 分析了一个双人场景,扩展到具有各种角色分配 (随机,固定,旋转) 的格子化群体.
主要成果:
- 公平在UG中显著出现,当时玩家重视经验和未来的奖励.
- 成功交易的概率随着更高的报价而增加,与经验观察一致.
- 该系统表现出两个阶段,稳定到公平或合理的策略中,在不同的条件下强大.
结论:
- 强化学习框架中的内生激励措施足以解释最后通游戏中公平的出现.
- 在经济决策中观察到的公平行为不需要外部因素来解释.
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