多玩家网络信任游戏的动态演变,带有分级惩罚.
Juan Wang1,2, Zhuo Liu1,2, Yan Xu3
1Tianjin Key Laboratory for Control Theory and Complicated Industry Systems, Tianjin University of Technology, Tianjin 300384, China.
Chaos (Woodbury, N.Y.)
|March 3, 2025
概括
一个分级的惩罚策略有效地减少了网络信任游戏中不可信任的行为. 平衡罚款费用和金额是维持和改善社会信任水平的关键.
科学领域:
- 行为经济学是一种行为经济学.
- 网络科学 网络科学
- 游戏理论 游戏理论
背景情况:
- 信任是社会功能的基础.
- 在自私的个人之间保持信任是很有挑战性的.
- 网络信任游戏模拟社会互动.
研究的目的:
- 调查分级惩罚对信任的影响.
- 观察网络N玩家信任游戏中的信任动态.
- 确定有效增强信任的条件.
主要方法:
- 将分级惩罚策略纳入网络N玩家信任游戏中.
- 使用蒙特卡洛模拟来分析行为进展.
- 检查惩罚者在排除不值得信赖的行为者方面的作用.
主要成果:
- 渐进的惩罚大大减少了不可信行为.
- 该策略有可能消除不可信行为.
- 人口内部的整体信任水平有所提高.
结论:
- 渐进的惩罚是培养信任的有效工具.
- 平衡罚款成本和金额对于系统稳定至关重要.
- 这项研究为增强对网络社会信任提供了见解.
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