在公共物品游戏中有限的学习和规划
Prakhar Godara1, Stephan Herminghaus1
1Max Planck Institute for Dynamics and Self-Organization (MPIDS), Am Faßberg 17, D-37077 Göttingen, Germany.
Physical review. E
|June 17, 2023
概括
这项研究增强了具有学习和记忆边界的代理模型,揭示了噪音对公共产品游戏 (PGG) 合作产生积极影响. 这些发现为未来的PGG实验提供了可测试的预测.
科学领域:
- 基于代理的建模模型
- 行为经济学是一种行为经济学.
- 游戏理论 游戏理论
背景情况:
- 之前的代理模型使用了有限的理性规划.
- 代理记忆的局限性以前没有在学习方面被探索过.
- 了解公共产品游戏 (PGG) 中的合作动态至关重要.
研究的目的:
- 通过结合具有边界记忆的学习来扩展现有的代理模型.
- 调查学习对PGG合作的具体影响,特别是在扩展游戏场景中.
- 通过同步的行动,为PGG提供实验可测试的预测.
主要方法:
- 开发了一个基于代理的模型,结合了学习和有限的记忆.
- 分析了学习对延长游戏时间的合作的影响.
- 理论上解释了与群体规模和人均平均收益率 (MPCR) 相关的实验发现.
主要成果:
- 学习,特别是具有有限记忆的学习,显著影响了在PGG中的合作.
- 玩家贡献中的噪音可以意外地增强小组合作.
- 该模型成功地解释了现有的关于组大小和MPCR对合作的影响的实验数据.
结论:
- 代理学习和记忆界限是PGG合作的关键因素.
- 将受控噪声引入PGG可能是一个促进合作的策略.
- 扩展代理模型为了解PGG动态提供了一个强大的框架.
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