在没有完整信息的网络上模仿动态
Xiaochen Wang1, Lei Zhou2, Alex McAvoy3,4
1Center for Systems and Control, College of Engineering, Peking University, Beijing, 100871, China.
Nature communications
|November 17, 2023
概括
在社会困境中,合作策略以不同的方式发展,基于模仿规则. 忽视个人信息并使用外部社交线索最能促进网络内对对互动的合作.
科学领域:
- 进化游戏理论 进化游戏理论
- 社交网络分析 社交网络分析
- 行为经济学是一种行为经济学.
背景情况:
- 模仿是社会系统中一个关键的学习机制.
- 不同的模仿策略对合作的影响仍然不清楚.
- 了解模仿的信息要求对于合作动态至关重要.
研究的目的:
- 开发网络系统中不完全信息的模拟动态的一般模型.
- 为了统一经典模仿更新规则.
- 调查不同信息的可用性如何对合作的发展产生定量影响.
主要方法:
- 开发了一种仿真动态的一般模型.
- 统一的经典更新规则 (死亡出生,对对比).
- 在不同的信息条件下分析了复杂网络上的合作演变.
主要成果:
- 集体合作是最大限度地当个人忽略个人信息在双对互动.
- 当外部信息丰富时,将个人信息纳入有利于合作者进化.
- 在集团互动中,在集群较少的网络中,优先考虑个人信息而不是外部信息,可以加强合作.
结论:
- 这项研究提供了关于模仿动态和合作的统一视角.
- 信息处理策略显著影响合作的演变.
- 网络结构和交互类型调节了合作的最佳模仿策略.
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