具有代理行为和局部拓配置的共同进化的复杂适应系统的不变分布
Hebing Zhang1, Xiaojing Zheng2
1School of Intelligent Manufacture, Taizhou University, Jiaojiang 318000, Zhejiang, China.
Mathematical biosciences and engineering : MBE
|March 8, 2024
概括
本研究介绍了一个动态的多局域世界 (MLW) 复杂的适应系统,用于预测在不断发展的网络中战略的融合. 研究结果显示,不变分布是由代理行为,网络拓和系统动态所塑造的.
科学领域:
- 复杂的自适应系统.
- 网络科学 网络科学
- 游戏理论的游戏理论.
背景情况:
- 了解动态系统中的代理行为收对于预测新出现的属性至关重要.
- 现有的模型往往缺乏代理策略和网络拓学的共同进化.
- 现实世界的经济和管理系统表现出复杂的适应性行为,目前的理论并没有完全捕捉到它们.
研究的目的:
- 开发一个动态的多局部世界 (MLW) 复杂的适应性系统模型.
- 研究代理行为和局部拓结构的共同进化.
- 预测战略的趋同,并推导出最佳战略分布的条件.
主要方法:
- 构建了一个包含代理行为和局部网络拓学的马尔科夫过程模型.
- 利用随机过程方法来导出系统的不变分布.
- 模拟了三种代理行为 (智能,正常,非理性) 以证明战略的融合.
主要成果:
- 证明代理策略趋于可预测的不变分布.
- 确定了影响这种分布的关键因素:代理行为,网络拓,行为噪音和人口规模.
- 展示了基于行为噪音和人口规模参数的独特不变分布.
结论:
- 开发的MLW系统准确地模拟了代理行为和网络结构的共同进化动态.
- 由此得出的不变分布为理解复杂的适应性系统中的策略选择提供了一个框架.
- 调查结果为城市规划和技术创新等不同领域的管理人员提供了实际见解.
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