人工自我组织系统中的复杂性控制:在管理流行病传染时,自下而上的干预与自上而下的干预的情况
Korosh Mahmoodi1, James K Hazy2
1University of North Texas, Denton.
Nonlinear dynamics, psychology, and life sciences
|January 4, 2025
概括
在囚犯的困境模拟中,自私的算法代理 (SA-agent) 显示出新兴的智能和集体代理. 这些适应性系统证明了对人工病毒的弹性,比较了自上而下的和自下而上的控制策略.
科学领域:
- 复杂的系统复杂的系统.
- 基于代理人的建模.
- 计算社会科学 计算社会科学
背景情况:
- 基于代理的建模 (ABM) 对于模拟复杂系统至关重要.
- 自私的算法代理 (SA-代理) 为研究新出现的行为提供了一个框架.
- 囚犯的困境游戏模拟了战略互动和合作.
研究的目的:
- 在一个多轮的囚犯困境游戏中使用SA代理来建模一个基于适应的代理环境.
- 在SA-代理互动中调查新兴情报和集体机构.
- 评估集体网络应对环境变化的适应能力,特别是病毒感染.
主要方法:
- 在基于代理的环境中利用了自私的算法代理 (SA-agent).
- 模拟多轮囚犯困境游戏,观察代理互动和网络动态.
- 引入了一种人工病毒来测试集体的适应能力和弹性,比较自上而下的和自下而上的控制策略.
主要成果:
- 观察到新兴情报和集体机构作为SA-agent集体的属性.
- 证明了集体在应对不断变化的环境条件 (病毒传播) 时重组其网络结构的能力.
- 分析了病毒感染对集体奖励寻求表现的影响,并比较了不同控制策略的有效性.
结论:
- SA-代理模型可以展示新兴的智能和集体代理,反映生物系统的特性.
- 集体的适应性允许网络重组以在压力下保持性能.
- 外源的自上而下的自我隔离策略和内源的自下而上的自我隔离策略都可以用于管理集体内的传染.
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