记忆丧失可以防止游戏动态中的混乱
Jakub Bielawski1, Thiparat Chotibut2, Fryderyk Falniowski1
1Department of Mathematics, Krakow University of Economics, Rakowicka 27, 31-510 Kraków, Poland.
Chaos (Woodbury, N.Y.)
|January 29, 2024
概括
记忆丧失可以抑制拥堵游戏中的混乱行为. 提高学习率可能会破坏系统的稳定性,但记忆丧失可能导致可预测的结果,帮助资源配置策略.
科学领域:
- 经济建模经济建模
- 游戏理论的游戏理论.
- 复杂系统分析 复杂系统分析
背景情况:
- 高学习率的拥堵游戏可能会导致不可预测的混乱动态.
- 了解代理学习行为对于系统稳定至关重要.
研究的目的:
- 调查两种资源拥堵游戏中的学习动态.
- 分析学习率,记忆丧失和成本不对称性对系统可预测性的影响.
主要方法:
- 使用简化经验加权吸引算法进行代理学习.
- 使用选择强度 (学习率) 和折扣因子 (记忆丧失) 建模代理行为.
- 研究了成本函数不对称性对系统动态的影响.
主要成果:
- 更高的选择强度会破坏系统的稳定,而不管是折扣因素.
- 系统可预测性 (混乱与可预测) 取决于内存损失和成本不对称.
- 增加的记忆丧失可以将动态从混乱转移到稳定的周期-2轨道.
结论:
- 记忆丧失在缓解拥堵游戏中的混乱方面发挥着至关重要的作用.
- 结果为设计资源分配系统中的控制策略提供了洞察力.
- 代理记忆和成本结构显著影响系统稳定性和可预测性.
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