We investigate the hypothesis that long-term memory in populations of agents can lead to counterproductive emergent properties at the system level. Our investigation is framed in the context of a discrete, one-dimensional road-traffic congestion model: we investigate the influence of simple cognition in a population of rational commuter agents that use memory to optimise their departure time, taking into account congestion delays on previous trips. Our results differ from the well-known minority game in that crowded slots do not carry any explicit penalty. We use Markov chain analysis to uncover fundamental properties of this model and then use the gained insight as a benchmark. Then, using Monte Carlo simulations, we study two scenarios: one in which “myopic” agents only remember the outcome (delay) of their latest commute, and one in which their memory is practically infinite. We show that there exists a trade-off, whereby myopic memory reduces congestion but increases uncertainty, while infinite memory does the opposite. We evaluate the performance against the optimal distribution of departure times (i.e. where both delay and uncertainty are minimised simultaneously). This optimal but unstable distribution is identified using a genetic algorithm.

中文翻译：

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内存引起的长期同步可能会削弱拥塞系统中的整体性能

我们调查的假设，即长期在代理商群体中记忆会导致系统水平上适得其反的紧急情况。我们的研究是在一个离散的一维道路交通拥堵模型的背景下进行的：我们研究简单认知对使用内存来优化其出发时间的理性通勤者群体的影响，同时考虑了先前交通拥堵的延迟旅行。我们的结果与著名的少数派游戏不同，因为拥挤的老虎机没有任何明显的惩罚。我们使用马尔可夫链分析发现该模型的基本属性，然后将获得的见解用作基准。然后，使用蒙特卡洛模拟，我们研究了两种情况：一种是“近视”特工仅记住其最新通勤的结果（延迟），他们的记忆几乎是无限的。我们表明存在一个折衷，即近视记忆减少了拥塞但增加了不确定性，而无限记忆则相反。我们根据出发时间的最佳分布（即同时将延迟和不确定性最小化）评估性能。使用遗传算法可以确定这种最佳但不稳定的分布。