Long-term memory-induced synchronisation can impair collective performance in congested systems
article
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. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
Topics
CongestionEmergenceGenetic algorithmsMarkov chainMemoryMonte Carlo simulationMulti-agentOptimisationSynchronisationData storage equipmentEconomic and social effectsGenetic algorithmsIntelligent systemsMarkov processesMulti agent systemsTraffic congestionCongested systemsCongestionFundamental propertiesMarkov chain analysisOptimal distributionsOptimisations
TNO Identifier
862575
ISSN
19353812
Source
Swarm Intelligence
Publisher
Springer New York LLC
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