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Long-term memory-induced synchronisation can impair collective performance in congested systems

Research output: Contribution to journalArticle

  • F Saffre
  • G Gianini
  • H Hildmann
  • J Davies
  • Seth Bullock
  • E Damiani
  • JL Deneubourg
Original languageEnglish
Pages (from-to)95-114
Number of pages20
JournalSwarm Intelligence
Issue number2
Early online date22 Feb 2019
DateAccepted/In press - 8 Feb 2019
DateE-pub ahead of print - 22 Feb 2019
DatePublished (current) - 15 Jun 2019


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.

    Research areas

  • Multi-agent, congestion, Synchronisation, memory, emergence, optimisation, Markov chain, monte carlo simulation, genetic algorithms



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