Collective decision making in dynamic environments

Judhi Prasetyo, Giulia De Masi, Eliseo Ferrante

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
170 Downloads (Pure)

Abstract

Collective decision making is the ability of individuals to jointly make a decision without any centralized leadership, but only relying on local interactions. A special case is represented by the best-of-n problem, whereby the swarm has to select the best option among a set of n discrete alternatives. In this paper, we perform a thorough study of the best-of-n problem in dynamic environments, in the presence of two options ( n=2 ). Site qualities can be directly measured by agents, and we introduce abrupt changes to these qualities. We introduce two adaptation mechanisms to deal with dynamic site qualities: stubborn agents and spontaneous opinion switching. Using both computer simulations and ordinary differential equation models, we show that: (i) The mere presence of the stubborn agents is enough to achieve adaptability, but increasing its number has detrimental effects on the performance; (ii) the system adaptation increases with increasing swarm size, while it does not depend on agents’ density, unless this is below a critical threshold; (iii) the spontaneous switching mechanism can also be used to achieve adaptability to dynamic environments, and its key parameter, the probability of switching, can be used to regulate the trade-off between accuracy and speed of adaptation.
Original languageEnglish
Pages (from-to)217-243
Number of pages27
JournalSwarm Intelligence
Volume13
Issue number3-4
Early online date26 Jun 2019
DOIs
Publication statusPublished - 1 Dec 2019

Keywords

  • Dynamic environments
  • Collective decision making
  • Best-of-n
  • Swarm robotics
  • Complex adaptive systems

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