Source of Publication
© 2019, The Author(s). 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.
Springer New York LLC
Business | Social and Behavioral Sciences
Best-of-n, Collective decision making, Complex adaptive systems, Dynamic environments, Swarm robotics
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Prasetyo, Judhi; De Masi, Giulia; and Ferrante, Eliseo, "Collective decision making in dynamic environments" (2019). All Works. 965.
Indexed in Scopus
Open Access Type
Hybrid: This publication is openly available in a subscription-based journal/series