The Best-of-n Problem with Dynamic Site Qualities: Achieving Adaptability with Stubborn Individuals

Collective decision making in dynamic environments

“…We use multi-agent simulations where the spatial dimension is taken into account, and each robot is abstracted by an agent. We extend the study done by Prasetyo et al (2018b) in several directions. First, we consider two mechanisms (rather than one) to tackle the problem.…”

“…In this paper, we extend our work on the best-of-n in the dynamic environment first introduced by Prasetyo et al (2018b). As in that preliminary study, we consider a problem where the environment is static and symmetric with respect to option distribution, but the quality is asymmetric and abruptly changes at a given moment in time.…”

“…The spontaneous switching mechanism acts as negative feedback and again has the effect to add exploratory capabilities to the swarm, analogously to the abandonment component already seen in different collective decision-making models (Reina et al 2015b(Reina et al , 2017. We study the first mechanism in more detail compared to the work of Prasetyo et al (2018b): After confirming the effect of swarm size, of the proportion of stubborn individuals, and of the ratio between the option qualities in larger swarms than those considered by Prasetyo et al (2018b), we now also confirm that the decision making is indeed affected by the swarm size alone and not by increased agent density, as we find that the agent density does not play a role unless it is below a critical threshold. We also discover that in large swarms and when options are difficult to discern, increasing the overall number of stubborn individuals has a detrimental effect.…”

“…For high quality ratio the convergence to one option is reached but there is no adaptation to the change of opinion quality It shows that quality ratio has a stronger effect than the percentage of stubborn ratio, the swarm converged to whichever site had the optimal value at the beginning of the simulation run, and agents could not adapt to changes in the environment. Figure 3 reports the results of runs for four different cases of systems of 100 agents, as shown by Prasetyo et al (2018b), but with new runs that lasted four times longer than in the original study. Across rows, we vary the ratio ρ A /ρ B from low (1.05) to high (3).…”

“…First, we consider two mechanisms (rather than one) to tackle the problem. The first one, already introduced by Prasetyo et al (2018b), considers stubborn agents, that is, agents that never change their opinion. Stubborn agents can be seen as scouts, constantly exploring their favorite opinion, irrespective of the opinion of others and of the consensus state of the swarm.…”