Mobility helps problem-solving systems to avoid Groupthink
Paulo F. Gomes, Sandro M. Reia, Francisco A. Rodrigues, Jos\'e F., Fontanari

TL;DR
This study uses an agent-based model to show that mobility among problem-solving agents can prevent groupthink and improve performance on complex problems, though it may slightly hinder easy problem-solving.
Contribution
It demonstrates how agent mobility influences the ability to avoid local maxima in NK-fitness landscapes, highlighting benefits for difficult problems.
Findings
Mobility slightly hampers easy problem-solving.
Mobility prevents trapping in local maxima for difficult problems.
Mobility improves performance on complex landscapes.
Abstract
Groupthink occurs when everyone in a group starts thinking alike, as when people put unlimited faith in a leader. Avoiding this phenomenon is a ubiquitous challenge to problem-solving enterprises and typical countermeasures involve the mobility of group members. Here we use an agent-based model of imitative learning to study the influence of the mobility of the agents on the time they require to find the global maxima of NK-fitness landscapes. The agents cooperate by exchanging information on their fitness and use this information to copy the fittest agent in their influence neighborhoods, which are determined by face-to-face interaction networks. The influence neighborhoods are variable since the agents perform random walks in a two-dimensional space. We find that mobility is slightly harmful for solving easy problems, i.e. problems that do not exhibit suboptimal solutions or local…
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