Mobility helps problem-solving systems to avoid Groupthink

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 maxima. For difficult problems, however, mobility can prevent the imitative search being trapped in suboptimal solutions and guarantees a better performance than the independent search for any system size.