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Groupthink occurs when everyone in a group starts thinking alike, as whenpeople put unlimited faith in a leader. Avoiding this phenomenon is aubiquitous challenge to problem-solving enterprises and typical countermeasuresinvolve the mobility of group members. Here we use an agent-based model ofimitative learning to study the influence of the mobility of the agents on thetime they require to find the global maxima of NK-fitness landscapes. Theagents cooperate by exchanging information on their fitness and use thisinformation to copy the fittest agent in their influence neighborhoods, whichare determined by face-to-face interaction networks. The influenceneighborhoods are variable since the agents perform random walks in atwo-dimensional space. We find that mobility is slightly harmful for solvingeasy problems, i.e. problems that do not exhibit suboptimal solutions or localmaxima. For difficult problems, however, mobility can prevent the imitativesearch being trapped in suboptimal solutions and guarantees a betterperformance than the independent search for any system size.

Mobility helps problem-solving systems to avoid Groupthink