  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.
