Mobility helps problem-solving systems to avoid groupthink

Author(s)

Gomes, Paulo F. ; Reia, Sandro M. ; Rodrigues, Francisco A. ; Fontanari, Jose F.

• Document type: Journal article
• Source: PHYSICAL REVIEW E; v. 99, n. 3, p. 10-pg., 2019-03-06.

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. (AU)

• FAPESP's process: 17/23288-0 - Topics on Collective Intelligence: Brainstorming, Vox Populi and Physarum polycephalum
• Grantee: José Fernando Fontanari
• Support Opportunities: Regular Research Grants
• FAPESP's process: 16/25682-5 - Information spreading in complex networks
• Grantee: Francisco Aparecido Rodrigues
• Support Opportunities: Regular Research Grants
• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 15/17277-0 - Diffusion of Innovations: a computational approach based on Axelrod's model
• Grantee: Sandro Martinelli Reia
• Support Opportunities: Scholarships in Brazil - Post-Doctoral