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The Strength of the Strongest Ties in Collaborative Problem Solving

Posted on by Brandon Klein

Complex problems in science, engineering, or business are being solved by teams of people working closely with one another, each with the help of their network. In science, modern experiments require the collaboration and specialization of many individuals1. For example, a modern Nature paper can have more than 100 co-authors2 and the number of co-authors of PNAS papers has more than doubled over the last 20 years, reaching an average of 8.4 co-authors per paper3. In businesses, teams of knowledge workers have become the basic unit carrying out work4. Our ability to solve complex problems increasingly depends on teams of scientists, engineers, or knowledge workers and their extended information networks.

 

Accessing to the right piece of information is central to solving complex problems. This information, however, often only exists in the form of advice, expertise, implicit knowledge, or experience and flows through social ties. Consequently, the structure of social interactions has been shown to enhance or hinder access to such resources. Building on advances in social network analysis, empirical research showed the impact of an individual's information or collaboration network on her performance. Amongst others, the impact of an individual position in the information network has been investigated through measures of node degree, centrality, structural holes, closure, and social diversity

 

Complex problem solving in science, engineering, and business has become a highly collaborative endeavor. Teams of scientists or engineers collaborate on projects using their social networks to gather new ideas and feedback. Here we bridge the literature on team performance and information networks by studying teams' problem solving abilities as a function of both their within-team networks and their members' extended networks. We show that, while an assigned team's performance is strongly correlated with its networks of expressive and instrumental ties, only the strongest ties in both networks have an effect on performance. Both networks of strong ties explain more of the variance than other factors, such as measured or self-evaluated technical competencies, or the personalities of the team members. In fact, the inclusion of the network of strong ties renders these factors non-significant in the statistical analysis. Our results have consequences for the organization of teams of scientists, engineers, and other knowledge workers tackling today's most complex problems.