Team Assembly Mechanisms Determine Collaboration Network Structure and Team Performance

Abstract

Agents in creative enterprises are embedded in networks that inspire, support, and evaluate their work. Here, we investigate how the mechanisms by which creative teams self-assemble determine the structure of these collaboration networks. We propose a model for the self-assembly of creative teams that has its basis in three parameters: team size, the fraction of newcomers in new productions, and the tendency of incumbents to repeat previous collaborations. The model suggests that the emergence of a large connected community of practitioners can be described as a phase transition. We find that team assembly mechanisms determine both the structure of the collaboration network and team performance for teams derived from both artistic and scientific fields.

Collaboration: Group theory

“…Looking at a data set of nearly 90,000 papers published between 1955 and 2004 by 115,000 authors in 32 journals spread across the fields of social psychology, economics, ecology and astronomy, Luis Amaral, a network scientist at Northwestern, and his colleagues measured the proportion of authors who had worked with each other before 4 . Papers in high-impact journals showed a strikingly lower proportion of these repeated interactions than did papers in low-impact journals.…”

Using decision trees to enhance interdisciplinary team work: the case of oncofertility

“…Further, the complexities behind decision-making, the immaturity of the technology, and the nature of reproductive medicine itself create implicit ethical and legal considerations. Thus, a diverse team of scholars has been mobilized to try to make an intractable problem (fertility preservation for cancer patients) tractable [1][2][3][4][5][6][7][8][9][10].…”

“…The advantages of interdisciplinary teamwork are routinely touted, presenting collaborative approaches as the ideal mechanism to drive progress and innovation [8]. In the context of science, interdisciplinary teamwork is often necessary, as academic science and medicine creates specialties by nature.…”

Event-driven modelling of evolving social networks

“…The new node copies a number of links from a 'prototype' node that is selected randomly from the existing nodes whereas choosing the remaining neighbours is random. Similar graph growth mechanisms also include models that implicitly or explicitly rely on the locality heuristics (Leskovec et al, 2005;Guimera et al, 2005;Kossinets and Watts, 2006;Krapivsky and Redner, 2001;Liben-Nowell and Kleinberg, 2007;Watts et al, 2002) or specified feature similarity (correlation) between nodes (Xuan et al, 2007).…”

“…In other words, the DDG model uses a 'continuous' measure for locality. Guimera et al (2005) propose a team assembly mechanism by investigating the interplay between 'incumbents' and 'newcomers' in the context of collaboration networks. The model implicitly incorporates the evolution of node behaviour into modelling.…”