Abstract for: A Hybrid Social Network-System Dynamics Model of Team Learning
Social network analysis has fruitfully explored many aspects of inter-personal interaction, yet the most common methodologies associated with it prove problematic when studying the behavior of small teams. This paper begins to remedy this by proposing a hybrid system dynamics-agent based methodology to expand the power of social network analysis study with respect to analyzing the evolution and emergent behavior of teams. Accordingly, it discusses the advantages, disadvantages, and complementarities of both system dynamics and agent-based modeling for this purpose. Then, to illustrate the potential effectiveness of such an approach, the paper describes an initial proof-of-concept model based on this hybrid methodology and uses it to present illustrative simulations of several important team-science questions including the differential effects of hierarchy; the impact of overspecialization; the role of generalists; and the disruption created by team-member turnover.