Whether as team members brainstorming or cultures experimenting with new technologies, problem solvers communicate and share ideas. This paper examines how the structure of communication networks among actors can affect system-level performance. We present an agent-based computer simulation model of information sharing in which the less successful emulate the more successful. Results suggest that when agents are dealing with a complex problem, the more efficient the network at disseminating information, the better the short-run but the lower the long-run performance of the system. The dynamic underlying this result is that an inefficient network maintains diversity in the system and is thus better for exploration than an efficient network, supporting a more thorough search for solutions in the long run. For intermediate time frames, there is an inverted-U relationship between connectedness and performance, in which both poorly and well-connected systems perform badly, and moderately connected systems perform best. This curvilinear relationship between connectivity and group performance can be seen in several diverse instances of organizational and social behavior.


Originally published in Administrative Science Quarterly 52 (December 2007): 667–694.

David Lazer was affiliated with Northeastern University at the time of deposit.


information sharing, communication networks, agent-based computer simulation model, problem solving

Subject Categories

Group problem solving, Communication - Network analysis


Interpersonal and Small Group Communication | Organizational Behavior and Theory | Organizational Communication

Publication Date


Rights Information

Copyright 2007

Rights Holder

Johnson Graduate School, Cornell University