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.• We live in a smaller world today than ever before, with more distant linkages that rapidly spread information from one corner of the globe to the other. Perhaps as a result, the last decade has witnessed a massive surge of interest in networks, both among academics (Borgatti and Foster, 2003) and in popular culture. Services to increase the efficiency with which we exploit our personal networks have proliferated, and armies of consultants have emerged to improve the efficiency of organizational networks. Implicit in this discourse is the idea that the more connected we are, the better: silos are to be eliminated, and the boundaryless organization is the wave of the future. Technologies that enable distant actors to access each other's knowledge should be utilized (Wenger, McDermott, and Snyder, 2002). Even the most hierarchical of organizations, the military, is ostensibly shifting to a more flexible, network-based system (Arquilla and Ronfeldt, 2001), with bottom-up, boundary-spanning, Web-based knowledge systems (Baum, 2005). Despite these evident secular trends, popular attention, and prescribed organizational network strategies, relatively little attention has been focused on the question of how network structures affect overall system performance and, in particular, collective problem solving-the pooling of individual abilities to solve a problem.In contrast, there is ample research that highlights the advantage that network position can provide those who occupy them. Diffusion of innovation research (Rogers, 2003) illustrates how the well connected tend to be early adopters. Acquiring information quickly, in turn, provides advantages to individuals. Thus, for example, Granovetter's (1973) landmark research highlights the role of relationships in funneling information to individuals about employment opportunities, and Burt's (1995) research foc...

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Whether as team members brainstorming, or cultures experimenting with new technologies, problem solvers communicate and share ideas. This paper examines how the structure of these communication networks can affect system-level performance. We present an agent-based model of information sharing, where the less successful emulate the more successful. Results suggest that where agents are dealing with a complex problem, the more efficient the network at disseminating information, and the higher the velocity of information over that network, the better the short run and lower the long run performance of the system. The dynamic underlying this result is that an inefficient network is better at exploration than an efficient network, supporting a more thorough search for solutions in the long run. This suggests that the efficient network is the harethe fast starter-and the poorly connected network is the tortoise-slow at the start of the race, but ultimately triumphant.Networks provide the architecture of systemic adaptation. In any social system, the units of that system seek success-whether those criteria for success are determined by evolutionary processes or by (boundedly rational) goal-seeking processes. Networks provide the architecture of success in social systems because information about success that flows through the network provides one of the primary mechanisms for improvement in the system. This paper explores the implications of this architecture for system-level success (e.g., Putnam 2000), rather than nodal-level success (e.g., Burt 1995). In particular, using computational experiments, it demonstrates how network architecture affects the aggregation of problem solving within a system. It finds that small world (small number of degrees of separation) networks (the hare) are better at quickly converging on the best solution that initially exists in the network. However, small worlds perform very poorly in the long run, because they so quickly squeeze diversity out of the system. Larger world networks (the tortoise) perform better in the long run because they explore more of the solution space. More generally, we find that the smaller the world, the better the system in the short run, and the worse it is in the long run. Finally, we find that the velocity of information is negatively related to performance, especially in highly connected worlds.We argue that these findings are robust across a wide variety of settings, offering illustrations from the diffusion of pre-written history agricultural innovations and creative groups.The first section of the paper examines the role that networks play in the diffusion of information, focusing in particular how networks affect the aggregation of information. The second part of the paper develops a simulation model, basing the solution space on NK models (Kauffman 1995), to understand the implications of

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