Communication Networks

Why Study Communication Networks?

The overwhelming majority of research on group dynamics has studied interacting groups in which communications from each member are sent to the entire group, with no constraints on communication (a prototypical example would be a jury sitting around a table). This type of communication network is common in many small real-world groups, such as juries; however, it is not common in larger groups and institutions. In larger groups communication is likely asymmetrical, where different individuals receive and transmit information heterogeneously across the entire group. First, many groups do not necessarily meet in a specific location at the same time, but rather consist of a number of asynchronous communications within subsets of group members. Second, as group size increases, there may be evolutionary constraints on the optimal number of group members performing any given task. Robin Dunbar has speculated that as groups increase in size, the brain's processing capacity may constrain the number of individuals with whom one can optimally communicate. This evolved constraint likely leads to recurrent and structural patterns across all groups in terms of communication networks. Therefore, it is important to study the development and effects of communication networks within groups, especially among larger groups (for example, within organizations).

Classic Research

The first systematic research on communication networks was conducted by Harold J. Leavitt of MIT and Alex Bavelas of Bell Laboratories in the 1950s. This work was stimulated by formal mathematical models derived from graph theory. By placing partitions between participants seated at a table, Leavitt and Bavelas manipulated communication structures within groups of varying sizes. For example, in a five-person group, members could communicate within a circle structure, in which each person can only share messages with those on either side of him or her. Alternatively, communication could take place within the structure of a wheel, with one central member (the hub) through which all communications must pass. One consistent result in such early research was that the centralization of a communication network (i.e., the degree to which some members of the group had more communication partners than others; e.g., the wheel is more centralized than the circle network) was a strong predictor of the efficiency of problem solving; that is, the more centralized a network, the more efficient the group was at solving problems. Later research by Marvin E. Shaw qualified this finding to show that centralized groups solve relatively simple problems better than decentralized groups, but when problems become more complex, centralization can hamper problem solving. Another consistent finding of this research was that more centralized group members are more satisfied with the group process than are more peripheral members.

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