communication network

communication network, the structure and flow of communication and information between individuals within a group. Within many groups (e.g., in a typical office), formal and informal communication is often characterized by a top-down hierarchical pattern, in which members direct communication to others at the same level or below but not above.

Classic studies

The first systematic research on communication networks was conducted by American organizational psychologists Harold J. Leavitt and Alex Bavelas in the 1950s. That 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 circular structure in which each person could only share messages with those on either side. Alternatively, communication could take place within the structure of a wheel with one central member (the hub) through which all communications must pass. Later research by the American social psychologist Marvin E. Shaw showed that centralized groups solve relatively simple problems better than decentralized groups. When problems become more complex, however, Shaw found that centralization can hamper problem solving.

Later research

Most of the research on small-group decision making from the 1950s through the 1980s was conducted in groups with symmetrical communication networks in which each member’s communications were received by the entire group. Bibb Latané, an American social psychologist, and his colleagues revived interest in communication networks in the late 1980s by pointing out that individual members of large groups cannot easily communicate with the entire group at the same time. Latané developed what came to be known as dynamic social-impact theory. It includes a principle of immediacy, which assumes that influence between any two members in a group is predicted by the likelihood that they can easily share communications.

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Latané tested the implications of his theory by conducting computer simulations in which agents were situated in a two-dimensional space where the strongest influence between agents occurred with immediate neighbours. Each agent was randomly assigned a binary opinion on an issue. In keeping with other assumptions of the theory, individual agents in the simulations also varied in strength (i.e., some were more influential than others), and agents were influenced by the number of other agents sharing or opposing their preferences.

After simulating some rounds of communication in which each agent’s opinion was compared with the opinions of fellow agents, the researchers found that opinions were either maintained or changed as a function of the strength, immediacy, and number of other agents. In addition, two significant group-level phenomena emerged. Whichever opinion was most commonly held within the group became even more common after simulated communication. And, because communication networks constrained communication, opinions also became regionally clustered, such that agents shared opinions with other agents who were physically close to them in the two-dimensional space.

Latané and his colleagues then tested whether those phenomena also occur within actual groups discussing issues in communication networks configured via e-mail exchanges. Both of the group-level phenomena observed in the computer simulations—consolidation and clustering—also emerged within groups of people discussing issues. The “geometry” of communication networks—how they are organized—can determine the extent to which a group’s opinions will consolidate and cluster as a function of communication. For example, as communication networks become more “clumpy,” or hierarchical, consolidation and clustering of opinions tend to increase.

Mathematicians and physicists have also used computer simulation to test constrained communication networks within large groups. The Australian sociologist Duncan Watts and his colleagues used computer simulation to solve the “small-world problem” (posited by the American social psychologist Stanley Milgram): if most people communicate with others within local networks (as social-impact theory assumes), what accounts for the fact that any two randomly chosen people within the larger group are connected by a surprisingly small number of links? (The phrase “six degrees of separation,” coined by the Hungarian writer Frigyes Karinthy, refers to this phenomenon.) Watts showed that simply adding a small number of random communication links to a computer simulation of a large group would create such small-world networks.

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The Hungarian-born physicist Albert-László Barabasi and his colleagues showed that communication networks within large groups share properties with what are known as “scale-free” networks. In a scale-free network, some individuals within the larger group have many more communication partners than others; in the terms of earlier work on communication networks, such members can be said to be more centralized. Scale-free networks are another way to solve the small-world problem: when a small number of members within a large group have a large number of communication partners, it takes a relatively small number of links to join any two randomly chosen group members.