The emergence of social consensus in Boolean networks

Social order and unity require consensus among individuals about cooperation and other issues. Boolean network models (BN) help to explain the role played by peer interactions in the emergence of consensus. BN models represent a society as a network in which individuals are the nodes (with two states, e.g. agree/disagree) and social relationships are the edges. BN models highlight the influence of peer interactions on social cooperation, in contrast to models, such as prisoner's dilemma, that focus on individual strategies. In BN models, the behavior that emerges from peer interactions differs in subtle, but important ways from equivalent mathematical models (e.g. Markov, dynamic systems). Despite their simplicity, BN models provide potentially important insights about many social issues. They confirm that there is an upper limit to the size of groups within which peer interactions can create and maintain consensus. In large social groups, a combination of peer interaction and enforcement is needed to achieve consensus. Social consensus is brittle in the face of global influences, such as mass media, with the peer network at first impeding the spread of alternative views, then accelerating them once a critical point is passed. BN models are sensitive both to the network topology, and to the degrees of influence associated with peer-peer connections.