DYNAMICAL PHASE TRANSITIONS IN OPINION NETWORKS: COEXISTENCE OF OPPORTUNISTS AND CONTRARIANS

We study a model for the emergence of collective decision making, consisting of N interacting agents, whose opinions are described by Ising spin variables. In particular, we present dynamical phase transitions from ordered to chaotic behavior in the space-time evolution of the binary choice network. One focus of this study is the determination of critical parameters, where the network is placed at the edge of chaos, i.e., at a subtle compromise between stability and flexibility, where the system has both, the necessary stability and the potential for evolutionary improvements.