Over the past few years there has been a rapidly growing interest in analysis, design and optimization of various types of collective behaviors in networked dynamic systems. Collective phenomena (such as flocking, schooling, rendezvous, synchronization, and formation flight) have been studied in a diverse set of disciplines, ranging from computer graphics and statistical physics to distributed computation, and from robotics and control theory to ecology, evolutionary biology, social sciences and economics. A common underlying goal in such studies is to understand the emergence of some global phenomena from local rules and interactions.In this talk, I will expand on such developments and present and analyze new models of consensus and agreement in random networks as well as new algorithms for information aggregation tailored to opinions and beliefs in social networks. Specifically, I will present a model of social learning in which an agent acts as rational and Bayesian with respect to her own observations, but exhibits a bias towards the average belief of its neighbors to reflect the "network effect". When the underlying social network is strongly connected all agents reach consensus in there beliefs. Moreover, I will show that when each agent's observed signal is independent from others, agents will "learn" like a Bayesian who has access to global information, hence information is correctly aggregated.
Joint work with Pooya Molavi, Alireza Tahbaz-Salehi, Alvaro Sandroni and Victor Preciado