Modifying trust dynamics through cooperation and defection in evolving social networks

We present a model of social network that shows a dynamic emergent behavior simulating actors that exchange knowledge based on their preferences, expertise and friendship relations. The network presents a stochastic interaction behavior that tends to create communities, driven by the assortative mixing and triadic closures. Our first research goal is to investigate the features driving the formation of communities and their characteristics under different configurations of the network. In particular we focus on trust which we analyze qualitatively as dependent on the frequency and pattern of interactions. To this aim, we ran simulations of different network configurations and analyzed the resulting statistics. The second research goal is to study the effects of node deception and cooperation on the social network behavior; our primary metric is trust and we evaluated how, under specific conditions, it is possible to manipulate trust in some non trivial ways.