Belief propagation analysis in two-player games for peer-influence social networks

Abstract

This thesis considers approaches to influencing population opinions during counterinsurgency efforts in Afghanistan. A discrete time, agent-based threshold model is developed to analyze the propagation of beliefs in the social network, whereby each agent has a belief and a threshold value, which indicts the willingness to be influenced by the peers. Agents communicate in stochastic pairwise interactions with their neighbors. A dynamic, two player game is formulated whereby each player strategically controls the placement of one stubborn agent over time in order to maximally influence the network according to one of two different payoff functions. The stubborn agents have opposite, immutable beliefs and exert significant influence in the network. We demonstrate the characteristics of strategies chosen by the players to improve their payoffs through simulation. Determining strategies for the players in large, complex networks in which each stubborn agent has multiple connections is difficult due to exponential increases in the strategy space that is searched. We implement two heuristic methods which are shown to significantly reduce the run time needed to find strategies without significantly reducing the quality of the strategies. Lastly, we introduce population-focused actions, such as economic stimulus projects, which when used by the players result in long-lasting changes in the beliefs of the agents in the network.