Consensus and Information Cascades in Game-Theoretic Imitation Dynamics with Static and Dynamic Network Topologies
Speaker: Chris Griffin, Penn State University
Abstract: We construct a model of strategic imitation in an arbitrary network of players who interact through an additive game. Assuming a discrete time update, we show a condition under which the resulting difference equations converge to consensus. Two conjectures on general convergence will also be discussed. We then consider the case where players not only may choose their strategies, but also affect their local topology. We show that for prisoner's dilemma, the graph structure converges to a set of disconnected cliques and strategic consensus occurs in each clique. Several examples from various matrix games will be provided. A variation of the model is then used to create a simple model for the spreading of trends, or information cascades in (e.g., social) networks. We provide theoretical and empirical results on the trend-spreading model. As time permits, we may discuss the relationship to recurrent neural networks.
Room Reservation Information
Room Number: 106 McAllister
Time: 2:30pm - 3:30pm