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Stability Theory of Stochastic Models in Opinion Dynamics


We consider a certain class of nonlinear maps that preserve the probability simplex, i.e., stochastic maps, which are inspired by the DeGroot-Friedkin model of belief/opinion propagation over influence networks. The corresponding dynamical models describe the evolution of the probability distribution of interacting species. Such models where the probability transition mechanism depends nonlinearly on the current state are often referred to as nonlinear Markov chains. In this paper, we develop stability results and study the behavior of representative opinion models. The stability certificates are based on the contractivity of the nonlinear evolution in the ℓ1-metric. We apply the theory to two types of opinion models where the adaptation of the transition probabilities to the current state is exponential and linear - both of these can display a wide range of behaviors. We discuss continuous-time and other generalizations.

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