Stability Theory of Stochastic Models in Opinion Dynamics
Provides theoretical stability guarantees for nonlinear Markov chain models of opinion dynamics, relevant to researchers studying social influence and belief propagation.
This paper develops stability theory for a class of nonlinear stochastic maps inspired by the DeGroot-Friedkin opinion dynamics model, proving contractivity in the ℓ1-metric. The theory is applied to two opinion models with exponential and linear adaptation, showing a wide range of behaviors.
We consider a certain class of nonlinear maps that preserve the probability simplex, i.e., stochastic maps, that 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 {\em 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 $\ell_1$-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, respectively--both of these can display a wide range of behaviors. We discuss continuous-time and other generalizations.