# Stability Theory of Stochastic Models in Opinion Dynamics

## TL;DR

This paper develops stability results for nonlinear opinion models based on stochastic maps, analyzing their behavior using contractivity in the -metric, with applications to exponential and linear transition probability models.

## Contribution

It introduces a stability framework for nonlinear Markov chain models of opinion dynamics using -contractivity, applicable to various transition mechanisms and continuous-time extensions.

## Key findings

- Stability certificates based on -contractivity are established.
- The theory applies to both exponential and linear transition models.
- Models can exhibit diverse behaviors, including convergence and complex dynamics.

## Abstract

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.