Multidimensional Opinion Dynamics with Confirmation Bias: A Multi-Layer Framework

TL;DR

This paper introduces a multi-layer framework for modeling multidimensional opinion dynamics with confirmation bias, analyzing convergence, steady states, and the impact of source influence in social networks.

Contribution

It develops a novel multi-layer model incorporating confirmation bias without thresholds, providing conditions for convergence and methods to compute steady states.

Findings

Convergence to a unique steady state under Lipschitz source-influence functions.

Explicit steady state computation for affine confirmation-bias functions.

Numerical and real-world examples highlight the impact of confirmation bias on opinion dynamics.

Abstract

We study multidimensional opinion dynamics under confirmation bias in social networks. Each agent holds a vector of correlated opinions across multiple topic layers. Peer interaction is modeled through a static, informationally symmetric social channel, while external information enters through a dynamic, informationally asymmetric source channel. Source influence is described by nonnegative state-dependent functions of agent--source opinion mismatch, which captures confirmation bias without hard thresholds. For general Lipschitz source-influence functions, we give sufficient conditions under which the dynamics are contractive and converge to a unique steady state independent of the initial condition. For affine confirmation-bias functions, we show that the steady state can be computed through a finite sign-consistency search and identify a regime in which it admits a closed form. For broader classes of bounded nonlinear source-influence functions, we derive explicit lower and upper bounds on the fixed point. Numerical examples and a study on a real-world adolescent lifestyle network illustrate the role of multidimensional coupling and show that source-design conclusions can change qualitatively when confirmation bias is ignored.