Control of Agreement and Disagreement Cascades with Distributed Inputs

TL;DR

This paper investigates how opinion cascades form in networked groups responding to distributed inputs, highlighting the roles of network structure and attention dynamics in triggering collective agreement or disagreement.

Contribution

It introduces a nonlinear opinion dynamics model with dynamic attention modulation, revealing how network spectral properties influence cascade triggering and decision outcomes.

Findings

Opinion cascades depend on input and network centrality.

Attention dynamics create implicit thresholds for cascade activation.

Network spectral properties determine collective response.

Abstract

For a group of autonomous communicating agents, the ability to distinguish a meaningful input from disturbance, and come to collective agreement or disagreement in response to that input, is paramount for carrying out coordinated objectives. In this work we study how a cascade of opinion formation spreads through a group of networked decision-makers in response to a distributed input signal. Using a nonlinear opinion dynamics model with dynamic feedback modulation of an attention parameter, we show how the triggering of an opinion cascade and the collective decision itself depend on both the distributed input and the node agreement and disagreement centrality, determined by the spectral properties of the network graph. We further show how the attention dynamics introduce an implicit threshold that distinguishes between distributed inputs that trigger cascades and ones that are rejected as disturbance.