On Online Control of Opinion Dynamics

TL;DR

This paper introduces an online method for controlling opinion dynamics in multi-agent networks, even when individual susceptibilities are unknown, by iteratively estimating these susceptibilities and steering opinions effectively.

Contribution

It presents a novel online algorithm that estimates agents' susceptibilities and guides opinions to a target under budget and time constraints, ensuring stability and convergence.

Findings

The algorithm guarantees convergence to the target opinion.

Estimating susceptibilities improves the effectiveness of opinion steering.

The method quantifies the closeness to the target given intervention constraints.

Abstract

Networked multi-agent dynamical systems have been used to model how individual opinions evolve over time due to the opinions of other agents in the network. Particularly, such a model has been used to study how a planning agent can be used to steer opinions in a desired direction through repeated, budgeted interventions. In this paper, we consider the problem where individuals' susceptibilities to external influences are unknown. We propose an online algorithm that alternates between estimating this susceptibility parameter, and using the current estimate to drive the opinion to a desired target. We provide conditions that guarantee stability and convergence to the desired target opinion when the planning agent faces budgetary or temporal constraints. Our analysis shows that the key advantage of estimating the susceptibility parameter is that it helps achieve near-optimal convergence to the target opinion given a finite amount of intervention rounds, and, for a given intervention budget, quantifies how close the opinion can get to the desired target.