On Online Control of Opinion Dynamics

2026-03-17

AI Summary

This work addresses the challenge of effectively steering the collective opinion of a networked multi-agent system toward a desired target when individual agents’ sensitivities to external influence are unknown and intervention resources—such as budget or time—are limited. The paper proposes an online joint estimation-and-control algorithm that alternates between learning agents’ sensitivity parameters and applying opinion interventions based on the current estimates. This approach is the first to enable online guidance of opinion dynamics under unknown sensitivity, integrating parameter estimation with feedback control to drive the system’s opinion to a neighborhood of the target within a finite number of intervention rounds. Theoretical guarantees are provided, showing that convergence accuracy depends on estimation quality and that the overall performance nearly achieves optimality.

Technology Category

• Multiagent Systems: Adversarial Agents
• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• opinion dynamics
• online control
• susceptibility estimation
• multi-agent systems
• budgeted intervention

Innovation

• online control
• opinion dynamics
• susceptibility estimation
• multi-agent systems
• budgeted intervention