On Online Control of Opinion Dynamics
This work addresses the challenge of controlling opinion dynamics in social networks with unknown parameters, which is incremental as it builds on existing models by introducing online estimation.
The paper tackles the problem of steering opinions in a network when individual susceptibilities to influence are unknown, proposing an online algorithm that estimates these parameters and uses them to drive opinions toward a target, achieving near-optimal convergence with finite intervention rounds and quantifying closeness to the target given a budget.
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.