Opinion Dynamics Steering using Stochastic Search

TL;DR

This paper introduces a stochastic search framework for controlling opinion dynamics in populations, demonstrating its effectiveness and robustness through simulations and comparing different control policies.

Contribution

It develops a novel stochastic optimization approach for opinion steering, including adaptive feedback policies, and validates them in simulation scenarios.

Findings

The framework effectively steers opinion dynamics in simulations.

Adaptive feedback policy shows robustness to active agent set size.

Compared policies outperform hand-designed feedback in control effectiveness.

Abstract

In this paper, we apply the stochastic search dynamic optimization framework for steering opinion dynamics in a partially active population. The framework is grounded on stochastic optimization theory and relies on sampling of candidate solutions from distributions of the exponential family. We derive the distribution parameter update for an open loop, a feedback, and a novel adaptive feedback policy. All three policies are tested on two different opinion dynamics steering scenarios in simulation and compared against a hand designed feedback policy. The results showcase the effectiveness the framework for opinion dynamics control and the robustness of the adaptive feedback policy with respect to the active agent set size.