Opinion shaping in social networks using reinforcement learning

TL;DR

This paper develops reinforcement learning methods to influence opinions in social networks with unknown interaction matrices, addressing resource constraints and comparing different optimization and learning algorithms.

Contribution

It introduces novel RL-based algorithms for opinion shaping in social networks, including convex and non-convex optimization approaches with convergence guarantees.

Findings

Proposed two-time scale RL schemes that converge to optimal solutions.

Compared stochastic gradient descent with RL schemes for efficiency.

Numerical results demonstrate convergence and effectiveness of the algorithms.

Abstract

In this paper, we study how to shape opinions in social networks when the matrix of interactions is unknown. We consider classical opinion dynamics with some stubborn agents and the possibility of continuously influencing the opinions of a few selected agents, albeit under resource constraints. We map the opinion dynamics to a value iteration scheme for policy evaluation for a specific stochastic shortest path problem. This leads to a representation of the opinion vector as an approximate value function for a stochastic shortest path problem with some non-classical constraints. We suggest two possible ways of influencing agents. One leads to a convex optimization problem and the other to a non-convex one. Firstly, for both problems, we propose two different online two-time scale reinforcement learning schemes that converge to the optimal solution of each problem. Secondly, we suggest stochastic gradient descent schemes and compare these classes of algorithms with the two-time scale reinforcement learning schemes. Thirdly, we also derive another algorithm designed to tackle the curse of dimensionality one faces when all agents are observed. Numerical studies are provided to illustrate the convergence and efficiency of our algorithms.