Multiagent Learning for Competitive Opinion Optimization

TL;DR

This paper models competitive opinion optimization as a two-player zero-sum Stackelberg game and analyzes the convergence of multiagent learning algorithms within this framework.

Contribution

It introduces a novel game-theoretic model for opinion optimization and studies the convergence properties of multiagent learning algorithms in this context.

Findings

Modeling opinion optimization as a Stackelberg game

Analysis of convergence of Optimistic Gradient Descent Ascent

Insights into strategic interactions in social networks

Abstract

From a perspective of designing or engineering for opinion formation games in social networks, the "opinion maximization (or minimization)" problem has been studied mainly for designing subset selecting algorithms. We define a two-player zero-sum Stackelberg game of competitive opinion optimization by letting the player under study as the leader minimize the sum of expressed opinions by doing so-called "internal opinion design", knowing that the other adversarial player as the follower is to maximize the same objective by also conducting her own internal opinion design. We furthermore consider multiagent learning, specifically using the Optimistic Gradient Descent Ascent, and analyze its convergence to equilibria in the simultaneous version of competitive opinion optimization.