Multiagent Learning for Competitive Opinion Optimization

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 opt...

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Bibliographic Details
Published in:arXiv.org 2022-12
Main Authors: Po-An, Chen, Chi-Jen, Lu, Chuang-Chieh Lin, Ke-Wei, Fu
Format: Article
Language:English
Subjects:
Algorithms
Combinatorial analysis
Game theory
Optimization
Randomization
Randomized algorithms
Social networks
Strategy
Zero sum games
Online Access:Get full text
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Summary: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.
ISSN:2331-8422