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 seeding algorithms that aim at selecting a subset of nodes to control their opinions. We first define a two-player z...

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Bibliographic Details
Published in:Theoretical computer science 2024-11, Vol.1017, p.114787, Article 114787
Main Authors: Chen, Po-An, Lu, Chi-Jen, Lin, Chuang-Chieh, Teng, An-Tzu, Fu, Ke-Wei
Format: Article
Language:English
Subjects:
Competitive opinion optimization
Multiagent learning
Optimistic gradient descent ascent
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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 seeding algorithms that aim at selecting a subset of nodes to control their opinions. We first 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-game version of competitive opinion optimization.
ISSN:0304-3975
DOI:10.1016/j.tcs.2024.114787