Influence Maximization and Equilibrium Strategies in Election Network Games


Zhang A, Perrault A. Influence Maximization and Equilibrium Strategies in Election Network Games, in AI for Social Good Workshop. ; 2020.


Social media has become an increasingly important political domain in recent years, especially for campaign advertising. In this work, we develop a linear model of advertising influence maximization in two-candidate elections from the viewpoint of a fully-informed social network platform, using several variations on classical DeGroot dynamics to model different features of electoral opinion formation. We consider two types of candidate objectives—margin of victory (maximizing total votes earned) and probability of victory (maximizing probability of earning the majority)—and show key theoretical differences in the corresponding games, including advertising strategies for arbitrarily large networks and the existence of pure Nash equilibria. Finally, we contribute efficient algorithms for computing mixed equilibria in the margin of victory case as well as influence-maximizing best-response algorithms in both cases and show that in practice, as implemented on the Adolescent Health Dataset, they contribute to campaign equality by minimizing the advantage of the higherspending candidate.

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