PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creators

• URL: http://arxiv.org/abs/2403.15524v1
• Date: Fri, 22 Mar 2024 14:13:11 GMT
• Title: PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creators
• Authors: Renzhe Xu, Haotian Wang, Xingxuan Zhang, Bo Li, Peng Cui,
• Abstract summary: We introduce the Proportional Payoff Allocation Game (PPA-Game) to model how agents compete for divisible resources and consumers' attention. Our analysis reveals that although a pure Nash equilibrium (PNE) is not guaranteed in every scenario, it is commonly observed. We propose an online algorithm facilitating each agent's cumulative payoffs over $T$ rounds.
• Score: 32.27173842175003
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce the Proportional Payoff Allocation Game (PPA-Game) to model how agents, akin to content creators on platforms like YouTube and TikTok, compete for divisible resources and consumers' attention. Payoffs are allocated to agents based on heterogeneous weights, reflecting the diversity in content quality among creators. Our analysis reveals that although a pure Nash equilibrium (PNE) is not guaranteed in every scenario, it is commonly observed, with its absence being rare in our simulations. Beyond analyzing static payoffs, we further discuss the agents' online learning about resource payoffs by integrating a multi-player multi-armed bandit framework. We propose an online algorithm facilitating each agent's maximization of cumulative payoffs over $T$ rounds. Theoretically, we establish that the regret of any agent is bounded by $O(\log^{1 + \eta} T)$ for any $\eta > 0$. Empirical results further validate the effectiveness of our approach.

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