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

2024-03-22

arXiv.org

AI Summary

This paper investigates competitive behavior among content creators (e.g., YouTube, TikTok influencers) under heterogeneous content quality in the attention economy. Addressing the scarcity of platform resources and user attention—both allocated proportionally to creators’ quality-weighted contributions—we propose the Proportional Payoff Allocation Game (PPA-Game), the first divisible-resource game incorporating heterogeneous quality weights. We prove the universal existence of pure Nash equilibria (PNE). Further, we integrate multi-player multi-armed bandits (MP-MAB) with online learning to design the first distributed learning algorithm achieving a logarithmic regret upper bound of $O(log^{1+eta} T)$. Theoretical analysis and stochastic simulations confirm both high PNE occurrence rates and substantial long-term payoff improvement. Our framework provides a provably optimal, dynamic decision mechanism for attention resource allocation.

Technology Category

Game Theory and Economic Paradigms: Fair DivisionMultiagent Systems: Mechanism DesignMachine Learning: Online Learning & Bandits

Application Category

Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environmentsResponsible Web: Human-perceived consequences of algorithmic deployment on the webWeb Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

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.

Problem

• Modeling competition for divisible resources among agents
• Analyzing Nash equilibrium existence in competitive scenarios
• Developing online learning for maximizing cumulative payoffs

Innovation

• Proportional Payoff Allocation Game models resource competition
• Game-theoretical analysis identifies Nash equilibrium conditions
• Online learning algorithm maximizes cumulative payoffs efficiently