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

arXiv: https://arxiv.org/abs/2403.15524 · HTML: https://mlsanity.com/paper/2403.15524/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 41 of its 78 references that are in the map.
- First posted on arXiv 22 Mar 2024.

## Builds on (most cited first, 30 of 41)

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- [Causal Bandits: Learning Good Interventions via Causal Inference](https://mlsanity.com/paper/1606.03203/) (2016)
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- [FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms](https://mlsanity.com/paper/2002.10764/) (2020)
- [Cascading Bandits: Learning to Rank in the Cascade Model](https://mlsanity.com/paper/1502.02763/) (2015)
- [Competing Bandits in Matching Markets](https://mlsanity.com/paper/1906.05363/) (2019)
- [SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits](https://mlsanity.com/paper/1809.08151/) (2018)
- [Bandit Learning in Decentralized Matching Markets](https://mlsanity.com/paper/2012.07348/) (2020)
- [Modeling Content Creator Incentives on Algorithm-Curated Platforms](https://mlsanity.com/paper/2206.13102/) (2022)
- [Learning Equilibria in Matching Markets from Bandit Feedback](https://mlsanity.com/paper/2108.08843/) (2021)
- [A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers](https://mlsanity.com/paper/1806.00955/) (2018)
- [Supply-Side Equilibria in Recommender Systems](https://mlsanity.com/paper/2206.13489/) (2022)
- [Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach](https://mlsanity.com/paper/2008.00104/) (2020)
- [Beyond log2(T) Regret for Decentralized Bandits in Matching Markets](https://mlsanity.com/paper/2103.07501/) (2021)
- [How Bad is Top-K Recommendation under Competing Content Creators?](https://mlsanity.com/paper/2302.01971/) (2023)
- [Decentralized Multi-player Multi-armed Bandits with No Collision Information](https://mlsanity.com/paper/2003.00162/) (2020)
- [Multiplayer bandits without observing collision information](https://mlsanity.com/paper/1808.08416/) (2018)
- [A survey on multi-player bandits](https://mlsanity.com/paper/2211.16275/) (2022)
- [Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization](https://mlsanity.com/paper/2110.14622/) (2021)
- [Selfish Robustness and Equilibria in Multi-Player Bandits](https://mlsanity.com/paper/2002.01197/) (2020)
- [Game of Thrones: Fully Distributed Learning for Multi-Player Bandits](https://mlsanity.com/paper/1810.11162/) (2018)
- [Multiplayer Bandit Learning, from Competition to Cooperation](https://mlsanity.com/paper/1908.01135/) (2019)
- [Towards Optimal Algorithms for Multi-Player Bandits without Collision Sensing Information](https://mlsanity.com/paper/2103.13059/) (2021)
- [Decentralized Heterogeneous Multi-Player Multi-Armed Bandits with Non-Zero Rewards on Collisions](https://mlsanity.com/paper/1910.09089/) (2019)
- [Multi-Player Multi-Armed Bandits with Finite Shareable Resources Arms: Learning Algorithms & Applications](https://mlsanity.com/paper/2204.13502/) (2022)
- [Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial?](https://mlsanity.com/paper/2306.07893/) (2023)
- [Multi-User Multi-Armed Bandits for Uncoordinated Spectrum Access](https://mlsanity.com/paper/1807.00867/) (2018)
- [From Recommendation Systems to Facility Location Games](https://mlsanity.com/paper/1809.02931/) (2018)
- [Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities](https://mlsanity.com/paper/2105.02377/) (2021)
- [Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributions](https://mlsanity.com/paper/2106.13669/) (2021)

## Abstract (arXiv)

In this paper, we present the Proportional Payoff Allocation Game (PPA-Game), which characterizes situations where agents compete for divisible resources. In the PPA-game, agents select from available resources, and their payoffs are proportionately determined based on heterogeneous weights attributed to them. Such dynamics simulate content creators on online recommender systems like YouTube and TikTok, who compete for finite consumer attention, with content exposure reliant on inherent and distinct quality. We first conduct a game-theoretical analysis of the PPA-Game. While the PPA-Game does not always guarantee the existence of a pure Nash equilibrium (PNE), we identify prevalent scenarios ensuring its existence. Simulated experiments further prove that the cases where PNE does not exist rarely happen. 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(log1 + η T) for any η > 0. Empirical results further validate the effectiveness of our online learning approach.