Creator Incentives in Recommender Systems: A Cooperative Game-Theoretic Approach for Stable and Fair Collaboration in Multi-Agent Bandits

This project is an academic research contribution (8 days old, 0 stars, 4 forks) rather than a production-ready software tool. It addresses a sophisticated niche: the intersection of Cooperative Game Theory and Multi-Agent Bandits within recommendation engines. Defensibility is low (2) because the primary value is the mathematical proof and algorithmic approach, which are easily reproducible by any researcher or platform engineer once the paper is public. There is no software moat, network effect, or proprietary data. Frontier risk is low as OpenAI and Anthropic are currently focused on general-purpose reasoning rather than the micro-economics of content platform incentives. However, platform domination risk is high; the logic described here is only useful to incumbents who already own recommendation ecosystems (YouTube, TikTok, Spotify). These platforms are likely to implement similar logic in-house if they haven't already. The project's 'novel_combination' status comes from applying TU-game stability (The Core) to the cumulative regret of bandit coalitions. While technically rigorous, it lacks the 'gravity' of a project with community adoption or infrastructure-level utility. It is best viewed as a reference implementation for specialized PhD-level researchers in algorithmic fairness.