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Content Creation with Spillovers: An Incentive Design Approach

arXiv 2603.14372 ↗First posted 15 Mar 2026cs.AIcited by 0

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Abstract (arXiv)

The rise of AI amplifies the economic phenomenon of positive spillovers: when creators contribute content that can be reused and adapted by LLMs, one creator's effort may improve the content quality of others through recombination. While such spillovers can improve content quality, they also reshape incentives, as creators may reduce effort when they can benefit from others' contributions. We introduce the Content Creation with Spillovers (CCS) model, a game between a platform and strategic creators. In this game, each creator chooses an effort level, and qualities are jointly determined by all creators' efforts. The platform contracts on qualities while seeking to maximize social welfare. We show that standard contest mechanisms can be unstable, and propose a parametrized family of Provisional Allocation mechanisms that guarantee a Pareto-dominant equilibrium. Although optimizing welfare within this family is hard to approximate in general, we develop approximation algorithms whose guarantees hold either deterministically across structured spillover classes or with high probability under random instances.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 38 references that are in the map. First posted on arXiv 15 Mar 2026.

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19 of 38 references.

References · 19

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2020Scaling Laws for Neural Language Modelscited by 6,411
  3. 2017Revisiting Unreasonable Effectiveness of Data in Deep Learning Eracited by 916
  4. 2017Deep Learning Scaling is Predictable, Empiricallycited by 556
  5. 2023The Curse of Recursion: Training on Generated Data Makes Models Forgetcited by 343
  6. 2023Self-Consuming Generative Models Go MADcited by 239
  7. 2019A Constructive Prediction of the Generalization Error Across Scalescited by 202
  8. 2024Scaling Retrieval-Based Language Models with a Trillion-Token Datastorecited by 54
  9. 2022Modeling Content Creator Incentives on Algorithm-Curated Platformscited by 35
  10. 2018A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providerscited by 33
  11. 2022Supply-Side Equilibria in Recommender Systemscited by 33
  12. 2024Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?cited by 13
7 more
  1. 2018From Recommendation Systems to Facility Location Gamescited by 9
  2. 2023Online Learning in a Creator Economycited by 8
  3. 2023Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Modelscited by 7
  4. 2024Unveiling User Satisfaction and Creator Productivity Trade-Offs in Recommendation Platformscited by 6
  5. 2024How to Strategize Human Content Creation in the Era of GenAI?cited by 4
  6. 2025Networked Digital Public Goods Games with Heterogeneous Players and Convex Costscited by 2
  7. 2025Collaborating with GenAI: Incentives and Replacementscited by 1

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