Contests with Spillovers: Incentivizing Content Creation with GenAI

2026-03-15

AI Summary

This study addresses the erosion of creators’ incentives to exert genuine effort due to content spillovers induced by generative artificial intelligence (GenAI). The authors propose a Content Creation Spillover (CCS) model in which platforms observe only output quality, not underlying effort, and design incentive mechanisms that maximize social welfare while guaranteeing the existence of a pure-strategy Nash equilibrium. To overcome the equilibrium non-existence issues inherent in traditional contest mechanisms—such as winner-takes-all or Tullock contests—they innovatively introduce a parameterized provisional allocation mechanism. Combining game theory with mechanism design, they develop efficient algorithms tailored to bounded and tree-structured spillover networks, including a greedy cost-selection algorithm running in linearithmic time. This approach provides strong worst-case social welfare guarantees and achieves near-optimal performance on average, offering theoretical foundations for sustaining human creativity in the GenAI era.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

The rise of GenAI amplifies the economic phenomenon of positive spillovers. When creators contribute content that can be reused and adapted by Large Language Models (LLMs), each creator's effort can enhance the content quality of others by enabling easy imitation and recombination of existing content. On the one hand, such spillovers create value for the entire ecosystem; on the other hand, they risk undermining creators' incentives to invest genuine effort, as others may freely benefit from their contributions. To address this problem, we introduce the Content Creation with Spillovers (CCS) model. In our model, each creator chooses an effort level that, together with the efforts of others, determines her content quality. The platform aims to maximize the social welfare of consumers under stable behavior of the creators (pure Nash equilibrium), but can only observe the resulting qualities and not the underlying efforts. Interestingly, simple mechanisms like winner-takes-all and Tullock lead to the non-existence of equilibrium. In response, we propose the parametrized family of Provisional Allocation mechanisms, guaranteeing equilibrium existence and a unique Pareto-dominant equilibrium. While maximizing the social welfare under this family is NP-hard, we develop approximation algorithms that apply to a broad class of spillover structures and provide strong welfare guarantees. Specifically, in the worst-case analysis, we devise efficient algorithms for bounded spillovers and tree-structure spillovers. We also introduce Greedy Cost Selection, a linearithmic time algorithm that achieves approximately optimal results in the average case analysis. Together, our results provide game-theoretic foundations for sustaining human content creation in the era of GenAI.

Problem

• spillovers
• content creation
• GenAI
• incentives
• Nash equilibrium

Innovation

• Provisional Allocation mechanisms
• spillover effects
• content creation incentives
• approximation algorithms
• Nash equilibrium