研究AI时代内容创作的溢出效应，设计激励机制提升整体内容质量。
Content Creation with Spillovers: An Incentive Design Approach
- 构建平台与创作者博弈模型，考虑内容可复用带来的正向溢出。
- 提出临时分配机制，确保均衡结果优于传统竞赛模式。
- 开发近似算法，在结构化或随机场景下保障社会福利最优性。
AI的发展加剧了内容创作中的正向溢出现象：创作者贡献的内容可被大模型重用和重组，使他人内容质量受益。然而，这也改变了激励机制，导致创作者可能减少投入。本文提出「内容创作溢出模型」（CCS），刻画平台与策略型创作者间的博弈关系。每个创作者选择努力程度，内容质量由全体创作者的努力共同决定。平台通过合约控制质量，目标是最大化社会福利。研究表明，标准竞赛机制可能不稳定；为此提出一类参数化「临时分配」机制，保证帕累托占优均衡。尽管在一般情形下优化福利难以近似，但本文设计的近似算法在结构化溢出类别中具确定性保证，或在随机实例下以高概率成立。
原文摘要 · Abstract (English)
The rise of AI amplifies the economic phenomenon of \emph{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 \emph{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 \emph{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.
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