Contests with Spillovers: Incentivizing Content Creation with GenAI

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.

Figures (3)

• Figure 1: Results for varying $N$ with $q^\star = 1$. Left: Social welfare scales linearly in $N$. Right: Number of incentivized players grows proportionally to $N$. Markers indicate theoretical predictions. Equal Allocation shown for $r=0.8$.
• Figure 2: Results for varying $r$ with $q^\star = 1$. Left: Social welfare. Right: Number of active players. Markers indicate the theoretical predictions $N(q^\star r)^3/2$ and $rq^\star N$, respectively. Equal Allocation shown for $N=1000$.
• Figure 3: Results for varying $q^\star$ with $N = 100$ and $r = 0.5$. Left: Social welfare increases with $q^\star$. Right: Larger $q^\star$ enables larger incentivized sets.

Theorems & Definitions (71)

• Theorem 1: Informal; see Theorem \ref{['thm:stability_selection']}
• Theorem 2: Informal; see Theorems \ref{['thm:approx_bounded']}, \ref{['thm random alg']}, and \ref{['thm:hop']}
• Example 1
• Example 2
• Lemma 3
• Definition 1
• Proposition 4
• proof : Proof Sketch of \ref{['tullock simple instances no PNE']}
• Definition 2
• Theorem 5: Stability and Selection