We introduce a game-theoretic framework examining strategic interactions between a platform and its content creators in the presence of AI-generated content. Our model's main novelty is in capturing creators' dual strategic decisions: The investment in content quality and their (possible) consent to share their content with the platform's GenAI, both of which significantly impact their utility. To incentivize creators, the platform strategically allocates a portion of its GenAI-driven revenue to creators who share their content. We focus on the class of full-sharing equilibrium profiles, in which all creators willingly share their content with the platform's GenAI system. Such equilibria are highly desirable both theoretically and practically. Our main technical contribution is formulating and efficiently solving a novel optimization problem that approximates the platform's optimal revenue subject to inducing a full-sharing equilibrium. A key aspect of our approach is identifying conditions under which full-sharing equilibria exist and a surprising connection to the Prisoner's Dilemma. Finally, our simulations demonstrate how revenue-allocation mechanisms affect creator utility and the platform's revenue.

翻译：本文引入了一个博弈论框架，用以考察在人工智能生成内容存在的情况下，平台与其内容创作者之间的战略互动。我们模型的主要创新点在于捕捉创作者的双重战略决策：对内容质量的投资以及他们（可能）同意将其内容分享给平台的生成式人工智能，这两者都显著影响其效用。为了激励创作者，平台战略性地将其生成式人工智能驱动收入的一部分分配给分享其内容的创作者。我们聚焦于完全分享均衡配置这一类别，即所有创作者都自愿将其内容分享给平台的生成式人工智能系统。此类均衡在理论和实践上都具有高度吸引力。我们的主要技术贡献在于提出并高效求解了一个新颖的优化问题，该问题在诱导完全分享均衡的条件下近似求解平台的最优收入。我们方法的一个关键方面是识别完全分享均衡存在的条件，以及其与囚徒困境之间令人惊讶的关联。最后，我们的仿真模拟展示了收入分配机制如何影响创作者效用和平台收入。