Learning Dynamics of Strategic Publishers in Generative AI Ecosystems

Generative AI (GenAI) search systems are transforming how users access information. Given a user's question, GenAI systems generate an answer, often accompanied by attributed sources (e.g., in the form of citations). Content creators (publishers) seeking to increase exposure might behave strategically and compete with each other for users' attention. In these generative systems, the incentives take on a novel form: publishers may gain exposure through generated responses and/or attributions to these responses. We introduce a novel game-theoretic model of the emerging GenAI ecosystem in which publishers compete for attribution-based exposure. We study the learning dynamics of strategic content creators under better-response dynamics. We associate the convergence of learning dynamics to equilibrium with ecosystem stability. Using the concept of potential games, we study the stability of GenAI ecosystems under several known content selection mechanisms. We demonstrate the instability of mechanisms representing real-world modern systems and characterize a mechanism that induces a stable ecosystem. We conduct extensive simulations to analyze the stability and welfare of GenAI ecosystems under various mechanisms. The simulations support our theoretical findings and reveal an interplay among stability, publisher welfare, and user welfare. We then introduce a study illustrating how platforms can select GenAI mechanisms to achieve desired welfare trade-offs.