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Modern recommender systems lie at the heart of complex ecosystems that couplethe behavior of users, content providers, advertisers, and other actors.Despite this, the focus of the majority of recommender research -- and mostpractical recommenders of any import -- is on the local, myopic optimization ofthe recommendations made to individual users. This comes at a significant costto the long-term utility that recommenders could generate for its users. Weargue that explicitly modeling the incentives and behaviors of all actors inthe system -- and the interactions among them induced by the recommender'spolicy -- is strictly necessary if one is to maximize the value the systembrings to these actors and improve overall ecosystem "health". Doing sorequires: optimization over long horizons using techniques such asreinforcement learning; making inevitable tradeoffs in the utility that can begenerated for different actors using the methods of social choice; reducinginformation asymmetry, while accounting for incentives and strategic behavior,using the tools of mechanism design; better modeling of both user anditem-provider behaviors by incorporating notions from behavioral economics andpsychology; and exploiting recent advances in generative and foundation modelsto make these mechanisms interpretable and actionable. We propose a conceptualframework that encompasses these elements, and articulate a number of researchchallenges that emerge at the intersection of these different disciplines.

Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models