  Modern recommender systems lie at the heart of complex ecosystems that couple
the behavior of users, content providers, advertisers, and other actors.
Despite this, the focus of the majority of recommender research -- and most
practical recommenders of any import -- is on the local, myopic optimization of
the recommendations made to individual users. This comes at a significant cost
to the long-term utility that recommenders could generate for its users. We
argue that explicitly modeling the incentives and behaviors of all actors in
the system -- and the interactions among them induced by the recommender's
policy -- is strictly necessary if one is to maximize the value the system
brings to these actors and improve overall ecosystem "health". Doing so
requires: optimization over long horizons using techniques such as
reinforcement learning; making inevitable tradeoffs in the utility that can be
generated for different actors using the methods of social choice; reducing
information asymmetry, while accounting for incentives and strategic behavior,
using the tools of mechanism design; better modeling of both user and
item-provider behaviors by incorporating notions from behavioral economics and
psychology; and exploiting recent advances in generative and foundation models
to make these mechanisms interpretable and actionable. We propose a conceptual
framework that encompasses these elements, and articulate a number of research
challenges that emerge at the intersection of these different disciplines.
