Recommendation systems (RSs) are the principal ingredient of many online services and platforms like Youtube, Quora, Substack, and Medium. Algorithmicall y, those platforms treat the task of recommendation as a matching problem. RSs match a user's con text, i.e., their past interactions, demographics, etc., to an item from a predetermined list of i tems, e.g., news articles, which will hopefully satisfy that user. The quality of a user-content m atch is initially unclear, so many data-driven approaches have been proposed to determine a matchin g's quality; for instance, collaborate filtering [ 23 ], matrix completion [ 37 ], and online learning [ 7 ]. However, due to their rapid adoption in commercial applications, many RSs are now dynamic economic systems with multiple stakeholders, facing challenges beyond dissolving uncertainty in matchi ng. Fairness [ 6, 15, 18, 35 ], misinformation [ 17 ], user incentives [ 3, 24 ], and privacy [ 21 ] are only some of the challenges RSs face. A recent body of research addresses tradeoffs among stakehol ders [ 9, 10, 28 ]. Online platforms have three main stakeholders: The commercial company that r uns the platform, content consumers, and content providers. Content consumers, which we refer to as users for simplicity, enjoy the RSs' content.