Congestion is a common failure mode of markets, where consumers compete inefficiently on the same subset of goods (e.g.,  chasing the same small set of properties on a vacation rental platform). The typical economic story is that prices  decongest by balancing supply and demand. But in modern online marketplaces, prices are typically set in a decentralized way by sellers, and the information about items is inevitably partial. The power of a platform  is limited to controlling *representations*---the subset of information about items presented by default to users. This motivates the present study of *decongestion by representation*, where a platform seeks to learn representations that reduce  congestion and thus improve social welfare. The technical challenge is twofold: relying only on revealed preferences from the choices of consumers, rather than true preferences; and the combinatorial problem associated with representations that  determine the  features to reveal in the default view.  We tackle both challenges by proposing a *differentiable proxy of welfare* that can be trained end-to-end on consumer choice data. We develop sufficient conditions for when decongestion promotes welfare, and present the results of extensive experiments on both synthetic and real data that demonstrate the utility of our approach.
Decongestion by Representation: Learning to Improve Economic Welfare in Marketplaces
Omer Nahum Gali Noti David C. Parkes Nir Rosenfeld