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Congestion is a common failure mode of markets, where consumers competeinefficiently on the same subset of goods (e.g., chasing the same small set ofproperties on a vacation rental platform). The typical economic story is thatprices solve this problem by balancing supply and demand in order to decongestthe market. But in modern online marketplaces, prices are typically set in adecentralized way by sellers, with the power of a platform limited tocontrolling representations -- the information made available about products.This motivates the present study of decongestion by representation, where aplatform uses this power to learn representations that improve social welfareby reducing congestion. The technical challenge is twofold: relying only onrevealed preferences from users' past choices, rather than true valuations; andworking with representations that determine which features to reveal and areinherently combinatorial. We tackle both by proposing a differentiable proxy ofwelfare that can be trained end-to-end on consumer choice data. We providetheory giving sufficient conditions for when decongestion promotes welfare, andpresent experiments on both synthetic and real data shedding light on oursetting and approach.

Decongestion by Representation: Learning to Improve Economic Welfare in Marketplaces