  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.
