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 solve this problem by balancing supply and demand in order to decongest the market. But in modern online marketplaces, prices are typically set in a decentralized way by sellers, with the power of a platform limited to controlling representations -- the information made available about products. This motivates the present study of decongestion by representation, where a platform uses this power to learn representations that improve social welfare by reducing congestion. The technical challenge is twofold: relying only on revealed preferences from users' past choices, rather than true valuations; and working with representations that determine which features to reveal and are inherently combinatorial. We tackle both by proposing a differentiable proxy of welfare that can be trained end-to-end on consumer choice data. We provide theory giving sufficient conditions for when decongestion promotes welfare, and present experiments on both synthetic and real data shedding light on our setting and approach.

翻译：拥堵是市场常见的失效模式，此时消费者在同一子集商品上低效竞争（例如在度假租赁平台上追逐同一小部分房产）。典型的经济学叙事认为，价格通过平衡供需来缓解拥堵。但在现代在线市场中，价格通常由卖家分散设定，平台的能力仅限于控制表征——即向用户提供的商品信息。这促使我们研究"通过表征缓解拥堵"这一课题：平台利用表征控制能力，学习能通过减少拥堵提升社会福祉的表征方式。技术挑战有二：其一，需仅依赖用户历史选择所揭示的偏好而非真实估值；其二，需处理决定特征展示方式且具有天然组合特性的表征。我们提出可微的社会福利代理函数，该函数可基于消费者选择数据进行端到端训练。我们从理论上给出了拥堵缓解促进社会福利的充分条件，并通过合成数据与真实数据的实验揭示了研究场景与方法的有效性。