Search Design and Broad Matching
研究了消费者根据噪声信号搜索企业池的分散机制，发现剩余最大化的搜索池可在对称纳什均衡中实现，且最优机制可通过广泛匹配的关键词拍卖模拟。
We study decentralized mechanisms for allocating firms into search pools. The pools are created in response to noisy preference signals provided by consumers, who then browse the pools via costly random sequential search. Surplus-maximizing search pools are implementable in symmetric Nash equilibrium. Full extraction of the maximal surplus is implementable if and only if the distribution of consumer types satisfies a set of simple inequalities, which involve the relative fractions of consumers who like different products and the Bhattacharyya coefficient of similarity between their conditional signal distributions. The optimal mechanism can be simulated by a keyword auction with broad matching.