Decentralized College Admissions
研究了学生偏好不确定下分散式大学录取的均衡策略，发现大学会策略性避开竞争、过度加权低相关维度，而限制申请或候补名单虽缓解不确定性但导致低效和不公，集中匹配虽高效公平却可能损害部分大学利益。
We study decentralized colleges admissions in the face of uncertain student preferences. Enrollment uncertainty causes colleges to strategically target their admissions, forgoing students sought after by others and seeking students overlooked by others. When students' types are multidimensional, colleges avoid head-on competition by placing excessive weights on less correlated dimensions. Restricting the number of applications or allowing for wait-listing alleviates enrollment uncertainty, but the resulting assignments of decentralized matching are inefficient and unfair. A centralized matching via Gale and Shapley's deferred acceptance algorithm attains efficiency and fairness, but some colleges can be worse off relative to decentralized matching.