We investigate the hiring problem where a sequence of applicants is sequentially interviewed, and a decision on whether to hire an applicant is immediately made based on the applicant's score. For the maximal and average improvement strategies, the decision depends on the applicant's score and the scores of all employees, i.e., previous successful applicants. For local improvement strategies, an interviewing committee randomly chosen for each applicant makes the decision depending on the score of the applicant and the scores of the members of the committee. These idealized hiring strategies capture the challenges of decision-making under uncertainty. We probe the average score of the best employee, the probability of hiring all first $N$ applicants, the fraction of superior companies in which, throughout the evolution, every hired applicant has a score above expected, etc.

翻译：本文研究招聘问题：面试官按顺序面试一系列申请人，并根据申请人的分数立即决定是否录用。对于最大改进和平均改进策略，录用决策取决于申请人的分数以及所有已录用员工（即先前成功申请人）的分数。对于局部改进策略，每个申请人随机组成的面试委员会将根据申请人分数及委员会成员分数做出决策。这些理想化的招聘策略捕捉了在不确定性下决策制定的核心挑战。我们探究了最佳员工的平均分数、前$N$位申请人全部被录用的概率、在整个发展过程中每位录用者分数均高于期望值的优质企业比例等指标。