Hiring Strategies

TL;DR

This paper analyzes various hiring strategies involving sequential decision-making based on applicant scores, exploring their effectiveness and probabilistic outcomes in idealized scenarios.

Contribution

It introduces and evaluates different hiring strategies, including maximal, average, and local improvement methods, capturing decision-making under uncertainty.

Findings

Average score of best employee analyzed

Probability of hiring all first N applicants computed

Fraction of superior companies identified

Abstract

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.