Building a Good Team: Secretary Problems and the Supermodular Degree

TL;DR

This paper extends the secretary problem to arbitrary non-negative monotone functions under matroid constraints, using supermodular degree to achieve better approximation guarantees for functions that are not submodular.

Contribution

It introduces algorithms parametrized by supermodular degree for secretary problems with general functions, providing polynomial approximation guarantees.

Findings

Algorithms with polynomial guarantees based on supermodular degree

Extension of secretary problem to non-submodular functions

Analysis of approximation bounds in new model

Abstract

In the Secretary Problem, one has to hire the best among n candidates. The candidates are interviewed, one at a time, at a random order, and one has to decide on the spot, whether to hire a candidate or continue interviewing. It is well known that the best candidate can be hired with a probability of 1/e (Dynkin, 1963). Recent works extend this problem to settings in which multiple candidates can be hired, subject to some constraint. Here, one wishes to hire a set of candidates maximizing a given set function. Almost all extensions considered in the literature assume the objective set function is either linear or submodular. Unfortunately, real world functions might not have either of these properties. Consider, for example, a scenario where one hires researchers for a project. Indeed, it can be that some researchers can substitute others for that matter. However, it can also be that some combinations of researchers result in synergy (see, e.g, Woolley et al., Science 2010, for a research about collective intelligence). The first phenomenon can be modeled by a submoudlar set function, while the latter cannot. In this work, we study the secretary problem with an arbitrary non-negative monotone function, subject to a general matroid constraint. It is not difficult to prove that, generally, only very poor results can be obtained for this class of objective functions. We tackle this hardness by combining the following: 1.Parametrizing our algorithms by the supermodular degree of the objective function (defined by Feige and Izsak, ITCS 2013), which, roughly speaking, measures the distance of a function from being submodular. 2.Suggesting an (arguably) natural model that permits approximation guarantees that are polynomial in the supermodular degree (as opposed to the standard model which allows only exponential guarantees).