Sequential Observation and Selection with Rank-Dependent Payoffs: An Experimental Study

Abstract

We consider a class of sequential observation and selection decision problems in which applicants are interviewed one at a time, decision makers only learn the applicant's quality relative to the applicants that have been interviewed and rejected, only a single applicant is selected, and payoffs increase in the absolute quality of the selected applicant. Compared to the optimal decision policy, which we compute numerically, results from two experiments show that subjects terminated their search too early. We competitively test three behavioral decision rules and find that a multithreshold rule, which has the same form as the optimal decision policy but is parameterized differently, best accounts for the data. Results from a probability estimation task show that subjects tend to overestimate the absolute quality of early applicants and give insufficient consideration to the yet-to-be-seen applicants.