A multi-attribute extension of the secretary problem: Theory and experiments

We present a generalization of a class of sequential search problems with ordinal ranks, referred to as “secretary” problems, in which applicants are characterized by multiple attributes. We then present a procedure for numerically computing the optimal search policy and test it in two experiments with incentive-compatible payoffs. With payoffs dependent on the absolute ranks of the attributes, we test the optimal search model with both symmetric (Experiment 1) and asymmetric (Experiment 2) search problems. In both experiments we find that, relative to the optimal search policy, subjects stop the search too early. Our results show that this bias is largely driven by a propensity to stop prematurely on applicants of intermediate (relative) quality.