J. Neil Bearden
Associate Professor of Decision Sciences
Sequential search; Secretary problem; Dynamic programming;
The authors 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.They 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, the authors test the optimal search model with both symmetric (Experiment 1) and asymmetric (Experiment 2) search problems.In both experiments the authors find that, relative to the optimal search policy, subjects stop the search too early. Their results show that this bias is largely driven by a propensity to stop prematurely on applicants of intermediate (relative) quality.