Sequential Selection of Candidates: An Experimental Investigation

Information Systems and Operations Management Department 

Intervenante: Morvarid Rahmani (Georgia Tech)

Salle T004

Abstract

Managers often face sequential selection challenges, making choices among candidates without knowing the quality of future options. This dilemma is exemplified by the U.S. Army’s officer evaluation system, where supervisors must rate subordinates under strict quotas. Using a theoretical model and experimental data, the study finds that while individuals tend to follow optimal decision rules with extreme performers, they struggle to evaluate non-extreme candidates, particularly early in the sequence. Counter-intuitively, larger candidate pools lead to lower per capita performance, driven by behavioral mechanisms such as search fatigue and limited forward-looking.