Learning from Coarse Information: Biased Contests and Career Profiles

Abstract

An organization's promotion decision between two workers is modelled as a problem of boundedly-rational learning about ability. The decision-maker can bias noisy rank-order contests sequentially, thereby changing the information they convey. The optimal final-period bias favours the “leader”, reinforcing his likely ability advantage. When optimally biased rank-order information is a sufficient statistic for cardinal information, the leader is favoured in every period. In other environments, bias in early periods may (i) favour the early loser, (ii) be optimal even when the workers are equally rated, and (iii) reduce the favoured worker's promotion chances.