Asymmetric sequential search under incomplete information

We study a multistage sequential search model with n agents who compete for one job. The agents arrive sequentially, each one in a different stage. The agents' abilities, which are private information, are derived from heterogeneous distribution functions. In each stage, the designer chooses an ability threshold. If an agent has a higher ability than the threshold in the stage in which he arrives, he gets the job and the search is over. The agent's ability is not revealed when he wins the job and the designer has only an estimation of this ability according to the threshold placed by him. We analyze the optimal ability thresholds imposed by the designer who wishes to maximize the ability estimation of the agent who gets the job net of the search cost. We also investigate the relation between the optimal ability thresholds as well as the optimal order of agents in all stages according to the agents' distributions of abilities.