Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
This paper studies preference identification in a general framework that allows for partial observability of optimal choices: Decision makers select some optimal alternatives, but not necessarily all of them. While partial observability is a methodologically appealing assumption for empirical applications, it makes recovering preferences much harder. The main result provides abstract conditions on classes of preferences and decision problems ensuring identification. The result is applied to several standard settings demonstrating the power of the method.