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I introduce a dynamic model of learning and random meetings between along-lived agent with unknown ability and heterogeneous projects withobservable qualities. The outcomes of the agent's matches with the projectsdetermine her posterior belief about her ability (i.e., her reputation). In aself-type learning framework with endogenous outside option, I find the optimalproject selection strategy of the agent, that determines what types of projectsthe agent with a certain level of reputation will accept. Sections of theoptimal matching set become increasing intervals, with different cutoffs acrossdifferent types of the projects. Increasing the meeting rate has asymmetriceffects on the sections of the matching sets: it unambiguously expands thesection for the high type projects, while on some regions, it initially expandsand then shrinks the section of the low type projects.

Reputation, Learning and Project Choice in Frictional Economies