Reputation, Learning and Project Choice in Frictional Economies

I introduce a dynamic model of learning and random meetings between a long-lived agent with unknown ability and heterogenous projects with observable types. There is incomplete yet symmetric information about the agent's ability. She needs to accept the contacting projects and create success to learn her type. Alternatively, lack of success during a match leads to a reputational loss followed from Bayesian learning, in that the reputation is interpreted as the posterior belief about the agent's ability. Developing a self-type learning framework with endogenous outside option, I find the optimal matching strategy of the agent, that determines what types of projects the agent with a certain level of reputation will accept. Comparing with a perfect information benchmark, I show learning incentives lead to larger matching sets in the optimum