Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We consider a signaling model where the senderʼs continuation value after signaling depends on his type, for instance because the receiver is able to update his posterior belief. As a leading example, we introduce Bayesian learning in a variety of environments ranging from simple two-period to continuous-time models with stochastic production. Signaling equilibria present two major departures from those obtained in models without learning. First, new mixed-strategy equilibria involving multiple pooling are possible. Second, pooling equilibria can survive the Intuitive Criterion when learning is efficient enough.