In "Bayesian Economists ... Bayesian Agents I" (BBI), we generalized the results on Bayesian learning based on the martingale convergence theorem from the repeated to the sequential framework. In BBI, we showed that the variability introduced by the sequential framework is sufficient under very mild identifiability conditions to circumvent the incomplete learning results that characterize the literature. In this paper, we demonstrate that result in the neo-classical single sector growth model under even weaker identifiability conditions. We study the evolution of agent-beliefs in that model and show that, under reasonable conditions, the dependence of the current capital stock on the previous capital stock induces enough variability for our complete learning results to become relevant. Not only does complete learning take place from the subjective point of view of the agents' priors, but it also takes place from the point of view of an objective observer (modeling economist) who knows the true structure
