Dynamic social learning under graph constraints

We introduce a model of graph-constrained dynamic choice with reinforcement modeled by positively \alpha-homogeneous rewards. We show that its empirical process, which can be written as a stochastic approximation recursion with Markov noise, has the same probability law as a certain vertex reinforced random walk. Thus the limiting differential equation... Show more