  We introduce and study a model of long-run convention formation for rare
interactions. Players in this model form beliefs by observing a
recency-weighted sample of past interactions, to which they noisily best
respond. We propose a continuous state Markov model, well-suited for our
setting, and develop a methodology that is relevant for a larger class of
similar learning models. We show that the model admits a unique asymptotic
distribution which concentrates its mass on some minimal CURB block
configuration. In contrast to existing literature of long-run convention
formation, we focus on behavior inside minimal CURB blocks and provide
conditions for convergence to (approximate) mixed equilibria conventions inside
minimal CURB blocks.
