  We examine the long-term behavior of a Bayesian agent who has a misspecified
belief about the time lag between actions and feedback, and learns about the
payoff consequences of his actions over time. Misspecified beliefs about time
lags result in attribution errors, which have no long-term effect when the
agent's action converges, but can lead to arbitrarily large long-term
inefficiencies when his action cycles. Our proof uses concentration
inequalities to bound the frequency of action switches, which are useful to
study learning problems with history dependence. We apply our methods to study
a policy choice game between a policy-maker who has a correctly specified
belief about the time lag and the public who has a misspecified belief.
