Rational inattention dynamics: Inertia and delay in decision-making

We solve a general class of dynamic rational-inattention problems in which
an agent repeatedly acquires costly information about an evolving state and selects actions. The solution resembles the choice rule in a dynamic logit model, but it is biased towards an optimal default rule that is independent of the realized state. The model provides the same fit to choice data as dynamic logit, but, because of the bias, yields different counterfactual predictions. We apply the general solution to the study of (i) the status quo bias; (ii) inertia in actions leading to lagged adjustments to shocks; and (iii) the trade off between accuracy and delay in decision-making.