Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
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 toward 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 tradeoff between accuracy and delay in decision‐making.