When and what to learn in a changing world

About this event

A decision-maker periodically acquires information about a changing state, controlling both the timing and content of updates. I characterise optimal policies using a recursive decomposition into optimal stopping and static information acquisition. Eventually, information acquisition either stops or follows a simple cycle in which updates occur at regular intervals to restore prescribed levels of relative certainty, which enables precise analysis of long run dynamics across environments. To tame the rich predictions of the general model, I study the limit as fixed costs of information vanish. This provides a new tractable approach to solving the limit problem, which is shown to be a good approximation of the case with small fixed cost. Belief changes become lumpy in the limit and it is optimal to either wait or acquire information which exactly confirms current beliefs until rare news prompts a sudden change. The long run solution has a closed-form characterisation in terms of the “virtual flow payoff". I highlight an illustrative application to portfolio diversification.