We propose an active cognition approach to bounded rationality, in
which agents use a calculation algorithm to improve on the forecasts
provided by a purely adaptive
learning rule such as least-squares
learning. Agents' choices of calculation intensity depend on their
estimates of the benefits of improved forecasts relative to
calculation costs. Using an asset-pricing model, we show how more
rapid adjustment to rational expectations and forward-looking
behavior arise naturally when there are large
anticipated structural
changes such as policy shifts. We also give illustrative applications
in which the severity of asset price bubbles and the intensity of
hyperinflationary
episodes are related to the cognitive ability of
the agents.