This paper develops a calibrated theory of economic decision complexity. For any decision problem, the framework delivers a measure of its complexity with no free parameters, making it highly portable across settings. We illustrate its use in central economic tasks, including dealing with taxes, forecasting, consumption, intertemporal financial planning, and the choice of financial products with opaque features.

The microfoundation builds on a macro-style production framework that treats the mind as a cognitive economy. Complexity is defined as the inverse productivity of thought: it increases with the number of importance-weighted components and decreases when outcomes hinge on a small set of dominant factors. More complex problems generate larger errors and reduced sensitivity to underlying parameters. We complement the theory with an experimental methodology for measuring subjective perceptions of complexity, and an experiment that confirms its main predictions. The measure provides a portable formula for predicting ex ante and quantifying ex post decision complexity across economic domains, potentially useful both theoretical and empirical applications.