We propose a theory of the complexity of economic decisions. Leveraging a macroeconomic framework of production functions we conceptualize the mind as a cognitive economy, where a task’s complexity is determined by its composition of basic cognitive operations, such as information retrieval and mental algebra. The complexity of the task emerges as the inverse of the total factor productivity of thinking about that task. Complexity increases in the number of importance-weighted components and decreases when the e↵ect of one or few components dominates those of all others. Higher complexity generates larger decision errors and behavioral attenuation to variation in problem parameters. The model applies both to continuous and discrete choice. In addition, we develop a theory-guided experimental methodology for measuring subjective perceptions of complexity that is simple and portable. A series of experiments tests and confirms the central predictions of our model for perceptions of complexity, behavioral dampening to parameters, and decision errors. We apply our framework to economic issues, including consumption over time, the complexity of a tax system, the complexity of consumption planning as a function of age, forecasting, and discrete choice between lotteries or financial products. These applications demonstrate how our complexity measure can be applied in future empirical and theoretical work. (with Thomas Graeber)