We propose a framework to study regulatory complexity, based on concepts from computer science. We distinguish different dimensions of complexity, classify existing measures, develop new ones, compute them on three examples — Basel I, the Dodd–Frank Act, and the European Banking Authority’s reporting rules — and test them using experiments and a survey on compliance costs. We highlight two measures that capture complexity beyond the length of a regulation. We propose a quantitative approach to the policy trade-off between regulatory complexity and precision.