  We study the complexity of closure operators, with applications to machine
learning and decision theory. In machine learning, closure operators emerge
naturally in data classification and clustering. In decision theory, they can
model equivalence of choice menus, and therefore situations with a preference
for flexibility. Our contribution is to formulate a notion of complexity of
closure operators, which translate into the complexity of a classifier in ML,
or of a utility function in decision theory.
