This paper utilizes a Banach-type fixed point theorem in a functorial context to develop
Universal Choice Spaces for addressing decision problems, focusing on expected utility and
preference uncertainty. This generates an infinite sequence of optimal selection problems
involving probability measures on utility sets. Each solution at a given stage addresses
the preference ambiguity from the previous stage, enabling optimal choices at that level.
The Universal Choice Space is characterized as a collection of finite-dimensional vectors of
probability distributions, with the mth component being an arbitrary probability measure
relevant to the mth stage of the problem. The space is derived as the canonical fixed point
of a suitable endofunctor on an enriched category and simultaneously as the colimit of the
sequence of iterations of this functor, starting from a suitable object.