Kan extensions are partial colimits

TL;DR

This paper formalizes the idea that left Kan extensions can be viewed as partial colimits, using monads and bar constructions to precisely characterize this intuition in category theory.

Contribution

It introduces a detailed categorical framework connecting Kan extensions with partial colimits via pseudomonads and defines an 'image' morphism of monads to generalize colimit invariance concepts.

Findings

Kan extensions are characterized as partial evaluations of colimits.

A monad morphism called 'image' is constructed to relate different colimit concepts.

The main theorem links pointwise Kan extensions to partial colimit evaluations.

Abstract

One way of interpreting a left Kan extension is as taking a kind of "partial colimit", whereby one replaces parts of a diagram by their colimits. We make this intuition precise by means of the "partial evaluations" sitting in the so-called bar construction of monads. The (pseudo)monads of interest for forming colimits are the monad of diagrams and the monad of small presheaves, both on the (huge) category CAT of locally small categories. Throughout, particular care is taken to handle size issues, which are notoriously delicate in the context of free cocompletion. We spell out, with all 2-dimensional details, the structure maps of these pseudomonads. Then, based on a detailed general proof of how the "restriction-of-scalars" construction of monads extends to the case of pseudoalgebras over pseudomonads, we define a morphism of monads between them, which we call "image". This morphism allows us in particular to generalize the idea of "confinal functors" i.e. of functors which leave colimits invariant in an absolute way. This generalization includes the concept of absolute colimit as a special case. The main result of this paper spells out how a pointwise left Kan extension of a diagram corresponds precisely to a partial evaluation of its colimit. This categorical result is analogous to what happens in the case of probability monads, where a conditional expectation of a random variable corresponds to a partial evaluation of its center of mass.