Axiom of Replacement in Category Theory

I had a slightly ironic experience on Math Overflow. A couple of months ago, I started wondering to what extent you could develop category theory “below a cardinal”. When you consider the category of groups (for example), you’re probably not literally interested in groups of arbitrarily large sizes — you just want enough space so that you can perform any operation you need to. I started writing this post here arguing that for concrete categories, sets smaller than a limit cardinal were big enough. Limit cardinals are not usually large cardinals in the sense of set theory, but they’re pretty big — the category of sets smaller than a limit cardinal is closed under the power set operation, for example.

Before I finished the post, I thought I should check the claim and look over some proofs in a category theory book. I realized that, under the usual definition of a diagram in the literature, my proposed restriction would make the category of sets fail to be either complete or cocomplete — even countable diagrams could have to have limits or colimits. You could finesse the issue by changing the definition of diagram, but I thought “No one will stand for that”. Under the standard definition, the construction of limits or colimits requires the Axiom of Replacement, which means that the right condition is inaccessibility, or equivalently you need Grothendieck universes.

So now I thought I understood the big picture. Completeness required replacement, which leads naturally to Groethendieck universes, which explains why the main competitor in textbooks to either Goedel-Bernays or Morse-Kelley set theory is to postulate one or more Grothendieck universes. The only thing that puzzled me was that while people using category theory seemingly made casual use of replacement, people would also argue that replacement is never used in ordinary mathematics. I thought that maybe I was confused on some issue, so I asked on Math Overflow.

It turns out that at least some people really don’t want to use replacement. They would rather change the definition of what it means to be a small diagram so as to be able to avoid the axiom. Avoiding replacement has lots of little consequences. For example, you have to require that the image of a small diagram is a set. Even with the corrected definition, the General Adjoint Functor Theorem becomes false as stated, and you have to strengthen the solution set condition. It means lots of fiddly little details have to be changed. You also no longer have as clean of a distinction between large and small. (You can have categories that are locally small, and have only countably many objects, and yet are not small categories, for example.)

But I could have stuck with my original idea for this post.

Universal Differential Equation

Here’s something I didn’t know. There exists nonlinear (but algebraic) ordinary differential equations such that solutions to that differential equation are dense in the space of continuous functions. These are known as universal differential equations. An explicit construction of one is given in this preprint by Keith Briggs. If I understand the construction correctly, the trick seems to be that the nonlinearity gives you branch points where you have a choice for the direction in the solution. This allows you to paste together solutions in enough ways that you can achieve density.

Forcing Truth

This thread at Math Overflow has the feel of advanced alien technology. Forcing is a technique for constructing models of set theory where various hypotheses fail. For example, forcing can be used to construct a model of set theory where the continuum hypothesis is violated.

There are some statements whose value cannot be affected by forcing. These statements are known as absolute. Forcing is useless for establishing such a statement is independent, but this can be a virtue. If you can create a model using forcing such that you can prove that an absolute statement is true in that model, then it must already be true in the universe of ordinary sets. The thread gives several specific examples of theorems you can prove this way.

Reversible Markov Chains

Here’s a pretty idea. A Markov chain is one of the simplest forms of dependence in random variables: an infinite sequence of dependent random variables, where the probability distribution of the next random variable only depends on the value of the current random variable. If you reverse the sequence of variables, you get another Markov chain, the reverse Markov chain. Some Markov chains, reversible Markov chains, have the property that when you reverse them, you get back the same chain. Markov chains represent processes that have no history, in that future is determined solely by the present, not the past. A reversible Markov chain not only has no history, but time has no direction.

Here is a draft of a book by Aldous and Fill on the theory of reversible Markov chains.

The Algebra of Possibilities

There is a notion in symbolic dynamics of a “topological Markov chain” that is analogous to a Markov chain in probability theory. It’s occurred to me that you can extend the analogy to a complete analogy with probability theory. We’re still interested in sets of events, but now we’re no longer interested in the probability of an event, but just whether or not an event is possible.

Start with a σ-algebra of sets, as usual. Instead of associating a probability with each set, associate a member of the set {Not Possible, Possible}. The empty set is assigned the value Not Possible, while the whole space is assigned the value Possible. A countable disjoint union of sets is Possible if and only if at least one of the individual sets is Possible.

A measure takes values in the semigroup of the nonnegative real numbers closed under addition. Here, we’ve replaced that semigroup with the semigroup of {Not Possible, Possible} under the commutative binary operation +, with multiplication table:

+ Not Possible Possible
Not Possible Not Possible Possible
Possible Possible Possible

I’ll explain the relationship with topological Markov chains in a future post.