# Two Different Flavors of Type Theory

So summer seems to be about over. I’m very happy with mine, I learned quite a lot. In particular over the last few months I’ve been reading and fiddling with a different kind of type theory than I was used to: computational type theory. This is the type theory that underlies Nuprl (or JonPRL cough cough).

One thing that stood out to me was that you could do all these absolutely crazy things in this system that seemed impossible after 3 years of Coq and Agda. In this post I’d like to sketch some of the philosophical differences between CTT and a type theory more in the spirit of CiC.

## Formal Type Theory and Props-as-Types #1

First things first, let’s go over the more familiar notion of type theory. To develop one of these type theories you start by discussing some syntax. You lay out the syntax for some types and some terms

```
A ::= Σ x : A. A | Π x : A. A | ⊤ | ⊥ | ...
M ::= M M | λ x : A. M | <M, M> | π₁ M | ⋆ | ...
```

And now we want to describe the all important `M : A`

relation. This tells us that some term has some type. It’s is inductively defined from a finite set of inferences. Ideally, it’s even decidable for philosophical reasons I’ve never cared too much about. In fact, it’s this relation that really governs our whole type theory, everything else is going to stem from this.

As an afterthought, we may decide that we want to identify certain terms which other terms this is called definitional equality. It’s another inductively defined (and decidable) judgment `M ≡ N : A`

. Two quick things to note here

- Definitional equality is completely arbitrary; it exists in the way it does because we defined it that way and for no other reason
- The complexity of proving
`M ≡ N : A`

is independent of the complexity of`A`

The last point is some concern because it means that equality for functions is never going to be right for what we want. We have this uniformly complex judgment `M ≡ N : A`

but when `A = Π x : B. C`

the complexity *should* be greater and dependent on the complexity of `B`

and `C`

. That’s how it works in math after all, equality at functions is defined pointwise, something we can’t really do here if `≡`

is to be decidable or just be of the same complexity no matter the type.

Now we can do lots of things with our theory. One thing we almost always want to do is now go back and build an operational semantics for our terms. This operational semantics should be some judgment `M ↦ M`

with the property that `M ↦ N`

will imply that `M ≡ N`

. This gives us some computational flavor in our type theory and lets us run the pieces of syntax we carved out with `M : A`

.

But these terms that we’ve written down aren’t really programs. They’re just serializations of the collections of rules we’ve applied to prove a proposition. There’s no ingrained notion of “running” an `M`

since it’s built on after the fact. What we have instead is this `≡`

relation which just specifies which symbols we consider equivalent but even it is was defined arbitrarily. There’s no reason we `≡`

needs to be a reasonable term rewriting system or anything. If we’re good at our jobs it will be, sometimes (HoTT) it’s not completely clear what that computation system is even though we’re working to find it. So I’d describe a (good) formal type theory as an axiomatic system like any other that we can add a computational flavor to.

This leads to the first interpretation of the props-as-types correspondence. This states that the inductively defined judgments of a logic give rise to a type theory whose terms are proof terms for those same inductively defined judgments. It’s an identification of similar looking syntactic systems. It’s useful to be sure if you want to develop a formal type theory, but it gives us less insight into the computational nature of a logic because we’ve reflected into a type theory which we have no reason to suspect has a reasonable computational characterization.

## Behavioural/Computational Type Theory and Props-as-Types #2

Now we can look at a second flavor of type theory. In this setting the way we order our system is very different. We start with an programming language, a collection of terms and an untyped evaluation relation between them. We don’t necessarily care about all of what’s in the language. As we define types later we’ll say things like “Well, the system has to include at least X” but we don’t need to exhaustively specify all of the system. It follows that we have actually no clue when defining the type theory how things compute. They just compute *somehow*. We don’t really even want the system to be strongly normalizing, it’s perfectly valid to take the lambda calculus or Perl (PerlPRL!).

So we have some terms and `↦`

, on top of this we start by defining a notion of equality between terms. This equality is purely computational and has no notion of types yet (like `M ≡ N : A`

) because we have no types yet. This equality is sometimes denoted `~`

, we usually define it as `M ~ N`

if and only if `M ↦ O(Ms)`

if and only if `N ↦ O(Ns)`

and if they terminate than `Ms ~ Ns`

. By this I mean that two terms are the same if they compute in the same way, either by diverging or running to the same value built from `~`

equal components. For more on this, you could read Howe’s paper.

So now we still have a type theory with no types.. To fix this we go off an define inferences to answer three questions.

- What other values denote types equal to it? (
`A = B`

) - What values are in the type? (
`a ∈ A`

) - What values are considered equal
**at that type**? (`a = b ∈ A`

)

The first questions is usually answered in a boring way, for instance, we would say that `Π x : A. B = Π x : A'. B'`

if we know that `A = A'`

and `B = B'`

under the assumption that we have some `x ∈ A`

. We then specify two and three. There we just give the rules for demonstrating that some value, which is a program existing entirely independently of the type we’re building, is in the type. Continuing with functions, we might state that

```
e x ∈ B (x ∈ A)
———————————————————
e ∈ Π x : A. B
```

Here I’m using `_ (_)`

as syntax for a hypothetical judgment, we have to know that `e ∈ B`

under the assumption that we know that `x ∈ A`

. Next we have to decide what it means for two values to be equal as functions. We’re going to do this behaviourally, by specifying that they behave as equal programs when used as functions. Since we use functions by applying them all we have to do is specify that they behave equally on application

```
v x = v' x ∈ B (x ∈ A)
————————————————————————
v = v' ∈ Π x : A. B
```

Equality is determined on a per type basis. Furthermore, it’s allowed to use the equality of smaller types in its definition. This means that when defining equality for `Π x : A. B`

we get to use the equalities for `A`

and `B`

! We make no attempt to maintain either decidability or uniform complexity in the collections of terms specified by `_ = _ ∈ _`

as we did with `≡`

. As another example, let’s have a look at the equality type.

```
A = A' a = a' ∈ A b = b' ∈ A
————————————————————————————————
I(a; b; A) = I(a'; b'; A')
a = b ∈ A
——————————————
⋆ ∈ I(a; b; A)
a = b ∈ A
——————————————————
⋆ = ⋆ ∈ I(a; b; A)
```

Things to notice here, first off the various rules depend on the rules governing membership and equality in `A`

as we should expect. Secondly, `⋆`

(the canonical occupant of `I(...)`

) has no type information. There’s no way to reconstruct whatever reasoning went into proving `a = b ∈ A`

because there’s no computational content in it. The thing on the left of the `∈`

only describes the portions of our proof that involve computation and equalities in computational type theory are always computationally trivial. Therefore, they get the same witness no matter the proof, no matter the types involved. Finally, the infamous equality reflection rule is really just the principle of inversion that we’re allowed to use in reasoning about hypothetical judgments.

This leads us to the second cast of props-as-types. This one states that constructive proof has computational character. Every proof that we write in a logic like this gives us back an (untyped) program which we can run as appropriate for the theorem we’ve proven. This is the idea behind Kleene’s realizability model. Similar to what we’d do with a logical relation we define what each type means by defining the class of appropriate programs that fit its specification. For example, we defined functions to be the class of things that apply and proofs of equality are ⋆ when the equality is true and there are no proofs when it’s false. Another way of phrasing this correspondence is types-as-specs. Types are used to identify a collection of terms that may be used in some particular way instead of merely specifying the syntax of their terms. To read a bit more about this see Stuart Allen and Bob Harper’s work on the do a good job of explaining how this plays out for type theory.

## Building Proof Assistants

A lot of the ways we actually interact with type theories is not on the blackboard but through some proof assistant which mechanizes the tedious aspects of using a type theory. For formal type theory this is particularly natural. It’s decidable whether `M : A`

holds so the user just writes a term and says “Hey this is a proof of `A`

” and the computer can take care of all the work of checking it. This is the basic experience we get with Coq, Agda, Idris, and others. Even `≡`

is handled without us thinking about it.

With computational type theory life is a little sadder. We can’t just write terms like we would for a formal type theory because `M ∈ A`

isn’t decidable! We need to help guide the computer through the process of validating that our term is well typed. This is the price we pay for having an exceptionally rich notion of `M = N ∈ A`

and `M ∈ A`

, there isn’t a snowball’s chance in hell of it being decidable ^{1}. To make this work we switch gears and instead of trying to construct terms we start working with what’s called a program refinement logic, a PRL. A PRL is basically a sequent calculus with a central judgment of

`H ≫ A ◁ e`

This is going to be set up so that `H ⊢ e ∈ A`

holds, but there’s a crucial difference. With `∈`

everything was an input. To mechanize it we would write a function accepting a context and two terms and checking whether one is a member of the other. With `H ≫ A ◁ e`

only `H`

and `A`

are inputs, `e`

should be thought of as an output. What we’ll do with this judgment is work with a tactic language to construct a derivation of `H ≫ A`

without even really thinking with that `◁ e`

and the system will use our proof to *construct the term for us*. So in Agda when I want to write a sorting function what I might do is say

I just give the definition and Agda is going to do the grunt work to make sure that I don’t apply a nat to a string or something equally nutty. In a system like (Jon|Nu|Meta|λ)prl what we do instead is define the type that our sorting function ought to have and use tactics to prove the existence of a realizer for it. By default we don’t really specify what exactly that realizer. For example, if I was writing JonPRL maybe I’d say

```
||| Somehow this says a list of nats is a sorted version of another
Operator sorting : (0; 0).
Theorem sort : [(xs : List Nat) {ys : List Nat | is-sorting(ys; xs)}] {
||| Tactics go here.
}
```

I specify a sufficiently strong type so that if I can construct a realizer for it then I clearly have constructed a sorting algorithm. Of course we have tactics which let us say things “I want to use *this* realizer” and then we have to go off and show that the candidate realizer is a validate realizer. In that situation we’re actually acting as a type checker, constructing a derivation implying `e ∈ A`

.

## Wrap Up

Well, that’s this summer in a nutshell. Before I finish I had one more possible look on things. Computational type theory is not concerned with something being provable in an axiomatic system, rather it’s about describing constructions. Brouwer’s core idea is that a proof is a mental construction and computational type theory is a system for proving that a particular a computable process actually builds the correct object. It’s a translation of Brouwer’s notion of proof into terms a computer scientist might be interested in.

To be clear, this is the chance of the snowball not melting. Not the snowball’s chances of being able to decide whether or not

`M ∈ A`

holds. Though I suppose they’re roughly the same.↩