> {-# LANGUAGE RankNTypes, MultiParamTypeClasses, TypeOperators #-}

**Introduction**

After I spoke at BayHac 2014 about free monads I was asked about cofree comonads. So this is intended as a sequel to that talk. Not only am I going to try to explain what cofree comonads are. I'm also going to point out a very close relationship between cofree comonads and free monads.

At the beginning of the talk the Google Hangout software seems to have switched to the laptop camera so you can't see the slides in the video. However the slides are here.

**Cothings as machines**

I often think of coalgebraic things as machines. They have some internal state and you can press buttons to change that internal state. For example here is a type class for a machine with two buttons that's related to a magma:

> class TwoButton a where > press :: a -> (a, a)The idea is that the state of the machine is given by some type

`a`and you could press either the left button or the right button. The result of pressing one or other button is given by these two functions:

> pressLeft, pressRight :: TwoButton a => a -> a > pressLeft = fst . press > pressRight = snd . press(As with many metaphors used to explain Haskell type classes your mileage may vary. Sometimes you'll have to stretch your imagination to see what the set of buttons is for a particular cothing.)

**Comonads**

Just as monads are a kind of generalised algebraic structure (for example see my talk), comonads are a generalised kind of machine. The idea is that for any state of the machine there is a bunch of buttons we could press. But we don't have two buttons, or any fixed number of buttons. We instead have a functorful of buttons (if you think of functors by analogy with containers). We also don't get to directly see the internal state of the machine but instead we get to make observations.

Here's the type class:

> class Comonad w where > extract :: w a -> a > duplicate :: w a -> w (w a)The state of the machine is given by

`w a`. We observe the state using the

`extract`function. And when we come to press a button, we have a functorful of new states that it could end up in. The

`duplicate`function gives the container of those new states.

For example, various kinds of zipper give rise to comonads. Zippers allow you to "focus" on a part of a data structure. The `extract` operation allows you to observe the point that currently has focus. There is one button for every position in the structure where the focus could be. Pressing the corresponding button moves the focus to that point. Similarly the `Store` comonad has one button for each value you can store in the field it represents. Press the button and the value gets stored in the field.

**Cofreeness as a way to memoise**

Cofree coalgebras can be thought of as memoised forms of elements of coalgebras. For example, the `TwoButton` machine above has a function, `press`, as part of its definition. Memoising an element of such a thing means tabulating everything that could possibly happen if you pressed the buttons so we no longer need the `press` function. One approach is to try something like this:

data CofreeTwoButton = Memo CofreeTwoButton CofreeTwoButtonThe structure contains two

`CofreeTwoButton`s, each giving the result of pressing one of the two buttons. Any element of

`CofreeTwoButton`may now be memoised like so:

memoiseTwoButton :: TwoButton m => m -> CofreeTwoButton memoiseTwoButton m = Memo (memoiseTwoButton (pressLeft m)) (memoiseTwoButton (pressRight m))It definitely tabulates the result of pressing buttons. But it has a major flaw. We have no way of seeing what's stored in the table! To make this useful we want to also store some data in the table that we can peek at. So here is a better definition:

> data CofreeTwoButton a = Memo a (CofreeTwoButton a) (CofreeTwoButton a) > memoiseTwoButton :: TwoButton m => (m -> a) -> m -> CofreeTwoButton a > memoiseTwoButton f m = Memo (f m) (memoiseTwoButton f (pressLeft m)) (memoiseTwoButton f (pressRight m))The first argument to

`memoiseTwoButton`says what we want to store in the table and then

`memoiseTwoButton`goes ahead and stores it. We can use the identity function if we want to store the original elements.

Note how this is like `foldMap`:

foldMap :: Monoid m => (a -> m) -> t a -> mif we replace

`t`by the list functor and remember that lists are free monoids. The main difference is that arrows have been reversed. Where

`foldMap`takes an element of a free monoid and interprets it as an element of another monoid,

`memoiseTwoButton`packs an element of a

`TwoButton`into a cofree structure. The "interpretation" and "packing" here are both homomorphisms for their respective structures. Homomorphisms respect equations so if an equation holds between elements of a free monoid we expect it to also hold when interpreted in another monoid. But any element of a free monoid can be interpreted in any other monoid meaning that any equation that holds between elements of a free monoid must hold in any monoid. That's why free monoids are designed so that the only equations that hold between elements are those that follow from the monoid laws.

With the `TwoButton` we have a dualised version of the above. Every element of every `TwoButton` can be packed into the `CofreeTwoButton`. So every equation in the original structure will still hold after the packing. So every equation that holds in some `TwoButton` must have some solution in `CofreeTwoButton`. That gives an idea of what a `CofreeTwoButton` is by analogy with the free monoid.

**Cofree comonads**

A cofree comonad is basically a memoised comonad. So the data structure is:

> data Cofree f a = Cofree a (f (Cofree f a))At each point in the "table" we store some observable value of type

`a`. And we have a functorful of buttons, so we expect to have a functorful of new states we could transition to. The

`Functor`instance looks like:

> instance Functor f => Functor (Cofree f) where > fmap f (Cofree a fs) = Cofree (f a) (fmap (fmap f) fs)We apply

`f`to the observable value and then push the

`fmap f`down to the child nodes.

The `duplicate` function takes a memoised state and replaces the observable stored at each position with the memoised state that gives rise to the observable.

> instance Functor f => Comonad (Cofree f) where > extract (Cofree a _) = a > duplicate c@(Cofree _ fs) = Cofree c (fmap duplicate fs)Now by analogy with

`memoiseTwoButton`we can memoise comonads.

> memoiseComonad :: (Comonad w, Functor f) => > (forall x.w x -> f x) -> (forall x.w x -> Cofree f x) > memoiseComonad f w = Cofree (extract w) (fmap (memoiseComonad f) (f (duplicate w)))So that's what a cofree comonad is: it's a type that can be used to memoise all of the states that are accessible from a state in a comonad by pressing its buttons.

**Cofree comonad meets free monad**

But that's not all. There is a close relationship between cofree comonads and free monads. So to get going, here's a free monad type:

> data Free f a = Id a | Free (f (Free f a))Now I'll define a kind of pairing between functors. Given a way to combine two kinds of element, the pairing gives a way to combine a pair of containers of those elements.

> join' :: Functor f => Free f (Free f a) -> Free f a > join' (Id x) = x > join' (Free fa) = Free (fmap join' fa)

> instance Functor f => Functor (Free f) where > fmap f (Id x) = Id (f x) > fmap f (Free fa) = Free (fmap (fmap f) fa)

> instance Functor f => Monad (Free f) where > return = Id > m >>= f = join' (fmap f m)

> class (Functor f, Functor g) => Pairing f g where > pair :: (a -> b -> r) -> f a -> g b -> rGiven a pairing between

> data Identity a = Identity a > instance Functor Identity where > fmap f (Identity x) = Identity (f x)

> instance Pairing Identity Identity where > pair f (Identity a) (Identity b) = f a b

> data (f :+: g) x = LeftF (f x) | RightF (g x) > instance (Functor f, Functor g) => Functor (f :+: g) where > fmap f (LeftF x) = LeftF (fmap f x) > fmap f (RightF x) = RightF (fmap f x)

> data (f :*: g) x = f x :*: g x > instance (Functor f, Functor g) => Functor (f :*: g) where > fmap f (x :*: y) = fmap f x :*: fmap f y

> instance (Pairing f f', Pairing g g') => Pairing (f :+: g) (f' :*: g') where > pair p (LeftF x) (a :*: _) = pair p x a > pair p (RightF x) (_ :*: b) = pair p x b

> instance (Pairing f f', Pairing g g') => Pairing (f :*: g) (f' :+: g') where > pair p (a :*: _) (LeftF x) = pair p a x > pair p (_ :*: b) (RightF x) = pair p b x

> instance Pairing ((->) a) ((,) a) where > pair p f = uncurry (p . f)

`f`and

`g`we get one between

`Cofree f`and

`Free g`.

> instance Pairing f g => Pairing (Cofree f) (Free g) where > pair p (Cofree a _) (Id x) = p a x > pair p (Cofree _ fs) (Free gs) = pair (pair p) fs gsAn element of

`Free g`can be thought of as an expression written in a DSL. So this pairing gives a way to apply a monadic expression to a memoised comonad. In other words, if you think of comonads as machines, monads give a language that can be used to compute something based on the output of the machine.

Here's an almost trivial example just so you can see everything working together. A reasonable definition of a comagma structure on the type `a` is `a -> UpDown a` with `UpDown` defined as:

> data UpDown a = Up a | Down aA well known comagma structure on the positive integers is given by the famous Collatz conjecture:

> instance Functor UpDown where > fmap f (Up a) = Up (f a) > fmap f (Down a) = Down (f a)

> type CofreeComagma a = Cofree UpDown a

> collatz :: Integer -> UpDown Integer > collatz n = if even n then Down (n `div` 2) else Up (3*n+1)We can memoise this as a cofree comonad:

> memoisedCollatz :: Integer -> CofreeComagma Integer > memoisedCollatz n = Cofree n (fmap memoisedCollatz (collatz n))Here's a picture of

`memoisedCollatz 12`:

Now let's make the dual functor in readiness for building the dual monad:

> data Two a = Two a a > instance Functor Two where > fmap f (Two a b) = Two (f a) (f b)And here we set up a pairing:

> instance Pairing UpDown Two where > pair f (Up a) (Two b _) = f a b > pair f (Down a) (Two _ c) = f a cThis gives rise to a free monad isomorphic to the one in my talk:

> execute :: Cofree UpDown x -> Free Two (x -> r) -> r > execute w m = pair (flip ($)) w m

> data Direction = WentUp | WentDown deriving ShowAnd here's an example of some code written in the corresponding DSL:

> choose :: Free Two Direction > choose = Free (Two (return WentUp) (return WentDown))

> ex1 :: Free Two (Integer -> String) > ex1 = do > x <- choose > y <- choose > case (x, y) of > (WentDown, WentDown) -> return (\z -> "Decreased twice " ++ show z) > _ -> return showIt can be represented as:

And here's what happens when they meet:

> go1 :: String > go1 = execute (memoisedCollatz 12) ex1This can be understood through the combined picture:

**References**

On getting monads from comonads more generally see Monads from Comonads. For more on memoising and how it's really all about the Yoneda lemma see Memoizing Polymorphic Functions. I'm waiting for Tom Leinster to publish some related work. The pairing above gives a way for elements of free monads to pick out elements of cofree comonads and is a special case of what I'm talking about here. But I think Tom has some unpublished work that goes further.

If you think of a comonad as a compressed object that is decompressed by a monadic decision tree, then you'd expect some form of information theoretical description to apply. That makes me think of Convex spaces and an operadic approach to entropy.

## 8 comments:

I used to have a version of your Pairing class in an earlier version of the adjunctions package, under the less informative name 'Zap'.

http://comonad.com/reader/2008/zapping-strong-adjunctions/

I general it is just about asking for a sum from one of the types whenever the other is a product and vice versa and it can annihilate values with functions from that value

The problem I had with it as a class was that both

instance Adjunction f g => Zap f g

instance Adjunction f g => Zap g f

are reasonable.

This caused me to eventually switch it over to a data type carrying the mapping:

http://hackage.haskell.org/package/adjunctions-0.6.0/docs/Data-Functor-Zap.html

But with very few users I eventually dropped the feature; I think only Sjoerd Visscher ever noticed it was there. ;)

I could reintroduce it without the automated lifting from Adjunction if folks are interested.

A quick search revealed this paper: http://math.ucr.edu/home/baez/information_loss.pdf

@heisenbug Yeah, I know about those papers because I followed their unfolding at the n-category cafe. But I think coalgebras may add a twist. I think random processes are nicely described coalgebraically but the decisions tree driven by such processes are nicely thought of as algebraic.

The relationship between Pairing functors looks a lot like the relationship between `w` and `Co w`.

@Unknown That's why I linked to Ed's page about Co :-)

It's a special case of that relationship. But I think it's nice to unpack it for free monads and cofree comonads because they give a lot of insight into what's happening more generally.

The only instance that is a Pairing but not an Adjunction that I can think of is where you have two adjunctions intertwined, i.e.

instance (Adjunction f1 g1, Adjunction f2 g2) => Pairing (Compose f1 g2) (Compose f2 g1)

Are there others?

Sjoerd:

There are also

instance (Zap f g, Zap h i) => Pairing (Sum f h) (Product g i)

instance (Zap f g, Zap h i) => Pairing (Product g i) (Sum f h)

which aren't simply instances of the only Hask->Hask adjunction in disguise and which swaps sums for products.

But even that first 'intertwining' leads to whole families of such twistings, e.g. StateT vs. StoreT should give rise to something like:

instance Pairing m w => Pairing (StateT m s) (StoreT w s)

and vice versa.

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