Monoids are Composable List Summarizers


Let’s look at some examples:

  • Counts: I can summarize a finite list by counting its elements. If I have the counts of two lists, I can compute the count of their concatenation by adding the two counts together.
  • Sums: I can summarize a finite list by its sum. If I have the sum of two lists, I can compute the sum of their concatenation, again by adding.
  • Minima and Maxima: I can summarize a finite list by its minimum and maximum elements. (Lists may be empty, but we can define the minimum and maximum of an empty list to be ∞ and -∞, respectively). And again, if I have the minima and maxima of two lists, I can compute the minimum and maximum of their concatenation, by taking the least of the two minima and the greater of the two maxima.
  • GCDs: If I’m doing something with number theory, I might summarize a finite list with its greatest common divisor. If I have the greatest common divisors of two lists, I can compute the greatest common divisor of their concatenation, by computing the gcd of the gcds.
  • Heads: I can summarize a list by its head (i.e., first element), if it exists! If the list is empty, I will just note that it has no head. Given the heads of two lists, I can tell you the first element of their concatenation: it’s the head of the left-hand list if it exists, and the head of the right-hand list otherwise.
  • The Whole List: This one may seem a bit silly, but I’ll reference it later. You can trivially “summarize” a list by just keeping the whole list. Your summarizer here is the identity function! That obviously gives you enough information to know the concatenation, as well, so it’s a perfectly good monoid.

Counter-examples and how to fix them

At this point, our examples of monoids include counts, sums, minima, maxima, gcds, and heads. With so many examples, one might start to suspect that any function on lists is a monoid. But of course, that’s not the case. We can look at some instructive counterexamples.

  • Means: If you’re thinking of ways to summarize a list of numbers, one that probably comes to mind is the mean. The mean is not quite a monoid, because knowing the means of two lists isn’t enough information to compute the mean of their concatenation. Just averaging the averages doesn’t work, unless the lists happen to be the same length!
  • Medians: Going down the list of averages, you might also want to know the median of a list. Now things get more dicey, but also more intriguing! Once again, the median itself is not a monoid: knowing the medians of two lists still doesn’t tell you the median of the concatenation.
  • First, is that even a monoid? Well, (a) given a list I can always sort it. And (b) If I have the sorted versions of two lists, I can merge them to get the sorted concatenation of those lists. So yes, it’s a monoid.
  • Not only is it a monoid, but the monoid structure is helpful. It’s actually easier to merge sorted lists than it is to sort the concatenation of the originals — this is the whole basis of merge sort, after all.
  • Finally, this monoid helps with computing a median. Although we don’t save on the size of data, it’s trivial to find the median of a list after sorting it.

Why is this equivalent to abstract monoids?

I haven’t yet justified calling these list-summarizers monoids. Are they really the same thing as monoids in their traditional mathematical definition? They are, indeed. The short justification is that finite lists are free monoids.

  • f maps finite lists with elements in E onto S.
  • If ⧺ denotes concatenation of finite lists, then f(ab) = f(a) ∗ f(b).
  1. The ∗ operation is associative. That is, for any x, y, and z in S, (xy) ∗ z = x ∗ (yz). To show this, we use the fact that f is onto to find lists a, b, and c so that x = f(a), y = f(b), and z = f(c). Then we have this chain of equalities: (xy) ∗ z = (f(a) ∗ f(b)) ∗ f(c) = f(a b) ∗ f(c) = f((a b) c) = f(a (b c)) = f(a) ∗ f(bc) = f(a) ∗ (f(b) ∗ f(c)) = x ∗ (yz).
  2. The ∗ operation has an identity. For any x in S, we again use the fact that f is onto to find x with x = f(a). Then if e is the empty list, we have x = f(a) = f(a e) = f(a) ∗ f(e) = x f(e), and similarly on the other side. So f(e) is the identity.
  1. That this f is onto. It is, because you can get any x in S by applying f to the one-element list containing x.
  2. That f is a monoid homomorphism, so f(ab) = f(a) ∗ f(b). We need only expand the definition of f to get f(a) and f(b) as chains of the monoid operation, and then appeal to the associative property to arrange the parentheses as needed.

Where next?

This line of thought leads more places, as well. If monoids are composable summarizers of finite lists, then what about some other structures? In general, any algebraic structure should be describable as homomorphic images of the free structure. But what makes this interesting is that free monoids have a particularly simple meaning. So this is the same as asking which other structures have particularly simple free constructions.

  • Semigroups are summarizers of nonempty finite lists.
  • Commutative monoids are summarizers of bags (aka multisets), where elements can occur more than once, but the order is not significant.
  • Magmas (that is, sets with a binary operation but no axioms at all) are summarizers of binary trees.
type Free :: (Type -> Constraint) -> (Type -> Type)
type Free cls a = (forall x. cls x => (a -> x) -> x)
type List :: Type -> Type
type List a = Free Monoid a
toList :: Foldable f => f a -> List a
toList = flip foldMap



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Chris Smith

Chris Smith


Software engineer, volunteer K-12 math and computer science teacher, author of the CodeWorld platform, amateur ring theorist, and Haskell enthusiast.