Like most people, I’ve found monads hard to pin down. I know things I can do with them, but I don’t understand them at an intuitive and flexible level. Here I’ll try to build my mental model by relating to other tools.

Monads are originally a math concept, but I learned about them in a programming context and want to use them practically as a developer. However, they aren’t like other programming structures I know. It isn’t really a statement, data structure, or type. What makes a monad special is the behavioral properties that live outside of the obvious syntax or structures.

It is like a pattern, but it doesn’t relate closely to any patterns I know. This may be because patterns reduce common solutions and often feel familiar. Patterns usually don’t have strong conceptual consequences. On the other hand, Monads aren’t something you’d easily deduce. They stem from a theoretical construct whose useful consequences filtered down into practice.

Relating to programming structures just isn’t cutting it, so instead, let’s lean on my math background to try to build an understanding.


Scott Wlaschin likes to talk the functional way of “lifting” into the monad world, and keeping it there as long as possible before converting back.

This makes me think of an isomorphism. An isomorphism is a “structure-preserving mapping between two structures of the same type that can be reversed by an inverse mapping”. In simpler terms, it is a set of actions that are effectively the same for two different types, and you can map between the types.

It’s like a shift cipher (where you assign each letter to a different letter). The new alphabet looks different but acts the same as the original and can always be mapped back no matter how much we re-arrange the letters.

So are monads an isomorphism? No, result-types prove that they aren’t. Result-types take advantage of the “lifting” to let us define a series of actions that might have errors, and not worry about the errors until we need to extract the final result.

// divide by zero example

let map f = 
    fun resultInstance -> 
        match resultInstance with
        | Ok okVal -> Ok (f okVal)
        | Error errVal -> Error errVal

let safeDivide (divisor:int) (dividend:Result<int, string>): Result<int, string> = 
    if divisor = 0
    then Result.Error "Can't divide by zero"
    else (map (fun x -> x / divisor) dividend) 

let result = 
    Result.Ok 50 
    |> safeDivide 2
    |> safeDivide 0
    |> safeDivide 5

match result with
| Ok success -> printfn "%i" success
| Error message -> printfn "%s" message
// prints "Can't divide by zero"

This threw me for a loop for a bit. I originally thought it wasn’t an isomorphism. I assumed the starting domain to only be success values (e.g. numbers if we’re calculating).

Then I realized that the monad actually maps to and from the sum of the success and error typespaces.

However, it still isn’t an isomorphism even though the value mapping isn’t broken. We can’t perform the same operations in either typespace and expect to get the same result. The whole point of the result monad is to handle breaking errors in the original typespace. Since the operations are not always equivalent, it is not an isomorphism.

While an isomorphism wasn’t quite accurate, this is a useful parallel for how the lifting works. We map values to a more useful form to operate and map back. Mapping back from the monad even helps us discover cases that aren’t intuitive in the original typespace.


Now we have to make an important distinction. A bijection would mean every monad value corresponds to a distinct value in the original typespace. It is similar to isomorphism, but only requires an equivalence between values, not operations.

This gets pretty murky. Monads are defined to be a bijection in their cateogry theory definition. The programming version, however, says nothing about an inverse map from the monad to original typespaces.

Can we decide to add a reverse map and make into a bijection? Well, first we need to guarantee that the monad is injective.


The monad laws require a return method for elevating any value into the monad space. However, many of the definitions I was reading were unclear. I couldn’t find out if a bind and return that simply mapped everything to a constant value made a valid monad.

let return val = ForgetfulMonad.Unit
let bind f x = ForgetfulMonad.Unit

I factored out the monad laws from the FsCheck test suite and adapted them to take an arbitrary return and bind. Then I plugged in the constant monad. It passed.

This definition is the clearest I’ve seen so far and Scott Wlaschin has an excellent explanation. It clarifies that the unit laws are looking for return (and bind) to produce semantically equivalent values.

In other words, if we mapped the identity function into the monad world, it would be the same as directly implementing the identity function in the monad world. It should be a function that returns whatever it was given. This also is more in line with the original concept of a monad from category theory.

Notice that our definition from before can actually be rewritten

let return val = ForgetfulMonad.Unit
let bind f x = x

Nothing changes because the only possible value of x and the only possible return value of f is ForgetfulMonad.Unit. For this reason, my ForgetfulMonad is a valid monad. It would not be valid if any other values were allowed in the monad.

This clarification means that the programming definition of a monad is neither injective nor bijective by necessity. However, the return function at least guarantees programming monads project into the monad space. We can always map any value into the monad, but we may have limited values that can be mapped back.

In practice, useful monads will allow you to map meaningful information back to normal typespaces. It may not be as simple as just wrapping then unwrapping to get the original value.

This is interesting, but it’s missing something. It’s great that we can map, but just mapping isn’t more valuable than other data structures.


This draws focus to bind. Bind lets us map operations into the monad space and guarantee that they return values in the monad.

The fact that we can reliably apply functions to a monad value and get another monad value is very useful. This allows us to chain without fear that some step will return a “new kind” of value. We can always continue to operate just as we were before.

This is why the result-type is so powerful. It allows us to ignore error states until the very end, producing simple and readable code.

This reminds me of rings in Group Theory. The basic idea is that you have a set of operations that will always produce the same kind of value they were given. That property is called closure. In this way monads are very much like rings.


Most people are probably satisfied with knowing

  • We can always map to a monad (but be careful for extra cases mapping back)
  • Monads are always chainable
  • Monads let us transform values to fit our usecase, operate error-free, then map back (e.g. async, lists, error-handling, etc)

However, I find the parallel to other math concepts develops a deeper intuition for why monads work and what they are useful for.

Ah, it feels good to math again.

Further Reading