Reduce Need Not Be Hard
In my book, I argue that all people are software engineers. Even if it’s not their profession, they program all the time—in their heads. Here’s an example of what I mean.
Programmers sometimes struggle with the
reduce function. Perhaps one reason is that it’s incredibly powerful and versatile, so it isn’t always clear why or when one would use it.
What may surprise you is that you already have inexplicit knowledge (cf. David Deutsch's The Beginning of Infinity ch. 14) of how to use
reduce—because you use it all the time, in your head, even if you have never written a line of code.
Imagine you are hungry but don’t know what to eat. You open your pantry and scan various items. You see:
What is it, exactly, that happens in your mind as your eyes scan your pantry for something you feel like eating? You start with a preference of “unknown.” Then you see rolls and ask yourself: “Do I feel like eating rolls?” Let’s say the answer is “no,” so you don’t update your preference. It’s still “unknown.” Then you see noodles and think to yourself, “that’s the best option I’ve seen so far!” So you update your preference to “noodles.” Lastly, you see “chili” and ask yourself: “Am I more in the mood for chili than noodles?” If the answer is “yes,” you update your preference to “chili.” If it’s “no,” you leave your preference as is.
reduce does. You have some initial value—in this case, your preference—that’s “unknown.” Then you start iterating over some options and update the initial value based on some criterion. You do this at every step, comparing each option with the previous value, and what you’re left with at the end is the “reduced” value.
Many programming languages have a
reduce function. In Clojure, the process looks like this:
(reduce (fn [acc curr] (if (do-i-feel-like-eating? curr) curr acc)) nil ["rolls" "noodles" "chili"])
reduce iterates over the last argument (the vector of different foods), takes an initial value of
nil (representing the initially “unknown” preference) and then runs the
do-i-feel-like-eating? function for each item. Whenever that function returns true, that current item (
curr) is set as the preference. Whenever it returns true, the previously set preference (
acc) remains the same. (The details of the
do-i-feel-like-eating? function need to concern us here, and in reality, it may take both
acc to compare them, but doesn’t matter all that much. We’re often unclear about why we feel like eating one thing and not another.)
You do this every time you decide what to eat from a given set of foods, or what movies to watch from a given set of movies, etc.
That does not mean that human decision making can be reduced (no pun intended) to the simple algorithm above, because humans do much more: they create the
do-i-feel-like-eating? criterion and the options in the first place, they can interrupt the above algorithm and change the options by removing some or adding new ones and then run the algorithm again—especially if they find that the algorithm returns
nil, meaning they haven’t found anything they like yet—they can decide to focus on another problem altogether, and so on. But once they have (tentatively) settled on a set of options and a criterion, they typically use something like the above algorithm to make a decision.
You knew how
reduce worked all along. Much of programming is about making what you already know explicit.
Another example of using reduce in one’s head is when you’re prompted to listen to options on the phone. You know, the thing where an automated voice says “please listen to the following options…” and then you make a selection by pressing a number.
The voice helps you iterate over the different options, and as you listen you remember the option that best fits your intent so far, which is the current “reduced” value at every stage.