Last week I read an article from the MIT Technology Review that looked into what causes people to make mistakes. The approach was one I hadn’t seen before: it used data mining on a set of chess games. Their rationale was that:
- There’s a huge database to feed your model from,
- Chess is a deterministic domain where you can objectively evaluate good and bad moves,
- There are clear criteria for the skill level of the participants, even when disparate, which allows you to balance.
As spoiled by the title, complexity was the main factor whenever a player made a mistake.
The bottom line is that the difficulty of the decision is the most important factor in determining whether a player makes a mistake.
So what’s the big deal?
Here’s the original text for “Flexibility Through Immutability“, for those of you who prefer reading to watching a video.
It’s a bit of a long read, so I’ve divided it into two parts. The first one focuses on the motivations behind preferring immutable data, and the second one (which will go up on Wednesday) on how to go about it.
While functional programming has a lot of advantages, the more I worked on the paradigm, the more surprised I was at how much thinking in a functional manner made refactoring easier.
As happy as I am to sell people on Clojure, I realized that teams could benefit from adopting some functional programming ideas even on their current language. I decided to try a different approach, and gave a talk at the 2016 ITAKE Unconference in Bucharest, elaborating on the benefits of immutable data. I wanted to focus on how the functional paradigm has a value in-and-of-itself. A secondary goal was to provide some tips that teams who can’t yet pull away from C# or Java can put into effect to help.
You’ll find my screen recording, as well as the slides, below.