Why Human-Centred Approaches Lead to Better Algorithm Design | by John Loewen, PhD | Apr, 2024

Some lessons I learned about quantifying qualitative thinkingDall-E Image: “Impressionist painting of Adams River in British Columbia”Algorithms often evoke fearful thoughts of cold, hard mathematical formulas beyond the minds of many.This is the approach taught in many computer science courses and textbooks — it was what I learned when I was a Comp Sci student in the 1990s.Conceptually, this approach works well — for searching, sorting, calculating, organizing, etc. There are categories of well-established algorithms to get the job done.But what about for modelling an environment where qualitative data is predominant? For example, when modelling data that deals with informal approximations.Traditionally, this is how humans solve problems in the face of many variables. Here, the decisions made are far more complex and intertwined with the fabric of society than many assume.A massive failure while working on my PhD made me realize this, and it forever transformed my approach to algorithmic design.I went to school to become a computer scientist. This was back in the 1990s when most in the field were purists, taking the view that algorithm design was purely a mathematics endeavour.The main purpose of algorithm design being to improve efficiency and to optimize what ever needed to be better. Audio algorithms, storage algorithms, compression algorithms, speed algorithms.My first job out of college as a programmer/analyst was to create an employee scheduling algorithm for industrial saw mills. All of the data was quantitative and was easily plugged in as input to our algorithm.And presto! The weekly schedule was generated.A few years later, back at school working on a PhD, I used this style of approach to algorithm design in my own research, and it was a disaster.But maybe not in the way you think.Let me explain.