I decided to learn the programming language Python. I was steered towards the MIT OpenCourseware ‘Introduction to Computer Science and Programming’ 6.00 course, taught by Prof. Eric Grimson and Prof. John Guttag (they say it is a course about computational thinking.)
As the first lecture felt a bit basic, at great personal risk of uncovering ‘a spoiler’, I skipped on to the course summary in the last lecture to see if it was worth sticking around. I found it inspirational. Prof. John Guttag explains computation in the context of ‘The Scientific Method’. I’ve since realised that his explanation maps with great accurately onto Agile iterative methods. Agilists aren’t engineers, we’re scientists again. Engineering Project Management uses experience of similar previous projects. Why would you ever write similar software twice? Most of the work is already done. Every change to a computational system should be R&D.
Every Scrum Sprint is a suite of computational experiments. The Product Owner is our test subject. This feels right. I never felt like a computer scientist. In James Gleich’s ‘The Information’, he explains that Alan Turing introduced Babbage’s mechanical Difference Engine when talking to non-specialist, to emphasise that computing is an abstract concept, independent from computers and electricity. I’ve always had a computational scientist trying to get out <Woo asplodes>.
On The Outside 
Interesting ideas, but I don’t see how a sprint is a computational experiment. I would hope that a deliverable wouldn’t be considered an experiment. Either it satisfies the acceptance criteria of the user stories or it doesn’t.
Sometimes it doesn’t. One of the great advantages of agility is ‘failing fast’, as science sometimes fails to prove a hypothesis but the results may open a new line of enquiry. I plan to come back took this topic to demonstrate the similarities between agility and the scientific method.