Science and Magick

Wednesday, June 5th, 2013

You found my old blog. Thanks for visiting! For my new writing, visit

This series is about science.

A lot of people, when they read that, expect to hear about magick and quantum physics. But that’s a particular field of contemporary science, not science itself.

Science isn’t any one field of knowledge or set of terms. Science is a way of understanding the world. It’s about thinking through an explanation, then testing to see if it’s right. Then using that explanation to build a useful solution to a problem — a solution you wouldn’t have seen without the explanation. And, eventually, finding some place where the explanation doesn’t work anymore, and repeating the process to understand a bit more of the world.

If we’re going to use a scientific mindset to explore magick, then it would help to understand science first. That’s what this series is about.

My Science Credentials

It’s only natural to wonder if I’m really a scientist, or if I just play one on the internet.

I have a MS in computer science, and published my thesis in a peer-reviewed conference. (In computer science, we do conferences rather than journals.) Then I worked for 5 years at the national labs in New Mexico, researching genetic programming and a bit of quantum computing, and publishing several papers. So, yes, I’ve both trained and worked as a scientist.

(After that, I got into computer consulting. The money’s better. Mea culpa. And I like being able to take time off from consulting to write about magick, which you can’t do as a staff researcher.)

Science Topics

Here are some of the topics we’ll cover in this series:

  • Modeling vs Trial and Error: The difference between science vs testing options until something works.
  • Isolating the Hypothesis: Most of the effort goes into finding a hypothesis worth testing. Like 90-99% of it. Once you’ve done that, testing it is relatively easy.
  • Why Prediction Matters: Why science uses prediction as the gold standard for actually understanding a problem.
  • Using Negative Predictions: It’s easy to see when a model predicts something that happens. But what about things a model says ought to happen, but it doesn’t? That’s where the real science lives.
  • Complexity Isn’t Free: Simple objects do simple actions. If you want a complex action, you need a complex object. Why proposing an object that “just does” this complex thing isn’t allowed.
If you liked this post, consider visiting my current blog at