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In a car, the pedals are the interface, and engine is the implementation. Here’s how to design tests to explore magick’s engine.
This series is about two things:
- Why my model of magick is useful.
- How I know it’s accurate.
To answer those, I’m going to show you each part, what it does, what it predicts, and non-obvious techniques I’ve developed based on those predictions.
But first, we need a few concepts. My model focuses on magick’s implementation, rather than its interface. And my testing focuses on predicting new techniques, rather than A / B testing. Those aren’t standard terms from other forms of magick, let’s cover them before diving into the details of my model.
If you’re new here, read this quick intro before reading this series.
Interfaces and Implementations
Interfaces are about information and instructions. A computer’s interface — the mouse and keyboard — is about telling the computer what to do. Same with a car’s interface: The pedals and steering wheel. Interfaces are where users normally focus.
Implementations are what’s going on under the hood. A computer’s implementation is transistors, drivers and code. A car’s implementation is the engine, rack-and-pinion steering, and disk brakes. Implementations are normally hidden from users. It’s where engineers focus.
In magick, the interface is how you signal your intent: Rituals, visualizations, and other uses of symbols. The implementation is, well, whatever happens externally to produce the results. That’s the focus of this series.
What about testing rituals to uncover “natural laws,” like the effects of certain symbols? Aren’t natural laws fundamental, irreducible truths?
Usually, natural laws describe a particular interface. But if you work exclusively with one interface, it’s easy to forget there’s an engine behind it, doing the actual work.
Think about it this way: If you’d never heard of a car engine, but someone told you that the pedals operate by natural laws, you’d test the relationship between foot pressure and speed, accelerating in different road conditions, and if it’s better to press slowly or quickly. Then you might try those tests on different cars, and trains and jet skis. But it would take a real leap to step outside the car, pop the hood and examine something that’s intentionally hidden.
I don’t want to put down anyone who experiments with rituals. Mastering an interface is important. I use three systems (the forces mages channel) almost every day, and I’m glad to know which commands they respond to. But I’m hoping you also want to understand how those instructions become changes in the world, both out of human curiosity, and so you can build better techniques. Because that’s my passion, and that’s why I focus on magick’s implementation.
You don’t find magick’s implementation by experimenting on rituals any more than you figure out engines by testing pedals, or learn to build a motherboard by using software. No, you need to pop the hood. That’s what we’ll do in this series.
Here’s more on interfaces and implementations. For more on “natural laws,” see my favorite philosophy blog, Less Wrong. “Natural law” is a semantic stopsign similar to “elan vital.”
Scientific Models
A good scientific model has two properties:
- It predicts a specific, testable outcome.
- That outcome is non-obvious, and not predicted by other models.
A model is like a map, and the only way to see if the map matches the territory is to ask it what will happen in a bunch of specific situations. Because in order to be correct in all those situations, a model needs to closely align to how the world actually works.
Imagine a weather model that reliably predicts the exact minute the rain will start. The only way it could do that is by matching the way rain actually happens.
But imagine a model that says “It will rain sometime in April.” Sure, the prediction was correct, but it was also obvious. Any model can make correct, vague predictions, even if it’s totally out of sync with reality. That’s why we don’t give any points for obvious answers.
And a model that makes every outcome equally likely, like “Whatever you believe will happen, happens”? Well, that’s not really a model at all.
Testing Magick Models
I realized I need to discuss different testing methods after reading Ananael’s comment:
I can say “I hypothesize that set A of ritual forms should produce a higher probability shift than set B” without too much difficulty, but […] I need [to do] several hundred of those [rituals] for my sample.
I’ll call this “A/B testing” (a marketing term), because you’re testing between two specific options (A and B). It’s a form of “black box testing,” which means you don’t need to understand the inner-workings of the thing you’re testing, you just try it and see what happens.
To do A/B testing, you need to be able to enumerate the options. This is fine for rituals, where you’re deciding whether to do a pentagon or hexagon, and in which direction. It’s a problem for exploring magick’s implementation, where you can’t even list all the moving parts at first.
The real problem is that black box testing doesn’t include an actual model. There are no moving parts, so you can’t think through what would happen in different situations. All it can tell you is whether A works better than B, not why, and not what would happen in situations C or D.
I don’t want to single Ananael out. I’m sure he does a variety of tests. I just used his comment because it got me thinking about this.
In other words, A/B testing is a great tool if you want to refine a ritual, but it’s the wrong tool for understanding the inner-workings of magick.
Instead, I mostly rely on direct observation and technique prediction:
Direct observation means using sensory connections to watch mental muscles and systems as they work. Like how dissection was the first step to modern biology and observing planetary motion was the first step to modern physics, direct observation is the start of understanding magick’s implementation.
Just like observing planets accurately requires a telescope and observing cells requires staining, directly observing magick requires its own technique: sensory connections. They let you see energy signatures and the structures that energy flows through, including mental muscles, energy paths through the body, and everything else that systems do to implement magick. We’ll discuss them later in this series. For now, just know that there is a (moderately advanced) technique to observing magick accurately.
One thing I want to make clear up front: Direct observation is not closing your eyes and visualizing how magick works. Those visualizations come from your already-existing expectations, not the external world. They’re guesses, not observations.
How do I know my observations are accurate? Because they (and the models they suggest) predict techniques. This is similar to A/B testing, in that you try a technique you haven’t done before and see how it works. But instead of black-box testing, where you guess at what changes to make, this is white-box: You have a model of the world, simulate it in your thoughts, and see what you should do to accomplish some task. If your technique produces the expected outcome, that suggests your model is on the right track*, especially if no other model would have expected that technique to be effective.
*A model can never be correct. “The right track” is all we can aspire to. Like Newtonian physics, to be replaced by Einstein 300 years later.
But wait. If I expect X to work, and then I see X work, how do you know my results aren’t just “Whatever I expect to happen, happens”? Simple: Because X doesn’t always work. I have a pretty good success rate, because my predictions are based on direct observations. But it’s far from perfect. And my confidence has little to do with the results: Sometimes I’ll be pretty sure of a technique, and it fails. And sometimes I’ll just try something to see what happens, and it works. The result is in the actual technique, not in my expectations.
More on how I avoid coincidence, placebo and more coincidence in my testing.
Comments
See a flaw in any of that? Leave a comment. I’m always looking to improve my methods.
Next
With those terms taken care of, we’re ready for details and experiments with my model.
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