This is the first in what will hopefully be a series of posts about why you should try things, and with strategies against common reasons for not doing so.
You’ve probably read about how to properly turn information into beliefs, and how to squeeze every last bit from your data. There's been less attention on the importance of going and getting data.
This article is about how personal experience is an incredibly useful form of data, and in particular how in many activities going out and trying something is more marginally useful than doing more exploratory research into it. In particular, I'll examine how personal experience is useful because it makes information more tangible and easier to learn, is good practice, and exposes common circumstances that you didn't build your models to handle.
For precise and well-defined fields and problems, clear thinking and reasoning will get you really far. Mathematics departments don’t use that much equipment, and they’ve been going on pretty well for hundreds of years.
Rationality is about how to turn data into maps. But this still requires data. I think that in a wide variety of not particularly theoretical subjects (like sports, social interactions, negotiation, cooking, etc.) rationality needs to be augmented by personal experience. Instrumental Rationality turns models into high-utility actions, but before you can do that you need a model.
Personal experience is useful in fields where there are certain building blocks which are treated as conceptual atoms when thinking consciously about it, but where said building blocks are hard to acquire without experience. In these fields, experience is needed before advice can be particularly useful.
One thing about these kinds of fields is that your brain has a lot of circuitry built in for dealing with things related to them. Sports involve muscle memory and your kinesthetic sense. Social interactions involve a lot of non-verbal cues that your brain is optimized to handle. If you’re unaware of these basic concerns, then further progression is difficult. You can give the best explanation in the world, but if you missed that the other person didn’t understand your first assertion then it doesn’t matter.
The best way that I’ve found for me to be able to recognize and learn things like that is for me to go out and experience them. Consider the difference between a smile and a smirk. Language is terrible at communicating the difference, but your brain has a high bandwidth way of gathering data about them by experiencing it.
People can definitely communicate about these topics, but they often assume a comprehension of the basic words used to describe them, and gloss over the subtleties. Was that an “I feel uncomfortable” laugh, or an “I’m becoming comfortable” one? If you can’t tell the difference between these things, then you’re in for some trouble. When someone in soccer tells you to go down the side and then pass the ball to the center, you’re going to fail if you don’t know how to dribble, pass, or deal with defenders.
Practice is important. As any akrasiatic or novice would know, knowledge in a field or domain doesn’t translate directly to success in it. In muscle memory, you need practice in order to get your brain to incorporate what you know to the point that you can use it automatically. Consciously thinking about what you’re doing while you’re doing it tends to cause lag and awkwardness, and in some fields (like conversation or physical activities) is a pretty large detriment.
This is even true in fields like math. When someone walks you through a math problem or proof and explains what’s going on and what tools are being used when and why, it’s a lot easier to understand. And you might totally understand it when the person is walking you through, but then flounder if given a similar problem without their supervision. Without practice, you don’t know what needs to be applied when, and actually acting on your knowledge is made much more difficult.
For me, the most obvious example of this comes from learning how to write. A teacher would recommend a particular technique or device, like appositive phrases, and then I would notice them everywhere. And then I’d overuse them. And then I’d sound silly, or just wind up with a bunch of really long sentences in a row. I needed practice before I could make better judgments about where they should and shouldn’t be used, rather than indiscriminately apply them everywhere.1
Pointing out Problems:
One of your strengths as a rationalist is to be able to learn from your data. Theorizing helps you turn data into strategies. Experience is very salient data. When you combine the two, you can become incredibly responsive.
When you try something, you can see how your map works in practice. When you encounter a setback, you can find an inaccuracy in your map, or just a region of the territory that you didn't cover. As a rationalist, you can better understand and use this information to improve.
Trying something out is vital to noticing that there are things for which your model does not account, but are important. One of the easiest ways to find out if you’re forgetting something important is to try and see what goes wrong. Anything that consistently causes problems is by definition important. This can settle a lot of arguments.
One of the times the importance of this rang most true for me was during a robotics competition in this game. We had built a vacuum for our robot to allow it to hold balls while aiming and maneuvering. When competition came, two major problems made themselves obvious. The first was that we couldn’t see the balls from where the drivers drove, and the second was that the vacuum head was incredibly delicate to small deviations in position (which could be caused by pushing a ball against a wall), and would be rendered inoperable almost every game. Both of these issues would have been obvious had we tried playing under game conditions beforehand, but our “tests” (holding the vacuum to a soccer ball and observing that we could then lift the ball with the vacuum, and trying to place a ball on the vacuum while it was mounted to a stationary robot) obscured several very important and relevant details.
It didn’t take very long to fix these problems for our second competition, and doing so made our robot dramatically more effective. Had we tested out for real beforehand, we could have been awesome from day one.
 A slight caveat was that I still needed to use them a lot before I was able to learn where I shouldn’t use them.
Thanks to everyone who gave me feedback on the previous incarnation of this article, it was helpful and very motivating.