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This is: 16 types of useful predictions, published by Julia_Galef on the LessWrong.
How often do you make predictions (either about future events, or about information that you don't yet have)? If you're a regular Less Wrong reader you're probably familiar with the idea that you should make your beliefs pay rent by saying, "Here's what I expect to see if my belief is correct, and here's how confident I am," and that you should then update your beliefs accordingly, depending on how your predictions turn out.
And yet. my impression is that few of us actually make predictions on a regular basis. Certainly, for me, there has always been a gap between how useful I think predictions are, in theory, and how often I make them.
I don't think this is just laziness. I think it's simply not a trivial task to find predictions to make that will help you improve your models of a domain you care about.
At this point I should clarify that there are two main goals predictions can help with:
Improved Calibration (e.g., realizing that I'm only correct about Domain X 70% of the time, not 90% of the time as I had mistakenly thought).
Improved Accuracy (e.g., going from being correct in Domain X 70% of the time to being correct 90% of the time)
If your goal is just to become better calibrated in general, it doesn't much matter what kinds of predictions you make. So calibration exercises typically grab questions with easily obtainable answers, like "How tall is Mount Everest?" or "Will Don Draper die before the end of Mad Men?" See, for example, the Credence Game, Prediction Book, and this recent post. And calibration training really does work.
But even though making predictions about trivia will improve my general calibration skill, it won't help me improve my models of the world. That is, it won't help me become more accurate, at least not in any domains I care about. If I answer a lot of questions about the heights of mountains, I might become more accurate about that topic, but that's not very helpful to me.
So I think the difficulty in prediction-making is this: The set {questions whose answers you can easily look up, or otherwise obtain} is a small subset of all possible questions. And the set {questions whose answers I care about} is also a small subset of all possible questions. And the intersection between those two subsets is much smaller still, and not easily identifiable. As a result, prediction-making tends to seem too effortful, or not fruitful enough to justify the effort it requires.
But the intersection's not empty. It just requires some strategic thought to determine which answerable questions have some bearing on issues you care about, or -- approaching the problem from the opposite direction -- how to take issues you care about and turn them into answerable questions.
I've been making a concerted effort to hunt for members of that intersection. Here are 16 types of predictions that I personally use to improve my judgment on issues I care about. (I'm sure there are plenty more, though, and hope you'll share your own as well.)
Predict how long a task will take you. This one's a given, considering how common and impactful the planning fallacy is.
Examples: "How long will it take to write this blog post?" "How long until our company's profitable?"
Predict how you'll feel in an upcoming situation. Affective forecasting – our ability to predict how we'll feel – has some well known flaws.
Examples: "How much will I enjoy this party?" "Will I feel better if I leave the house?" "If I don't get this job, will I still feel bad about it two weeks later?"
Predict your performance on a task or goal.
One thing this helps me notice is when I've been trying the same kind of approach repeatedly without success. Even just the act of making the prediction can spark the realization that I need a bett...
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