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I'm getting incredibly sick of the "rationalist" affectation/verbal tic of "statistically" "quantifying" your predictions in contexts where this is completely meaningless.
"But I think it would still have over a 40% chance of irreparably harming your relationship with Drew"
"Nonlinear's threatening to sue Lightcone for Ben's post is completely unacceptable, decreases my sympathy for them by about 98%"
What it does mean to have a 40% chance of irreparably harming her relationship with Drew? Does that mean that there's a 60%, 70% etc. chance of it harming her relationship with Drew, but in a way that could be fixed, given enough time and effort? What information could she be presented with that would cause her to update her 40% prediction up or down?
The numbers are made up and the expressions of confidence don't matter. It's just cargo cult bullshit, applying a thin veneer of "logic" and "precision" to a completely intuitive gut feeling of the kind everyone has all the time.
Here's the rationalist theory:
Let's say you do this ten thousand times over the course of a few years. Make a list of every prediction, and count how many predictions in the '40%' category were true. If it ends up as '40%', you're making good predictions. Or, take all your predictions and score them according to a brier score or another scoring rule, and if you have a low score you're making good predictions.
The theory is that you can take statements and make predictions, and often the best you can do is '60%' or '20%' while maintaining a good score, and that this says something about the structure of decisionmaking.
I don't like it personally. I think the complexities you need to explore are mostly unrelated to the exact numbers. But you probably can, after the fact, in most scenarios say 'yeah, her relationship was harmed' or 'no, it wasn't', and then score your prediction, and if your calibration is reasonable and you're not manipulating them then it might mean something!
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I disagree. Even if the numbers are somewhat made up, having a ballpark figure that tells you the relative probability of certain events that would result from a decision you’re planning to make.
Going to the Drew example, if I think that doing something (say going to school in another city and trying to have a LDR is going to result in a 40% chance that I’ll lose the relationship entirely, and a 60% chance that I’ll damage it in away that would be difficult but not impossible to fix, then I can use that to decide if that would be more important to me than the job opportunities, the scholarships, or whatever else I gain from going to school away from him. Might doesn’t give you enough information for a true reality check imo, because it treats low probability events equally to large probability events. Even using verbal categories like low, medium and high probability, especially when making a group decision aren’t precise enough to communicate what I’m actually thinking. Low is how low? For you it might be 5%, for me it’s 20%. We can’t communicate that well if we don’t know what the terms are.
I see an opening parenthesis without a closing one. Is your comment unfinished?
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I think part of the point is the numerical values convey an unwarranted degree of precision based on the process that generated them. Say your estimate is 20% probability for X. Why not 21%? 19%? 25%? 15%? What's the size of the error term on your estimate? Is your forecasting of this outcome so good as to warrant a <1% margin? Of course, estimation of that error term itself has the same problems as generating the initial estimate.
I don't think this is a good objection. Numbers are often approximate. 20% means 'somewhere between 10% and 30%' as much as 'around a hundred pounds' might mean '75-125 pounds'. On the other hand, I usually think it's better to actually say what ideas and conditionals inform your judgement rather than just saying a number, and I'm not sure what the number adds to the former.
The numbers at least for me give me a ballpark estimate of what I think will actually happen given a certain set of conditions. If I say 25% (which in my mind is generally within 10% of the number I give) that communicates in a way that “low probability” doesn’t because “low” doesn’t mean anything. My low might be 25%, your low might be 5%. And making decisions, in a group setting especially, requires precision so that when weighing options you can know with some degree of certainty what people think are likely and unlikely and to what degree. This allows you to discuss whether an X% (+/-10%) risk of something happening being serious enough to make that decision a bad idea. If low can mean anything between 5% and 35%, it’s going to cause people to either overestimate the risk and be too cautious, or underestimate it and take risks that they might not take otherwise.
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My point is that it's important to make that uncertainty explicit because not everyone talking to you is going to understand that. Maybe you think 20% is shorthand for 10-30% but someone else thinks it's precisely 20% or is actually 15-25% or some other range. I think the "around a hundred pounds" is a good example because "around" conveys a degree of uncertainty on the "hundred pounds." If I was quoted a price of some good at "a hundred pounds" (no "around") and later found out it was actually 125 I would feel like I was deceived.
Probability already inherently indicates uncertainty though! You can just say you're combining the different 'levels' of uncertainty (what that means is debatable), and the average of [10..30] is 20.
But the average of [5..35] and [0..40] are also 20. Do you think all three of these ranges are conveying the same information because their average is 20? I don't.
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