[Epistemic status: Not original to me. Also, I might be getting it wrong.]
A lot of responses to my Friday post on overconfidence centered around this idea that we shouldn’t, we can’t, use probability at all in the absence of a well-defined model. The best we can do is say that we don’t know and have no way to find out. I don’t buy this:
“Mr. President, NASA has sent me to warn you that a saucer-shaped craft about twenty meters in diameter has just crossed the orbit of the moon. It’s expected to touch down in the western United States within twenty-four hours. What should we do?”
“How should I know? I have no model of possible outcomes.”
“Should we put the military on alert?”
“Maybe. Maybe not. Putting the military on alert might help. Or it might hurt. We have literally no way of knowing.”
“Maybe we should send a team of linguists and scientists to the presumptive landing site?”
“What part of ‘no model’ do you not understand? Alien first contact is such an inherently unpredictable enterprise that even speculating about whether linguists should be present is pretending to a certainty which we do not and cannot possess.”
“Mr. President, I’ve got our Israeli allies on the phone. They say they’re going to shoot a missile at the craft because ‘it freaks them out’. Should I tell them to hold off?”
“No. We have no way of predicting whether firing a missile is a good or bad idea. We just don’t know.”
In real life, the President would, despite the situation being totally novel and without any plausible statistical model, probably make some decision or another, like “yes, put the military on alert”. And this implies a probability judgment. The reason the President will put the military on alert, but not, say, put banana plantations on alert, is that in his opinion the aliens are more likely to attack than to ask for bananas.
Fine, say the doubters, but surely the sorts of probability judgments we make without models are only the most coarse-grained ones, along the lines of “some reasonable chance aliens will attack, no reasonable chance they will want bananas.” Where “reasonable chance” can mean anything from 1% to 99%, and “no reasonable chance” means something less than that.
But consider another situation: imagine you are a director of the National Science Foundation (or a venture capitalist, or an effective altruist) evaluating two proposals that both want the same grant. Proposal A is by a group with a long history of moderate competence who think they can improve the efficiency of solar panels by a few percent; their plan is a straightforward application of existing technology and almost guaranteed to work and create a billion dollars in value. Proposal B is by a group of starry-eyed idealists who seem very smart but have no proven track record; they say they have an idea for a revolutionary new kind of super-efficient desalinization technology; if it works it will completely solve the world’s water crisis and produce a trillion dollars in value. Your organization is risk-neutral to a totally implausible degree. What do you do?
Well, it seems to me that you choose Proposal B if you think it has at least a 1/1000 chance of working out; otherwise, you choose Proposal A. But this requires at least attempting to estimate probabilities in the neighborhood of 1/1000 without a model. Crucially, there’s no way to avoid this. If you shrug and take Proposal A because you don’t feel like you can assess proposal B adequately, that’s making a choice. If you shrug and take Proposal B because what the hell, that’s also making a choice. If you are so angry at being placed in this situation that you refuse to choose either A or B and so pass up both a billion and a trillion dollars, that’s a choice too. Just a stupid one.
Nor can you cry “Pascal’s Mugging!” in order to escape the situation. I think this defense is overused and underspecified, but at the very least, it doesn’t seem like it can apply in places where the improbable option is likely to come up over your own lifespan. So: imagine that your organization actually reviews about a hundred of these proposals a year. In fact, it’s competing with a bunch of other organizations that also review a hundred or so such proposals a year, and whoever’s projects make the most money gains lots of status and new funding. Now it’s totally plausible that, over the course of ten years, it might be a better strategy to invest in things that have a one in a thousand chance of working out. Indeed, maybe you can see the organizations that do this outperforming the organizations that don’t. The question really does come down to your judgment: are Project B’s odds of success greater or less than 1/1000?
Nor is this a crazy hypothetical situation. A bunch of the questions we have to deal with come down to these kinds of decisions made without models. Like – should I invest for retirement, even though the world might be destroyed by the time I retire? Should I support the Libertarian candidate for president, even though there’s never been a libertarian-run society before and I can’t know how it will turn out? Should I start learning Chinese because China will rule the world over the next century? These questions are no easier to model than ones about cryonics or AI, but they’re questions we all face.
The last thing the doubters might say is “Fine, we have to face questions that can be treated as questions of probability. But we should avoid treating them as questions of probability anyway. Instead of asking ourselves ‘is the probability that the desalinization project will work greater or less than 1/1000’, we should ask ‘do I feel good about investing this money in the desalinization plant?’ and trust our gut feelings.”
There is some truth to this. My medical school thesis was on the probabilistic judgments of doctors, and they’re pretty bad. Doctors are just extraordinarily overconfident in their own diagnoses; a study by Bushyhead, who despite his name is not a squirrel, found that when doctors were 80% certain that patients had pneumonia, only 20% would turn out to have the disease. On the other hand, the doctors still did the right thing in most every case, operating off of algorithms and heuristics that never mentioned probability. The conclusion was that as long as you don’t force doctors to think about about what they’re doing in mathematical terms, everything goes fine – something I’ve brought up before in the context of the Bayes mammogram problem. Maybe this generalizes. Maybe people are terrible at coming up with probabilities for things like investing in desalinization plants, but will generally make the right choice.
But refusing to frame choices in terms of probabilities also takes away a lot of your options. If you use probabilities, you can check your accuracy – the foundation director might notice that of a thousand projects she had estimated as having 1/1000 probabilities, actually about 20 succeeded, meaning that she’s overconfident. You can do other things. You can compare people’s success rates. You can do arithmetic on them (“if both these projects have 1/1000 probability, what is the chance they both succeed simultaneously?”), you can open prediction markets about them.
Most important, you can notice and challenge overconfidence when it happens. I said last post that when people say there’s only a one in a million chance of something like AI risk, they are being stupendously overconfident. If people just very quietly act as if there’s a one in a million chance of such risk, without ever saying it, then no one will ever be able to call them on it.
I don’t want to say I’m completely attached to using probability here in exatly the normal way. But all of the alternatives I’ve heard fall apart when you’ve got to make an actual real-world choice, like sending the military out to deal with the aliens or not.
[EDIT: Why regressing to meta-probabilities just gives you more reasons to worry about overconfidence]
[EDIT-2: “I don’t know”]
[EDIT-3: A lot of debate over what does or doesn’t count as a “model” in this case. Some people seem to be using a weak definition like “any knowledge whatsoever about the process involved”. Others seem to want a strong definition like “enough understanding to place this event within a context of similar past events such that a numerical probability can be easily extracted by math alone, like the model where each flip of a two-sided coin has a 50% chance of landing heads”. Without wanting to get into this, suffice it to say that any definition in which the questions above have “models” is one where AI risk also has a model.]