As a third data point, I used to be very confused about your ideas about causality, but your recent writing has helped a lot. To make embarassingly clear how very wrong I've been able to be, some years ago when you'd told us about TDT but not given details, I thought you had a fully worked-out and justified theory about how a decision agent could use causal graphs to model its uncertainty about the output of platonic computations, and use do() on its own output to compute the utility of different courses of action, and I got very frustrated when I simply couldn't figure out how to fill in the details of that...
...hmm. (I should probably clarify: when I say "use causal graphs to reason about", I don't mean in the 'trivial' sense you are actually using where the platonic computations cause other things but are themselves uncaused in the model; I mean some sort of system where different computations and/or logical facts about computations form a non-degenerate graph, and where do() severs one node somewhere in the middle of that graph from its parents.) "And", I was going to say, "when you finally did tell us more, I had a strong oh moment when you said that you still weren't able to give a completely satisfying theory/justification, but were reasonably satisfied with the version you had. But I still continued to think that my picture of what you had been trying to do had been correct, only you didn't have a fully worked-out theory of it, either." The actual quote that turned into this memory of things seems to be,
Note that this does not solve the remaining open problems in TDT (though Nesov and Dai may have solved one such problem with their updateless decision theory). Also, although this theory goes into much more detail about how to compute its counterfactuals than classical CDT, there are still some visible incompletenesses when it comes to generating causal graphs that include the uncertain results of computations, computations dependent on other computations, computations uncertainly correlated to other computations, computations that reason abstractly about other computations without simulating them exactly, and so on.
But there's also this:
The three-sentence version is: Factor your uncertainty over (impossible) possible worlds into a causal graph that includes nodes corresponding to the unknown outputs of known computations; condition on the known initial conditions of your decision computation to screen off factors influencing the decision-setup; compute the counterfactuals in your expected utility formula by surgery on the node representing the logical output of that computation.
And later:
Those of you who've read the quantum mechanics sequence can extrapolate from past experience that I'm not bluffing.
Huh. In retrospect I can see how this matches my current understanding of what you're doing, but comparing this to what I wrote in the first paragraph above (before searching for that post), it's actually surprisingly nonobvious where the difference is between what you wrote back then and what I wrote just now to explain the way in which I had horribly misunderstood you...
Anyway. As for what you wrote in the great-grandparent, I had to read it slowly, but most of it makes perfect sense to me; the last paragraph I'm not quite as sure about, but there too I think I understand what you mean.
There is, however, one major point on which I currently feel confused. You seem to be saying that causal reasoning should be seen as a very fundamental principle of epistemology, and on your list of open problems, you have "Better formalize hybrid of causal and mathematical inference." But it seems to me that if you just do inference about logical uncertainty, and the mathematical object you happen to be interested in is a cellular automaton or the PDE giving the time evolution of some field theory, then your probability distribution over the state at different times will necessarily happen to factor in such a way that it can be represented as a causal model. So why treat causality as something fundamental in your epistemology, and then require deep thinking about how to integrate it with the rest of your reasoning system, rather than treating it as an efficient way to compress some probability distributions, which then just automatically happens to apply to the mathematical objects representing our actual physics? (At this point, I ask this question not as a criticism, but simply to illustrate my current confusion.)
So why treat causality as something fundamental in your epistemology, and then require deep thinking about how to integrate it with the rest of your reasoning system, rather than treating it as an efficient way to compress some probability distributions, which then just automatically happens to apply to the mathematical objects representing our actual physics?
Because causality is not about efficiently encoding anything. A causal process a -> b -> c is equally efficiently encoded via c -> b -> a.
...But it seems to me that if you just do in
Part of the sequence: Rationality and Philosophy
Thomas Kelly
Jason Brennan
After millennia of debate, philosophers remain heavily divided on many core issues. According to the largest-ever survey of philosophers, they're split 25-24-18 on deontology / consequentialism / virtue ethics, 35-27 on empiricism vs. rationalism, and 57-27 on physicalism vs. non-physicalism.
Sometimes, they are even divided on psychological questions that psychologists have already answered: Philosophers are split evenly on the question of whether it's possible to make a moral judgment without being motivated to abide by that judgment, even though we already know that this is possible for some people with damage to their brain's reward system, for example many Parkinson's patients, and patients with damage to the ventromedial frontal cortex (Schroeder et al. 2012).1
Why are physicists, biologists, and psychologists more prone to reach consensus than philosophers?2 One standard story is that "the method of science is to amass such an enormous mountain of evidence that... scientists cannot ignore it." Hence, religionists might still argue that Earth is flat or that evolutionary theory and the Big Bang theory are "lies from the pit of hell," and philosophers might still be divided about whether somebody can make a moral judgment they aren't themselves motivated by, but scientists have reached consensus about such things.
In its dependence on masses of evidence and definitive experiments, science doesn't trust your rationality:
Sometimes, you can answer philosophical questions with mountains of evidence, as with the example of moral motivation given above. But or many philosophical problems, overwhelming evidence simply isn't available. Or maybe you can't afford to wait a decade for definitive experiments to be done. Thus, "if you would rather not waste ten years trying to prove the wrong theory," or if you'd like to get the right answer without overwhelming evidence, "you'll need to [tackle] the vastly more difficult problem: listening to evidence that doesn't shout in your ear."
This is why philosophers need rationality training even more desperately than scientists do. Philosophy asks you to get the right answer without evidence that shouts in your ear. The less evidence you have, or the harder it is to interpret, the more rationality you need to get the right answer. (As likelihood ratios get smaller, your priors need to be better and your updates more accurate.)
Because it tackles so many questions that can't be answered by masses of evidence or definitive experiments, philosophy needs to trust your rationality even though it shouldn't: we generally are as "stupid and self-deceiving" as science assumes we are. We're "predictably irrational" and all that.
But hey! Maybe philosophers are prepared for this. Since philosophy is so much more demanding of one's rationality, perhaps the field has built top-notch rationality training into the standard philosophy curriculum?
Alas, it doesn't seem so. I don't see much Kahneman & Tversky in philosophy syllabi — just light-weight "critical thinking" classes and lists of informal fallacies. But even classes in human bias might not improve things much due to the sophistication effect: someone with a sophisticated knowledge of fallacies and biases might just have more ammunition with which to attack views they don't like. So what's really needed is regular habits training for genuine curiosity, motivated cognition mitigation, and so on.
(Imagine a world in which Frank Jackson's famous reversal on the knowledge argument wasn't news — because established philosophers changed their minds all the time. Imagine a world in which philosophers were fine-tuned enough to reach consensus on 10 bits of evidence rather than 1,000.)
We might also ask: How well do philosophers perform on standard tests of rationality, for example Frederick (2005)'s CRT? Livengood et al. (2010) found, via an internet survey, that subjects with graduate-level philosophy training had a mean CRT score of 1.32. (The best possible score is 3.)
A score of 1.32 isn't radically different from the mean CRT scores found for psychology undergraduates (1.5), financial planners (1.76), Florida Circuit Court judges (1.23), Princeton Undergraduates (1.63), and people who happened to be sitting along the Charles River during a July 4th fireworks display (1.53). It is also noticeably lower than the mean CRT scores found for MIT students (2.18) and for attendees to a LessWrong.com meetup group (2.69).
Moreover, several studies show that philosophers are just as prone to particular biases as laypeople (Schulz et al. 2011; Tobia et al. 2012), for example order effects in moral judgment (Schwitzgebel & Cushman 2012).
People are typically excited about the Center for Applied Rationality because it teaches thinking skills that can improve one's happiness and effectiveness. That excites me, too. But I hope that in the long run CFAR will also help produce better philosophers, because it looks to me like we need top-notch philosophical work to secure a desirable future for humanity.3
Next post: Train Philosophers with Pearl and Kahneman, not Plato and Kant
Previous post: Intuitions Aren't Shared That Way
Notes
1 Clearly, many philosophers have advanced versions of motivational internalism that are directly contradicted by these results from psychology. However, we don't know exactly which version of motivational internalism is defended by each survey participant who said they "accept" or "lean toward" motivational internalism. Perhaps many of them defend weakened versions of motivational internalism, such as those discussed in section 3.1 of May (forthcoming).
2 Mathematicians reach even stronger consensus than physicists, but they don't appeal to what is usually thought of as "mountains of evidence." What's going on, there? Mathematicians and philosophers almost always agree about whether a proof or an argument is valid, given a particular formal system. The difference is that a mathematician's premises consist in axioms and in theorems already strongly proven, whereas a philosopher's premises consist in substantive claims about the world for which the evidence given is often very weak (e.g. that philosopher's intuitions).
3 Bostrom (2000); Yudkowsky (2008); Muehlhauser (2011).