I found myself becoming more fascinated with this book as I read it, thinking “I don’t quite agree with this book, but the subject matter is interesting, the author starts off with axioms like my own, and I can’t put my finger precisely on why I don’t agree, so I am compelled to keep thinking about it.”
Since the first part of my book review I have changed my mind on whether this book overrates rationality. As long as you define rationality as “making the correct decisions in every circumstance” you can’t really overrate rationality. The real question is whether the Bayesian method described in this book is actually rational. That I think the author overrates.
This book goes into three areas and tries to take a hyper-Bayesian methodology to get a rational approach for each of them. Quantum mechanics, evolutionary psychology, and the author’s personal life.
The discussion of quantum mechanics is focused on whether the “many worlds” interpretation of quantum mechanics is correct, as opposed to the “waveform collapse” theory. The author’s stance is that not only is the “many worlds” theory correct, but it is so clearly correct that the fact that many people don’t agree with “many worlds” shows that they are insufficiently rational.
The evolutionary psychology discussion is similar. Yudkowsky claims that scientists are constantly hoping for evolution to favor morality, which leads to a bias in favor of more pleasant interpretations. The underlying claim is that for unsettled areas of science, there is still one rational interpretation that is superior, and if scientists disagree on how to interpret findings on the frontier of science, it is because they are not rational enough.
The author frequently cites Aumann’s agreement theorem - that two perfectly rational people with the same knowledge cannot disagree. Therefore in practice two rationalists should not disagree. It seems like the thing for them to do is to argue incessantly, and if they cannot come to an agreement then each should conclude that the other is not rational enough. That feels a bit wrong.
The last section of the book discusses more personal issues and the trouble with growing this rationalist movement. Yudkowsky mentions he has trouble “getting things done” and he also has trouble getting groups of people to work together. To me, both of these seem like problems with what I would colloquially describe as “overthinking it”.
To the Yudkowsky-rationalist mind, there is no such thing as “overthinking a problem”. You keep thinking, you get to a more intelligent solution. The problem is, mental energy is a limited resource. If every statement and action, you feel obliged to analyze it to perfection, then you’re going to end up more exhausted than if you let yourself make quick decisions and prefer to be 80% correct immediately than 90% correct slowly. No wonder the author laments that he has trouble working as much as he intends to.
Sometimes your goal really does take precedence over rationally rethinking all of your premises. If you really rethink everything you do until it’s 100% correct, you will constantly be stalled and frustrated in making progress. Eliezer writes, for example, that he was quite discouraged in trying to raise donations from a group of rationalists that the general consensus was that it was irrational to donate money. Is it actually rational for rationalists to donate money? Yudkowsky hesitates to attack that question, probably out of fear of concluding that it is indeed not rational and thus rationalizing himself out of a job.
I’m not saying that no nonprofit should collect donations. I just think the money-raisers should not constantly angst about whether it is really the most rational thing to collect money. They should not expect 100% of the followers to think alike and be willing to donate. They should just practically see what methods work for raising money, and which don’t, and use the methods that work rather than assuming that arguing about rationalism is the way to solve every problem.
The core paradox at the heart of Eliezer-style rationalism is that, when you define “rational” as using the best strategy available, once you add any additional principles to your philosophy of rationality, it is inevitable that in some situations, disregarding that principle is the most effective. Yudkowsky loves Bayesianism because in a limited number of situations it does provide a perfect analysis of what to do. But beyond that limited number of simple situations, it does not seem that a Bayesian approach to a problem is actually the most effective way of solving it. So why try to be more Bayesian in your life?
I have a more technical criticism here too. Even in a situation where you are just focused on decisions and you have a clear set of input variables, a Bayesian model may very well not be the most effective. For example, let’s say you have a large enough number of inputs n that you can process all of them which is O(n), but you can’t process all the pairs of them which is O(n2). You have some boolean output you are trying to decide on. And many of the variables may be correlated. Logistic regression is probably a better fit here than naive Bayes, because you’ll end up capturing much of the input correlation implicitly if not explicitly.
When you apply this to a practical situation, you end up with a system that locally appears to violate Bayesian statistics. You will have an input that, statistically when X happens, Y happens 60% of the time. But, your gut tells you that when X happens, it’s actually an indicator of not-Y. Maybe your gut is doing logistic regression on a large number of hidden variables and coming to a more successful strategy than your local Bayesian analysis is. Should you really cheat on a test when your probability estimate tells you it’s worth it? Or should you listen to your gut telling you to be ethical? Even if you can’t verbalize all the reasons encapsulated in this gut instinct, it doesn’t mean that rejecting it will lead to better outcomes. Even if you mathematically analyze every visible variable, it still doesn’t mean that rejecting your gut instinct will lead to better outcomes.
I do want my criticism to be falsifiable. So, what would convince me would be seeing that adopting this rationalist philosophy actually does lead to better outcomes at some practical endeavor. This does not yet appear to be happening.
All of that said, the book is quite compelling and contains many arguments that make me rethink some of my own basic principles. It is worth reflecting on your own decisionmaking processes, even if you don’t agree with the hyper-Bayesian methodology advocated here.
If you want a quick hit of this book without reading 1800 pages of it, try this essay on the twelve virtues of rationality.
This book also left me curious about the author’s theories on artificial intelligence. How would one build a “friendly AI”? What would it look like to get 10% of the way there? My suspicion is that working on “how to make AI friendly” will indeed be a very valuable thing to do, but you can’t really make much progress unless you have some basic architecture of how any AI would be built, and it doesn’t really seem like humanity is there yet. We need the equivalent of the Von Neumann architecture - what parts will lead to a whole that can do humanlike things. Learning functions from vector spaces to booleans is neat but it’s like we’ve only built a CPU and we haven’t figured out that we’ll also need some permanent storage, some I/O devices, and a way to enter programs.
This book also left me thinking about cryonics. In passing the author claims that signing up for cryonics is such a good decision that everyone should do it. I do not have a good counterargument, yet I have not signed up for cryonics. The pro-cryonics argument might be the most compelling practical part of this book; I wish Eliezer had spent as much time on that as he did on quantum mechanics and evolution.
One last note - this book reminded me a lot of A New Kind Of Science. Both have quite complex and deep thoughts which diverge a lot from the mainstream. Both discuss how a hypermathematical approach could cause a paradigm shift in a different field. Both are convinced their work is revolutionary but the concrete evidence is not enough to convince the world of it. Both are insanely long in a way that discourages normal people from reading them.
I would like to see more books like this.