I used to feel similarly, but then a few things changed for me and now I am pro-textbook. There are caveats—namely that I don’t work through them continuously.
Textbooks seem overly formal at points
This is a big one for me, and probably the biggest change I made is being much more discriminating in what I look for in a textbook. My concerns are invariably practical, so I only demand enough formality to be relevant; otherwise I am concerned with a good reputation for explaining intuitions, graphics, examples, ease of reading. I would go as far as to say that style is probably the most important feature of a textbook.
As I mentioned, I don’t work through them front to back, because that actually is homework. Instead I treat them more like a reference-with-a-hook; I look at them when I need to understand the particular thing in more depth, and then get out when I have what I need. But because it is contained in a textbook, this knowledge now has a natural link to steps before and after, so I have obvious places to go for regression and advancement.
I spend a lot of time thinking about what I need to learn, why I need to learn it, and how it relates to what I already know. This does an excellent job of helping things stick, and also of keeping me from getting too stuck because I have a battery of perspectives ready to deploy. This enables the reference approach.
I spend a lot of time what I have mentally termed triangulating, which is deliberately using different sources/currents of thought when I learn a subject. This winds up necessitating the reference approach, because I always wind up with questions that are neglected or unsatisfactorily addressed in a given source. Lately I really like founding papers and historical review papers right out of the gate, because these are prone to explaining motivations, subtle intuitions, and circumstances in a way instructional materials are not.
Rough draft for Scott’s Secular Cycles post:
This doesn’t qualify as criticism per se, but might offer some help for coloring in the edges. The only real suspicion I have about Turchin’s work is that it follows the traditional model of only looking at agrarian empires, even though better information is available now outside of this traditional focus.
Significant change in the understanding of mongols and other nomadic empires
From Needy Nomad (of material goods for survival) to Tradey Nomad (of luxury goods for maintaining their social organization), from Thomas Barfield in The Perilous Frontier.
Under this lens, a large unified agrarian state provides enough luxury trade and raiding for large nomadic confederacies to form.
Beckwith goes further in Empires of the Silk Road (into controversy), arguing that nomads are the drivers of Eurasian commerce. This is on the basis of records detailing huge importations of finished goods, including things like iron weapons and armor, into China. Further, he argues an inverse relationship between the agrarian states and the nomad confederacies—noting that the confederacies grew larger first, posits that the formation of a large confederacy creates a kind of Silk Road free-trade zone, which generates enough surplus wealth in the agrarian kingdoms to fund wars of unification.
A little detail about the Han-Xiongnu Wars provides some context about a stupendous crisis that might devour a golden age.
Ah man, sorry your joke bombed.
On the basis of this line alone, I regret nothing!
I said “good thing I have another good eye” on the way to the hospital
HA! After I crawled away from the truck, I was laying between the engine block and the cab, and my gunner was kneeling a little ways away pulling security. After a little time I said to him, “After due and careful consideration, I have decided explosions are even more exciting from the inside.”
He was unamused. Too bad—not a lot of opportunities to deliver that joke.
The two things I knew beforehand were that episodes of spontaneously reliving the event are the classic example of consequences I did not want, and that there is a technique called exposure therapy, which usually entails deliberately exposing yourself to some trigger until you normalize to it again. Doing it on purpose was like exposing myself to no trigger, I figure. I’m confident this isn’t how it actually works, but I kind of felt like every one I went through deliberately was one less I would have to go through while driving in the car or something.
I have found that it does a really good job of separating the feelings then from the feelings now, because I can just keep verifying to myself that I’m actually fine, and so is everyone else.
How do you feel about the surprise of the event? I feel like the dominant feature in my recovery is the preparation I had beforehand: I knew when I joined this was a thing that happens, it being the most publicized part of the war; there are a hundred thousand people it has happened to before me who described it; we have general emergency medical training and maximum-intensity safety gear; we have hours of specific training for how to respond to it; I personally had the habit of visualizing it when I sat down in the truck; I knew that day we were going to drive until it happened to someone. I was about as prepared as humanly possible, and getting blown up still sucked. Yet I never had to deal with feeling like I couldn’t believe it even happened.
I think that is plausible, and I think the factors you mention are definitely a virtue of the MCB approach. A further one is that even if we were to produce too few, the ones we did produce would still result in marginal gains. I also agree that most of the cost will be at the beginning; even more so if it is done correctly.
But I point out the error in estimating how many boats will be needed is completely independent of the error in estimating the timeline and costs for setting up production; we aren’t at liberty to assume they will even approximately balance out. I think it is reasonable to infer that the longer the delay until operations start, the more boats will be needed to achieve the goal. This means the risk is lopsided primarily on the side of costs increasing; there’s no particular likelihood of things being much cheaper or faster than expected, like we expected production to start in five years and it mysteriously happened in three.
These are all solvable problems, mind; the core of my criticism is that there are specific issues that arise from the bigness of challenges alone, and that we need to account for them deliberately. This is not done in baseline cost or time estimates, and rarely done even among people who are experienced in tackling big challenges, so we aren’t at liberty to assume that we can hand it off to experienced practitioners and they will handle it.
As it happens, coordinating a large assembly-line project is fairly standard megaproject material. Ships, aircraft, and semiconductors are good examples.
The hitch is your example assumes a WWII-grade of funding and coordination. Do you think that can be achieved quickly enough, cheaply enough, and reliably enough to be ignored when proposing such a project?
I had a severe back injury far from home in 2011, the circumstances surrounding which have been detailed elsewhere. Irritatingly, my glasses were lost and I spent a week at a hospital in Germany unable to see anything clearly and bedridden.
I also had a sensitivity to loud noises, and thought about the event a lot. With nothing better to do, and being forewarned that post-traumatic stress was a severe problem, I got control of the situation by deliberately reliving the event over and over again until the adrenaline stopped.
I still do it, sometimes; if I hit a pothole and that surprises me, or if I find myself otherwise unaccountably stressed and unfocused, I go back and smooth it out again.
One of the things I like about this idea is how it specifically triggers thinking about two different modes of communication, the words and the pictures. I feel like when I think about displaying information it is usually either showing something I already know in word form, or alternatively to get at information I cannot grok otherwise like data points in a large table. I almost never think about giving one idea both barrels from the get-go.
To my knowledge, none. This is because to my knowledge there has never been such a project.
I claim that there is no reason to expect geoengineering to be different than any other field in project outcomes. I claim further there are strong causal reasons to expect them to be the same. Large projects behave similarly regardless of whether we are talking civil infrastructure, oil & gas, energy, mining, aerospace, entertainment or defense. There is no trait of geoengineering which can differentiate it from this pattern.
This is because the problems are not driven by the field from which the project originates, but by the irreducible complexity that comes with size. Absent a specific commitment to dealing with irreducible complexity problems, we should expect budget and timeline estimates to be badly wrong.
Note this doesn’t make geoengineering a bad field or their projects worse than other projects; the thing I am pointing to is that we need separate expertise to make it work the way we need. We cannot afford to spend multiple projects worth of budget on only one project, and we really cannot afford to be surprised by a 10 year delay.
The implications are significantly different if $10B turns into $30B while the project is underway, which is the norm. The timeline is also significant, and delays of 2-10 years matter a great deal to how successful the project is going to be.
It does not follow that because climate models are bad to an unknown degree, geoengineering projects will overperform to a symmetric degree. This is a common assumption among large projects, but it is also a specific failure mode.
No, with ~95% confidence.
The central problem of geoengineering projects is that there is no reason to even entertain the notion that they will perform differently than regular projects. They will be over budget by about the same amount, miss their timelines by about the same amount, and miss their performance targets by about the same amount. This last point is the real crux of the matter, because we are talking about large scale, irreversible changes to the environment. The only way to remedy an error is by using the same error-prone process that caused it in the first place.
That being said, there are new methods available for managing huge and complex projects. Then it is a matter of adopting the methods.
I agree with you about the non-decision value of forecasting. My claim is that the decision value of forecasting is neglected, rather than that decisions are the only value. I strongly feel that neglecting the decisions aspect is leaving money on the table. From Ozzie:
My impression is that some groups have found it useful and a lot of businesses don’t know what to do with those numbers. They get a number like 87% and they don’t have ways to directly make that interact with the rest of their system.
I will make a stronger claim and say that the decisions aspect is the highest value aspect of forecasting. From the megaproject management example: Bent Flyvbjerg (of Reference Class Forecasting fame) estimates that megaprojects account for ~8% of global GDP. The time and budget overruns cause huge amounts of waste, and eyeballing his budget overrun numbers it looks to me like ~3% of global GDP is waste. I expect the majority of that can be resolved with good forecasting; by comparison with modelling of a different system which tries to address some of the same problems, I’d say 2⁄3 of that waste.
So I currently expect that if good forecasting became the norm only in projects of $1B or more, excluding national defense, it would conservatively be worth ~2% of global GDP.
Looking at the war example, we can consider a single catastrophic decision: disbanding the Iraqi military. I expect reasonable forecasting practices would have suggested that when you stop paying a lot of people who are in possession of virtually all of the weaponry, that they would have to find other ways to get by. Selling the weapons and their fighting skills, for example. This decision allowed an insurgency to unfold into a full-blown civil war, costing some 10^5 lives and 10^6 displaced people and moderately intense infrastructure damage.
Returning to the business example from the write-up, if one or more projects were to succeed in delivering this kind of value, I expect a lot more resources would be available for the pursuing true-beliefs-aspect of forecasting. I go as far as to say it would be a very strong inducement for people who do not currently care about having true beliefs to start doing so, in the most basic big pile of utility sense.
I approve of this write-up, and would like to see more of this kind of content.
I feel like the most neglected part of forecasting is how it relates to anything else. The working assumption is that if it works well and is widely available, it will enable a lot of really cool stuff; I agree with this assumption, but I don’t see much effort to bridge the gap between ‘cool stuff’ and ‘what is currently happening’. I suspect that the reason more isn’t being invested in this area is that we mostly won’t use it regardless of how well it works.
There are other areas where we know how to achieve good, or at least better, outcomes in the form of best practices, like software and engineering. I think it is uncontroversial to claim that most software or engineering firms do not follow most best practices, most of the time.
But that takes effort, and so you might reason that perhaps trying to predict what will happen is more common when the responsibility is enormous and rewards are fabulous, to the tune of billions of dollars or percentage points of GDP. Yet that is not true—mostly people doing huge projects don’t bother to try.
Perhaps then a different standard, where hundreds of thousands of lives are on the line and where nations hang in the balance. Then, surely, the people who make decisions will think hard about what is going to happen. Alas, even for wars it is not the case.
When we know the right thing to do, we often don’t do it; and whether the rewards are great or terrible, we don’t try to figure out if we will get them or not. The people who would be able to make the best use of forecasting in general follow a simpler rule: predict success, then do whatever they would normally do.
There’s an important ambiguity at work, and the only discussion of it I have read is in the book Prediction Machines. This book talks about what the overall impact of AI will be, and they posit that the big difference will be a drop in cost of prediction. The predictions they talk about are mostly of the routine sort, like how much inventory is needed or expected number of applications, which is distinct from the forecasting questions of GJP. But the point they made that I thought was valuable is how deeply entwined predictions and decisions are in our institutions and positions, and how this will be a barrier to taking advantage of the new trends for businesses. We will have to rethink how decisions are made once we separate out the prediction component.
So what I would like to see from forecasting platforms, companies, and projects is a lot more specifics about how forecasting relates to the decisions that need to be made, and how it improves them. As it stands, forecasting infrastructure probably looks a lot like a bridge to nowhere from the perspective of its beneficiaries.
The front page features are very useful for getting up to speed. Recently Curated is newer posts the mods thought were important, From the Archives are old posts that were well received, and Continue Reading helps keep track of the sequences (the core content of the site) so you can consume them over time.
I expect this information is reasonably well documented in histories of particular currents of thought. I have no idea how often it happens in absolute terms, but I feel it must be relatively common because I have encountered it as an amateur reader of papers.
A good example is ET Jaynes’ work on maximum caliber, which is a variational principle for dynamical systems. It might be cheating because it is well-understood to be a controversial concept, but the insights concerned entropy. Jaynes’ specialty was Statistical Mechanics, for which he had employed information-theoretic notions of entropy in order to account for the lack of knowledge of the microstates. When Jaynes’ was writing, physics used the Clausius formulation of 2nd Law of Thermodynamics, which he found unsatisfactory for the problem of prediction because it says nothing about intermediate states before reaching equilibrium. In Physical Chemistry they used a different one, which came from work by G.N. Lewis, who used the Gibbs formulation of the 2nd Law. It is insights drawn from the subtleties of Gibbs’ work concerning entropy that gave Jaynes the predictive power he was interested in. Lastly, Jaynes had the work of Clifford Truesdell who was writing around the same time in the field of Continuum Mechanics, and working to expand that approach to fully cover thermodynamics. Truesdell’s work persuaded Jaynes that the other approaches were in fact wrong.
So here was a case where one physics researcher (Jaynes) borrowed math ideas from communication (Shannon), then read older work from chemistry (Lewis), leading to much older work in early thermodynamics that had new insights (Gibbs), and confirmed by more recent work in a different field of math (Truesdell). All of these insights went into his work on maximum caliber.
In a similar vein, a lot of Truesdell’s writing consists of going back to the early days of thermodynamics and finely sifting the insights therein. He writes well and carefully, but is animated and polemical; I recommend reading him to anyone interested in thermodynamics.
I affirm Scharre’s interpretation.
Anecdote: during deployment when we arrive in country, we are given briefings about the latest tactics being employed in the area where we will be operating. When I went to Iraq in 2008 one of these briefings was about young girls wearing suicide vests, which was previously unprecedented.
The tactic consisted of taking a family hostage, and telling the girl that if she did not wear this vest and go to X place at Y time, her family would be killed. Then they would detonate the vest by remote.
We copped to it because sometimes we had jammers on which prevented the detonation, and one of the girls told us what happened. Of course, we didn’t have jammers everywhere. Then the calculus changes from whether we can take the hit in order to spare the child, to one child or many (suicide bombings target crowds).
The obvious wrongness of killing children does not change; nor that of allowing children to die. So one guy eats the sin, and the others feel ashamed for letting him.
I don’t understand the source of your concern.
Is it not at all concerning that aliens with no knowledge of Earth or humanity could plausibly guess that a movement dedicated to a maximizing, impartial, welfarist conception of the good would also be intrinsically attracted to learning about idealized reasoning procedures?
This is not at all concerning. If we are concerned about this then we should also be concerned that aliens could plausibly guess a movement dedicated to space exploration would be intrinsically attracted to learning about idealized dynamical procedures. It seems to me this is just a prior that groups with a goal investigate instrumentally useful things.
My model of your model so far is this: because the EA community is interested in LessWrong, and because LessWrong facilitated the group that work on HRAD research, the EA community will move their practices closer to implications of this research even in the case where it is wrong. Is that accurate?
My expectation is that EAs will give low weight to the details of HRAD research, even in the case where it is a successful program. The biggest factor is the timelines: HRAD research is in service of the long term goal of reasoning correctly about AGI; EA is about doing as much good as possible, as soon as possible. The iconic feature of the EA movement is the giving pledge, which is largely predicated on the idea that money given now is more impactful than money given later. There is a lot of discussion about alternatives and different practices, for example the donor’s dilemma and mission hedging, but these are operational concerns rather than theoretical/idealized ones.
Even if I assume HRAD is a productive line of research, I strongly expect that the path to changing EA practice leads from some surprising result, evaluated all the way up to the level of employment and investment decisions. This means the result would need to be surprising, then it would need to withstand scrutiny, then it would need to lead to conclusions big enough to shift activity like donations, employment, and investments, cost of change included and all. I would be deeply shocked if this happened, and then further shocked if it had a broad enough impact to change the course of EA as a group.