War is more likely when the population has a higher fraction of young men (e.g. see Angry Young Men Are Making the World Less Stable ). That’s doesn’t quite say that young men vote more for war, but it’s suggestive.More war could easily overwhelm any benefits from weighted voting.
IPOs are strongly dependent on an expanding economy. Cryptocurrency bubbles are somewhat more likely in an expanding economy.
The impact of IPOs and Bitcoin on other markets is much smaller than the impact of the economy on IPOs and Bitcoin.
I’ll guess that EA giving is a bit more sensitive to the economy than other giving, because a disproportionate amount of EA giving comes from IPO-related wealth and cryptocurrency bubbles.
No, I expected that no rigorous research had been done on NLP as of 2014, and I don’t know how rigorous the more recent research has been.
I don’t know whether it has been published. I heard it from Rick Schwall (http://shfhs.org/aboutus.html).
I’ve contributed small amounts of money to MAPS , but I haven’t been thinking of those as EA donations.My doubts overlap a fair amount with those of Scott Alexander , but I’ll focus on somewhat different reasoning which led me there.It sounds like MAPS has been getting impressive results, and MAPS would likely qualify as an EA charity if FDA approval were the main obstacle to extending those results to the typical person who seeks help with PTSD. However, I suspect there are other important obstacles.I know a couple of people, who I think consider themselves EAs, who have been trying to promote an NLP-based approach to treating PTSD, which reportedly has a higher success rate than MAPS has reported. The basic idea behind it has been around for years , without spreading very widely, and without much interest from mainstream science.Maybe the reports I hear involve an improved version of the basic technique, and it will take off as soon as the studies based on the new version are published.Or maybe the glowing reports are based on studies that attracted both therapists and patients who were unusually well suited for NLP, and don’t generalize to random therapists and random PTSD patients. And maybe the MAPS study has similar problems.Whatever the case is there, the ease with which I was able to stumble across an alternative to psychedelics that sounds about equally promising is some sort of evidence against the hypothesis that there’s a shortage of promising techniques to treat PTSD.I suspect there are important institutional problems in getting mental help professionals to adopt techniques that provide quick fixes. I doubt it’s a complete coincidence that the number of visits required for for successful therapy happens to resemble a number that maximizes revenue per patient.If that were simply a conspiracy of medical professionals, and patients were eager to work around them, I’d be vaguely hopeful of finding a way to do so. But I’m under the impression that patients have a weak tendency to contribute to the problem, by being more likely to recommend to their friends a therapist who they see for long time, than they would be to recommend a therapist who they stop seeing after a month because they were cured that fast. And I don’t see lots of demand for alternative routes to finding therapists that have good track records.None of these reasons for doubt is quite sufficient by itself to decide that MAPS isn’t an EA charity, but they outline at least half of my intuitions for feeling somewhat pessimistic about this cause area.
I suspect that principal–agent problems are the biggest single obstacle to alignment. That leads me to suspect it’s less tractable than you indicate.I’m interested in what happened with Netflix. Ten years ago their recommendation system seemed focused almost exclusively on maximizing user ratings of movies. That dramatically improved my ability to find good movies.Yet I didn’t notice many people paying attention to those benefits. Netflix has since then shifted toward less aligned metrics. I’m less satisfied with Netflix now, but I’m unclear what other users think of the changes.
Sleep loss is an important problem, but it’s unclear whether any charity should focus on it directly.
The problem of driving while sleep-deprived will likely be solved by robocars more than by any altruistic efforts.
The rest of the problem seems better tackled by focusing more on the stresses that cause sleep problems, and by relatively decentralized efforts to shift our cultures to be more sleep-friendly.
Sleep is something to keep in mind when asking whether EAs should donate to mental health charities or to meditation charities such as Monastic Academy. I’m very uncertain whether these charities should be considered effective enough to be EA causes.
>For anyone who’s had some experience with depression or anxiety, as well as with “some problems walking about,” it should be obvious that moderate depression or anxiety are (much) worse than moderate mobility problems, pound for pound.
That’s obvious for rich people, but not at all obvious for someone who risks hunger as a result of mobility problems.
I assume that by “cash-flow positive”, you mean supported by fees from workshop participants?I don’t consider that to be a desirable goal for CFAR.Habryka’s analysis focuses on CFAR’s track record. But CFAR’s expected value comes mainly from possible results that aren’t measured by that track record.My main reason for donating to CFAR is the potential for improving the rationality of people who might influence x-risks. That includes mainstream AI researchers who aren’t interested in the EA and rationality communities. The ability to offer them free workshops seems important to attracting the most influential people.
>which means that what everyone else is doing doesn’t matter all that muchEarning to give still matters a moderate amount. That’s mostly what I’m doing. I’m saying that average EA should start with the outside view that they can’t do better than earning to give, and then attempt some more difficult analysis to figure out how they compare to average.And it’s presumably possible to matter more than the average earning to give EA, by devoting above-average thought to vetting new charities.
I’m unimpressed by the arguments for random funding of research proposals. The problems with research funding are mostly due to poor incentives, rather than people being unable to do much better than random guessing. EA organizations don’t have ideal incentives, and may be on the path to unreasonable risk-aversion, but they still have a fairly sophisticated set of donors setting their incentives, and don’t yet appear to be particularly risk-averse or credential-oriented.Unless something has changed in the last few years, there are still plenty of startups with plausible ideas that don’t get funded by Y Combinator or anything similar. Y Combinator clearly evaluates a lot more startups than I’m willing or able to evaluate, but it’s not obvious that they’re being less selective than I am about which ones they fund.I mentioned Nick Bostrom and Eric Drexler because they’re widely recognized as competent. I didn’t mean to imply that we should focus more funding on people who are that well known—they do not seem to be funding constrained now.Let me add some examples of funding I’ve done that better characterize what I’m aiming for in charitable donations (at the cost of being harder for many people to evaluate):My largest donations so far have been to CFAR, starting in early 2013, when their track record was rather weak, and almost unknown outside of people who had attended their workshops. That was based largely on impressions of Anna Salamon that I got by interacting with her (for reasons that were only marginally related to EA goals).Another example is Aubrey de Grey. I donated to the Methuselah Mouse Prize for several years starting in 2003, when Aubrey had approximately no relevant credentials beyond having given a good speech at the Foresight Institute and a similar paper on his little-known website.Also, I respected Nick Bostrom and Eric Drexler fairly early in their careers. Not enough to donate to their charitable organizations at their very beginning (I wasn’t actively looking for effective charities before I heard of GiveWell). But enough that I bought and read their first books, primarily because I expected them to be thoughtful writers.
Speaking for why I haven’t donated, this is close to the key question:>Then the question is (roughly) whether, given £60,000, it makes more sense to fund 1 researcher who’s cleared the EA hiring bar, or 10 who haven’t (and are in D).My intuition has been that if those 10 are chosen at random, then I’m moderately confident that it’s better to fund the 1 well-vetted researcher.EA is talent-constrained in the sense that it needs more people like Nick Bostrom or Eric Drexler, but much less in the sense of needing more people who are average EAs to do direct EA work.I’ve done some angel investing in startups. I initially took an approach of trying to fund anyone who has a a good idea. But that worked poorly, and I’ve shifted, as good VCs advise, to looking for signs of unusual competence in founders. (Alas, I still don’t have much reason to think I’m good at angel investing). And evaluating founder’s competence feels harder than evaluating a business idea, so I’m not willing to do it very often.I use a similar approach with donating to early-stage charities, expecting to see many teams with decent ideas, but expecting the top 5% to be more than 10 times as valuable than the average. And I’m reluctant to evaluate more pre-track-record projects than I’m already doing.With the hotel, I see a bunch of little hints that it’s not worth my time to attempt an in-depth evaluation of the hotel’s leaders. E.g. the focus on low rent, which seems like a popular meme among average and below average EAs in the bay area, yet the EAs whose judgment I most respect act as if rent is a relatively small issue.I can imagine that the hotel attracts better than random EAs, but it’s also easy to imagine that it selects mainly for people who aren’t good enough to belong at a top EA organization.Halffull has produced a better argument for the EA Hotel, but I find it somewhat odd that he starts with arguments that seem weak to me, and only in the middle did he get around to claims that are relevant to whether the hotel is better than a random group of EAs.Also, if donors fund any charity that has a good idea, I’m a bit concerned that that will attract a larger number of low-quality projects, much like the quality of startups declined near the peak of the dot-com bubble, when investors threw money at startups without much regard for competence.
Here are a few examples of strategies that look (or looked) equally plausible, from the usually thoughtful blog of my fellow LessWronger Colby Davis .This blog post recommends:- emerging markets, which overlaps a fair amount with my advice- put-writing, which sounds reasonable to me, but he managed to pick a bad time to advocate it- preferred stock, which looks appropriate today for more risk-averse investors, but which looked overpriced when I wrote my post.This post describes one of his failures. Buying XIV was almost a great idea. It was a lot like shorting VXX, and shorting VXX is in fact a good idea for experts who are cautious enough not to short too much (alas, the right amount of caution is harder to know than most people expect). I expect the rewards in this area to go only to those who accept hard-to-evaluate risks.This post has some strategies that require more frequent trading. I suspect they’re good, but I haven’t given them enough thought to be confident.
Hi, I’m Bayesian Investor.I doubt that following my advice would be riskier than the S&P 500 - the low volatility funds reduce the risk in important ways (mainly by moving less in bear markets) that roughly offset the features which increase risk.It’s rational for most people to ignore my advice, because there’s lots of other (somewhat conflicting) advice out there that sounds equally plausible to most people.I’ve got lots of evidence about my abilities (I started investing as a hobby in 1980, and it’s been my main source of income for 20 years). But I don’t have an easy way to provide much evidence of my abilities in a single blog post.
I’m a little confused by this reply. Did you think I was complaining that you over-estimated the costs of weight loss? Let me emphasize that I was complaining about the actual resources devoted to weight loss, not your estimates of it. I’ll guess that you under-estimated those costs, by focusing on money spent, rather than trying to evaluate the psychological costs.
My main point is that we should focus more on getting people to switch from typical weight loss approaches to ones that are easier and more effective.
I’m unsure what to infer from your weight satisfaction evidence. It might mean that some people notice that obesity is harming them (via sleep apnea? romantic problems?) and that’s what causes them to worry. Or it might mean they’re just more responsive to peer pressure, and it’s the peer pressure, not the obesity, that’s harmful.
I suspect you underestimate the cost of obesity.
But there’s something seriously wrong with the cost of the typical weight loss approach, and your ROI estimate might be close to the right answer for that.
I believe it’s possible to adopt a much better than average approach to weight loss, by focusing more on switching to healthier foods (based on the Satiety Index, or on high fiber content), and/or some form of intermittent fasting.
I expect that good software engineers are more likely to figure out for themselves how to be more efficient than they are to figure out how to increase their work quality. So it’s not obvious what to infer from “it’s harder for an employer to train people to work faster”—does it just mean that the employer has less need to train the slow, high quality worker?
Regulations shouldn’t be much of a problem for subsidized prediction markets. The regulations are designed to protect people from losing their investments. You can avoid that by not taking investments—i.e. give every trader a free account. Just make sure any one trader can’t create many accounts.
Alas, it’s quite hard to predict how much it will cost to generate good predictions, regardless of what approach you take.
Drexler would disagree with some of Richard’s phrasing, but he seems to agree that most (possibly all) of (somewhat modified versions of) those 6 reasons should cause us to be somewhat worried. In particular, he’s pretty clear that powerful utility maximisers are possible and would be dangerous.