Here are some things I’ve learned from spending the better part of the last 6 months either forecasting or thinking about forecasting, with an eye towards beliefs that I expect to be fairly generalizable to other endeavors.
Note that I assume that anybody reading this already has familiarity with Phillip Tetlock’s work on (super)forecasting, particularly Tetlock’s 10 commandments for aspiring superforecasters.
1. Forming (good) outside views is often hard but not impossible. I think there is a common belief/framing in EA and rationalist circles that coming up with outside views is easy, and the real difficulty is a) originality in inside views, and also b) a debate of how much to trust outside views vs inside views.
I think this is directionally true (original thought is harder than synthesizing existing views) but it hides a lot of the details. It’s often quite difficult to come up with and balance good outside views that are applicable to a situation. See Manheim and Muelhauser for some discussions of this.
2. For novel out-of-distribution situations, “normal” people often trust centralized data/ontologies more than is warranted. See here for a discussion. I believe something similar is true for trust of domain experts, though this is more debatable.
3. The EA community overrates the predictive validity and epistemic superiority of forecasters/forecasting.
(Note that I think this is an improvement over the status quo in the broader society, where by default approximately nobody trusts generalist forecasters at all)
I’ve had several conversations where EAs will ask me to make a prediction, I’ll think about it a bit and say something like “I dunno, 10%?”and people will treat it like a fully informed prediction to make decisions about, rather than just another source of information among many.
I think this is clearly wrong. I think in any situation where you are a reasonable person and you spent 10x (sometimes 100x or more!) time thinking about a question then I have, you should just trust your own judgments much more than mine on the question.
To a first approximation, good forecasters have three things: 1) They’re fairly smart. 2) They’re willing to actually do the homework. 3) They have an intuitive sense of probability.
This is not nothing, but it’s also pretty far from everything you want in a epistemic source.
4. The EA community overrates Superforecasters and Superforecasting techniques. I think the types of questions and responses Good Judgment .* is interested in is a particular way to look at the world. I don’t think it is always applicable (easy EA-relevant example: your Brier score is basically the same if you give 0% for 1% probabilities, and vice versa), and it’s bad epistemics to collapse all of the “figure out the future in a quantifiable manner” to a single paradigm.
Likewise, I don’t think there’s a clear dividing line between good forecasters and GJP-certified Superforecasters, so many of the issues I mentioned in #3 are just as applicable here.
I’m not sure how to collapse all the things I’ve learned on this topic in a few short paragraphs, but the tl;dr is that I trusted superforecasters much more than I trusted other EAs before I started forecasting stuff, and now I consider their opinions and forecasts “just” an important overall component to my thinking, rather than a clear epistemic superior to defer to.
5. Good intuitions are really important. I think there’s a Straw Vulcan approach to rationality where people think “good” rationality is about suppressing your System 1 in favor of clear thinking and logical propositions from your system 2. I think there’s plenty of evidence for this being wrong*. For example, the cognitive reflection test was originally supposed to be a test of how well people suppress their “intuitive” answers to instead think through the question and provide the right “unintuitive answers”, however we’ve later learned (one fairly good psych study. May not replicate, seems to accord with my intuitions and recent experiences) that more “cognitively reflective” people also had more accurate initial answers when they didn’t have the time to think through the question.
On a more practical level, I think a fair amount of good thinking is using your System 2 to train your intuitions, so you have better and better first impressions and taste for how to improve your understanding of the world in the future.
*I think my claim so far is fairly uncontroversial, for example I expect CFAR to agree with a lot of what I say.
6. Relatedly, most of my forecasting mistakes are due to emotional rather than technical reasons. Here’s a Twitter thread from May exploring why; I think I mostly still stand by this.
Consider making this a top-level post! That way, I can give it the “Forecasting” tag so that people will find it more often later, which would make me happy, because I like this post.
Thanks for the encouragement and suggestion! Do you have recommendations for a really good title?
Titles aren’t my forte. I’d keep it simple. “Lessons learned from six months of forecasting” or “What I learned after X hours of forecasting” (where “X” is an estimate of how much time you spent over six months).
I second this.
cross-posted from Facebook.
Sometimes I hear people who caution humility say something like “this question has stumped the best philosophers for centuries/millennia. How could you possibly hope to make any progress on it?”. While I concur that humility is frequently warranted and that in many specific cases that injunction is reasonable , I think the framing is broadly wrong.In particular, using geologic time rather than anthropological time hides the fact that there probably weren’t that many people actively thinking about these issues, especially carefully, in a sustained way, and making sure to build on the work of the past. For background, 7% of all humans who have ever lived are alive today, and living people compose 15% of total human experience  so far!!! It will not surprise me if there are about as many living philosophers today as there were dead philosophers in all of written history.For some specific questions that particularly interest me (eg. population ethics, moral uncertainty), the total research work done on these questions is generously less than five philosopher-lifetimes. Even for classical age-old philosophical dilemmas/”grand projects” (like the hard problem of consciousness), total work spent on them is probably less than 500 philosopher-lifetimes, and quite possibly less than 100.There are also solid outside-view reasons to believe that the best philosophers today are just much more competent  than the best philosophers in history, and have access to much more resources.Finally, philosophy can build on progress in natural and social sciences (eg, computers, game theory).Speculating further, it would not surprise me, if, say, a particularly thorny and deeply important philosophical problem can effectively be solved in 100 more philosopher-lifetimes. Assuming 40 years of work and $200,000/year per philosopher, including overhead, this is ~800 million, or in the same ballpark as the cost of developing a single drug. Is this worth it? Hard to say (especially with such made-up numbers), but the feasibility of solving seemingly intractable problems no longer seems crazy to me. For example, intro philosophy classes will often ask students to take a strong position on questions like deontology vs. consequentialism, or determinism vs. compatibilism. Basic epistemic humility says it’s unlikely that college undergrads can get those questions right in such a short time.  https://eukaryotewritesblog.com/2018/10/09/the-funnel-of-human-experience/ Flynn effect, education, and education of women, among others. Also, just https://en.wikipedia.org/wiki/Athenian_democracy#Size_and_make-up_of_the_Athenian_population. (Roughly as many educated people in all of Athens at any given time as a fairly large state university). Modern people (or at least peak performers) being more competent than past ones is blatantly obvious in other fields where priority is less important (eg, marathon runners, chess). Eg, internet, cheap books, widespread literacy, and the current intellectual world is practically monolingual. https://en.wikipedia.org/wiki/Cost_of_drug_development
If a problem is very famous and unsolved, doesn’t those who tried solving it include many of the much more competent philosophers alive today? The fact that the problem has not been solved by any of them either would suggest to me it’s a hard problem.
Honest question: are there examples of philosophical problems that were solved in the last 50 years? And I mean solved by doing philosophy not by doing mostly unrelated experiments (like this one). I imagine that even if some philosophers felt they answered a question, other would dispute it. More importantly, the solution would likely be difficult to understand and hence it would be of limited value. I’m not sure I’m right here.
After a bit more googling I found this which maybe shows that there have been philosophical problems solved recently. I haven’t read about that specific problem though. It’s difficult to imagine a short paper solving the hard problem of consciousnesses though.
I enjoyed this list of philosophy’s successes, but none of them happened in the last 50 years.
You might be interested in the following posts on the subject from Daily Nous, an excellent philosophy blog:
“Why Progress Is Slower In Philosophy Than In Science”
“How Philosophy Makes Progress (guest post by Daniel Stoljar)”
“How Philosophy Makes Progress (guest post by Agnes Callard)”
“Whether Philosophical Questions Can Be Answered”
“Convergence as Progress in Philosophy”
I’ll be interested in having someone with a history of philosophy background weigh in on the Gettier question specifically. I thought Gettier problems were really interesting when I first heard about them, but I’ve also heard that “knowledge as justified true belief” wasn’t actually all that dominant a position before Gettier came along.
Catalyst (biosecurity conference funded by the Long-Term Future Fund) was incredibly educational and fun.
Random scattered takeaways:
1. I knew going in that everybody there will be much more knowledgeable about bio than I was. I was right. (Maybe more than half the people there had PhDs?)
2. Nonetheless, I felt like most conversations were very approachable and informative for me, from Chris Bakerlee explaining the very basics of genetics to me, to asking Anders Sandberg about some research he did that was relevant to my interests, to Tara Kirk Sell detailing recent advances in technological solutions in biosecurity, to random workshops where novel ideas were proposed...
3. There’s a strong sense of energy and excitement from everybody at the conference, much more than other conferences I’ve been in (including EA Global).
4. From casual conversations in EA-land, I get the general sense that work in biosecurity was fraught with landmines and information hazards, so it was oddly refreshing to hear so many people talk openly about exciting new possibilities to de-risk biological threats and promote a healthier future, while still being fully cognizant of the scary challenges ahead. I guess I didn’t imagine there were so many interesting and “safe” topics in biosecurity!
5. I got a lot more personally worried about coronavirus than I was before the conference, to the point where I think it makes sense to start making some initial preparations and anticipate lifestyle changes.
6. There was a lot more DIY/Community Bio representation at the conference than I would have expected. I suspect this had to do with the organizers’ backgrounds; I imagine that if most other people were to organize biosecurity conferences, it’d be skewed academic a lot more.
7. I didn’t meet many (any?) people with a public health or epidemiology background.
8. The Stanford representation was really high, including many people who have never been to the local Stanford EA club.
9. A reasonable number of people at the conference were a) reasonably interested in effective altruism b) live in the general SF area and c) excited to meet/network with EAs in the area. This made me slightly more optimistic (from a high prior) about the value of doing good community building work in EA SF.
10. Man, the organizers of Catalyst are really competent. I’m jealous.
11. I gave significant amounts of money to the Long-Term Future Fund (which funded Catalyst), so I’m glad Catalyst turned out well. It’s really hard to forecast the counterfactual success of long-reach plans like this one, but naively it looks like this seems like the right approach to help build out the pipeline for biosecurity.
12. Wow, evolution is really cool.
13. Talking to Anders Sandberg made me slightly more optimistic about the value of a few weird ideas in philosophy I had recently, and that maybe I can make progress on them (since they seem unusually neglected).
14. Catalyst had this cool thing where they had public “long conversations” where instead of a panel discussion, they’d have two people on stage at a time, and after a few minutes one of the two people get rotated out. I’m personally not totally sold on the format but I’d be excited to see more experiments like that.
15. Usually, conferences or other conversational groups I’m in have one of two failure modes: 1) there’s an obvious hierarchy (based on credentials, social signaling, or just that a few people have way more domain knowledge than others) or 2) people are overly egalitarian and let useless digressions/opinions clog up the conversational space. Surprisingly neither happened much here, despite an incredibly heterogeneous group (from college sophomores to lead PIs of academic biology labs to biotech CEOs to DiY enthusiasts to health security experts to randos like me)
16. Man, it seems really good to have more conferences like this, where there’s a shared interest but everybody come from different fields so it’s less obviously hierarchal/status-jockeying.
17. I should probably attend more conferences/network more in general.
18. Being the “dumbest person in the room” gave me a lot more affordance to ask silly questions and understand new stuff from experts. I actually don’t think I was that annoying, surprisingly enough (people seemed happy enough to chat with me).
19. Partially because of the energy in the conference, the few times where I had to present EA, I mostly focused on the “hinge of history/weird futuristic ideas are important and we’re a group of people who take ideas seriously and try our best despite a lot of confusion” angle of EA, rather than the “serious people who do the important, neglected and obviously good things” angle that I usually go for. I think it went well with my audience today, though I still don’t have a solid policy of navigating this in general.
20. Man, I need something more impressive on my bio than “unusually good at memes.”
Publication bias alert: Not everybody liked the conference as much as I did. Someone I know and respect thought some of the talks weren’t very good (I agreed with them about the specific examples, but didn’t think it mattered because really good ideas/conversations/networking at an event + gestalt feel is much more important for whether an event is worthwhile to me than a few duds).
That said, on a meta level, you might expect that people who really liked (or hated, I suppose) a conference/event/book to write detailed notes about it than people who were lukewarm about it.
I am glad to hear that! I sadly didn’t end up having the time to go, but I’ve been excited about the project for a while.
Thanks for your report! I was interested but couldn’t manage the cross country trip and definitely curious to hear what it was like.
I’d really appreciate ideas for how to try to confer some of what it was like to people who couldn’t make it. We recorded some of the talks and intend to edit + upload them, we’re writing a “how to organize a conference” postmortem / report, and one attendee is planning to write a magazine article, but I’m not sure what else would be useful. Would another post like this be helpful?
We recorded some of the talks and intend to edit + upload them, we’re writing a “how to organize a conference” postmortem / report, and one attendee is planning to write a magazine article
We recorded some of the talks and intend to edit + upload them, we’re writing a “how to organize a conference” postmortem / report, and one attendee is planning to write a magazine article
That all sounds useful and interesting to me!
Would another post like this be helpful?
Would another post like this be helpful?
I think multiple posts following events on the personal experiences from multiple people (organizers and attendees) can be useful simply for the diversity of their perspectives. Regarding Catalyst in particular I’m curious about the variety of backgrounds of the attendees and how their backgrounds shaped their goals and experiences during the meeting.
While talking to my manager (Peter Hurford), I made a realization that by default when “life” gets in the way (concretely, last week a fair amount of hours were taken up by management training seminars I wanted to attend before I get my first interns, this week I’m losing ~3 hours the covid vaccination appointment and in expectation will lose ~5 more from side effects), research (ie the most important thing on my agenda that I’m explicitly being paid to do) is the first to go. This seems like a bad state of affairs.I suspect that this is more prominent in me than most people, but also suspect this is normal for others as well. More explicitly, I don’t have much “normal” busywork like paperwork or writing grants and I try to limit my life maintenance tasks (of course I don’t commute and there’s other stuff in that general direction). So all the things I do are either at least moderately useful or entertaining. Eg, EA/work stuff like reviewing/commenting on other’s papers, meetings, mentorship stuff, slack messages, reading research and doing research, as well as personal entertainment stuff like social media, memes, videogames etc (which I do much more than I’m willing to admit).if “life” gets in the way and more of my hours get taken up so my normal tempo is interrupted, I find that by default I’m much more likely to sacrifice the important parts of my job (reading research and writing research) than I am to sacrifice timely things I sort of promised others on (eg, meetings, doing reviews, etc), or entertainment/life stuff. An obvious solution to this is either to scale all my work hours proportionally down or even disproportionately scale down meetings and reviews so more of my focused hours is spent on doing the “hard stuff”, but logistically this feels quite difficult and may not play nice with other people’s schedules. Another obvious solution is to have a much higher “bar” before letting life get in the way of work time, but this also seems unappealing or unrealistic.The only real consistent exception is if I’m on a tight external deadline (eg promise X funder to deliver Y by Z date), in which case I’m both more likely to apologetically sacrifice meetings etc and to cut down on entertainment (the latter of which is not fun and intuitively doesn’t feel sustainable, but perhaps I’m overestimating the unsustainability due to the obvious sources of motivated reasoning). So at the core, I believe that on top of my usual time management problems, I think I’m especially bad at doing important + nonurgent tasks when my normal flow is broken. This results in a pattern where I only get most of my real work done either a) on urgent timescales or b) when flow isn’t broken and I actually have my normal schedule uninterrupted. Since many important work isn’t urgent and having schedules interrupted is annoyingly common for me, I’m in search of a (principled) solution to this problem. Somewhat related: Maker’s schedule vs manager’s schedule (though I think the problem is much less extreme for me than most forms of programmer flow).
So framing this in the inverse way – if you have a windfall of time from “life” getting in the way less, you spend that time mostly on the most important work, instead of things like extra meetings. This seems good. Perhaps it would be good to spend less of your time on things like meetings and more on things like research, but (I’d guess) this is true whether or not “life” is getting in the way more.
This is a really good point, I like the reframing.
I liked this, thanks.I hear that this similar to a common problem for many entrepreneurs; they spend much of their time on the urgent/small tasks, and not the really important ones. One solution recommended by Matt Mochary is to dedicate 2 hours per day of the most productive time to work on the the most important problems.
I’ve occasionally followed this, and mean to more.
This resonated with me a lot. Unfortunately, I do not have a quick fix. However, what seems to help at least a bit for me is seperating planning for a day and doing the work. Every workday the last thing I do (or try to do) is look at my calendar and to do lists and figure out what I should be doing the next day. By doing this I think I am better at assessing at what is important, as I do not have to do it at that moment. I only have to think of what my future self will be capable of doing. When the next day comes and future self turns into present self I find it really helpful to already having the work for the day planned for me. I do not have to think about what is important, I just do what past me decided.
Not sure if this is just an obvious way to do this, but I thought it does not hurt to write it down.
This seems like a good topic for a call with a coach/coworker because there are a lot of ways to approach it. One easy-to-implement option comes from 80k’s podcast with Tom Kalil: “the calendar is your friend.”
In Tom’s case, it was Obama’s calendar! I have a more low-key version I use. When I want to prioritize research or writing, I will schedule a time with my manager or director to get feedback on a draft of the report I’m writing. It’s a good incentive to make some meaningful progress—hard to get feedback if you haven’t written anything! - and makes it a tiny bit easier to decline or postpone meetings, but it is still somewhat flexible, which I find really useful.
Side question: what was the management training you took, and would you recommend it?
I think that all of us RP intern managers took the same 12-hour management training from The Management Center. I thought that there was some high-quality advice in it but I’m not sure if it applied that much to our situation of managing research interns. I haven’t been to other such trainings so I can’t compare.
Thanks salius! I agree with what you said. In addition,
A lot of the value was just time set aside to thinking about management, so hard to separate that out without a control group
(but realistically, without the training, I would not have spent ~16 (including some additional work/loose threads after the workshop) hours thinking about management in one week.
So that alone is really valuable!
I feel like the implicit prioritization for some of the topics they covered possibly made more sense for experienced managers than people like me.
For example, about 1⁄3 of the time in the workshop was devoted to diversity/inclusion topics, and I’d be very surprised if optimal resource allocation for a new manager is anywhere close to 1⁄3 time spent on diversity/inclusion.
A really important point hammered throughout the training is the importance of clear and upfront communication.
Again, I think this is something I could have figured out through my usual combination of learning about things (introspection, asking around, internet research), but having this hammered in me is actually quite valuable.
I find a lot of the specific tools they suggested intuitively useful (eg, explicit MOCHA diagrams) , but I think I have to put in work to use them in my own management (and indeed failed to do this my first week as a manager).
Thanks, good to know!
Yeah, I can also relate a lot (doing my PhD). One thing I noticed is that my motivational system slowly but surely seems to update on my AI related worries and that this now and then helps keeping me focused on what I actually think is more important from the EA perspective. Not sure what you are working on, but maybe there are some things that come to your mind how to increase your overall motivation, e.g. by reading or thinking of concrete stories of why the work is important, and by talking to others why you care about the things you are trying to achieve.
Over a year ago, someone asked the EA community whether it’s valuable to become world-class at an unspecified non-EA niche or field. Our Forum’s own Aaron Gertler responded in a post, saying basically that there’s a bunch of intangible advantages for our community to have many world-class people, even if it’s in fields/niches that are extremely unlikely to be directly EA-relevant.
Since then, Aaron became (entirely in his spare time, while working 1.5 jobs) a world-class Magic the Gathering player, recently winning the DreamHack MtGA tournament and getting $30,000 in prize monies, half of which he donated to Givewell.
I didn’t find his arguments overwhelmingly persuasive at the time, and I still don’t. But it’s exciting to see other EAs come up with unusual theories of change, actually executing on them, and then being wildly successful.
Something that came up with a discussion with a coworker recently is that often internet writers want some (thoughtful) comments, but not too many, since too many comments can be overwhelming. Or at the very least, the marginal value of additional comments is usually lower for authors when there are more comments. However, the incentives for commentators is very different: by default people want to comment on the most exciting/cool/wrong thing, so internet posts can easily by default either attract many comments or none. (I think) very little self-policing is done, if anything a post with many comments make it more attractive to generate secondary or tertiary comments, rather than less.Meanwhile, internet writers who do great work often do not get the desired feedback. As evidence: For ~ a month, I was the only person who commented on What Helped the Voiceless? Historical Case Studies (which later won the EA Forum Prize). This will be less of a problem if internet communication is primarily about idle speculations and cat pictures. But of course this is not the primary way I and many others on the Forum engage with the internet. Frequently, the primary publication venue for some subset of EA research and thought is this form of semi-informal internet communication.Academia gets around this problem by having mandated peer review for work, with a minimum number of peer reviewers who are obligated to read (or at least skim) anything that passes through an initial filter. It is unclear how (or perhaps if?) we ought to replicate something similar in a voluntary internet community.On a personal level, the main consequence of this realization is that I intend to become slightly more inclined to comment more on posts without many prior comments, and correspondingly less to posts with many comments (especially if the karma:comment ratio is low).On a group level, I’m unsure what behavior we ought to incentivize, and/or how to get there. Perhaps this is an open question that others can answer?
I think these are useful observations and questions. (Though I think “too many comments” should probably be much less of a worry than “too few”, at least if the comments make some effort to be polite and relevant, and except inasmuch as loads of comments on one thing sucks up time that could be spent commenting on other things where that’d be more useful.)
I think a few simple steps that could be taken by writers are:
People could more often send google doc drafts to a handful of people specifically selected for being more likely than average to (a) be interested in reading the draft and (b) have useful things to say about it
People could more often share google doc drafts in the Effective Altruism Editing & Review Facebook group
People could more often share google doc drafts in other Facebook groups, Slack workspaces, or the like
E.g., sharing a draft relevant to improving institutional decision-making in the corresponding Facebook group
People could more often make posts/shortforms that include an executive summary (or similar) and a link to the full google doc draft, saying that this is still like a draft and they’d appreciate comment
Roughly this has been done recently by Joe Carlsmith and Ben Garfinkel, for example
This could encourage more comments that just posting the whole thing to the Forum as a regular post, since (a) this conveys that this is still a work-in-progress and that comments are welcome, and (b) google docs make it easier to comment on specific points
When people do post full versions of things on the Forum (or wherever), they could explicitly indicate that they’re interested in feedback, indicate roughly what kinds of feedback would be most valuable, and indicate that they might update the post in light of feedback (if that’s true)
People could implement the advice given in these two good posts:
I think a few simple steps that could be taken by potential reviewers are:
As you suggest, people could adjust their behaviours a bit more towards commenting on posts without many prior comments, and save time to do that by commenting a bit less on posts with many comments
Likewise, people could adjust their behaviours a bit more towards commenting on drafts they come across where the author is seeking feedback (e.g., drafts that were sent to the person directly or shared in some group the person is part of), especially if they don’t yet have many prior comments
People could implement the advice given in https://forum.effectivealtruism.org/posts/ZjiokgANEfu36LD6G/giving-and-receiving-feedback
There are presumably also other options one could come up with. And maybe something more systematic/institutionalised would be good.
One other semi-relevant thing from my post Notes on EA-related research, writing, testing fit, learning, and the Forum:
Sometimes people worry that a post idea might be missing some obvious, core insight, or just replicating some other writing you haven’t come across. I think this is mainly a problem only inasmuch as it could’ve been more efficient for you to learn things than slowly craft a post.So if you can write (a rough version of) the post quickly, you could just do that.Or you could ask around or make a quick Question post to outline the basic idea and ask if anyone knows of relevant things you should read.
Sometimes people worry that a post idea might be missing some obvious, core insight, or just replicating some other writing you haven’t come across. I think this is mainly a problem only inasmuch as it could’ve been more efficient for you to learn things than slowly craft a post.
So if you can write (a rough version of) the post quickly, you could just do that.
Or you could ask around or make a quick Question post to outline the basic idea and ask if anyone knows of relevant things you should read.
Reading Bryan Caplan and Zach Weinersmith’s new book has made me somewhat more skeptical about Open Borders (from a high prior belief in its value).
Before reading the book, I was already aware of the core arguments (eg, Michael Huemer’s right to immigrate, basic cosmopolitanism, some vague economic stuff about doubling GDP).
I was hoping the book will have more arguments, or stronger versions of the arguments I’m familiar with.
It mostly did not.
The book did convince me that the prima facie case for open borders was stronger than I thought. In particular, the section where he argued that a bunch of different normative ethical theories should all-else-equal lead to open borders was moderately convincing. I think it will have updated me towards open borders if I believed in stronger “weight all mainstream ethical theories equally” moral uncertainty, or if I previously had a strong belief in a moral theory that I previously believed was against open borders.
However, I already fairly strongly subscribe to cosmopolitan utilitarianism and see no problem with aggregating utility across borders. Most of my concerns with open borders are related to Chesterton’s fence, and Caplan’s counterarguments were in three forms:
1. Doubling GDP is so massive that it should override any conservativism prior.2. The US historically had Open Borders (pre-1900) and it did fine.3. On the margin, increasing immigration in all the American data Caplan looked at didn’t seem to have catastrophic cultural/institutional effects that naysayers claim.
I find this insufficiently persuasive.___Let me outline the strongest case I’m aware of against open borders:Countries are mostly not rich and stable because of the physical resources, or because of the arbitrary nature of national boundaries. They’re rich because of institutions and good governance. (I think this is a fairly mainstream belief among political economists). These institutions are, again, evolved and living things. You can’t just copy the US constitution and expect to get a good government (IIRC, quite a few Latin American countries literally tried and failed).
We don’t actually understand what makes institutions good. Open Borders means the US population will ~double fairly quickly, and this is so “out of distribution” that we should be suspicious of the generalizability of studies that look at small marginal changes.____I think Caplan’s case is insufficiently persuasive because a) it’s not hard for me to imagine situations bad enough to be worse than doubling GDP is good, 2)Pre-1900 US was a very different country/world, 3) This “out of distribution” thing is significant.
I will find Caplan’s book more persuasive if he used non-US datasets more, especially data from places where immigration is much higher than the US (maybe within the EU or ASEAN?).
I’m still strongly in favor of much greater labor mobility on the margin for both high-skill and low-skill workers. Only 14.4% of the American population are immigrants right now, and I suspect the institutions are strong enough that changing the number to 30-35% is net positive. [EDIT: Note that this is intuition rather than something backed by empirical data or explicit models]
I’m also personally in favor (even if it’s negative expected value for the individual country) of a single country (or a few) trying out open borders for a few decades and for the rest of us to learn from their successes and failures. But that’s because of an experimentalist social scientist mindset where I’m perfectly comfortable with “burning” a few countries for the greater good (countries aren’t real, people are), and I suspect the government of most countries aren’t thrilled about this.
Overall, 4⁄5 stars. Would highly recommend to EAs, especially people who haven’t thought much about the economics and ethics of immigration.
If you email this to him, maybe adding a bit more polish, I’d give ~40% odds he’ll reply on his blog, given how much he loves to respond to critics who take his work seriously.
It’s not hard for me to imagine situations bad enough to be worse than doubling GDP is good
I actually find this very difficult without envisioning extreme scenarios (e.g. a dark-Hansonian world of productive-but-dissatisfied ems). Almost any situation with enough disutility to counter GDP doubling seems like it would, paradoxically, involve conditions that would reduce GDP (war, large-scale civil unrest, huge tax increases to support a bigger welfare state).
Could you give an example or two of situations that would fit your statement here?
Almost any situation with enough disutility to counter GDP doubling seems like it would, paradoxically, involve conditions that would reduce GDP (war, large-scale civil unrest, huge tax increases to support a bigger welfare state).
I think there was substantial ambiguity in my original phrasing, thanks for catching that!
I think there are at least four ways to interpret the statement.
1. Interpreting it literally: I am physically capable (without much difficulty) of imagining situations that are bad to a degree worse than doubling GDP is good.
2. Caplan gives some argument for doubling of GDP that seems persuasive, and claims this is enough to override a conservatism prior, but I’m not confident that the argument is true/robust, and I think it’s reasonable to believe that there are possible bad consequences that are bad enough that even if I give >50% probability (or >80%), this is not automatically enough to override a conservatism prior, at least not without thinking about it a lot more.
3. Assume by construction that world GDP will double in the short term. I still think there’s a significant chance that the world will be worse off.
4. Assume by construction that world GDP will double, and stay 2x baseline until the end of time. I still think there’s a significant chance that the world will be worse off.
To be clear, when writing the phrasing, I meant it in terms of #2. I strongly endorse #1 and tentatively endorse #3, but I agree that if you interpreted what I meant as #4, what I said was a really strong claim and I need to back it up more carefully.
Makes sense, thanks! The use of “doubling GDP is so massive that...” made me think that you were taking that as given in this example, but worrying that bad things could result from GDP-doubling that justified conservatism. That was certainly only one of a few possible interpretations; I jumped too easily to conclusions.
That was not my intent, and it was not the way I parsed Caplan’s argument.
Do people have advice on how to be more emotionally resilient in the face of disaster?
I spent some time this year thinking about things that are likely to be personally bad in the near-future (most salient to me right now is the possibility of a contested election + riots, but this is also applicable to the ongoing Bay Area fires/smoke and to a lesser extent the ongoing pandemic right now, as well as future events like climate disasters and wars). My guess is that, after a modicum of precaution, the direct objective risk isn’t very high, but it’ll *feel* like a really big deal all the time.
In other words, being perfectly honest about my own personality/emotional capacity, there’s a high chance that if the street outside my house is rioting, I just won’t be productive at all (even if I did the calculations and the objective risk is relatively low).
So I’m interested in anticipating this phenomenon and building emotional resilience ahead of time so such issues won’t affect me as much.
I’m most interested in advice for building emotional resilience for disaster/macro-level setbacks. I think it’d also be useful to build resilience for more personal setbacks (eg career/relationship/impact), but I naively suspect that this is less tractable.
The last newsletter from Spencer Greenberg/Clearer Thinking might be helpful:
Wow, reading this was actually surprisingly helpful for some other things I’m going through. Thanks for the link!
I think it is useful to separately deal with the parts of a disturbing event over which you have an internal or external locus of control. Let’s take a look at riots:
An external part is them happening in your country. External locus of control means that you need to accept the situation. Consider looking into Stoic literature and exercises (say, negative visualizations) to come to peace with that possibility.
An internal part is being exposed to dangers associated with them. Internal locus of control means that you can take action to mitigate the risks. Consider having a plan to temporarily move to a likely peaceful area within your country or to another county.
Recently I was asked for tips on how to be less captured by motivated reasoning and related biases, a goal/quest I’ve slowly made progress on for the last 6+ years. I don’t think I’m very good at this, but I do think I’m likely above average, and it’s also something I aspire to be better at. So here is a non-exhaustive and somewhat overlapping list of things that I think are helpful:
Treat this as a serious problem that needs active effort.
Personally, I repeat the mantra “To see the world as it is, not as I wish it to be” on a near-daily basis. You may have other mantras or methods to invoke this mindset that works better for you.
In general, try to be more of a “scout” and less of a soldier/zealot.
As much as possible, try to treat ideas/words/stats as tools to aid your perception, not weapons to advance your cause or “win” arguments.
I’ve heard good things about this book by Julia Galef, but have not read it
Relatedly, have a mental check and try to notice when you are being emotional and in danger of succumbing to motivated reasoning. Always try to think as coherently as you can if possible, and acknowledge it (preferably publicly) when you notice you’re not.
sometimes, you don’t notice this in real-time, but only long afterwards.
If this happens, try to postmortem your mistakes and learn from them.
In my case, I noticed myself having a bunch of passion that’s neither truth- nor utility- tracking with the whole Scott Alexander/NYT debacle.
I hope to not repeat this mistake.
Be precise with your speech, and try not to hide behind imprecise words that can mean anything.
especially bad to have words/phrases that connotes a lot of stuff while having empty denotation
Relatedly, make explicit public predictions or monetary bets whenever possible.
Just the fact that you’re betting either your $s or your reputation on a prediction often reminds you to be concrete and aware of your own BS.
But it’s also helpful to learn from and form your own opinions on how to have relatively accurate+well-calibrated forecasts.
Surround yourself with people who you respect as people who care a lot about the truth and try to seek it
For humans (well, at least me), clear thinking is often at least a bit of a social practice.
Just being around them helps make you try to emulate their behavior
Even more helpful if they’re at least a little bit confrontational and willing to call you out on your BS.
If you are in a situation where surrounding yourself with truthseekers is not a practical solution for your offline interactions (eg live in a small town, most government bureaucracy work), make “people who are extremely invested in truth-seeking” a top priority in the content you consume (in blog posts, social media, books, videocalls, and so forth).
Conversely, minimize the number of conversations (online and otherwise) where you feel like your interlocutor can’t change your mind
Maybe this is just a rephrase of a soldier/scout mindset distinction?
But I find it a helpful heuristic anyway.
In general, I think conversations where I’m trying to “persuade” the other person while not being open to being persuaded myself are a) somewhat impolite/disrespectful and b) has a corrosive effect (not just costly in time) to my intellectual progress
This doesn’t apply to everything important (eg intro to EA talks)
But nonetheless a good heuristic to have. If your job/hobbies pushes you to be in an “expert talking to non-expert” roles too much, you need to actively fight the compulsion to getting high on your own epistemic supply.
My personal view is that this sense of righteous certainty is more corrosive with one-to-one conversations than one-to-many conversations, and worse online than offline, but it’s just a personal intuition and I haven’t studied this rigorously.
Be excited to learn facts about the world, and about yourself.
I mostly covered psychological details here, but learning true facts about the world a) is directly useful in limiting your ability to lie about facts and b) somewhat constrains your thinking to reasoning that can explain actual facts about the world.
Critical thinking is not just armchair philosophy, but involves empirical details and hard-fought experience.
See Zeynep on Twitter
A robust understanding of facts can at least somewhat make up for epistemic shortcomings, and not necessarily vice versa.
I think it might be interesting/valuable for someone to create “list of numbers every EA should know”, in a similar vein to Latency Numbers Every Programmer Should Know and Key Numbers for Cell Biologists.
One obvious reason against this is that maybe EA is too broad and the numbers we actually care about are too domain specific to specific queries/interests, but nonetheless I still think it’s worth investigating.
I think this is a great idea.
I love this idea! Lots of fun ways to make infographics out of this, too.
Want to start out by turning this into a Forum question where people can suggest numbers they think are important? (If you don’t, I plan to steal your idea for my own karmic benefit.)
Thanks for the karmically beneficial tip! I’ve now posted this question in its own right.
I find the unilateralist’s curse a particularly valuable concept to think about. However, I now worry that “unilateralist” is an easy label to tack on, and whether a particular action is unilateralist or not is susceptible to small changes in framing.
Consider the following hypothetical situations:
Company policy vs. team discretion
Alice is a researcher in a team of scientists at a large biomedical company. While working on the development of an HIV vaccine, the team accidentally created an air-transmissible variant of HIV. The scientists must decide whether to publish their discovery with the rest of the company, knowing that leaks may exist, and the knowledge may be used to create a devastating biological weapon, but also that it could help those who hope to develop defenses against such weapons, including other teams within the same company. Most of the team thinks they should keep it quiet, but company policy is strict that such information must be shared with the rest of the company to maintain the culture of open collaboration.
Alice thinks the rest of the team should either share this information or quit. Eventually, she tells her vice president her concerns, who relayed it to the rest of the company in a company-open document.
Alice does not know if this information ever leaked past the company.
Stan and the bomb
Stan is an officer in charge of overseeing a new early warning system intended to detect (nuclear) intercontinental ballistic missiles from an enemy country. A warning system appeared to have detected five missiles heading towards his homeland, quickly going through 30 early layers of verification. Stan suspects this is a false alarm, but is not sure. Military instructions are clear that such warnings must immediately be relayed upwards.
Stan decided not to relay the message to his superiors, on the grounds that it was probably a false alarm and he didn’t want his superiors to mistakenly assume otherwise and therefore start a catastrophic global nuclear war.
Listen to the UN, or other countries with similar abilities?
Elbonia, a newly founded Republic, has an unusually good climate engineering program. Elbonian scientists and engineers are able to develop a comprehensive geo-engineering solution that they believe can reverse the climate crisis at minimal risk. Further, the United Nations’ General Assembly recently passed a resolution that stated in no uncertain terms that any nation in possession of such geo-engineering technology must immediately a) share the plans with the rest of the world and b) start the process of lowering the world’s temperature by 2 °C.
However, there’s one catch: Elbonian intelligence knows (or suspects) that five other countries have developed similar geo-engineering plans, but have resolutely refused to release or act on them. Furthermore, four of the five countries have openly argued that geo-engineering is dangerous and has potentially catastrophic consequences, but refused to share explicit analysis why (Elbonia’s own risk assessment finds little evidence of such dangers).
Reasoning that he should be cooperative with the rest of the world, the prime minister of Elbonia made the executive decision to obey the General Assembly’s resolution and start lowering the world’s temperature.
Cooperation with future/past selves, or other people?
Ishmael’s crew has a white elephant holiday tradition, where individuals come up with weird and quirky gifts for the rest of the crew secretly, and do not reveal what the gifts are until Christmas. Ishmael comes up with a brilliant gift idea and hides it.
While drunk one day with other crew members, Ishmael accidentally lets slip that he was particularly proud of his idea. The other members egg him on to reveal more. After a while, Ishmael finally relents when some other crew members reveal their ideas, reasoning that he shouldn’t be a holdout. Ishmael suspects that he will regret his past self’s decision when he becomes more sober.
Putting aside whether the above actions were correct or not, in each of the above cases, have the protagonists acted unilaterally?
I think this is a hard question to answer. My personal answer is “yes,” but I think another reasonable person can easily believe that the above protagonists were fully cooperative. Further, I don’t think the hypothetical scenarios above were particularly convoluted edge cases. I suspect that in real life, figuring out whether the unilateralist’s curse applies to your actions will hinge on subtle choices of reference classes. I don’t have a good solution to this.
I really like this (I think you could make it top level if you wanted). I think these of these are cases of multiple levels of cooperation. If you’re part of an organization that wants to be uncooperative (and you can’t leave cooperatively), then you’re going to be uncooperative with one of them.
Good point. Now that you bring this up, I vaguely remember a Reddit AMA where an evolutionary biologist made the (obvious in hindsight, but never occurred to me at the time) claim that with multilevel selection, altruism on one level is often means defecting on a higher (or lower) level. Which probably unconsciously inspired this post!
As for making it top level, I originally wanted to include a bunch of thoughts on the unilateralist’s curse as a post, but then I realized that I’m a one-trick pony in this domain...hard to think of novel/useful things that Bostrom et. al hasn’t already covered!
Edit: By figuring out ethics I mean both right and wrong in the abstract but also what the world empirically looks like so you know what is right and wrong in the particulars of a situation, with an emphasis on the latter.I think a lot about ethics. Specifically, I think a lot about “how do I take the best action (morally), given the set of resources (including information) and constraints (including motivation) that I have.” I understand that in philosophical terminology this is only a small subsection of applied ethics, and yet I spend a lot of time thinking about it.
One thing I learned from my involvement in EA for some years is that ethics is hard. Specifically, I think ethics is hard in the way that researching a difficult question or maintaining a complicated relationship or raising a child well is hard, rather than hard in the way that regularly going to the gym is hard.
When I first got introduced to EA, I believed almost the opposite (this article presents something close to my past views well): that the hardness of living ethically is a matter of execution and will, rather than that of constantly making tradeoffs in a difficult-to-navigate domain.
I still think the execution/will stuff matters a lot, but now I think it is relatively more important to be making the right macro- and micro- decisions regularly.
I don’t have strong opinions on what this means for EA at the margin, or for individual EAs. For example, I’m not saying that this should push us much towards risk aversion or other conservatism biases (there are tremendous costs, too, to inaction!) Perhaps this is an important lesson for us to communicate to new EAs, or to non-EAs we have some influence over. But there are so many useful differences/divergences, and I’m not sure this should really be prioritized all that highly as an explicit introductory message.But at any rate, I feel like this is an important realization in my own growth journey, and maybe it’d be helpful for others on this forum to realize that I made this update.
I’m worried about a potential future dynamic where an emphasis on forecasting/quantification in EA (especially if it has significant social or career implications) will have adverse effects on making people bias towards silence/vagueness in areas where they don’t feel ready to commit to a probability forecast.
I think it’s good that we appear to be moving in the direction of greater quantification and being accountable for probability estimates, but I think there’s the very real risk that people see this and then become scared of committing their loose thoughts/intuitive probability estimates on record. This may result in us getting overall worse group epistemics because people hedge too much and are unwilling to commit to public probabilities.
See analogy to Jeff Kaufman’s arguments on responsible transparency consumption:
I’ve started trying my best to consistently address people on the EA Forum by username whenever I remember to do so, even when the username clearly reflects their real name (eg Habryka). I’m not sure this is the right move, but overall I think this creates slightly better cultural norms since it pushes us (slightly) towards pseudonymous commenting/”Old Internet” norms, which I think is slightly better for pushing us towards truth-seeking and judging arguments by the quality of the arguments rather than be too conscious of status-y/social monkey effects. (It’s possible I’m more sensitive to this than most people).
I think some years ago there used to be a belief that people will be less vicious (in the mean/dunking way) and more welcoming if we used Real Name policies, but I think reality has mostly falsified this hypothesis.
Malaria kills a lot more people >age 5 than I would have guessed (Still more deaths <=5 than >5, but a much smaller ratio than I intuitively believed). See C70-C72 of GiveWell’s cost-effectiveness estimates for AMF, which itself comes from the Global Burden of Disease Study.
I’ve previously cached the thought that malaria primarily kills people who are very young, but this is wrong.
I think the intuition slip here is that malaria is a lot more fatal for young people. However, there are more older people than younger people.
Should there be a new EA book, written by somebody both trusted by the community and (less importantly) potentially externally respected/camera-friendly?Kinda a shower thought based on the thinking around maybe Doing Good Better is a bit old right now for the intended use-case of conveying EA ideas to newcomers.I think the 80,000 hours and EA handbooks were maybe trying to do this, but for whatever reason didn’t get a lot of traction?I suspect that the issue is something like not having a sufficiently strong “voice”/editorial line, and what you want for a book that’s a)bestselling and b) does not sacrifice nuance too much is one final author + 1-3 RAs/ghostwriters.
Does the Precipice count? And I think Will Macaskill is writing a new book.
But I have the vague sense that public-facing books may be good for academics’ careers anyway. Evidence for this intuition:
(1) Where EA academics have written them, they seem to be more highly cited than a lot of their other publications, so the impact isn’t just “the public” (see Google Scholar pages for Will Macaskill, Toby Ord, Nick Bostrom, Jacy Reese Anthis—and let me know if there are others who have written public-facing books! Peter Singer would count but has no Google Scholar page)
(2) this article about the impact of Wikipedia. It’s not about public-facing books but fits into my general sense that “widely viewed summary content by/about academics can influence other academics” https://conference.druid.dk/acc_papers/2862e909vshtezgl6d67z0609i5bk6.pdf
Plus all the usual stuff about high fidelity idea transmission being good.
So yes, more EA books would be good?
I think The Precipice is good, both directly and as a way to communicate a subsection of EA thought, but EA thought is not predicated on a high probability of existential risk, and the nuance might be lost on readers if The Precipice becomes the default “intro to EA” book.
In the Precipice, Toby Ord very roughly estimates that the risk of extinction from supervolcanoes this century is 1⁄10,000 (as opposed to 1⁄10,000 from natural pandemics, 1⁄1,000 from nuclear war, 1⁄30 from engineered pandemics and 1⁄10 from AGI). Should more longtermist resources be put into measuring and averting the worst consequences of supervolcanic eruption?
More concretely, I know a PhD geologist who’s interested in doing an EA/longtermist career and is currently thinking of re-skilling for AI policy. Given that (AFAICT) literally zero people in our community currently works on supervolcanoes, should I instead convince him to investigate supervolcanoes at least for a few weeks/months?
If he hasn’t seriously considered working on supervolcanoes before, then it definitely seems worth raising the idea with him.
I know almost nothing about supervolcanoes, but, assuming Toby’s estimate is reasonable, I wouldn’t be too surprised if going from zero to one longtermist researcher in this area is more valuable than adding an additional AI policy researcher.
The biggest risk here I believe is anthropogenic; supervolcanoes could theoretically be weaponized.
What will a company/organization that has a really important secondary mandate to focus on general career development of employees actually look like? How would trainings be structured, what would growth trajectories look like, etc?
When I was at Google, I got the distinct impression that while “career development” and “growth” were common buzzwords, most of the actual programs on offer were more focused on employee satisfaction/retention than growth. (For example, I’ve essentially never gotten any feedback on my selection of training courses or books that I bought with company money, which at the time I thought was awesome flexibility, but in retrospect was not a great sign of caring about growth on the part of the company).
Edit: Upon a reread I should mention that there are other ways for employees to grow within the company, eg by having some degree of autonomy over what projects they want to work on.
I think there are theoretical reasons for employee career growth being underinvested by default. Namely, that the costs of career growth are borne approximately equally between the employer and the employee (obviously this varies from case to case), while the benefits of career growth are mostly accrued by the employee and their future employers.
This view will predict that companies will mostly only invest in general career development/growth of employees if one of a number of conditions are met:
The investment is so valuable that it pays for itself over the expected length of the employee’s tenor
The “investment” has benefits to the company (and hopefully the employee as well) other than via building the employee’s skillsets. Eg, it makes the employee more likely to stay at the company.
Relatedly, the investment only grows (or disproportionately grows) the employee’s skillset in ways that’s relevant to the work of the company, and not that of other workplaces (even close competitors)
There are principal-agent problems within the company, such that managers are not always acting in the company’s best interest when they promote career development for their underlings.
I suppose that in contrast to companies, academia is at least incentivized to focus on general career development (since professors are judged at least somewhat on the quality of their graduate students’ outputs/career trajectories). I don’t know in practice how much better academia is than industry however. (It is at least suggestive that people often take very large pay cuts to do graduate school).
I think the question of how to do employee career development well is particularly interesting/relevant to EA organizations, since there’s a sense in which developing better employees is a net benefit to “team EA” even if your own org doesn’t benefit, or might die in a year or three. A (simplified) formal view of this is that effective altruism captures the value of career development over the expected span of someone continuing to do EA activities.*
*eg, doing EA-relevant research or policy work, donating, working at an EA org, etc.
Definitely agreed. That said, I think some of this should probably be looked through the lens of “Should EA as a whole help people with personal/career development rather than specific organizations, as the benefits will accrue to the larger community (especially if people only stay at orgs for a few years).I’m personally in favor of expensive resources being granted to help people early in their careers. You can also see some of this in what OpenPhil/FHI funds; there’s a big focus on helping people get useful PhDs. (though this helps a small minority of the entire EA movement)
I’m interested in a collection of backchaining posts by EA organizations and individuals, that traces back from what we want—an optimal, safe, world—back to specific actions that individuals and groups can take.Can be any level of granularity, though the more precise, the better.
Interested in this for any of the following categories:
General GCR reduction
I think a sort-of relevant collection can be found in the answers to this question about theory of change diagrams. And those answers also include other relevant discussion, like the pros and cons of trying to create and diagrammatically represent explicit theories of change. (A theory of change diagram won’t necessarily exactly meet your criteria, in the sense that it may backchain from an instrumental rather than intrinsic goal, but it’s sort-of close.)
The answers in that post include links to theory of change diagrams from Animal Charity Evaluators (p.15), Convergence Analysis, Happier Lives Institute, Leverage Research, MIRI, and Rethink Priorities. Those are the only 6 research orgs I know of which have theory of change diagrams. (But that question was just about research orgs, and having such diagrams might be somewhat more common among non-research organisations.)
I think Leverage’s diagram might be the closest thing I know of to a fairly granular backchaining from one’s ultimate goals. It also seems to me quite unwieldy—I spent a while trying to read it once, but it felt annoying to navigate and hard to really get the overall gist of. (That was just my personal take, though.)
One could also argue that Toby Ord’s “grand strategy for humanity” is a very low-granularity instance of backchaining from one’s ultimate goals. And it becomes more granular once one connects the first step of the grand strategy to other specific recommendations Ord makes in The Precipice.
(I know you and I have already discussed some of this; this comment was partly for other potential readers’ sake.)
 For readers who haven’t read The Precipice, Ord’s quick summary of the grand strategy is as follows:
I think that at the highest level we should adopt a strategy proceeding in three phases:Reaching Existential SecurityThe Long ReflectionAchieving Our Potential
I think that at the highest level we should adopt a strategy proceeding in three phases:
Reaching Existential Security
The Long Reflection
Achieving Our Potential
The book contains many more details on these terms and this strategy, of course.
It has occurred to me that very few such documents exist.
I’m curious what it looks like to backchain from something so complex. I’ve tried it repeatedly in the past and feel like I failed.
I’d appreciate a 128kb square version of the lightbulb/heart EA icon with a transparent background, as a Slack emoji.
Not 128kb (Slack resized it for me) but this worked for me
I continue to be fairly skeptical that the all-things-considered impact of EA altruistic interventions differ by multiple ( say >2) orders of magnitude ex ante (though I think it’s plausible ex post). My main crux here is that I believe general meta concerns start dominating once the object-level impacts are small enough.This is all in terms of absolute value of impact. I think it’s quite possible that some interventions have large (or moderately sized) negative impact, and I don’t know how the language of impact in terms of multiplication best deals with this.
By “meta concerns”, do you mean stuff like base rate of interventions, risk of being wildly wrong, methodological errors/biases, etc.? I’d love it if you could expand a bit.
Also, did you mean that these dominate when object-level impacts are big enough?
By “meta concerns”, do you mean stuff like base rate of interventions, risk of being wildly wrong, methodological errors/biases, etc.?
Hmm I think those are concerns too, but I guess I was primarily thinking about meta-EA concerns like whether an intervention increases or decreases EA prestige, willingness of new talent to work on EA organizations, etc.
No. Sorry I was maybe being a bit confusing with my language. I mean to say that when comparing two interventions, the meta-level impacts of the less effective intervention will dominate if you believe the object-level impact of the less effective intervention is sufficiently small. Consider two altruistic interventions, direct AI Safety research and forecasting. Suppose that you did the analysis and think the object-level impact of AI Safety research is X (very high) and the impact of forecasting is only 0.0001X.
(This is just an example. I do not believe that the value of forecasting is 10,000 times lower than AI Safety research). I think it will then be wrong to think that the all-things-considered value of an EA doing forecasting is 10,000 times lower than the value of an EA doing direct AI Safety research, if for no other reason than because EAs doing forecasting has knock-on effects on EAs doing AI Safety. If the object-level impacts of the less effective intervention are big enough, then it’s less obvious that the meta-level impacts will dominate. If your analysis instead gave a value of forecasting as 3x less impactful than AIS research, then I have to actually present a fairly strong argument for why the meta-level impacts may still dominate, whereas I think it’s much more self-evident at the 10,000x difference level. Let me know if this is still unclear, happy to expand. Oh, also a lot of my concerns (in this particular regard) mirror Brian Tomasik’s, so maybe it’d be easier to just read his post.
Thanks, much clearer! I’ll paraphrase the crux to see if I understand you correctly:
If the EA community is advocating for interventions X and Y, then more resources R going into Y leads to more resources going into X (within about R/10^2).
Is this what you have in mind?
Yes, though I’m strictly more confident about absolute value than the change being positive (So more resources R going into Y can also eventually lead to less resources going into X, within about R/10^2).
And the model is that increased resources into main EA cause areas generally affects the EA movement by increasing its visibility, diverting resources from that cause area to others, and bringing in more people in professional contact with EA orgs/people—those general effects trickle down to other cause areas?
Yes that sounds right. There are also internal effects in framing/thinking/composition that by itself have flow-through effects that are plausibly >1% in expectation.For example, more resources going into forecasting may cause other EAs to be more inclined to quantify uncertainty and focus on the quantifiable, with both potentially positive and negative flow-through effects, more resources going into medicine- or animal welfare- heavy causes will change the gender composition of EA, and so forth.
Thanks again for the clarification!
I think that these flow-through effects mostly apply to specific targets for resources that are more involved with the EA-community. For example, I wouldn’t expect more resources going into efforts by Tetlock to improve the use of forecasting in the US government to have visible flow-through effects on the community. Or more resources going into AMF are not going to affect the community.
I think that this might apply particularly well to career choices.
Also, if these effects are as large as you think, it would be good to more clearly articulate what are the most important flow-through effects and how do we improve the positives and mitigate the negatives.
Minor UI note: I missed the EAIF AMA multiple times (even after people told me it existed) because my eyes automatically glaze over pinned tweets. I may be unusual in this regard, but thought it worth flagging anyway.
Do people have thoughts on what the policy should be on upvoting posts by coworkers? Obviously telling coworkers (or worse, employees!) to upvote your posts should be verboten, and having a EA forum policy that you can’t upvote posts by coworkers is too draconian (and also hard to enforce). But I think there’s a lot of room in between to form a situation like “where on average posts by people who work at EA orgs will have more karma than posts of equivalent semi-objective quality.” Concretely, 2 mechanisms in which this could happen (and almost certainly does happen, at least for me):
1. For various reasons, I’m more likely to read posts by people who are coworkers. Since EAF users have a bias towards upvoting more than downvoting, by default I’d expect this to lead to a higher expected karma for coworkers.2. I’m more likely to give people I know well a benefit of a doubt, and read their posts more charitably. This leads to higher expected karma.
3. I’m at least slightly more likely to respond to comments/posts by coworkers, since I have a stronger belief that they will reply. Since my default forum usage behavior is to upvote replies to my questions (as long as they are even remotely pertinent), this increases karma.#2 seems like a “me problem”, and is an internal problem/bias I am optimistic about being able to correct for. #3 and especially #1 on the other hand seems like something that’s fairly hard to correct for unless we have generalized policies or norms. (One example of a potential norm is to say that people should only upvote posts by coworkers if they think they’re likely to have read the post if they were working in a different field/org, or only upvote with a certain random probability proportional to such).
I’d prefer that people on the Forum not have to worry too much about norms of this kind. If you see a post or comment you think is good, upvote it. If you’re worried that you and others at your org have unequal exposure to coworkers’ content, make a concerted effort to read other Forum posts as well, or even share those posts within your organization.
That said, if you want to set a norm for yourself or suggest one for others, I have no problem with that — I just don’t see the Forum adopting something officially. Part of the problem is that people often have friends or housemates at other orgs, share an alma mater or cause area with a poster. etc. — there are many ways to be biased by personal connections, and I want to push us toward reading and supporting more things rather than trying to limit the extent to which people express excitement out of concern for these biases.
crossposted from LessWrong
There should maybe be an introductory guide for new LessWrong users coming in from the EA Forum, and vice versa.
I feel like my writing style (designed for EAF) is almost the same as that of LW-style rationalists, but not quite identical, and this is enough to be substantially less useful for the average audience member there.
For example, this identical question is a lot less popular on LessWrong than on the EA Forum, despite naively appearing to appeal to both audiences (and indeed if I were to guess at the purview of LW, to be closer to the mission of that site than that of the EA Forum).
I do agree that there are notable differences in what writing styles are often used and appreciated on the two sites.
Could this also be simply because of a difference in the extent to which people already know your username and expect to find posts from it interesting on the two sites? Or, relatedly, a difference in how many active users on each site you know personally?
I’m not sure how much those factors affect karma and comment numbers on either site, but it seems plausible that the have a substantial affect (especially given how an early karma/comment boost can set off a positive feedback loop).
Also, have you crossposted many things and noticed this pattern, or was it just a handful? I think there’s a lot of “randomness” in karma and comment numbers on both sites, so if it’s just been a couple crossposts it seems hard to be confident that any patterns would hold in future.
Personally, when I’ve crossposted something to the EA Forum and to LessWrong, those posts have decently often gotten more karma on the Forum and decently often the opposite, and (from memory) I don’t think there’s been a strong tendency in one direction or the other.
Could this also be simply because of a difference in the extent to which people already know your username and expect to find posts from it interesting on the two sites? Or, relatedly, a difference in how many active users on each site you know personally?
Yeah I think this is plausible. Pretty unfortunate though.
Also, have you crossposted many things and noticed this pattern, or was it just a handful? Hmm, I have 31 comments on LW, and maybe half of them are crossposts?
I don’t ever recall having a higher karma on LW than the Forum, though I wouldn’t be surprised if it happened once or twice.
Are there any EAA researchers carefully tracking the potential of huge cost-effectiveness gains in the ag industry from genetic engineering advances of factory farmed animals? Or (less plausibly) advances from better knowledge/practice/lore from classical artificial selection? As someone pretty far away from the field, a priori the massive gains made in biology/genetics in the last few decades seems like something that we plausibly have not priced in in. So it’d be sad if EAAs get blindsided by animal meat becoming a lot cheaper in the next few decades (if this is indeed viable, which it may not be).
Besides just extrapolating trends in cost of production/prices, I think the main things to track would be feed conversion ratios and the possibility of feeding animals more waste products or otherwise cheaper inputs, since feed is often the main cost of production. Some FCRs are already < 2 and close to 1, e.g. it takes less than 2kg of input to get 1kg of animal product (this could be measured in just weight, calories, protein weight, etc..), e.g. for chickens, some fishes and some insects.
I keep hearing that animal protein comes from the protein in what animals eat (but I think there are some exceptions, at least), so this would put a lower bound of 1 on FCR in protein terms, and there wouldn’t be much further to go for animals close to that.
I think a lower bound of around 1 for weight of feed to weight of animal product also makes sense, maybe especially if you ignore water in and out.
So, I think chicken meat prices could roughly at most halve again, based on these theoretical limits, and it’s probably much harder to keep pushing. Companies are also adopting less efficient breeds to meet welfare standards like the Better Chicken Commitment, since these breeds have really poor welfare due to their accelerated growth.
This might be on Lewis Bollard’s radar, since he has written about the cost of production, prices and more general trends in animal agriculture.
This post may be of interest, in case you haven’t seen it already.
Yep, aware of this! Solid post.
I’m now pretty confused about whether normative claims can be used as evidence in empirical disputes. I generally believed no, with the caveat that for humans, moral beliefs are built on a scaffolding of facts, and sometimes it’s easier to respond to an absurd empirical claim with the moral claim that has the gestalt sense of empirical beliefs if there isn’t an immediately accessible empirical claim.
I talked to a philosopher who disagreed, and roughly believed that strong normative claims can be used as evidence against more confused/less certain empirical claims, and I got a sense from the conversation that his view is much more common in academic philosophy than mine.
Would like to investigate further.
I haven’t really thought about it, but it seems to me that if an empirical claim implies an implausible normative claim, that lowers my subjective probability of the empirical claim.
Updated version on https://docs.google.com/document/d/1BDm_fcxzmdwuGK4NQw0L3fzYLGGJH19ksUZrRloOzt8/edit?usp=sharing
Cute theoretical argument for #flattenthecurve at any point in the distribution
What is #flattenthecurve?
The primary theory behind #flattenthecurve is assuming that everybody who will get COVID-19 will eventually get it anyway...is there anything else you can do?
It turns out it’s very valuable to
Delay the spread so that a) the peak of the epidemic spread is lower (#flattenthecurve)
Also to give public health professionals, healthcare systems, etc more time to respond (see diagram below)
A tertiary benefit is that ~unconstrained disease incidence (until it gets to herd immunity levels) is not guaranteed, with enough time to respond, aggressive public health measures (like done in Wuhan, Japan, South Korea etc) can arrest the disease at well below herd immunity levels
Why should you implement #flattenthecurve
If you haven’t been living under a rock, you’ll know that COVID-19 is a big deal
We have nowhere near the number of respirators, ICU beds, etc, for the peak of uncontrolled transmission (Wuhan ran out of ICU beds, and they literally built a dozen hospitals in a week, a feat Western governments may have trouble doing)
https://www.flattenthecurve.com/ has more detailed arguments
What are good #flattenthecurve policies?
The standard stuff like being extremely aggressive about sanitation and social distancing
https://www.flattenthecurve.com/ has more details
When should you implement #flattenthecurve policies?
A lot of people are waiting for specific “fire alarms” (eg, public health authorities sounding the bell, the WHO calling it a pandemic, X cases in a city) before they start taking measures.
I think this is wrong.
The core (cute) theoretical argument I have is that if you think #flattenthecurve is at all worth doing at any time, as long as you’re confident you are on the growth side of the exponential growth curve, slowing the doubling time from X days (say) to 2X days is good for #flattenthecurve and public health perspective no matter where you are on the curve.
Okay, let’s consider a few stricter versions of the problem
Exponential growth guaranteed + all of society
One way to imagine this is if #society all implemented your policy (because of some Kantian or timeless decision theory sense, say)
Suppose you are only willing to take measures for Y weeks, and for whatever reason the measures are only strong enough to slow down the virus’s spread rather than reverse the curve.
if the doubling rate is previously 3 days and everybody doing this can push it down to 8 days (or push it up to 2 days), then it’s roughly equally good (bad) no matter when on the curve you do those measures.
Exponential growth NOT guaranteed + all of society
Next, relax the assumption of exponential growth being guaranteed and assume that measures are strong enough to reverse the curve of exponential growth (as happened in China, South Korea, Japan)
I think you get the same effect where the cost of X weeks of your measures should be the same no matter where you are on the curve, plus now you got rid of the disease (with the added benefit that if you initiate your measures early, less people die/get sick directly and it’s easier to track new cases)
A downside is that a successful containment strategy means you get less moral credit/people will accuse you of fearmongering, etc.
NOT all of society
Of course, as a private actor you can’t affect all of society. Realistically (if you push hard), your actions will be correlated with only a small percentage of society. So relax the assumption that everybody does it, and assume only a few % of people will do the same actions as you.
But I think for #flattenthecurve purposes, the same arguments still roughly hold.
Now you’re just (eg) slowing the growth rate from 3 days to 3.05 days instead of 3 days to 8 days.
But the costs are ~ linear to the number of people who implement #flattenthecurve policies, and the benefits are still invariant to timing.
How do we know that we are on the growth side of the exponential/S curve?
Testing seems to lag actual cases a lot.
My claim is that approximately if your city has at least one confirmed or strongly suspected case of community transmission, you’re almost certainly on the exponential trajectory
Aren’t most other people’s actions different depending on where you are on the curve?
Sure, so maybe some mitigation actions are more effective depending on other people’s actions (eg, refusing to do handshakes may be more effective when not everybody has hand sanitizer than when everybody regularly uses hand sanitizer, for example)
I think the general argument is still the same however
Over the course of an epidemic, wouldn’t the different actions result in different R0 and doubling times, so you’re you’re then doing distancing or whatever from a different base?
Okay, I think this is the best theoretical argument against the clean exponential curve stuff.
I still think it’s not obvious that you should do more #flattenthecurve policies later on, if anything this pushes you to doing it earlier
If you think #flattenthecurve is worthwhile to do at all (which I did not argue for much here, but is extensively argued elsewhere), it’s at least as good to do it now as it is to do it later, and plausibly better to do soon rather than later.
I think it’s really easy to get into heated philosophical discussions about whether EAs overall use too much or too little jargon. Rather than try to answer this broadly for EA as a whole, it might be helpful for individuals to conduct a few quick polls to decide for themselves whether they ought to change their lexicon. Here’s my Twitter poll as one example.
Economic benefits of mediocre local human preferences modeling.
Epistemic status: Half-baked, probably dumb.
Note: writing is mediocre because it’s half-baked.
Some vague brainstorming of economic benefits from mediocre human preferences models.
Many AI Safety proposals include understanding human preferences as one of its subcomponents . While this is not obviously good, human modeling seems at least plausibly relevant and good.
Short-term economic benefits often spur additional funding and research interest [citation not given]. So a possible question to ask if we can get large economic benefits from a system with the following properties (each assumption can later be relaxed):
1. Can run on a smartphone in my pocket
2. Can approximate simple preference elicitations at many times a second
3. Low fidelity, has both high false-positive and false-negative rates
4. Does better on preferences with lots of training data (“in-distribution”)
5. Initially works better on simple preferences (preference elicitations takes me 15 seconds to think about an answer, say), but has continuous economic benefits from better and better models.
An *okay* answer to this question is recommender systems (ads, entertainment). But I assume those are optimized to heck already so it’s hard for an MVP to win.
I think a plausibly better answer to this is market-creation/bidding. The canonical example is ridesharing like Uber/Lyft, which sells a heterogeneous good to both drivers and riders. Right now they have a centralized system that tries to estimate market-clearing prices, but imagine instead if riders and drivers bid on how much they’re willing to pay/take for a ride from X to Y with Z other riders?
Right now, this is absurd because human preference elicitations take up time/attention for humans. If a driver has to scroll through 100 possible rides in her vicinity, the experience will be strictly worse.
But if a bot could report your preferences for you? I think this could make markets a lot more efficient, and also gives a way to price in increasingly heterogeneous preferences. Some examples:
1. I care approximately zero about cleanliness or make of a car, but I’m fairly sensitive to tobacco or marijuana smell. If you had toggles for all of these things in the app, it’d be really annoying.
2. A lot of my friends don’t like/find it stressful to make small talk on a trip, but I’ve talked to drivers who chose this job primarily because they want to talk on the job. It’d be nice if both preferences are priced in.
3. Some riders like drivers who speak their native language, and vice versa.
A huge advantage of these markets is that “mistakes” are pricey but not incredibly so. Ie, I’d rather not overbid for a trip that isn’t worth it, but the consumer/driver surplus from pricing in heterogeneous preferences at all can easily make up for the occasional (or even frequent) mispricing.
There’s probably a continuous extension of this idea to matching markets with increasingly sparse data (eg, hiring, dating).
One question you can ask is why is it advantageous to have this run on a client machine at all, instead of aggregative human preference modeling that lots of large companies (including Uber) already do?
The honest high-level answer is that I guess this is a solution in search of a problem, which is rarely a good sign...
A potential advantage of running it on your smartphone (imagine a plug-in app that runs “Linch’s Preferences” with an API other people can connect to) is that it legally makes the “Marketplace” idea for Uber and companies like Uber more plausible? Like right now a lot of them claim to have a marketplace except they look a lot like command-and-control economies; if you have a personalized bot on your client machine bidding on prices, then I think the case would be easier to sell.
I find it quite hard to do multiple quote-blocks in the same comment on the forum. For example, this comment took one 5-10 tries to get right.
What editor are you using? The default rich text editor? (EA Forum Docs)
What’s the issue?
The default rich text editor. The issue is that if I want to select one line and quote/unquote it, it either a) quotes (unquotes) lines before and after it, or creates a bunch of newlines before and after it. Deleting newlines in quote blocks also has the issue of quoting (unquoting) unintended blocks.Perhaps I should just switch to markdown for comments, and remember to switch back to a rich text editor for copying and pasting top-level posts?
Some ways that I do multiple blockquotes in posts/comments:
Start with nothing in blockquotes. Then, make sure anything I want to blockquote is in its own paragraph. Then, highlight the paragraph and format it as a quote. I haven’t ever seen this pick up earlier/later paragraphs, and any newlines that are created, I can just delete.
You can also start with everything in blockquotes. If you hit “enter” at the end of one paragraph, you’ll create a blank blockquote line between it and the next paragraph. If you hit “enter” from the newline, you’ll end up with two separate blockquoted sections with a non-blockquote line in the middle.
It sounds like you’re seeing some unexpected behavior when you try (1), but I haven’t been able to replicate it. If you want to jump on a quick call to investigate, that might be easier than trying to resolve the issue via text — you can set one up here.
On the forum, it appears to have gotten harder for me to do multiple quote blocks in the same comment. I now often have to edit a post multiple times so quoted sentences are correctly in quote blocks, and unquoted sections are not. Whereas in the past I do not recall having this problem?
I’m going to guess that the new editor is the difference between now and previously. What’s the issue you’re seeing? Is there a difference between the previewed and rendered text? Ideally you could get this to repro on LessWrong’s development server, which would be useful for bug reports, but no worries if not.
Cross-posted from Facebook
On the meta-level, I want to think hard about the level of rigor I want to have in research or research-adjacent projects.
I want to say that the target level of rigor I should have is substantially higher than for typical FB or Twitter posts, and way lower than research papers.
But there’s a very wide gulf! I’m not sure exactly what I want to do, but here are some gestures at the thing:
- More rigor/thought/data collection should be put into it than 5-10 minutes typical of a FB/Twitter post, but much less than a hundred or a few hundred hours on papers.- I feel like there are a lot of things that are worth somebody looking into it for a few hours (more rarely, a few dozen), but nowhere near the level of a typical academic paper?- Examples that I think are reflective of what I’m interested in are some of Georgia Ray and Nuno Sempere’s lighter posts, as well as Brian Bi’s older Quora answers on physics. (back when Quora was good)- “research” has the connotation of pushing the boundaries of human knowledge, but by default I’m more interested in pushing the boundaries of my own knowledge? Or at least the boundaries of my “group’s” knowledge.- If the search for truthful things shakes out to have some minor implications on something no other human currently knows, that’s great, but by default I feel like aiming for novelty is too constraining for my purposes.- Forecasting (the type done on Metaculus or Good Judgment Open) feels like a fair bit of this. Rarely do forecasters (even/especially really good ones) discover something nobody already knows; rather than the difficulty comes almost entirely in finding evidence that’s already “out there” somewhere in the world and then weighing the evidence and probabilities accordingly.- I do think more forecasting should be done. But forecasting itself provides very few bits of information (just the final probability distribution on a well-specified problem). Often, people are also interested in your implicit model, the most salient bits of evidence you discovered, etc. This seems like a good thing to communicate.- It’s not clear what the path to impact here is. Probably what I’m interested in is what Stefan Schubert calls “improving elite epistemics,” but I’m really confused on whether/why that’s valuable.- Not everything I or anybody does has to be valuable, but I think I’d be less excited to do medium rigor stuff if there’s no or minimal impact on the world?- It’s also not clear to me how much I should trust my own judgement (especially in out-of-distribution questions, or questions much harder to numerically specify than forecasting).- How do I get feedback? The obvious answer is from other EAs, but I take seriously worries that our community is overly insular.- Academia, in contrast, has a very natural expert feedback mechanism in peer review. But as mentioned earlier, peer review pre-supposes a very initially high level of rigor that I’m usually not excited about achieving for almost all of my ideas.- Also on a more practical level, it might just be very hard for me to achieve paper-worthy novelty and rigor in all but a handful of ideas?- In the few times in the past I reached out to experts (outside the EA community) for feedback, I managed to get fairly useful stuff, but I strongly suspect this is easier for precise well-targeted questions than some of the other things I’m interested in?- Also varies from field to field, for example a while back I was able to get some feedback on questions like water rights, but I couldn’t find public contact information from climate modeling scientist after a modest search (presumably because the latter is much more politicized these days)- If not for pre-existing connections and connections-of-connections, I also suspect it’d be basically impossible to get ahold of infectious disease or biosecurity people to talk to in 2020.- In terms of format, “blog posts” seems the most natural. But I think blog posts could mean anything from “Twitter post with slightly more characters” to “stuff Gwern writes 10,000 words on.” So doesn’t really answer the question of what to do about the time/rigor tradeoff.
Another question that is downstream of what I want to do is branding. Eg, some people have said that I should call myself an “independent researcher,” but this feels kinda pretentious to me? Like when I think “independent research” I think “work of a level of rigor and detail that could be publishable if the authors wanted to conform to publication standards,” but mostly what I’m interested in is lower quality than that? Examples of what I think of as “independent research” are stuff that Elizabeth van Nostrand, Dan Luu, Gwern, and Alexey Guzey sometimes work on (examples below).
Stefan Schubert on elite epistemics: https://twitter.com/StefanFSchubert/status/1248930229755707394
Negative examples (too little rigor):
- Pretty much all my FB posts?
Negative examples (too much rigor/effort):
- almost all academic papers
- many of Gwern’s posts
- eg https://www.gwern.net/Melatonin
(To be clear, by “negative examples” I don’t mean to associate them with negative valence. I think a lot of those work is extremely valuable to have, it’s just that I don’t think most of the things I want to do are sufficiently interesting/important to spend as much time on. Also on a practical level, I’m not yet strong enough to replicate most work on that level).