Participants in the 2008 FHI Global Catastrophic Risk conference estimated a probability of extinction from nano-technology at 5.5% (weapons + accident) and non-nuclear wars at 3% (all wars—nuclear wars) (the values are on the GCR wikipedia page). In the Precipice, Ord estimated the existential risk of Other anthropogenic risks (noted in the text as including but not limited to nano-technology, and I interpret this as including non-nuclear wars) as 2% (1 in 50). (Note that by definition, extinction risk is a sub-set of existential risk.)
Since starting to engage with EA in 2018 I have seen very little discussion about nano-technology or non-nuclear warfare as existential risks, yet it seems that in 2008 these were considered risks on-par with top longtermist cause areas today (nanotechnology weapons and AGI extinction risks were both estimated at 5%). I realize that Ord’s risk estimates are his own while the 2008 data is from a survey, but I assume that his views broadly represent those of his colleagues at FHI and others the GCR community.
My open question is: what new information or discussion over the last decade lead the GCR to reduce their estimate of the risks posed by (primarily) nano-technology and also conventional warfare?
I too find this an interesting topic. More specifically, I wonder why I’ve seen as little discussion published in the last few years (rather than from >10 years ago) of nanotech as I have. I also wonder about the limited discussion of things like very long-lasting totalitarianism - though there I don’t have reason to believe people recently had reasonably high x-risk estimates; I just sort-of feel like I haven’t yet seen good reason to deprioritise investigating that possible risk. (I’m not saying that there should be more discussion of these topics, and that there are no good reasons for the lack of it, just that I wonder about that.)
I realize that Ord’s risk estimates are his own while the 2008 data is from a survey, but I assume that his views broadly represent those of his colleagues at FHI and others the GCR community.
I’m not sure that’s a safe assumption. The 2008 survey you’re discussing seems to have itself involved widely differing views (see the graphs on the last pages). And more generally, the existential risk and GCR research community seems to have widely differing views on risk estimates (see a collection of side-by-side estimates here).
I would also guess that each individual’s estimates might themselves be relatively unstable from one time you ask them to another, or one particular phrasing of the question to another.
Relatedly, I’m not sure how decision-relevant differences of less than an order of magnitude between different estimates are. (Though such differences could sometimes be decision-relevant, and larger differences more easily could be.)
Thanks Michael, I had seen that but hadn’t looked at the links. Some comments:
The cause report from OPP makes the distinction between molecular nanotechnology and atomically precise manufacturing. The 2008 survey seemed to be explicitly considering weaponised molecular nanotechnology as an extinction risk (I assume the nanotechnology accident was referring to molecular nanotechnology as well). While there seems to be agreement that molecular nanotechnology could be a direct path to GCR/extinction, OPP presents atomically precise manufacturing as being more of an indirect risk, such as through facilitating weapons proliferation. The Grey goo section of the report does resolve my question about why the community isn’t talking about (molecular) nanotechnology as an existential risk as much now (the footnotes are worth reading for more details):
‘Grey goo’ is a proposed scenario in which tiny self-replicating machines outcompete organic life and rapidly consume the earth’s resources in order to make more copies of themselves.40 According to Dr. Drexler, a grey goo scenario could not happen by accident; it would require deliberate design.41 Both Drexler and Phoenix have argued that such runaway replicators are, in principle, a physical possibility, and Phoenix has even argued that it’s likely that someone will eventually try to make grey goo. However, they believe that other risks from APM are (i) more likely, and (ii) very likely to be relevant before risks from grey goo, and are therefore more worthy of attention.42 Similarly, Prof. Jones and Dr. Marblestone have argued that a ‘grey goo’ catastrophe is a distant, and perhaps unlikely, possibility.43
OPP’s discussion on why molecular nanotechnology (and cryonics) failed to develop as scientific fields is also interesting:
First, early advocates of cryonics and MNT focused on writings and media aimed at a broad popular audience, before they did much technical, scientific work …
Second, early advocates of cryonics and MNT spoke and wrote in a way that was critical and dismissive toward the most relevant mainstream scientific fields …
Third, and perhaps largely as a result of these first two issues, these “neighboring” established scientific communities (of cryobiologists and chemists) engaged in substantial “boundary work” to keep advocates of cryonics and MNT excluded …
It least in the case of molecular nanotechnology, the simple failure of the field to develop may have been lucky (at least from a GCR reduction perspective) as it seems that the research that was (at the time) most likely to lead to the risky outcomes was simply never pursued.
I just answered to UNESCO Public Online Consultation on the draft of a Recommendation on AI Ethics—it was longer and more complex than I thought.
I’d really love to know what other EA’s think of it. I’m very unsure about how useful it is going to be, particularly since US left the organization in 2018. But it’s the first Recommendation of a UN agency on this, the text address many interesting points (despite greatly emphasizing short-term issues, it does address “long-term catastrophic harms”), I haven’t seen many discussions of it (except for the Montreal AI Ethics Institute), and the deadline is July 31.
[UPDATED June 30, 10:00 pm EDT to reflect substantial improvements to the statistical approach and corresponding changes to the results]
I spent some time this weekend looking into the impact of COVID-19 on the 2020 U.S. presidential election, and I figured I might share some preliminary analysis here. I used data from The COVID Tracking Project and polling compiled by FiveThirtyEight to assemble a time series of Biden’s support head-to-head against Trump in 41 states (all those with adequate polling), along with corresponding COVID-19 data. I then implemented a collection of panel models in R evaluating the relationship between Biden’s performance against Trump in state polling and the severity of the pandemic in each state. My data, code, and regression output are on GitHub, and I’ve included some interpretive commentary below.
Interpretation of Results
When appropriately controlling for state-level fixed effects, time fixed effects, and heteroscedasticity, total COVID-19 cases and deaths are not significantly associated with support for Biden, nor are the number of ongoing COVID-19 hospitalizations (see Models A, B, and C in the 6.30 R script). However, controlling for state-level fixed effects, greater daily increases in cases and in deaths are significantly associated with higher support for Biden (see Models 2 and 3 in Table I). Breusch-Pagan tests indicate that we must also control for heteroscedasticity in those models, and when we do so, the results remain significant (see Models 2 and 3 in Table II), though only at a 90 percent confidence level.
These results do come with a caveat. While Lagrange FF multiplier tests indicate that there is no need to control for time fixed effects in Table I models 2 and 3, F-tests suggest the opposite conclusion. I lack the statistical acumen to know what to make of this, but it’s worth noting because when you control for both time fixed effects and heteroscedasticity, the results cease to be statistically significant, even at a 90 percent confidence level.
Interestingly, the state-level fixed effects identified by Table I models 2 and 3 are strikingly powerful predictors of support for Biden everwhere except for Arkansas, Florida, Georgia, North Carolina, Nevada, Ohio, and Texas, all of which (except for Arkansas) are currently considered general election toss-ups by RealClearPolitics. This makes sense — in a society as riven by political polarization as ours, you wouldn’t necessarily expect the impacts of the present pandemic to substantially shift political sympathies on the aggregate level in most states. The few exceptions where this seems more plausible would, of course, be swing states. In the case of Arkansas, the weak fixed effect identified by the models is likely attributable to the inadequacy of our data on the state.
A Hausman test indicates that when regressing the amount of COVID-19 tests performed in a state on the support for Biden there, a random effects model is more appropriate than a fixed effects model (because the state-level “fixed effects” identified are uncorrelated with the amount of tests performed in each state). Implementing this regression and controlling for heteroscedasticity yields the result featured under Model 1 in Table II: statistically significant at a 99 percent confidence level.
This is striking, both for the significance of the relationship and for how counterintuitive the result of the Hausman test is. One would assume that the fixed effects identified by a model like this one would basically reflect a state’s preexisting, “fundamental” partisan bent, and my prior had been that the more liberal a state was, the more testing it was doing. If that were true, one would expect the Hausman test to favor a fixed effects model over a random effects model. However, it turns out that my prior wasn’t quite right. A simple OLS regression of states’ 2016 Cook Partisan Voting Indexes on the amount of testing they had done as of June 26 (controlling for COVID-19 deaths as of that date and population) reveals no statistically significant relationship between leftist politics and tests performed (see Model 1 in Table III), and this result persists even when the controls for population and deaths are eliminated (see Model 2 in Table III).
This is odd. Hausman tests on Models A, B, and C in the 6.30 R script favor fixed effects models over random effects models, indicating that state-level fixed effects (i.e. each state’s underlying politics) are correlated with COVID-19 cases, hospitalizations, and deaths, but those same fixed effects are not correlated with COVID-19 tests. Moreover, when applying appropriate controls (e.g. for heteroscedasticity, time fixed effects, etc.), we find that while cases, hospitalizations, and deaths are not associated with support for Biden, testing is associated with support for Biden (basically the opposite of what I would have expected, under the circumstances). We can run a Breusch-Pagan Lagrange multiplier test on Table I’s Model 1 just to confirm for sure that a random effects model is appropriate (as opposed to an OLS regression), and it is. At that point, we are left with the question of what those random effects are that are associated with support for Biden but not with COVID-19 testing, as well as it’s corollary: Why aren’t the fixed effects in Models A, B, and C associated with testing (given that they are associated with cases, hospitalizations, and deaths)? Without the answers to these questions, it’s hard to know what to make of the robust association between total COVID-19 testing and support for Biden revealed by Table I’s Model 1.
The puzzling nature of the Table I, Model 1 results might incline some to dismiss the regression as somehow erroneous. I think jumping to that conclusion would be ill-advised. Among other things that speak in its favor, the random effects identified by the model, however mysterious, are remarkably consistent with common intuitions about partisanship at the state level, even more so, in fact, than the fixed effects identified by Models 2 and 3 in Table I. Unlike those fixed effects models, Model 1′s state-level random effects explain a considerable amount of Biden’s support in Georgia and Texas. I consider this a virtue of the model because Georgia and Texas have not been considered swing states in any other recent U.S. presidential elections. They are typically quite safe for the Republican candidate. Furthermore, Model 1 identifies particularly weak random effects in a few swing states not picked up by Models 2 and 3 — notably, Wisconsin, Pennsylvania, and Arizona. Wisconsin and Pennsylvania are genuine swing states: They went blue in 2008 and 2012 before going red in 2016. Arizona has been more consistently red over the last 20 years, but the signs of changing tides there are clear and abundant. Most notably, the state elected Kyrsten Sinema, an openly bisexual, female Democrat, to the Senate in 2018 to fill the seat vacated by Jeff Flake, who is none of those things.
I have one outstanding technical question about Table I, Model 1. When controlling for heteroscedasticity in R’s plm package, I understand that the “arellano” covariance estimator is generally preferred for data exhibiting both heteroscedasticity and serial correlation but is generally not preferred for random effects models (as opposed to fixed effects models). The “white” covariance estimators, on the other hand, are preferred for random effects models, though “white1” should not be used to control for heteroscedasticity in data that also exhibit serial correlation. A Breusch-Godfrey test indicates that Model 1 requires an estimator compatible with serial correlation, but it is of course a random effects model, not a fixed effects model. Would it be better to control for heteroscedasticity here with “white2“ or with “arellano?” Ultimately, it doesn’t matter much because both approaches yield a result that is statistically signicant at at least a 95 percent confidence level, but only the “white2” estimator is statistically significant at a 99 percent confidence level.
Thanks so much! I’m thrilled to hear you liked it. To be honest, my main reservation about doing anything non-anonymous with it is that I’m acutely aware of the difficulty of doing statistical analysis well and, more importantly, of being able to tell when you haven’t done statistical analysis well. I worry that my intro-y, undergrad coursework in stats didn’t give me the tools necessary to be able to pick up on the ways in which this might be wrong. That’s part of why I thought posting it here as a shortform would be a good first step. In that spirit, if anyone sees anything here that looks wrong to them, please do let me know!
After taking a closer look at the actual stats, I agree this analysis seems really difficult to do well, and I don’t put much weight on this particular set of tests. But your hypothesis is plausible and interesting, your data is strong, your regressions seem like the right general idea, and this seems proof of concept that this analysis could demonstrate a real effect. I’m also surprised that I can’t find any statistical analysis of COVID and Biden support anywhere, even though it seems very doable and very interesting. If I were you and wanted to pursue this further, I would figure out the strongest case that there might be an effect to be found here, then bring it to some people who have the stats skills and public platform to find the effect and write about it.
Statistically, I think you have two interesting hypotheses, and I’m not sure how you should test them or what you should control for. (Background: I’ve done undergrad intro stats-type stuff.)
Hypothesis A (Models 1 and 2) is that more COVID is correlated with more Biden support.
Hypothesis B (Model 3) is that more Biden support is correlated with more tests, which then has unclear causal effects of COVID.
I say “more COVID” to be deliberately ambiguous because I’m not sure which tracking metric to use. Should we expect Biden support to be correlated with tests, cases, hospitalizations, or deaths? And for each metric, should it be cumulative over time, or change over a given time period? What would it mean to find different effects for different metrics? Also, they’re all correlated with each other—does that bias your regression, or otherwise affect your results? I don’t know.
I also don’t know what controls to use. Controlling for state-level FEs seems smart, while controlling for date is interesting and potentially captures a different dynamic, but I have no idea how you should control for the correlated bundle of tests/cases/hospitalizations/deaths.
Without resolving these issues, I think the strongest evidence in favor of either hypothesis would be a bunch of different regressions that categorically test many different implementations of the overall hypothesis, with most of them seemingly supporting the hypothesis. I’m not sure what the right implementation is, I’d want someone with a strong statistics background to resolve these issues before really believing it, and this method can fail, but if most implementations you can imagine point in the same direction, that’s at least a decent reason to investigate further.
If you actually want to convince someone to look into this (with or without you), maybe do that battery of regressions, then write up a very generalized takeaway along the lines of “The hypothesis is plausible, the data is here, and the regressions don’t rule out the hypothesis. Do you want to look into whether or not there’s an effect here?”
Who’d be interested in this analysis? Strong candidates might include academics, think tanks, data journalism news outlets, and bloggers. The stats seem very difficult, maybe such that the best fit is academics, but I don’t know. News outlets and bloggers that aren’t specifically data savvy probably aren’t capable of doing this analysis justice. Without working with someone with a very strong stats background, I’d be cautious about writing this for a public audience.
Not sure if you’re even interested in any of that, but FWIW I think they’d like your ideas and progress so far. If you’d like to talk about this more, I’m happy to chat, you can pick a time here. Cool analysis, kudos on thinking of an interesting topic, seriously following through with the analysis, and recognizing its limitations.
Thank you so much for putting so much thought into this and writing up all of that advice! Your uncertainties and hesitations about the stats itself are essentially the same as my own. Last night, I passed this around to a few people who know marginally more about stats than I do, and they suggested some further robustness checks that they thought would be appropriate. I spent a bunch of time today implementing those suggestions, identifying problems with my previous work, and re-doing that work differently. In the process, I think I significantly improved my understanding of the right (or at least good) way to approach this analysis. I did, however, end up with a quite different (and less straightforward) set of conclusions than I had yesterday. I’ve updated the GitHub repository to reflect the current state of the project, and I will likely update the shortform post in a few minutes, too. Now that I think the analysis is in much better shape (and, frankly, that you’ve encouraged me), I am more seriously entertaining the idea of trying to get in touch with someone who might be able to explore it further. I think it would be fun chat about this, so I’ll probably book a time on your Calendly soon. Thanks again for all your help!
Glad to hear it! Very good idea to talk with a bunch of stats people, your updated tests are definitely beyond my understanding. Looking forward to talking (or not), and let me know if I can help with anything
Changing behaviour of people to make them more longtermist
Can we use standard behavioral economics techniques like loss aversion (e.g. humanity will be lost forever), scarcity bias, framing bias and nudging to influence people to make longtermist decisions instead of neartermist ones? Is this even ethical, given moral uncertainty?
It would be awesome if you could direct me to any existing research on this!
I think people already do some of it. I guess the rhetorical shift from x-risk reasoning (“hey, we’re all gonna die!”) to lontermist arguments (“imagine how wonderful the future can be after the Precipice...”) is based on that.
However, I think that, besides cultural challenges, the greatest obstacles for longtermist reasoning, in our societies (particularly in LMIC), is that we have an “intergenerational Tragedy of the Commons” aggravated by short-term bias (and hyperbolic discount) and representativeness heuristic (we’ve never observed human extinction). People don’t usually think about the longterm future—but, even when they do it, they don’t want to trade their individual-present-certain welfare for a collective (and non-identifiable), future and uncertain welfare.
Hi Ramiro, thanks for your comment. Based off this post, we can think of 2 techniques to promote longtermism. The first is what I mentioned—which is exploiting biases to get people inclined to longtermism. And the second is what you [might have] mentioned—a more rationality-driven approach where people are made aware of their biases with respect to longtermism. I think your idea is better since it is a more permanent-ish solution (there is security against future events that may attempt to bias an individual towards neartermism), has spillover effects into other aspects of rationality, and has lower risk with respect to moral uncertainity (correct me if I’m wrong).
I agree with the several biases/decision-making flaws that you mentioned! Perhaps, sufficient levels of rationality is a pre-requisite to one’s acceptance of longtermism. Maybe a promising EA cause area could be promoting rationality (such a cause area probably exists I guess).
[Context: This is a research proposal I wrote two years ago for an application. I’m posing it here because I might want to link to it. I plan to spend a few weeks looking into a subquestion: how heavy-tailed is EA talent, and what does this imply for EA community building?]
Research proposal: Assess claims that “impact is heavy-tailed”
Why is this valuable?
EAs frequently have to decide how much resources to invest to estimate the utility of their available options; e.g.:
How much time to invest to identify the best giving opportunity?
How much research to do before committing to a small set of cause areas?
When deciding whether to hire someone now or a more talented person in the future, when is it worth to wait?
One major input to such questions is how heavy-tailed the distribution of altruistic impact is: The better the best options are relative to a random option, the more valuable it is to identify the best options.
Claims like “impact is heavy-tailed” are widely accepted in the EA community—with major strategic consequences (e.g. [1], “Talent is high variance”)—but have sometimes been questioned [2, 3, 4, 5].
These claims are often made in an imprecise way, which makes it hard to estimate the extent of their practical implications (should you spend a month or a year doing research before deciding?), and hard to check if one actually disagrees about them. E.g., is the claim that we can now see that Einstein did much more for progress in physics than 90% of the world population at his time, or that in 1900 our subjective expected value for the progress Einstein would make would have been much higher than the value for a random physics graduate student, or something in between?
Suggested approach
1.Collect several claims of this type that have been made. 2. Review statistical measures of heavy-tailedness. 3. Limit the project’s scope appropriately. E.g., focus just on the claim that “talent is heavy-tailed” and its implications for community building. 4. Refine claims into precise candidate versions, i.e. something like “looking backwards, the empirical distribution of the number of published papers by researcher looks like it was sampled from a distribution that doesn’t have finite variance” rather than “researcher talent is heavy-tailed”. 5. Assess the veracity of those claims, based on published arguments about them and general properties of heavy-tailed distributions (e.g. [6]). Perhaps gather additional data. 6. Write up the results in an accessible way that highlights the true, precise claims and their practical implications.
Concerns
There probably are good reasons for why “impact is heavy-tailed” is widely accepted. I’m therefore unlikely to produce actionable results.
The proposed level of analysis may be too general.
This could be relevant. It’s not about the exact same question (it looks at the distribution of future suffering, not of impact) but some parts might be transferable.
There was an interesting discussion on whether EA organizations should reveal the authors of posts they publishhere. You may want to check it out if this is relevant to you (not just the linked comment, but also the replies.)
[WIP, not comprehensive] Collection of existing material on ‘impact being heavy-tailed’
Conceptual foundations
Newman (2005) provides a good introduction to powers laws, and reviews several mechanisms generating them, including: combinations of exponentials; inverses of quantities; random walks; the Yule process [also known as preferential attachment]; phase transitions and critical phenomena; self-organized criticality.
Clauset et al. (2009[2007]) explain why it is very difficult to empirically distinguish power laws from other heavy-tailed distributions (e.g. log-normal). In particular, seeing a roughly straight line in a log-log plot is not sufficient to identify a power law, despite such inferences being popular in the literature. Referring to power-law claims by others, they find that “the distributions for birds, books, cities, religions, wars, citations, papers, proteins, and terrorism are plausible power laws, but they are also plausible log-normals and stretched exponentials.” (p. 26)
Lyon (2014) argues that, unlike commonly believed, the Central Limit Theorem cannot explain why normal distributions are so common. (The critique also applies to the analog explanation for the log-normal distribution, an example of a heavy-tailed distribution.) Instead, an appeal to the principle of maximum entropy is suggested.
Discussion on this Shortform between me, Buck, and others, on whether “longtermism is bottlenecked by ‘great people’”.
Global health
Toby Ord’s classic paper The Moral Imperative toward Cost-Effectiveness in Global Health, in particular his observation that “According to the DCP2 data, if we funded all of these [health] interventions equally, 80 percent of the benefits would be produced by the top 20 percent of the interventions.” (p. 3)
Will MacAskill, in an interview by Lynette Bye, on the distribution of impact across work time for a fixed individual: “Maybe, let’s say the first three hours are like two thirds the value of the whole eight-hour day. And then, especially if I’m working six days a week, I’m not convinced the difference between eight and ten hours is actually adding anything in the long term.”
I did some initial research/thinking on this before the pandemic came and distracted me completely. Here’s a very broad outline that might be helpful.
You’re probably aware of this, but Anders Sandberg has done some thinking about this. Also presumably David Roodman based on his public writings (though I have not contacted him myself).
More broadly, I’m guessing that anybody who either you’ve referenced above, or who I’ve linked in my doc, would be helpful, though of course many of them are very busy.
This is a good link-list. It seems undiscoverable here though. I think thinking on how you can make such lists discoverable is useful. Making it a top-level post seems an obvious improvement.
Thanks for the suggestion. I plan to make this list more discoverable once I feel like it’s reasonably complete, e.g. by turning it into its own top-level post or appending it to a top-level post writeup of my research on this topic.
The long-term significance of reducing global catastrophic risks—Nick Beckstead, 2015 (Beckstead never actually writes “collapse”, but has very relevant discussion of probability of “recovery” and trajectory changes following non-extinction catastrophes)
Secret of Our Success—Henrich [book] (I don’t think this book is intended to be about civilizational collapse or recovery, but after reading this review I feel like the book might have some applicable insights)
I intend to add to this list over time. If you know of other relevant work, please mention it in a comment.
Shouldn’t we collect a sort of encyclopedia or manual of organizational best practices, to help EA organizations? A combination of research, and things we have learned?
Why couldn’t a manual of organizational best practices from non-EA organisations (I’m guessing there are probably many such manuals or other ways of communicating best practices) suffice? Which areas would it be unable to cover when applied directly to EA organisations? Are these areas particularly important to cover?
(Perhaps some Slate Star Codex posts? I can’t remember for sure.)
Notes
I intend to add to this list over time. If you know of other relevant work, please mention it in a comment.
Also, I’m aware that there has also been a vast amount of non-EA analysis of this topic. The reasons I’m collecting only EA analyses here are that:
their precise focuses or methodologies may be more relevant to other EAs than would be the case with non-EA analyses
links to non-EA work can be found in most of the things I list here
I’d guess that many collections of non-EA analyses of these topics already exist (e.g., in reference lists)
Excerpt from a Twitter thread about the Scott Alexander doxxing situation, but also about the power of online intellectual communities in general:
I found SlateStarCodex in 2015. immediately afterwards, I got involved in some of the little splinter communities online, that had developed after LessWrong started to disperse. I don’t think it’s exaggerating to say it saved my life.
I may have found my way on my own eventually, but the path was eased immensely by LW/SSC. In 2015 I was coming out of my only serious suicidal episode; I was in an unhappy marriage, in a town where I knew hardly anyone; I had failed out of my engineering program six months prior.
I had been peripherally aware of LW through a few fanfic pieces, and was directed to SSC via the LessWrong comments section.
It was the most intimidating community of people I had ever encountered—I didn’t think I could keep up.
But eventually, I realized that not only was this the first group of people who made me feel like I had come *home,* but that it was also one of the most welcoming places I’d ever been (IRL or virtual).
I joined a Slack, joined “rationalist” tumblr, and made a few comments on LW and SSC. Within a few months, I had *friends*, some of whom I would eventually count among those I love the most.
This is a community that takes ideas seriously (even when it would be better for their sanity to disengage).
This is a community that thinks everyone who can engage with them in sincere good faith might have something useful to say.
This is a community that saw someone writing long, in-depth critiques on the material produced on or adjacent to LW/SSC...and decided that meant he was a friend.
I have no prestigious credentials to speak of. I had no connections, was a college dropout, no high-paying job. I had no particular expertise, a lower-class background than many of the people I met, a Red-Tribe-Evangelical upbringing and all I had to do, to make these new friends, was show up and join the conversation.
[...]
The “weakness” of the LessWrong/SSC community is also its strength: putting up with people they disagree with far longer than they have to. Of course terrible people slip through. They do in every group—ours are just significantly more verbose.
But this is a community full of people who mostly just want to get things *right,* become *better people,* and turn over every single rock they see in the process of finding ways to be more correct—not every person and not all the time, but more than I’ve seen everywhere else.
The transhumanist background that runs through the history of LW/SSC also means that trans people are more accepted here than anywhere else I’ve seen, because part of that ideological influence is the belief that everyone should be able to have the body they want.
It is not by accident that this loosely-associated cluster of bloggers, weird nerds, and twitter shitposters were ahead of the game on coronavirus. It’s because they were watching, and thinking, and paying attention and listening to things that sound crazy… just in case.
There is a 2-part lesson this community held to, even while the rest of the world is forgetting it:
You can’t prohibit dissent
It’s sometimes worth it to engage someone when they have icky-sounding ideas
It was unpopular six months ago to think COVID might be a big deal; the SSC/LW diaspora paid attention anyways.
You can refuse to hang out with someone at a party. You can tell your friends they suck. But you can’t prohibit them from speaking *merely because their ideas make you uncomfortable* and there is value in engaging with dissent, with ideas that are taboo in Current Year.
(I’m not leaving a link or username, as this person’s Tweets are protected.)
An argument in favor of slow takeoff scenarios being generally safer is that we will get to see and experiment with the precursor AIs before they become capable of causing x-risks. Even if the behavior of this precursor AI is predictive of the superhuman AI’s, our ability to use it depends on the reaction to the potential dangers of this precursor AI. A society confident that there is no danger from increasing the capabilities of the machine that has been successfully running its electrical grid gains much less of an advantage from a slow takeoff (as opposed to the classic hard takeoff) than one with an awareness of its potential dangers.
Personally, I would expect a shift in attitudes towards AI as it becomes obviously more capable than humans in many domains. However, whether this shift involves being more careful or instead abdicating decisions to the AI entirely seems unclear to me. The way I play chess with a much stronger opponent is very different from how I play chess with a weaker or equally matched one. With the stronger opponent I am far more likely to expect obvious-looking blunders to actually be a set-up, for instance, and spend more time trying to figure out what advantage they might gain from it. On the other hand, I never bother to check my calculator’s math by hand, because the odds that it’s wrong is far lower than the chance that I will mess up somewhere in my arithmetic. If someone came up with an AI-calculator that gave occasional subtly wrong answers, I certainly wouldn’t notice.
Taking advantage of the benefits of a slow takeoff also requires the ability to have institutions capable of noticing and preventing problems. In a fast takeoff scenario, it is much easier for a single, relatively small project to unilaterally take off. This is, essentially, a gamble on that particular team’s capabilities. In a slow takeoff, it will be rapidly obvious that some project(s) seem to be trending in that direction, which makes it more likely that if the project seems unsafe there will be time to impose external control on it. How much of an advantage this is depends on how much you trust whichever institutions will be needed to impose those controls. Humanity’s track record in this respect seems to me to be mixed. Some historical precedents for cooperation (or lack thereof) in controlling dangerous technologies and their side-effects are the Asilomar Conference, nuclear proliferation treaties, and various pollution agreements. Asilomar, which seems to me the most successful of these, involved a relatively small scientific field voluntarily adhering to some limits on potentially dangerous research until more information could be gathered. Nuclear proliferation treaties reduce the cost of a zero-sum arms race, but it isn’t clear to me if they significantly reduced the risk of nuclear war. Pollution regulations have had very mixed results, with some major successes (eg acid rain) but on the whole failing to avert massive global change. Somewhat closer to home, the response to Covid-19 hasn’t been particularly encouraging. It is unclear to me which, if any, of these present a fair comparison, but our track record in cooperating seems decidedly mixed.
I found this interesting, and I think it would be worth expanding into a full post if you felt like it!
I don’t think you’d need more content: just a few more paragraph breaks, maybe a brief summary, and maybe a few questions to guide responses. If you have questions you’d want readers to tackle, consider including them as comments after the post.
I recommend placing questions for readers in comments after your posts.
If you want people to discuss/provide feedback on something you’ve written, it helps to let them know what types of discussion/feedback you are looking for.
If you do this through a bunch of scattered questions/notes in your post, any would-be respondent has to either remember what you wanted or read through the post again after they’ve finished.
If you do this with a list of questions at the end of the post, respondents will remember them better, but will still have to quote the right question in each response. They might also respond to a bunch of questions with a single comment, creating an awkward multi-threaded conversation.
If you do this with a set of comments on your own post—one for each question/request—you let respondents easily see what you want and discuss each point separately. No awkward multi-threading for you! This seems like the best method to me in most cases.
Intuitively, anti-science attitudes seem like a major barrier to solving many of the world’s most pressing problems: for example, climate change denial has greatly derailed the American response to climate change, and distrust of public health authorities may be stymying the COVID-19 response. (For instance, a candidate running in my district for State Senate is campaigning on opposition to contact tracing as well as vaccines.) I’m particularly concerned about anti-economics attitudes because they lead to bad economic policies that don’t solve the problems they’re meant to solve, such as protectionism and rent control, and opposition to policies that are actually supported by evidence. Additionally, I’ve heard (but can’t find the source for this) that economists are generally more reluctant to do public outreach in defense of their profession than scientists in other fields are.
Epistemic status: Almost entirely opinion, I’d love to hear counterexamples
When I hear proposals related to instilling certain values widely throughout a population (or preventing the instillation of certain values), I’m always inherently skeptical. I’m not aware of many cases where something like this worked well, at least in a region as large, sophisticated, and polarized as the United States.
You could point to civil rights campaigns, which have generally been successful over long periods of time, but those had the advantage of being run mostly by people who were personally affected (= lots of energy for activism, lots of people “inherently” supporting the movement in a deep and personal way).
If you look at other movements that transformed some part of the U.S. (e.g. bioethics or the conservative legal movement, as seen in Open Phil’s case studies of early field growth), you see narrow targeting of influential people rather than public advocacy.
Rather than thinking about “countering anti-science” more generally, why not focus on specific policies with scientific support? Fighting generically for “science” seems less compelling than pushing for one specific scientific idea (“masks work,” “housing deregulation will lower rents”), and I can think of a lot of cases where scientific ideas won the day in some democratic context.
This isn’t to say that public science advocacy is pointless; you can reach a lot of people by doing that. But I don’t think the people you reach are likely to “matter” much unless they actually campaign for some specific outcome (e.g. I wouldn’t expect a scientist to swing many votes in a national election, but maybe they could push some funding toward an advocacy group for a beneficial policy).
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One other note: I ran a quick search to look for polls on public trust in science, but all I found was a piece from Gallup on public trust in medical advice.
Putting that aside, I’d still guess that a large majority of Americans would claim to be “pro-science” and to “trust science,” even if many of those people actually endorse minority scientific claims (e.g. “X scientists say climate change isn’t a problem”). But I could be overestimating the extent to which people see “science” as a generally positive applause light.
EA-style discussion about AI seems to dismiss out of hand the possibility that AI might be sentient. I can’t find an example, but the possibility seems generally scoffed at in the same tone people dismiss Skynet and killer robot scenarios. Bostrom’s simulation hypothesis, however, is broadly accepted as at the very least an interestingly plausible argument.
These two stances seem entirely incompatible—if silicon can create a whole world inside of which are sentient minds, why can’t it just create the minds with no need for the framing device? It is plausible that sentience does not emerge unless you very precisely mimic natural (or “natural”) evolutionary pressures, but this seems unlikely. It’s likewise possible that something about the process by which we expect to create AI doesn’t allow for sentience, but in that case I think the burden of proof is on the people making the argument to identify this feature and argue for their reasons.
The strongest argument I can think off of the top of my head is that, if we expect a chance of future-AI created by something resembling modern machine learning methods to have a chance at sentience, we should likewise expect, say, worm-equivalent AIs to have it to. Is c. elegans sentient? Is OpenWorm? If you answered yes to the first and no to the second, what is OpenWorm missing that c. elegans has?
Many years ago, Eliezer Yudkowsky shared a short story I wrote (related to AI sentience) with his Facebook followers. The story isn’t great—I bring it up here only as an example of people being interested in these questions.
I think there are many examples of EAs thinking about the possibility that AI might be sentient by default. Some examples I can think of off the top of my head:
I don’t think people are disputing that it would be theoretically possible for AIs to be conscious, I think that they’re making the claim that AI systems we find won’t be.
Thanks for the links, I googled briefly before I wrote this to check my memory and couldn’t find anything. I think what formed my impression was that even in very detailed conversations/writing on AI, as far as I could tell by default there was no mention or implicit acknowledgement of the possibility. On reflection I’m not sure if I would expect it to be even if people did think it was likely, though.
Not all Effective Altruists are effective altruists and vice versa (where the capitalization means “part of the community” whereas the lowercase version means “does good effectively”).
Asking where Effective Altruists give makes sense, but checking where effective altruists are giving seems like it’s somewhat getting the causal arrow reversed. To know they are effective, you must first check which organizations are effective, and *then* you can determine that those who gave to those organizations were effective.
But I guess there’s also a more meaningful way to interpret the statement. Ex.: Where do smart strategic altruists give money? (you can still determine how smart and strategic they are in some direct ways without checking which organizations are the most effective). If you find some effective organizations first, you can also ask “Where else do those donors give” which might unveil charities you missed.
I was listening to the 80,000 Hours podcast today and heard Ben Todd say, “The issue is [longtermism is] a new idea.”
I’ve seen this view around EA a few times. It might be true about a certain narrow form of longtermism. It’s NOT true of longtermism broadly.
The first time I was introduced to long-termist ideas was in a university Native Studies class, discussing the traditional teaching that the current generation should focus on the well-being of seven generations in the future.
There’s even a specific term I can’t recall for intentional changes in the environment that a social group would make to domesticate a landscape and provide services for future. It will take me some time to find it.
On the other hand, besides the specifics of strong longtermism, I guess that the conjugation of these ideas is pretty recent: a) concern for humanity as a whole, b) a scope longer than 150 years, c) the existence of a trade-off between present and future welfare, d) the balance is tipped in favor of the long-term. [epistemic status: just an insight, would take me too long to look for a counter-example)
The efforts by https://1daysooner.org/ to use human challenge trials to speed up vaccine development make me think about the potential of advocacy for “human challenge” type experiments in other domains where consequentialists might conclude there hasn’t been enough “ethically questionable” randomized experimentation on humans. 2 examples come to mind:
My impression of the nutrition field is that it’s very hard to get causal evidence because people won’t change their diet at random for an experiment.
Why We Sleep has been a very influential book, but the sleep science research it draws upon is usually observational and/or relies on short time-spans. Alexey Guzey’s critique and self-experiment have both cast doubt on its conclusions to some extent.
Getting 1,000 people to sign up and randomly contracting 500 of them to do X for a year, where X is something like being vegan or sleeping for 6.5 hours per day, could be valuable.
At what point do feel with ~90% certainty you would have done more good by donating to animal charities than you’ve harmed by consuming a regular meat-filled diet?
It would be nice to know the numbers I have in my head somewhat conform to what smart people think.
Participants in the 2008 FHI Global Catastrophic Risk conference estimated a probability of extinction from nano-technology at 5.5% (weapons + accident) and non-nuclear wars at 3% (all wars—nuclear wars) (the values are on the GCR wikipedia page). In the Precipice, Ord estimated the existential risk of Other anthropogenic risks (noted in the text as including but not limited to nano-technology, and I interpret this as including non-nuclear wars) as 2% (1 in 50). (Note that by definition, extinction risk is a sub-set of existential risk.)
Since starting to engage with EA in 2018 I have seen very little discussion about nano-technology or non-nuclear warfare as existential risks, yet it seems that in 2008 these were considered risks on-par with top longtermist cause areas today (nanotechnology weapons and AGI extinction risks were both estimated at 5%). I realize that Ord’s risk estimates are his own while the 2008 data is from a survey, but I assume that his views broadly represent those of his colleagues at FHI and others the GCR community.
My open question is: what new information or discussion over the last decade lead the GCR to reduce their estimate of the risks posed by (primarily) nano-technology and also conventional warfare?
I too find this an interesting topic. More specifically, I wonder why I’ve seen as little discussion published in the last few years (rather than from >10 years ago) of nanotech as I have. I also wonder about the limited discussion of things like very long-lasting totalitarianism - though there I don’t have reason to believe people recently had reasonably high x-risk estimates; I just sort-of feel like I haven’t yet seen good reason to deprioritise investigating that possible risk. (I’m not saying that there should be more discussion of these topics, and that there are no good reasons for the lack of it, just that I wonder about that.)
I’m not sure that’s a safe assumption. The 2008 survey you’re discussing seems to have itself involved widely differing views (see the graphs on the last pages). And more generally, the existential risk and GCR research community seems to have widely differing views on risk estimates (see a collection of side-by-side estimates here).
I would also guess that each individual’s estimates might themselves be relatively unstable from one time you ask them to another, or one particular phrasing of the question to another.
Relatedly, I’m not sure how decision-relevant differences of less than an order of magnitude between different estimates are. (Though such differences could sometimes be decision-relevant, and larger differences more easily could be.)
In case you hadn’t seen it: 80,000 Hours recently released a post with a brief discussion of the problem area of atomically precise manufacturing. That also has links to a few relevant sources.
Thanks Michael, I had seen that but hadn’t looked at the links. Some comments:
The cause report from OPP makes the distinction between molecular nanotechnology and atomically precise manufacturing. The 2008 survey seemed to be explicitly considering weaponised molecular nanotechnology as an extinction risk (I assume the nanotechnology accident was referring to molecular nanotechnology as well). While there seems to be agreement that molecular nanotechnology could be a direct path to GCR/extinction, OPP presents atomically precise manufacturing as being more of an indirect risk, such as through facilitating weapons proliferation. The Grey goo section of the report does resolve my question about why the community isn’t talking about (molecular) nanotechnology as an existential risk as much now (the footnotes are worth reading for more details):
OPP’s discussion on why molecular nanotechnology (and cryonics) failed to develop as scientific fields is also interesting:
It least in the case of molecular nanotechnology, the simple failure of the field to develop may have been lucky (at least from a GCR reduction perspective) as it seems that the research that was (at the time) most likely to lead to the risky outcomes was simply never pursued.
I just answered to UNESCO Public Online Consultation on the draft of a Recommendation on AI Ethics—it was longer and more complex than I thought.
I’d really love to know what other EA’s think of it. I’m very unsure about how useful it is going to be, particularly since US left the organization in 2018. But it’s the first Recommendation of a UN agency on this, the text address many interesting points (despite greatly emphasizing short-term issues, it does address “long-term catastrophic harms”), I haven’t seen many discussions of it (except for the Montreal AI Ethics Institute), and the deadline is July 31.
[UPDATED June 30, 10:00 pm EDT to reflect substantial improvements to the statistical approach and corresponding changes to the results]
I spent some time this weekend looking into the impact of COVID-19 on the 2020 U.S. presidential election, and I figured I might share some preliminary analysis here. I used data from The COVID Tracking Project and polling compiled by FiveThirtyEight to assemble a time series of Biden’s support head-to-head against Trump in 41 states (all those with adequate polling), along with corresponding COVID-19 data. I then implemented a collection of panel models in R evaluating the relationship between Biden’s performance against Trump in state polling and the severity of the pandemic in each state. My data, code, and regression output are on GitHub, and I’ve included some interpretive commentary below.
Interpretation of Results
When appropriately controlling for state-level fixed effects, time fixed effects, and heteroscedasticity, total COVID-19 cases and deaths are not significantly associated with support for Biden, nor are the number of ongoing COVID-19 hospitalizations (see Models A, B, and C in the 6.30 R script). However, controlling for state-level fixed effects, greater daily increases in cases and in deaths are significantly associated with higher support for Biden (see Models 2 and 3 in Table I). Breusch-Pagan tests indicate that we must also control for heteroscedasticity in those models, and when we do so, the results remain significant (see Models 2 and 3 in Table II), though only at a 90 percent confidence level.
These results do come with a caveat. While Lagrange FF multiplier tests indicate that there is no need to control for time fixed effects in Table I models 2 and 3, F-tests suggest the opposite conclusion. I lack the statistical acumen to know what to make of this, but it’s worth noting because when you control for both time fixed effects and heteroscedasticity, the results cease to be statistically significant, even at a 90 percent confidence level.
Interestingly, the state-level fixed effects identified by Table I models 2 and 3 are strikingly powerful predictors of support for Biden everwhere except for Arkansas, Florida, Georgia, North Carolina, Nevada, Ohio, and Texas, all of which (except for Arkansas) are currently considered general election toss-ups by RealClearPolitics. This makes sense — in a society as riven by political polarization as ours, you wouldn’t necessarily expect the impacts of the present pandemic to substantially shift political sympathies on the aggregate level in most states. The few exceptions where this seems more plausible would, of course, be swing states. In the case of Arkansas, the weak fixed effect identified by the models is likely attributable to the inadequacy of our data on the state.
A Hausman test indicates that when regressing the amount of COVID-19 tests performed in a state on the support for Biden there, a random effects model is more appropriate than a fixed effects model (because the state-level “fixed effects” identified are uncorrelated with the amount of tests performed in each state). Implementing this regression and controlling for heteroscedasticity yields the result featured under Model 1 in Table II: statistically significant at a 99 percent confidence level.
This is striking, both for the significance of the relationship and for how counterintuitive the result of the Hausman test is. One would assume that the fixed effects identified by a model like this one would basically reflect a state’s preexisting, “fundamental” partisan bent, and my prior had been that the more liberal a state was, the more testing it was doing. If that were true, one would expect the Hausman test to favor a fixed effects model over a random effects model. However, it turns out that my prior wasn’t quite right. A simple OLS regression of states’ 2016 Cook Partisan Voting Indexes on the amount of testing they had done as of June 26 (controlling for COVID-19 deaths as of that date and population) reveals no statistically significant relationship between leftist politics and tests performed (see Model 1 in Table III), and this result persists even when the controls for population and deaths are eliminated (see Model 2 in Table III).
This is odd. Hausman tests on Models A, B, and C in the 6.30 R script favor fixed effects models over random effects models, indicating that state-level fixed effects (i.e. each state’s underlying politics) are correlated with COVID-19 cases, hospitalizations, and deaths, but those same fixed effects are not correlated with COVID-19 tests. Moreover, when applying appropriate controls (e.g. for heteroscedasticity, time fixed effects, etc.), we find that while cases, hospitalizations, and deaths are not associated with support for Biden, testing is associated with support for Biden (basically the opposite of what I would have expected, under the circumstances). We can run a Breusch-Pagan Lagrange multiplier test on Table I’s Model 1 just to confirm for sure that a random effects model is appropriate (as opposed to an OLS regression), and it is. At that point, we are left with the question of what those random effects are that are associated with support for Biden but not with COVID-19 testing, as well as it’s corollary: Why aren’t the fixed effects in Models A, B, and C associated with testing (given that they are associated with cases, hospitalizations, and deaths)? Without the answers to these questions, it’s hard to know what to make of the robust association between total COVID-19 testing and support for Biden revealed by Table I’s Model 1.
The puzzling nature of the Table I, Model 1 results might incline some to dismiss the regression as somehow erroneous. I think jumping to that conclusion would be ill-advised. Among other things that speak in its favor, the random effects identified by the model, however mysterious, are remarkably consistent with common intuitions about partisanship at the state level, even more so, in fact, than the fixed effects identified by Models 2 and 3 in Table I. Unlike those fixed effects models, Model 1′s state-level random effects explain a considerable amount of Biden’s support in Georgia and Texas. I consider this a virtue of the model because Georgia and Texas have not been considered swing states in any other recent U.S. presidential elections. They are typically quite safe for the Republican candidate. Furthermore, Model 1 identifies particularly weak random effects in a few swing states not picked up by Models 2 and 3 — notably, Wisconsin, Pennsylvania, and Arizona. Wisconsin and Pennsylvania are genuine swing states: They went blue in 2008 and 2012 before going red in 2016. Arizona has been more consistently red over the last 20 years, but the signs of changing tides there are clear and abundant. Most notably, the state elected Kyrsten Sinema, an openly bisexual, female Democrat, to the Senate in 2018 to fill the seat vacated by Jeff Flake, who is none of those things.
I have one outstanding technical question about Table I, Model 1. When controlling for heteroscedasticity in R’s plm package, I understand that the “arellano” covariance estimator is generally preferred for data exhibiting both heteroscedasticity and serial correlation but is generally not preferred for random effects models (as opposed to fixed effects models). The “white” covariance estimators, on the other hand, are preferred for random effects models, though “white1” should not be used to control for heteroscedasticity in data that also exhibit serial correlation. A Breusch-Godfrey test indicates that Model 1 requires an estimator compatible with serial correlation, but it is of course a random effects model, not a fixed effects model. Would it be better to control for heteroscedasticity here with “white2“ or with “arellano?” Ultimately, it doesn’t matter much because both approaches yield a result that is statistically signicant at at least a 95 percent confidence level, but only the “white2” estimator is statistically significant at a 99 percent confidence level.
This is really cool, maybe email it to 538 or Vox? I’ve had success contacting them to share ideas for articles before
Thanks so much! I’m thrilled to hear you liked it. To be honest, my main reservation about doing anything non-anonymous with it is that I’m acutely aware of the difficulty of doing statistical analysis well and, more importantly, of being able to tell when you haven’t done statistical analysis well. I worry that my intro-y, undergrad coursework in stats didn’t give me the tools necessary to be able to pick up on the ways in which this might be wrong. That’s part of why I thought posting it here as a shortform would be a good first step. In that spirit, if anyone sees anything here that looks wrong to them, please do let me know!
After taking a closer look at the actual stats, I agree this analysis seems really difficult to do well, and I don’t put much weight on this particular set of tests. But your hypothesis is plausible and interesting, your data is strong, your regressions seem like the right general idea, and this seems proof of concept that this analysis could demonstrate a real effect. I’m also surprised that I can’t find any statistical analysis of COVID and Biden support anywhere, even though it seems very doable and very interesting. If I were you and wanted to pursue this further, I would figure out the strongest case that there might be an effect to be found here, then bring it to some people who have the stats skills and public platform to find the effect and write about it.
Statistically, I think you have two interesting hypotheses, and I’m not sure how you should test them or what you should control for. (Background: I’ve done undergrad intro stats-type stuff.)
Hypothesis A (Models 1 and 2) is that more COVID is correlated with more Biden support.
Hypothesis B (Model 3) is that more Biden support is correlated with more tests, which then has unclear causal effects of COVID.
I say “more COVID” to be deliberately ambiguous because I’m not sure which tracking metric to use. Should we expect Biden support to be correlated with tests, cases, hospitalizations, or deaths? And for each metric, should it be cumulative over time, or change over a given time period? What would it mean to find different effects for different metrics? Also, they’re all correlated with each other—does that bias your regression, or otherwise affect your results? I don’t know.
I also don’t know what controls to use. Controlling for state-level FEs seems smart, while controlling for date is interesting and potentially captures a different dynamic, but I have no idea how you should control for the correlated bundle of tests/cases/hospitalizations/deaths.
Without resolving these issues, I think the strongest evidence in favor of either hypothesis would be a bunch of different regressions that categorically test many different implementations of the overall hypothesis, with most of them seemingly supporting the hypothesis. I’m not sure what the right implementation is, I’d want someone with a strong statistics background to resolve these issues before really believing it, and this method can fail, but if most implementations you can imagine point in the same direction, that’s at least a decent reason to investigate further.
If you actually want to convince someone to look into this (with or without you), maybe do that battery of regressions, then write up a very generalized takeaway along the lines of “The hypothesis is plausible, the data is here, and the regressions don’t rule out the hypothesis. Do you want to look into whether or not there’s an effect here?”
Who’d be interested in this analysis? Strong candidates might include academics, think tanks, data journalism news outlets, and bloggers. The stats seem very difficult, maybe such that the best fit is academics, but I don’t know. News outlets and bloggers that aren’t specifically data savvy probably aren’t capable of doing this analysis justice. Without working with someone with a very strong stats background, I’d be cautious about writing this for a public audience.
Not sure if you’re even interested in any of that, but FWIW I think they’d like your ideas and progress so far. If you’d like to talk about this more, I’m happy to chat, you can pick a time here. Cool analysis, kudos on thinking of an interesting topic, seriously following through with the analysis, and recognizing its limitations.
Thank you so much for putting so much thought into this and writing up all of that advice! Your uncertainties and hesitations about the stats itself are essentially the same as my own. Last night, I passed this around to a few people who know marginally more about stats than I do, and they suggested some further robustness checks that they thought would be appropriate. I spent a bunch of time today implementing those suggestions, identifying problems with my previous work, and re-doing that work differently. In the process, I think I significantly improved my understanding of the right (or at least good) way to approach this analysis. I did, however, end up with a quite different (and less straightforward) set of conclusions than I had yesterday. I’ve updated the GitHub repository to reflect the current state of the project, and I will likely update the shortform post in a few minutes, too. Now that I think the analysis is in much better shape (and, frankly, that you’ve encouraged me), I am more seriously entertaining the idea of trying to get in touch with someone who might be able to explore it further. I think it would be fun chat about this, so I’ll probably book a time on your Calendly soon. Thanks again for all your help!
Glad to hear it! Very good idea to talk with a bunch of stats people, your updated tests are definitely beyond my understanding. Looking forward to talking (or not), and let me know if I can help with anything
Thanks! I booked a slot on your Calendly—looking forward to speaking Thursday (assuming that still works)!
Changing behaviour of people to make them more longtermist
Can we use standard behavioral economics techniques like loss aversion (e.g. humanity will be lost forever), scarcity bias, framing bias and nudging to influence people to make longtermist decisions instead of neartermist ones? Is this even ethical, given moral uncertainty?
It would be awesome if you could direct me to any existing research on this!
I think people already do some of it. I guess the rhetorical shift from x-risk reasoning (“hey, we’re all gonna die!”) to lontermist arguments (“imagine how wonderful the future can be after the Precipice...”) is based on that.
However, I think that, besides cultural challenges, the greatest obstacles for longtermist reasoning, in our societies (particularly in LMIC), is that we have an “intergenerational Tragedy of the Commons” aggravated by short-term bias (and hyperbolic discount) and representativeness heuristic (we’ve never observed human extinction). People don’t usually think about the longterm future—but, even when they do it, they don’t want to trade their individual-present-certain welfare for a collective (and non-identifiable), future and uncertain welfare.
Hi Ramiro, thanks for your comment. Based off this post, we can think of 2 techniques to promote longtermism. The first is what I mentioned—which is exploiting biases to get people inclined to longtermism. And the second is what you [might have] mentioned—a more rationality-driven approach where people are made aware of their biases with respect to longtermism. I think your idea is better since it is a more permanent-ish solution (there is security against future events that may attempt to bias an individual towards neartermism), has spillover effects into other aspects of rationality, and has lower risk with respect to moral uncertainity (correct me if I’m wrong).
I agree with the several biases/decision-making flaws that you mentioned! Perhaps, sufficient levels of rationality is a pre-requisite to one’s acceptance of longtermism. Maybe a promising EA cause area could be promoting rationality (such a cause area probably exists I guess).
[Context: This is a research proposal I wrote two years ago for an application. I’m posing it here because I might want to link to it. I plan to spend a few weeks looking into a subquestion: how heavy-tailed is EA talent, and what does this imply for EA community building?]
Research proposal: Assess claims that “impact is heavy-tailed”
Why is this valuable?
EAs frequently have to decide how much resources to invest to estimate the utility of their available options; e.g.:
How much time to invest to identify the best giving opportunity?
How much research to do before committing to a small set of cause areas?
When deciding whether to hire someone now or a more talented person in the future, when is it worth to wait?
One major input to such questions is how heavy-tailed the distribution of altruistic impact is: The better the best options are relative to a random option, the more valuable it is to identify the best options.
Claims like “impact is heavy-tailed” are widely accepted in the EA community—with major strategic consequences (e.g. [1], “Talent is high variance”)—but have sometimes been questioned [2, 3, 4, 5].
These claims are often made in an imprecise way, which makes it hard to estimate the extent of their practical implications (should you spend a month or a year doing research before deciding?), and hard to check if one actually disagrees about them. E.g., is the claim that we can now see that Einstein did much more for progress in physics than 90% of the world population at his time, or that in 1900 our subjective expected value for the progress Einstein would make would have been much higher than the value for a random physics graduate student, or something in between?
Suggested approach
1. Collect several claims of this type that have been made.
2. Review statistical measures of heavy-tailedness.
3. Limit the project’s scope appropriately. E.g., focus just on the claim that “talent is heavy-tailed” and its implications for community building.
4. Refine claims into precise candidate versions, i.e. something like “looking backwards, the empirical distribution of the number of published papers by researcher looks like it was sampled from a distribution that doesn’t have finite variance” rather than “researcher talent is heavy-tailed”.
5. Assess the veracity of those claims, based on published arguments about them and general properties of heavy-tailed distributions (e.g. [6]). Perhaps gather additional data.
6. Write up the results in an accessible way that highlights the true, precise claims and their practical implications.
Concerns
There probably are good reasons for why “impact is heavy-tailed” is widely accepted. I’m therefore unlikely to produce actionable results.
The proposed level of analysis may be too general.
This could be relevant. It’s not about the exact same question (it looks at the distribution of future suffering, not of impact) but some parts might be transferable.
There was an interesting discussion on whether EA organizations should reveal the authors of posts they publish here. You may want to check it out if this is relevant to you (not just the linked comment, but also the replies.)
[See this research proposal for context. I’d appreciate pointers to other material.]
[WIP, not comprehensive] Collection of existing material on ‘impact being heavy-tailed’
Conceptual foundations
Newman (2005) provides a good introduction to powers laws, and reviews several mechanisms generating them, including: combinations of exponentials; inverses of quantities; random walks; the Yule process [also known as preferential attachment]; phase transitions and critical phenomena; self-organized criticality.
Terence Tao, in Benford’s law, Zipf’s law, and the Pareto distribution, offers a partial explanation for why heavy-tailed distributions are so common empirically.
Clauset et al. (2009[2007]) explain why it is very difficult to empirically distinguish power laws from other heavy-tailed distributions (e.g. log-normal). In particular, seeing a roughly straight line in a log-log plot is not sufficient to identify a power law, despite such inferences being popular in the literature. Referring to power-law claims by others, they find that “the distributions for birds, books, cities, religions, wars, citations, papers, proteins, and terrorism are plausible power laws, but they are also plausible log-normals and stretched exponentials.” (p. 26)
Lyon (2014) argues that, unlike commonly believed, the Central Limit Theorem cannot explain why normal distributions are so common. (The critique also applies to the analog explanation for the log-normal distribution, an example of a heavy-tailed distribution.) Instead, an appeal to the principle of maximum entropy is suggested.
Impact in general / cause-agnostic
Kokotajlo & Oprea (2020) argue that there also is a heavy left tail of harmful interventions.
Tobias Baumann, Is most expected suffering due to worst-case scenarios?
Owen Cotton-Barratt’s talk Prospecting for gold, section Heavy-tailed distributions
Brian Tomasik, Why Charities Usually Don’t Differ Astronomically in Expected Cost-Effectiveness
Discussion between Ben Pace and Richard Ngo on “how valuable it is for people to go into AI policy and strategy work”—but one of Ben’s premises is that impact generally has been heavy-tailed since the Industrial Revolution.
EA community building
CEA models of community building, especially the discussion of How much might these factors vary? in the ‘three-factor model’.
CEA’s current thinking [no longer endorsed by CEA], particularly Talent is high variance
Discussion between Ben Pace and Jan Kulveit on “whether you should expect to be able to identify individuals who will most shape the long term future of humanity”.
Discussion on this Shortform between me, Buck, and others, on whether “longtermism is bottlenecked by ‘great people’”.
Global health
Toby Ord’s classic paper The Moral Imperative toward Cost-Effectiveness in Global Health, in particular his observation that “According to the DCP2 data, if we funded all of these [health] interventions equally, 80 percent of the benefits would be produced by the top 20 percent of the interventions.” (p. 3)
Jeff Kaufman, The Unintuitive Power Laws of Giving (mostly based on the same DCP2 data)
Agenty Duck, Eva Vivalt Did Not Show QALYs/$ of Interventions Follow a Gaussian Curve
Jeff Kaufman, Effectiveness: Gaussian? (pushing back against the same claim by Robin Hanson as the Agenty Duck blog post above)
Misc
Will MacAskill, in an interview by Lynette Bye, on the distribution of impact across work time for a fixed individual: “Maybe, let’s say the first three hours are like two thirds the value of the whole eight-hour day. And then, especially if I’m working six days a week, I’m not convinced the difference between eight and ten hours is actually adding anything in the long term.”
I did some initial research/thinking on this before the pandemic came and distracted me completely. Here’s a very broad outline that might be helpful.
Great, thank you!
I saw that you asked Howie for input—are there other people you think it would be good to talk to on this topic?
You’re probably aware of this, but Anders Sandberg has done some thinking about this. Also presumably David Roodman based on his public writings (though I have not contacted him myself).
More broadly, I’m guessing that anybody who either you’ve referenced above, or who I’ve linked in my doc, would be helpful, though of course many of them are very busy.
This is a good link-list. It seems undiscoverable here though. I think thinking on how you can make such lists discoverable is useful. Making it a top-level post seems an obvious improvement.
Thanks for the suggestion. I plan to make this list more discoverable once I feel like it’s reasonably complete, e.g. by turning it into its own top-level post or appending it to a top-level post writeup of my research on this topic.
Collection of sources that seem very relevant to the topic of civilizational collapse and/or recovery
Civilization Re-Emerging After a Catastrophe—Karim Jebari, 2019 (see also my commentary on that talk)
Civilizational Collapse: Scenarios, Prevention, Responses—Denkenberger & Ladish, 2019
Update on civilizational collapse research—Ladish, 2020 (personally, I found Ladish’s talk more useful; see the above link)
The long-term significance of reducing global catastrophic risks—Nick Beckstead, 2015 (Beckstead never actually writes “collapse”, but has very relevant discussion of probability of “recovery” and trajectory changes following non-extinction catastrophes)
Various EA Forum posts by Dave Denkenberger (see also ALLFED’s site)
Aftermath of Global Catastrophe—GCRI, no date (this page has links to other relevant articles)
Civilisational collapse has a bright past – but a dark future—Luke Kemp, 2019
Are we on the road to civilisation collapse? - Luke Kemp, 2019
Civilization: Institutions, Knowledge and the Future—Samo Burja, 2018
Things about existential risk or GCRs more broadly, but with relevant parts
Toby Ord on the precipice and humanity’s potential futures − 2020 (the first directly relevant part is in the section on nuclear war)
The Precipice—Ord, 2020
Long-Term Trajectories of Human Civilization—Baum et al., 2019 (the authors never actually write “collapse”, but their section 4 is very relevant to the topic)
Defence in Depth Against Human Extinction: Prevention, Response, Resilience, and Why They All Matter—Cotton-Barratt, Daniel, Sandberg, 2020
Existential Risk Strategy Conversation with Holden Karnofsky, Eliezer Yudkowsky, and Luke Muehlhauser − 2014
Causal diagrams of the paths to existential catastrophe—Michael Aird (i.e., me), 2020
Stuart Armstrong interview − 2014 (the relevant section is 7:45-14:30)
Existential Risk Prevention as Global Priority—Bostrom, 2012
The Future of Humanity—Bostrom, 2007 (covers similar points to the above paper)
How Would Catastrophic Risks Affect Prospects for Compromise? - Tomasik, 2013/2017
Crucial questions for longtermists: Overview—Michael Aird (me), work in progress
Things that definitely sound relevant, but which I haven’t read/watched/listened to yet
Catastrophe, Social Collapse, and Human Extinction—Robin Hanson, 2007
How much could refuges help us recover from a global catastrophe? - Nick Beckstead, 2015
Islands as refuges for surviving global catastrophes—Turchin & Green, 2018
Existential Risks: Exploring a Robust Risk Reduction Strategy—Karim Jebari, 2015
Feeding Everyone No Matter What—Denkenberger [book]
Why and how civilisations collapse—Kemp [CSER]
https://en.wikipedia.org/wiki/Societal_collapse
https://en.wikipedia.org/wiki/Collapse:_How_Societies_Choose_to_Fail_or_Succeed [book]
Things which might be relevant, but which I haven’t read/watched/listened to yet
https://en.wikipedia.org/wiki/The_Knowledge:_How_to_Rebuild_Our_World_from_Scratch—Dartnell [book] (there’s also this TEDx Talk by the author, but I didn’t find that very useful from a civilizational collapse perspective)
Secret of Our Success—Henrich [book] (I don’t think this book is intended to be about civilizational collapse or recovery, but after reading this review I feel like the book might have some applicable insights)
I intend to add to this list over time. If you know of other relevant work, please mention it in a comment.
Guns, Germs, and Steel—I felt this provided a good perspective on the ultimate factors leading up to agriculture and industry.
Great, thanks for adding that to the collection!
Shouldn’t we collect a sort of encyclopedia or manual of organizational best practices, to help EA organizations? A combination of research, and things we have learned?
Why couldn’t a manual of organizational best practices from non-EA organisations (I’m guessing there are probably many such manuals or other ways of communicating best practices) suffice? Which areas would it be unable to cover when applied directly to EA organisations? Are these areas particularly important to cover?
Collection of EA analyses of political polarisation
EA considerations regarding increasing political polarization—Alfred Dreyfus, 2020
Adapting the ITN framework for political interventions & analysis of political polarisation—OlafvdVeen, 2020
Thoughts on electoral reform—Tobias Baumann, 2020
Risk factors for s-risks—Tobias Baumann, 2019
(Perhaps some Slate Star Codex posts? I can’t remember for sure.)
Notes
I intend to add to this list over time. If you know of other relevant work, please mention it in a comment.
Also, I’m aware that there has also been a vast amount of non-EA analysis of this topic. The reasons I’m collecting only EA analyses here are that:
their precise focuses or methodologies may be more relevant to other EAs than would be the case with non-EA analyses
links to non-EA work can be found in most of the things I list here
I’d guess that many collections of non-EA analyses of these topics already exist (e.g., in reference lists)
I’ve written some posts on related themes.
https://www.lesswrong.com/posts/k54agm83CLt3Sb85t/clearerthinking-s-fact-checking-2-0
https://forum.effectivealtruism.org/posts/pYaYtCT3Fc5H4rfWS/opinion-piece-on-the-swedish-network-for-evidence-based
https://forum.effectivealtruism.org/posts/CYyaQ3N4ipLFR4fzX/effective-altruism-s-fact-value-separation-as-a-weapon
https://forum.effectivealtruism.org/posts/yPkiBNW49NZvGvJ3q/political-debiasing-and-the-political-bias-test
Great, thanks for adding these to the collection!
Excerpt from a Twitter thread about the Scott Alexander doxxing situation, but also about the power of online intellectual communities in general:
(I’m not leaving a link or username, as this person’s Tweets are protected.)
Plant-based meat. Fun video from a youtuber which makes a strong case. Very sharable. https://youtu.be/-k-V3ESHcfA
An argument in favor of slow takeoff scenarios being generally safer is that we will get to see and experiment with the precursor AIs before they become capable of causing x-risks. Even if the behavior of this precursor AI is predictive of the superhuman AI’s, our ability to use it depends on the reaction to the potential dangers of this precursor AI. A society confident that there is no danger from increasing the capabilities of the machine that has been successfully running its electrical grid gains much less of an advantage from a slow takeoff (as opposed to the classic hard takeoff) than one with an awareness of its potential dangers.
Personally, I would expect a shift in attitudes towards AI as it becomes obviously more capable than humans in many domains. However, whether this shift involves being more careful or instead abdicating decisions to the AI entirely seems unclear to me. The way I play chess with a much stronger opponent is very different from how I play chess with a weaker or equally matched one. With the stronger opponent I am far more likely to expect obvious-looking blunders to actually be a set-up, for instance, and spend more time trying to figure out what advantage they might gain from it. On the other hand, I never bother to check my calculator’s math by hand, because the odds that it’s wrong is far lower than the chance that I will mess up somewhere in my arithmetic. If someone came up with an AI-calculator that gave occasional subtly wrong answers, I certainly wouldn’t notice.
Taking advantage of the benefits of a slow takeoff also requires the ability to have institutions capable of noticing and preventing problems. In a fast takeoff scenario, it is much easier for a single, relatively small project to unilaterally take off. This is, essentially, a gamble on that particular team’s capabilities. In a slow takeoff, it will be rapidly obvious that some project(s) seem to be trending in that direction, which makes it more likely that if the project seems unsafe there will be time to impose external control on it. How much of an advantage this is depends on how much you trust whichever institutions will be needed to impose those controls. Humanity’s track record in this respect seems to me to be mixed. Some historical precedents for cooperation (or lack thereof) in controlling dangerous technologies and their side-effects are the Asilomar Conference, nuclear proliferation treaties, and various pollution agreements. Asilomar, which seems to me the most successful of these, involved a relatively small scientific field voluntarily adhering to some limits on potentially dangerous research until more information could be gathered. Nuclear proliferation treaties reduce the cost of a zero-sum arms race, but it isn’t clear to me if they significantly reduced the risk of nuclear war. Pollution regulations have had very mixed results, with some major successes (eg acid rain) but on the whole failing to avert massive global change. Somewhat closer to home, the response to Covid-19 hasn’t been particularly encouraging. It is unclear to me which, if any, of these present a fair comparison, but our track record in cooperating seems decidedly mixed.
I found this interesting, and I think it would be worth expanding into a full post if you felt like it!
I don’t think you’d need more content: just a few more paragraph breaks, maybe a brief summary, and maybe a few questions to guide responses. If you have questions you’d want readers to tackle, consider including them as comments after the post.
I recommend placing questions for readers in comments after your posts.
If you want people to discuss/provide feedback on something you’ve written, it helps to let them know what types of discussion/feedback you are looking for.
If you do this through a bunch of scattered questions/notes in your post, any would-be respondent has to either remember what you wanted or read through the post again after they’ve finished.
If you do this with a list of questions at the end of the post, respondents will remember them better, but will still have to quote the right question in each response. They might also respond to a bunch of questions with a single comment, creating an awkward multi-threaded conversation.
If you do this with a set of comments on your own post—one for each question/request—you let respondents easily see what you want and discuss each point separately. No awkward multi-threading for you! This seems like the best method to me in most cases.
How pressing is countering anti-science?
Intuitively, anti-science attitudes seem like a major barrier to solving many of the world’s most pressing problems: for example, climate change denial has greatly derailed the American response to climate change, and distrust of public health authorities may be stymying the COVID-19 response. (For instance, a candidate running in my district for State Senate is campaigning on opposition to contact tracing as well as vaccines.) I’m particularly concerned about anti-economics attitudes because they lead to bad economic policies that don’t solve the problems they’re meant to solve, such as protectionism and rent control, and opposition to policies that are actually supported by evidence. Additionally, I’ve heard (but can’t find the source for this) that economists are generally more reluctant to do public outreach in defense of their profession than scientists in other fields are.
Combating antiscience: Are we preparing for the 2020s?
Fauci says “anti-science bias” is a problem in the US
How to Correct Misinformation, According to Science
Epistemic status: Almost entirely opinion, I’d love to hear counterexamples
When I hear proposals related to instilling certain values widely throughout a population (or preventing the instillation of certain values), I’m always inherently skeptical. I’m not aware of many cases where something like this worked well, at least in a region as large, sophisticated, and polarized as the United States.
You could point to civil rights campaigns, which have generally been successful over long periods of time, but those had the advantage of being run mostly by people who were personally affected (= lots of energy for activism, lots of people “inherently” supporting the movement in a deep and personal way).
If you look at other movements that transformed some part of the U.S. (e.g. bioethics or the conservative legal movement, as seen in Open Phil’s case studies of early field growth), you see narrow targeting of influential people rather than public advocacy.
Rather than thinking about “countering anti-science” more generally, why not focus on specific policies with scientific support? Fighting generically for “science” seems less compelling than pushing for one specific scientific idea (“masks work,” “housing deregulation will lower rents”), and I can think of a lot of cases where scientific ideas won the day in some democratic context.
This isn’t to say that public science advocacy is pointless; you can reach a lot of people by doing that. But I don’t think the people you reach are likely to “matter” much unless they actually campaign for some specific outcome (e.g. I wouldn’t expect a scientist to swing many votes in a national election, but maybe they could push some funding toward an advocacy group for a beneficial policy).
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One other note: I ran a quick search to look for polls on public trust in science, but all I found was a piece from Gallup on public trust in medical advice.
Putting that aside, I’d still guess that a large majority of Americans would claim to be “pro-science” and to “trust science,” even if many of those people actually endorse minority scientific claims (e.g. “X scientists say climate change isn’t a problem”). But I could be overestimating the extent to which people see “science” as a generally positive applause light.
EA-style discussion about AI seems to dismiss out of hand the possibility that AI might be sentient. I can’t find an example, but the possibility seems generally scoffed at in the same tone people dismiss Skynet and killer robot scenarios. Bostrom’s simulation hypothesis, however, is broadly accepted as at the very least an interestingly plausible argument.
These two stances seem entirely incompatible—if silicon can create a whole world inside of which are sentient minds, why can’t it just create the minds with no need for the framing device? It is plausible that sentience does not emerge unless you very precisely mimic natural (or “natural”) evolutionary pressures, but this seems unlikely. It’s likewise possible that something about the process by which we expect to create AI doesn’t allow for sentience, but in that case I think the burden of proof is on the people making the argument to identify this feature and argue for their reasons.
The strongest argument I can think off of the top of my head is that, if we expect a chance of future-AI created by something resembling modern machine learning methods to have a chance at sentience, we should likewise expect, say, worm-equivalent AIs to have it to. Is c. elegans sentient? Is OpenWorm? If you answered yes to the first and no to the second, what is OpenWorm missing that c. elegans has?
Many years ago, Eliezer Yudkowsky shared a short story I wrote (related to AI sentience) with his Facebook followers. The story isn’t great—I bring it up here only as an example of people being interested in these questions.
I think there are many examples of EAs thinking about the possibility that AI might be sentient by default. Some examples I can think of off the top of my head:
Brian Tomasik has written about why we might get sentient computer programs by default, eg https://reducing-suffering.org/why-your-laptop-may-be-marginally-sentient/
I’ve referred to this occasionally in the past in various posts of mine, eg http://shlegeris.com/2016/11/26/research
Eliezer Yudkowsky is worried about the related concern that AI systems might simulate humans in morally relevant ways https://arbital.com/p/mindcrime/?l=18h
Paul Christiano has written about whether unaligned AI is morally valuable https://ai-alignment.com/sympathizing-with-ai-e11a4bf5ef6e
I don’t think people are disputing that it would be theoretically possible for AIs to be conscious, I think that they’re making the claim that AI systems we find won’t be.
Thanks for the links, I googled briefly before I wrote this to check my memory and couldn’t find anything. I think what formed my impression was that even in very detailed conversations/writing on AI, as far as I could tell by default there was no mention or implicit acknowledgement of the possibility. On reflection I’m not sure if I would expect it to be even if people did think it was likely, though.
Not all Effective Altruists are effective altruists and vice versa (where the capitalization means “part of the community” whereas the lowercase version means “does good effectively”).
Asking where Effective Altruists give makes sense, but checking where effective altruists are giving seems like it’s somewhat getting the causal arrow reversed. To know they are effective, you must first check which organizations are effective, and *then* you can determine that those who gave to those organizations were effective.
But I guess there’s also a more meaningful way to interpret the statement. Ex.: Where do smart strategic altruists give money? (you can still determine how smart and strategic they are in some direct ways without checking which organizations are the most effective). If you find some effective organizations first, you can also ask “Where else do those donors give” which might unveil charities you missed.
x-post: Facebook—Effective Altruism Polls
I was listening to the 80,000 Hours podcast today and heard Ben Todd say, “The issue is [longtermism is] a new idea.”
I’ve seen this view around EA a few times. It might be true about a certain narrow form of longtermism. It’s NOT true of longtermism broadly.
The first time I was introduced to long-termist ideas was in a university Native Studies class, discussing the traditional teaching that the current generation should focus on the well-being of seven generations in the future.
Cool. Any special reason for 7?
There’s even a specific term I can’t recall for intentional changes in the environment that a social group would make to domesticate a landscape and provide services for future. It will take me some time to find it.
On the other hand, besides the specifics of strong longtermism, I guess that the conjugation of these ideas is pretty recent: a) concern for humanity as a whole, b) a scope longer than 150 years, c) the existence of a trade-off between present and future welfare, d) the balance is tipped in favor of the long-term. [epistemic status: just an insight, would take me too long to look for a counter-example)
The efforts by https://1daysooner.org/ to use human challenge trials to speed up vaccine development make me think about the potential of advocacy for “human challenge” type experiments in other domains where consequentialists might conclude there hasn’t been enough “ethically questionable” randomized experimentation on humans. 2 examples come to mind:
My impression of the nutrition field is that it’s very hard to get causal evidence because people won’t change their diet at random for an experiment.
Why We Sleep has been a very influential book, but the sleep science research it draws upon is usually observational and/or relies on short time-spans. Alexey Guzey’s critique and self-experiment have both cast doubt on its conclusions to some extent.
Getting 1,000 people to sign up and randomly contracting 500 of them to do X for a year, where X is something like being vegan or sleeping for 6.5 hours per day, could be valuable.
Challenge trials face resistance for very valid historical reasons—this podcast has a good summary. https://80000hours.org/podcast/episodes/marc-lipsitch-winning-or-losing-against-covid19-and-epidemiology/
At what point do feel with ~90% certainty you would have done more good by donating to animal charities than you’ve harmed by consuming a regular meat-filled diet?
It would be nice to know the numbers I have in my head somewhat conform to what smart people think.