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Metaculus is a community forecasting platform which popular among members of the EA and Rationalist communities. Many of the forecasts on the site are directly relevant to EAs, including the entirety of the pandemic subdomain, this series on global catastrophic risk, this recent tournament on AI progress, this large Animal Welfare series sponsored by Open Phil, and many others.

In addition, there has been some recent effort to use Metaculus as a platform to help EA-aligned organisations directly, by forecasting their success and/or decision making. It is my hope that this will become more common, because I think better forecasts should allow organisations to make better decisions about their future plans. Moreover, I think forecasting on important issues is a plausible candidate for Task Y, as it's interesting, scaleable, builds useful skills, and actually has tangible value.

As a result, I have collected forecasts about EA or EA-aligned organisations below, both because...

This is missing the Giving What We Can one:


In this post I share my career decision-making process. I hope that it will help others in their career decisions, by serving as a detailed case study. I believe that the general framework I used can be useful to a wide audience. Furthermore, in the last section (which comprises more than half of the post), I detail all of the options that I considered, many of which are not frequently discussed in EA, and I believe they will be particularly relevant for people with technological or scientific background. I encourage all community members to share their career decision-making process as well.

About half a year ago, I left my math PhD to pursue a more impactful career. In the past few months, I was working on generating a long-list of options, learning more about them and making a decision. Ultimately, I decided to start a PhD in computational healthcare, aiming to...

Really great post, thank you! You discuss the possibility of "part-time earning to give while simultaneously running side projects" and note that you've chosen to work part-time on a PhD in Computational Healthcare while also working a separate part-time job for earning to give. 

Part-time earning to give seems like an interesting possibility I hadn't considered before, mainly because I assumed there are very few part-time jobs that pay well. What has been your experience here? Do you have a unique opportunity that allows you to earn a lot part-time? P... (read more)

This is an update from the Centre for Effective Altruism on our work in the fourth quarter of 2020. Most of this information was not included in our annual review, which we began to draft in October.


Our mission is to build a community of students and professionals acting on EA principles, by nurturing high-quality discussion spaces.

In Q4, we began to focus on achieving our annual goals.

Going into the quarter, we had a much clearer scope and goals than before. We began to focus more on those goals, and test out new programs that could further them, like online fellowships and student-focused events. We also reviewed our progress in 2020, made plans for 2021, and began a major fundraising campaign.

Huw Thomas joined the groups team. Now that CEA’s scope and goals are clear, I think that we’re in a position to make several excellent hires. This will be a major focus for next...

8MaxDalton9hThat's interesting - I'm surprised by that and wonder if it's due to some differences between systems? In the UK people often begin to think about internships in their first or second years, and then look for jobs in the 3rd year, so I think there's quite a lot of ability to influence and discuss career plans early on. In the US degrees are longer, but early on people are trying to decide their major which is also a significant career decision. I also think that students have a lot more time and interest in engaging with new things, and they tend to be easier to reach (e.g. because they all come to activity fairs). How do you find/target these early-career people? And aren't they already normally in employment/set on a career path? CBGs remains open to non-student groups.

(I'm German, but have lived in the UK for 4.5 years now.)

My best guess is that you are both right, and large cultural differences are at play. I found this really bizarre when I moved to the UK. In Germany, you are an ambitious overachiever if you have a 'career plan' at 22. In the UK this is standard.

Among educated Germans, people take longer to finish their degrees, are more likely to take gap years, change degrees. Internships seem to be much rarer. The 'summer internship' system does not seem to exist as much in Germany, and just is not considered nece... (read more)

I recently read Tyler Cowen's Stubborn Attachments, which argues that we should be focusing on maximising the rate of sustainable economic growth and discusses why that is the most important thing to target. This sparked many questions for me about what we should be optimising for (GDP? Leisure time?). In this blogpost I take a stab at answering that question as a non-economist (so bear with me!).

Traditionally, most countries measure their GDP and target that as a measure of growth. GDP is a purely economic measure and has been under constant criticism for a variety of reasons. It doesn't include household work, doesn't value leisure time, and so on. Nevertheless, it has been and is the standard around the world for measuring economic growth.

There's no doubt in my mind that economic growth should be a central part of any reasonable solution to this question. Practically any important metric that you...

3Lars Mennen16hThanks for your comment! I agree with your point on how egalitarians can be in favour of growth. Good point, but I'm not sure that justifies a large number of policies that might lead to lower sustainable growth in the long run in favour of more equality. There's definitely a balance to strike here I think between making sure power does not get too unequally distributed and growth; it's not black and white. In particular, you also want to maintain a stable economy and democracy (necessary for growth to be sustainable), which does require solid institutions and I would argue also some degree of egalitarianism. Perhaps some metrics around stability can be added as early indicators to monitor while mainly focusing on sustainable growth? Curious for your thoughts on this. And thanks for the reading tip on Gerald Cohen, will add that to the list :)
2Lars Mennen16hThanks for the comment, that's a very fair point! There are certainly some issues on how such services are represented in these metrics. I think if we can find a better way to measure the state of the economy that accurately includes this, I would be in favour. This article [] for example has a proposal to do that. I don't think it changes the main argument for focusing on the sustainable economic growth (in that case measured by a more accurate metric) instead of focusing on metrics like happiness or life expectancy (or other metrics not directly measuring economic growth) though. What do you think?

Thanks for the article link. The proposed GDP-B indicator does seem like a step in the right direction. The European Commission is also working on developing a new indicator that does a better job at modelling the digital economy (feasibility study).

Yes, I'm not convinced that well-being metrics alone would do a good job either and your argument for emphasising sustainable economic growth seems quite convincing to me. 


I created a simple web-based tool which ranks animal species according to the harm caused by consuming them. The user can specify the relative priority of two subscales of harm: animal suffering and greenhouse gas emissions.

Numerous analyses have been published on how much suffering is caused by eating various animals. For example by Peter Hurford, Brian Tomasik, Charity Entrepreneurship and Dominik Peters. Results of these analyses hint at the small animal replacement problem which is the concern that advocating for reduced meat consumption for environmental reasons leads people to replace beef with smaller animals such as chicken and fish. This increases total suffering because more farmed animals are consumed for the same amount of calories.

I was inspired by Dominik Peters' tool and was wondering if a similar ranking could be developed which accounts for animal suffering, greenhouse gas emissions and human health. My main motivation was to better understand the...

This is really nicely done and it is exactly what many are looking for. Thank you so much! 

If it is to be shared more widely it might help to add a remark about how sensitive the results are to which country the animal products are from and whether they're organic or not. The reason for this being that many in the public sphere (and not infrequently wrongly) assume that this makes a crucial difference.

3VilleSokk5hThank you for the feedback, MichaelStJules! I added all of your ideas to my todo list. I definitely should have added probability of sentience to the model. I looked at Brian Tomasik's model which included sentience multipliers and I have read the OPP report you linked so I don't know why I didn't consider it. Jason Schukraft's "Differences in the Intensity of Valenced Experience across Species" was great and I will be happy to study his other research. Thank you for linking to it. I wish I was aware of kbog's post before I started. I managed to find multiple analyses of suffering but I didn't know someone had already devised a combined model!
1VilleSokk6hThank you for the thoughtful feedback, Benjamin! I will try to explain the model a bit more thouroughly than the methods section of the post. Let's forget normalising and weights for a moment. If we measure suffering in hours/kcal and emissions in CO2eq/kcal then the subscales have different units and can't be added (unless we have a conversion formula from one unit to the other somehow). A common solution in this case is to multiply the subscale values. If we do this a 1% change in suffering changes the combined score by the same amount that a 1% change in emissions would. We still might want to prioritise some subscales more than others. If we would have added subscale scores we could have multiplied the subscale scores by some constant weights beforehand. If instead we multiply subscale scores we would exponentiate the subscale scores by weights beforehand. This simple idea is called a weighted product model [] (WPM) in the multiple-criteria decision analysis discipline which studies how to make decisions when we have multiple conflicting criteria. This tool uses a weighted product model. The unnormalised suffering and emissions scores are: 1. exponentiated by their corresponding weight, 2. multiplied together to get a combined score, 3. the combined score is normalised to the 0-100 range for cleaner display. WPM is a dimensionless method used for ranking options when making decisions. That is, to answer questions like "is it more important to avoid chicken or beef" not "what is the cardinal utility of avoiding chicken". This model is only useful for prioritising if I have decided to reduce meat consumption but am only able to leave one species off my plate. I understand now that I should have made it more clear. Somehow measuring the utility of leaving a species off my plate would be much more interesting but seemed difficult considering the time and skills I had. I did consider using something like DALYs.

I think it can be useful to motivate longtermism by drawing an analogy to the prudential case — swapping out the entire future for your future, and only considering what would make your life go best.

Suppose that one day you learned that your ageing process had stopped. Maybe scientists identified the gene for ageing, and found that your ageing gene was missing. This amounts to learning that you now have much more control over how long you live than previously, because there's no longer a process imposed on you from outside that puts a guaranteed ceiling on... (read more)

This post originally appeared on LessWrong. It has been very lightly edited.

Megaproject management is a new-ish subfield of project management. Originally considered to be the special case of project management where the budgets were enormous (billions of dollars), it is developing into a separate specialization because of the high complexity and tradition of failure among such projects. The driving force behind treating it as a separate field appears to be Bent Flyvbjerg, previously known around here for Reference Class Forecasting as the first person to develop an applied procedure. That procedure was motivated by megaprojects. For context, these projects are things like powerplants, chip fabs, oil rigs, et cetera; in other words, the building blocks of modernity.

I will make a summary of the paper "What you should know about megaprojects, and why: an overview" from 2014. For casual reading, there is an article about it from the New Yorker here.



1ryan_b6hThis talk is great, and hits the exact same points as the paper. Would it be alright with you if I put the link to the talk in post with the other resources?

Of course!  :)

1ryan_b8hInvestigating the field more deeply this year is going to be one of my hobby projects, but my early impression is that a big part of the claim is that when you do regular project management things wrong, the penalty at least scales. It also looks like the cost of doing the analysis right, such as reference class forecasting, doesn't come remotely close to scaling with the needs of the project. I'm glad about this, because I was worried at first the whole inquiry might be useless except to people in a position of responsibility. Instead, it looks like there will be a lot of methods that are always a good idea but also scale really well. Bonus!

A big thank you to Birte Spekker, Alexander Herwix, Achim Voss, Florian Jehn, Manuel Allgaier, Peter Ruschhaupt, Rudi Zeidler, Ruslan Krenzler for commenting on drafts of this post.

Epistemic Status

Pretty convinced but with an uneasy feeling that I couldn't pass an ideological turing test. List of threats is tentative, only meant for illustration, and probably has blind spots.


In this post, I will argue that for many EA cause areas, having secure communication and collaboration platforms is an instrumental goal. And that Privacy, security and safety aspects are currently undervalued by the EA Community and too much weight is given to how wide-spread and easy to use these platforms are. I argue that self-hosted open-source collaboration tools are a good default alternative to the proprietary cloud services provided by tech companies.

Which platforms to use is a hard-to-reverse decision[1] in which we are trading off higher growth in the short term (1-5 years)...

Do all EA orgs need to use the same tools? 

No, and I hope I didn't imply that there is a one-size-fits-all solution that everybody needs to switch to.

can those with added security needs (e.g. those active in countries with government repression) just use different tools

Yes, that is of course possible, and I would expect that to happen automatically. Just note that this means in some cases that we will exclude those people with added security needs from community spaces.

Things that would make me less worried about "using whatever works best":

  • switching
... (read more)


In this post, you'll find why I think SENS Research Foundation (SRF) is great to finance from an EA perspective along with the interview questions I want to ask its Chief Science Officer, Aubrey de Grey. You are welcome to contribute with your own questions in the comments or through a private message. Here is a brief summary of each section:

Introduction: Aging research looks extremely good as a cause-area from an EA perspective. Under a total utilitarian view, it is probably second or third after existential risk mitigation. There are many reasons why it makes sense to donate to many EA cause-areas, such as to reduce risk, if there are particularly effective specific interventions, or if some cause-areas are already well funded.

SRF's approach to aging research: SRF selects its research following the SENS general strategy, which divides aging into seven categories of damage, each having a corresponding line of research....

1Florin20hToo much tau junk → too much cytoskeleton damage Too much lipofuscin/A2E → AMD That's LEV []'s job (SENS 2, 3, etc.). If you still think that there's any potential primary damage targets that SENS doesn't specifically mention, please let me know.
1InquilineKea16hThat's not the only thing that causes cytoskeleton damage. Ultimately one path forward is: how do you create the data-set/papers that can be used by a new version of GPT-3 to suggest potential interventions for aging. That's why ALL of the creative new technologies people use to treat genetic diseases or cancer (along with nanotechnology - yes UPenn people are already creating nanobots) can help, even if not originally designed for aging.

The point is that if the amount of tau/other junk could be kept low enough (by periodically removing it), then the accumulation of too much cytoskeleton damage should be avoided.

Another possible reason against might be:
In some countries there is a growing number of people who intentionally don't use Facebook. Even if their reasons for their decision may be flawed, it might make recruiting more difficult. While I perceive this as quite common among German academics, Germany might also just be an outlier.

Moving certain services found on Facebook to other sites: [...], making it easier for people to reach out to each other (e.g. EA Hub Community directory). Then it may be easier to move whatever is left (e.g. discussions) to a new pl

... (read more)
2Aaron Gertler15hI don't think the Forum is likely to serve as a good "group discussion platform" at any point in the near future. This isn't about culture so much as form; we don't have Slack's "infinite continuous thread about one topic" feature, which is also present on Facebook and Discord, and that seems like the natural form for an ongoing discussion to take. You can configure many bits of the Forum to feel more discussion-like (e.g. setting all the comment threads you see to be "newest first"), but it feels like a round peg/square hole situation. On the other hand, Slack seems reasonable for this!
1Tsunayoshi10hThere is also a quite active EA Discord server, which serves the function of "endless group discussions" fairly well, so another Slack workspace might have negligible benefits.