Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: 900+ Forecasters on Whether Russia Will Invade Ukraine, published by Metaculus on February 19, 2022 on The Effective Altruism Forum.

by Gaia Dempsey, Metaculus

Today, the world’s attention is riveted by the intensifying military brinkmanship playing out on the Russian-Ukrainian border. People are turning to geopolitical experts and analysts of all stripes to understand whether Russia will invade Ukraine — but few know about public forecasting platforms, which allow people to make quantitative predictions about future events, get scored on their performance over time, and compete for prizes in accuracy tournaments.

Metaculus is an open forecasting platform with a track record of making thousands of predictions on societal and scientific topics since 2015. Its unique approach brings the scientific method of modeling and prediction to critical, complex domains that affect public life.

To find a signal in all the noise, Metaculus aggregates the predictions of the crowd and gives more weight to those with the best track records and the most forecasting skill. The result is the Metaculus Prediction, a statistical bellwether that is hard to beat in terms of accuracy, and provides a great deal of information. Few quantitative predictive measures like it exist, least of all ones that can help us understand the most important and complex issues outside of markets. Decision-makers and the public would do well to pay attention.

Metaculus Odds Favor a Russian Invasion

The current Metaculus Prediction gives Russia a 65% chance of invading new territory in Ukraine in 2022. This is 14 percentage points below the current community median of 79% — both are fluctuating rapidly, but overall steadily increasing over the last week. A high degree of uncertainty remains, but an invasion currently seems likelier than not according to this aggregation of over 4,500 forecasts made by over 900 forecasters.

The majority of over 900 forecasters place the likelihood of a Russian invasion between 60–80%

Forecasters pay attention to a wide variety of signals, and their aggregate forecast updates in response to changes in the environment. Individual signals on their own are rarely decisive — indeed, the value of the crowd’s prediction is that it aggregates all of the information considered to be important into a single metric. Below, we’ll look at the most important recent events that forecasters believe may affect the final outcome.

Signals indicating an increasing likelihood of invasion:

There’s significant Russian ground and air power buildup near the border: Russia now allegedly has more than 60% of all its ground combat power and more than 50% of its air power close to the Ukraine border.” Here, a video shows a column of tanks near the Russian town of Striguny, 17-minutes from the border.

The US Embassy has accused Russia of shelling a kindergarten in the Donbas: A message yesterday morning said “Russia’s shelling of Stanytsia Luhanska in Ukrainian government-controlled territory in Donbas hit a kindergarten, injured two teachers, and knocked out power in the village.”

Russian units appear ready for attack: Russian military units, including long-range artillery and rocket launchers, had reportedly left their assembly areas to “move into attack positions” a few days ago with 40–50% now in attack positions as of today.

The US in both actions and words appears certain that a large-scale Russian invasion will happen: President Biden said today he is “convinced” that the decision to attack has been made. A few days ago, the US Embassy moved operations out of Kyiv — Secretary of State Anthony Blinken’s statement that the US is “in the process of temporarily relocating [.] Embassy operations in Ukraine from our Embassy in Kyiv to Lviv due to the dramatic acceleration in the buildup of Russian forces.”

...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: 12 interesting things I learned studying the discovery of nature's laws, published by Ben Pace on February 19, 2022 on LessWrong.

I've been thinking about out whether I can discover laws of agency and wield them to prevent AI ruin (perhaps by building an AGI myself in a different paradigm than machine learning).

So far I’ve looked into the history of the discovery of physical laws (gravity in particular) and mathematical laws (probability theory in particular). Here are 12 things I’ve learned or been surprised by.

1.

Data-gathering was a crucial step in discovering both gravity and probability theory. One rich dude had a whole island and set it up to have lenses on lots of parts of it, and for like a year he’d go around each day and note down the positions of the stars. Then this data was worked on by others who turned it into equations of motion.

2.

Relatedly, looking at the celestial bodies was a big deal. It was almost the whole game in gravity, but also a little helpful for probability theory (specifically the normal distribution was developed in part by noting that systematic errors in celestial measuring equipment followed a simple distribution).

It hadn’t struck me before, but putting a ton of geometry problems on the ceiling for the entire civilization led a lot of people to try to answer questions about it. (It makes Eliezer’s choice in That Alien Message apt.) I’m tempted in a munchkin way to find other ways to do this, like to write a math problem on the surface of the moon, or petition Google to put a prediction market on its home page, or something more elegant than those two.

3.

Probability theory was substantially developed around real-world problems! I thought math was all magical and ivory tower, but it was much more grounded than I expected.

After a few small things like accounting and insurance and doing permutations of the alphabet, games of chance (gambling) was what really kicked it off, with Fermat and Pascal trying to figure out the expected value of games (they didn’t phrase it like that, they put it more like “if the game has to stop before it’s concluded, how should the winnings be split between the players?“).

Other people who consulted with gamblers also would write down data about things like how often different winning hands would come up in different games, and discovered simple distributions, then tried to put equations to them. Later it was developed further by people trying to reason about gases and temperatures, and then again in understanding clinical trials or large repeated biological experiments.

Often people discovered more in this combination of “looking directly at nature” and “being the sort of person who was interested in developing a formal calculus to model what was going on”.

4.

Thought experiments about the world were a big deal too! Thomas Bayes did most of his math this way. He had a thought experiment that went something like this: his assistant would throw a ball on a table that Thomas wasn’t looking at. Then his assistant would throw more balls on the table, each time saying whether it ended up to the right or the left of the original ball. He had this sense that each time he was told the next left-or-right, he should be able to give a new probability that the ball was in any particular given region. He used this thought experiment a lot when coming up with Bayes’ theorem.

5.

Lots of people involved were full-time inventors, rich people who did serious study into a lot of different areas, including mathematics. This is a weird class to me. (I don’t know people like this today. And most scientific things are very institutionalized, or failing that, embedded within business.)

Here’s a quote I enjoyed from one of Pascal’s letters to Fermat when they founded the theory of probability. (For context: de Mere was the gam...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: How To Help Farmed Animals Without Going Vegan (A Happier World video), published by Jeroen W on February 18, 2022 on The Effective Altruism Forum.

Hi all!

Together with Jack Hancock from Humane Hancock we made a video on how to help farmed animals without going fully vegan.

We explore three main ways: Through your diet (cutting/reducing fish/chicken/eggs), your money (donating to effective animal organisation) and your career /free time (mentioning 80,000 Hours and Animal Advocacy Careers).

Feel free to give us any feedback in the comments here or on YouTube. I'm also curious to hear: did you learn any new things? Has it changed/updated your mind on anything?

If you liked the video, I would encourage you to share it with your friends (especially those who aren't in the effective altruist movement). You're also welcome to use it for any EA events you're hosting! In case you do, let us know how it went!

More on A Happier World in this earlier EA forum post.

Thanks to Gage Weston and Sarah Emminghaus for their help with the script.

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: We should consider funding well-known think tanks to do EA policy research, published by DavidZhang on February 19, 2022 on The Effective Altruism Forum.

Background: I am a Research Fellow at Europe's largest foreign policy think tank. Some of the points may not translate to a non-UK context, though I hope that most do. I'm very keen to hear comments especially from others working in policy and also open to discussing bilaterally.

Instead of funding EA organisations to do EA policy research, we should consider funding existing, well-known think tanks to do this work. Often this might be preferable to doing the work 'in house', through an EA-organisation such as FHI or CSER.

Examples of the kind of EA policy work we could fund think tanks to do include:

Assessing of the likelihood of WW3 (as per Stephen Clare's excellent post)

Comparing the merits of different voting systems

Researching the most effective policies to reduce air pollution in middle-income countries

Making the case for greater foreign aid contributions from developed countries

Promoting international, shared AI standards

This is not an original idea. A recent Founders Pledge report recommended funding Carnegie Endowment for International Peace to restart their Track II dialogue between the US and China. However, I think it is pretty rare for EAs to fund non-EA think tanks to do things, and I wanted to make the case from the perspective of a current think tanker.

The power of funders

One thing that has surprised me since I began working in a think tank is how much influence funders have over the scope and activities of a research project. It is not unusual for funders to be involved in:

Setting the parameters of the research

Specifying exactly what the research is used for, or whom

Recruitment for the project

Which organisations/partners are involved

The tone of the research, and in some cases even the conclusions/recommendations

Since the EA movement is somewhat cash-rich, and most of the bottlenecks are around time, expertise and capacity, our prior should be that paying experts to do valuable things is likely to be a good use of money. Think tanks seem like a clear case of experts and time which can be bought through funding.

Of course, being an active funder also takes time and some level of expertise, however it requires less of both compared to doing our own research.

Benefits of working through existing think tanks

Expertise

The most obvious benefit of working with existing think tanks is that they are full of experts who know a lot about issues that are of interest to EAs. This is particularly beneficial if we think that a distinction can be made between technical expertise and the ability to make good decisions. While EAs are good at the latter, they are often not the best at the former. For example, when it comes to US-China conflict (an issue I work on a bit), I would generally trust a well-informed EA to make better policy decisions, because they are likely to have good aims and do cost-benefit analysis well. However, I would not rank EA China experts above other China experts at Brookings or Chatham House in terms of their subject matter or technical expertise.

This distinction implies that we want to get EAs to a position where they can make decisions and do cost-benefit analysis (e.g. it would be great to have an EA-aligned US Secretary of State, or EA-aligned foreign policy advisers in Beijing), but the technical research on China's hypersonic weapons, the chances of war, Beijing's intentions for Taiwan, US public opinion towards China, etc., does not need to be done by EAs. In fact it is likely to be performed better by experts at the best foreign policy think tanks.

Experts are also aware of other ideas in the space and what is already popular or politically feasible, and may possess us...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Observation, published by LoganStrohl on February 19, 2022 on LessWrong.

Imagine that you meet someone you’re attracted to at a party. At one point, they smile at you, and you notice. You’re pretty sure they like you, but you really want to know whether they like you like you.

You don’t act on this in any particular way, but you do spend the whole next week thinking about it. You think about other people who have been into you, and about people who have not, and the differences between them. You muse about what sort of taste in romantic partners you imagine the person might have. By the end of the week, you’re weighing your virtues and vices, trying to decide whether you’re even worthy of love.

(If this seems alien to you, I hope it is at least true to your experiences of some humans.)

In the moment when you noticed you were attracted to the person, you made an observation. In the moment when you noticed their smile, you made another. In the moment when you noticed your curiosity, you made another.

But as soon as you vanished into your own musings, you were no longer making observations. You were no longer collecting data. Instead, you were interpolating, extrapolating, filling in the gaps with stories and guesses, processing and reprocessing. Everything that followed, in the week after the party, took place inside your map—analysis, interpretation, reasoning, reflection.

In Arthur Conan Doyle’s “A Scandal in Bohemia,” Sherlock Holmes lectures Watson on the difference between seeing and observing:

“You see, but you do not observe. The distinction is clear. For example, you have frequently seen the steps which lead up from the hall to this room.”

“Frequently.”

“How often?”

“Well, some hundreds of times.”

“Then how many are there?”

“How many? I don't know.”

“Quite so! You have not observed. And yet you have seen. That is just my point. Now, I know that there are seventeen steps, because I have both seen and observed.”

I don’t know how many steps there are on the staircase up to my own living room, either. Setting aside the question of prioritization, and whether I should be turning my attention there—what is it, exactly, that Watson and I are doing with the steps?

My guess is that we’ve taken some initial impressions—a few moments of impact from the external world—and used those points to draw a constellation. Every time we walk up the steps, we do almost all of our processing on the constellation, rather than on the points of light in the sky.

Most of our “seeing” the stairs is happening inside of our maps. We observe just enough to recognize that we’re about to encounter the well-understood “stairs” entity, and then we superimpose our “stairs” concept over whatever sensations are happening to us, and stop paying attention. To the extent that our brains record anything, it’s that we “climbed up the stairs,” rather than that we felt some number of impacts under each of our feet, while the muscles in our legs contracted and our heart rate climbed slightly, etc.

Imagine that you do end up asking the cute person from the party to meet you for coffee, but when the day comes, you’re extremely distracted by a disaster at work, one you’ll have to return to as soon as the date is over. Despite a whole hour of conversation, you leave feeling like you’ve learned almost nothing about them.

Crucial data was all around you, but while you saw it, you failed to observe any of it.

It is hardest to make fresh observations about things you have seen many times. The stairs, long-held beliefs, attitudes you were raised with. The more often you superimpose your drawing of a constellation over points of light in the sky, the more opaque your drawing becomes.

It probably doesn’t really matter that I have seen-but-failed-to-observe my stairs. I never miss a step, and I’m not in a murder myste...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Should We Studio – high-quality, EA-aligned video content, published by Elizabeth Cox on February 18, 2022 on The Effective Altruism Forum.

I (Elizabeth Cox) recently founded Should We Studio and wanted to share the first roles I'm hiring for.

Should We Studio is an independent video production company focused entirely on projects that will raise awareness of the most important issues facing humanity–especially those that aren’t getting enough attention.

To do that, we’re placing as much emphasis on great storytelling and design as on intellectual rigor and journalistic integrity. Every video we make, we’ll craft to motivate someone who’s never even heard of the subject to learn more. While we’ll be strict in focus, we’ll be flexible in form, creating characters, worlds, and stories that explore consequential topics with exuberance.

Our goal is to expand the frame of reference for anyone who might consider devoting their education, career, time, or money to improving our collective present and future.

I know that description is very high level, so if you're interested in applying to one of the positions below but want to understand a bit more about the approach, please get in touch via [email protected] and I'm happy to discuss.

Animation Director (full-time, min comp $110,000/year)

Our immediate focus will be on short-form, 2D animated content, though a broader skill set is always a plus. A strong candidate will likely have 5+ years of experience creating and directing 2D animation professionally. This could be a good fit for someone who's a relatively accomplished freelance/independent filmmaker looking for a full time gig that still has a lot of creative freedom, or a mid-level employee at a smallish studio looking for more responsibility and decision-making power. That said, the exact number of years or context of prior work is less important than versatility of style and medium, enthusiasm for trying new techniques and approaches, and ability to take a hands-on role at any stage of the production pipeline.

Production Manager (full-time, min comp $80,000/year)

The Production Manager will be responsible for building, optimizing, and maintaining our content and digital asset management systems from the ground up. The ideal candidate is a proactive problem solver with a detailed understanding of the production pipeline, who is able to anticipate and meet a range of needs. This is a great role for someone who is excited to try on many hats, including some unglamorous ones, and there’s an opportunity to evolve the role to include other aspects of operations and business development over time. A strong candidate will likely have 3+ years of experience working in video production (ideally in animation) at a studio, agency, or comparable professional environment.

Research Assistant (10-20 hours/week, $30/hour)

This role is a great fit for someone with a research background (in either an educational or professional context–current students are welcome to apply) who is at least somewhat engaged with effective altruism and wants to learn more about video production or is considering a career in media. They'll conduct a variety of research tasks as needed to support the script writing process. The most important qualities are a no-stone-unturned spirit of inquiry, wide-ranging curiosity, and strong organization and communication skills.

All roles are remote, starting as soon as possible. Full descriptions and applications forms here. If you're excited about the work but none of the open positions are the right fit, there's a general (and very brief) application form on the hiring page as well and I'd be happy to hear from you!

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Simulation arguments, published by Joe Carlsmith on February 18, 2022 on The Effective Altruism Forum.

“If Nature herself proves artificial, where will you go to seek wildness? Where is the real out-of-doors?”

– C.S. Lewis

(Cross-posted from Hands and Cities. Content warning: weird.)

Summary:

This post examines “simulation arguments” – i.e., arguments that we should assign significant probability to living in a computer simulation.

I distinguish between two types. The first type starts with our “standard story” about the world, and tries to argue that on this story, unless something prevents the creation of suitably many sims suitably like us, we’re probably sims. I think this version fails: on our standard story, none of the sims will make our observations. For example, they won’t observe the specific set of books on my bookshelf.

This sort of objection feels cheatsy – and ultimately, I think, it doesn’t work on the best version of the argument. But I use a series of cases to bring out its similarity to less cheatsy-feeling cases: for example, cases in which it appears that all the sims that will ever get made are squid-people with tentacle arms. Simulation arguments, I suggest, should say the same thing about all these cases.

But the “indifference principles” that classic simulation arguments rely on get confused about this. In particular, they end up having to hand-wave about which things we count as “knowing,” and/or about which “reference classes” are “admissible.” This confusion makes it seem like classic arguments require an unprincipled kind of “selective skepticism” – one that risks undermining itself.

I think we do better to focus on a second type of simulation argument, which relies centrally on a claim about what sorts of priors it’s reasonable to have over “structurally similar” worlds that contain your observations. In particular, instead of saying “on your standard story, you’re probably a sim,” this version says: “if that’s your story, you should put lots of weight on these alternative, structurally similar stories, too – on priors, such stories are similarly likely, and they contain your observations, too. But on those stories, you’re a sim.”

I think this second type of argument works better – and that it brings more to the table than a standard skeptical “what if.” Indeed, I think that classic formulations of the simulation argument are implicitly leaning on something in the vicinity. But bringing out the underlying assumptions about “reasonable priors” more explicitly helps to avoid the confusion that classic formulations often create.

That said, Type 2 arguments raise their own questions – for example, about how the relevant notion of “structural similarity” should be understood and applied, and about serious flirtations with “epistemic pascal’s muggings.” These questions (and other heuristics) leave me wary of Type 2 arguments, too.

I also discuss two further simulation arguments. The first is from Thomas (2021), who argues that we’re almost certainly sims, because conditional on being non-sims, the expected ratio of sims to non-sims is very high. I think this argument rests on learning the lesson of the classic argument in one breath, but forgetting this lesson in the next.

The other argument is that even conditional on finding yourself (apparently) living in earth’s early history, finding yourself as an “unusually something” person (e.g., unusually influential, entertaining, etc) should be some kind of substantive additional update in favor of being a sim. I don’t currently see how this argument is supposed to work; and on the classic argument, it seems like Donald Trump’s credence on “I’m a sim” should be basically the same as that of a janitor in Des Moines (even if there will be many more Trump sims than janitor sims).

I conclude with a few ...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Five steps for quantifying speculative interventions, published by NunoSempere on February 18, 2022 on The Effective Altruism Forum.

Summary

Currently, we can’t compare the impact of speculative interventions in a principled way. When making a decision about where to work or donate, longtermists or risk-neutral neartermists may have to choose an organization based on status, network effects, or expert opinion. This is, obviously, not ideal.

I propose a simple solution, if not an easy one. First, estimate the impact of an intervention in narrow units (such as micro-covids, or estimates of research quality). Then, convert those narrow units to more and more general units (such as QALYs, or percentage reduction in x-risk).

Quantifying the value of speculative interventions to a standard similar to GiveWell’s represents a lot of work on a messy problem. In this post, I’ll break it down to these five steps:

Create narrow units for specific types of interventions

Use narrow units to evaluate interventions

Create more general units, and conversion factors from narrow units to general units

Resolve or quantify crucial considerations in order to generalize further

Scale-up evaluations: do more evaluations, better, more cheaply, about more things.

As we make progress on these subproblems, relative value comparisons would become more robust, principled and transparent, which would improve the quality of our decision-making around funding and prioritization. Decisions about where to work or where to donate might still be informed by some subjective factors (e.g., personal fit, value differences), but they would be more grounded in research and expected utility calculations.

This proposal grew out of my frustrations with quantitatively evaluating longtermist organizations or EA projects more generally without a developed framework. Nobody has really been doing this kind of evaluation, so the infrastructure and know-how is just not there.

It’s not even clear what the bar for funding longtermist interventions should be–we don’t know how much good "the last longtermist dollar" will accomplish. Without that key number, funders have to make grants to the best of their abilities by using heuristics and intuitions, which naturally has limitations.

It has been argued that expected utility calculations can be misleading or counter-productive. But these calculations don't have to be perfect, they just have to be better than the alternative—whatever non-quantitative methods people would have used instead. It also doesn’t matter in practice whether one can reach expected value calculations in all their glory, as long as the efforts towards quantification end up paying off (e.g., in terms of better decisions).

So from my perspective, one of the most powerful tools in the EA arsenal has been left gathering dust, mostly for unclear reasons.

In the short term, intuition or heuristics can fill in the gap. But in the long term, as EA moves billions of additional dollars, we will need to upgrade intuition-based human factors to auditable, scalable and more powerful evaluation methods.

Step 1: Create narrow units for specific types of interventions

The simplest and cheapest way to start seems with units tailored to one particular intervention or type of intervention. I’m going to call these “narrow units”, as opposed to more general (e.g., QALYs, which could denominate many types of interventions) or abstract ones (e.g., measures of “research value”).

With narrow units, we can ask if a unit captures most of what we care about in an intervention, and evaluate a new unit on that metric. In the case of research at EA organizations, we care about how it directly influences decisions, but also about the further research it enables, the mentorship around it, the prestige that the authors attain, etc. A un...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Monks of Magnitude, published by Duncan Sabien on February 18, 2022 on LessWrong.

Sometimes I encounter a concept and it immediately embeds itself in my culture, such that it feels like I always knew it. This is a short description of such a concept; it was a near-perfect match for a way I already thought about things, and has since become a useful handle that I find myself explaining to others (so that I can subsequently reference it) roughly once a month.

There is a certain story, which I will not name here in order to reduce the spoiler-y nature of the following description.

In that story, there is a monastery, and the monastery is divided into tiers, or levels.

(I may not be precisely representing the story, here, but rather how the thing in the story ended up being recorded by my brain.)

Some monks are 1-day monks. They come out from their seclusion once a day, and mingle with the regular people, and share their insights, and make new observations, and then retreat back to their private spaces to muse and meditate.

Some monks are 10-day monks. They are much like the 1-day monks, except they come out only every 10 days.

The 10-day monks tend to think longer thoughts, and wrestle with subtler or more complex problems, than the 1-day monks. This is treated by the culture of the monastery as natural and correct. Of course the 10-day monks address a different set of problems; if 10-day monks and 1-day monks were good for the same purposes, the two different Orders wouldn't need to exist.

There are also 100-day monks, who come out only a few times per year.

There are also 1,000-day monks, who come out only every few years.

There are also 10,000-day monks, who come out only every thirty years or so.

There are also 100,000-day monks (the structure of this society has led them to be better at solving problems overall, which has allowed for some advances in longevity tech).

"Come out" may be a bit of a misnomer; in fact, it is the case that the most valuable insights of a given Order tend to be fully comprehensible only to monks of one, mmmmaaaaybe two Orders below them. So the 100,000-day monks, when they report in, mostly speak only to the 10,000-day monks, who are responsible for distilling and transferring relevant insights to the 1,000-day monks, who are responsible for distilling and transferring relevant insights to the 100-day monks, etc.

(It's also the case, as some readers have pointed out in the comments below, that certain problems cannot be solved by isolated thought alone, and require feedback loops or regular contact with the territory. For monks working on such problems, it is less that they sequester themselves completely for thousands of days at a time and more that, during those thousands of days, none can make demands of them.)

Duncan-culture works this way.

(By "Duncan-culture," I mean a culture composed entirely of Duncans; a culture made up of people who, whatever their other differences, take for granted everything that I, Duncan Sabien, find intuitively obvious and believe I could convey to a ten-year-old version of me in a few hours' time. This society lives on a large island a few hours' sailing off the coast of Dath Ilan.)

If there were indeed 1,000 literal Duncan-copies available, to found a monastery or any other endeavor, they would immediately stratify themselves into 1, 10, 100, and 1,000-day groups at the very least, and probably there would be nonzero 10,000-day Duncans as well.

The key here is that each of these strata focuses on a set of largely non-overlapping issues, with largely non-overlapping assumptions.

To a 1-day or 10-day monk, questions like "maybe this is all a simulation, though" are almost entirely meaningless. They are fun to ponder at parties, but they aren't relevant to the actual working-out-of-how-things-work. 1-day a...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: We need 40000h or maybe even 20000h, published by martyna on February 18, 2022 on The Effective Altruism Forum.

I'm a great admirer of the 80000hours initiative, ever since I've learned about it. And that's the thing, I was introduced to EA and 80kh relatively late in my life imo. I'm not a student, graduate, or even junior in the field. I'm two decades older senior-level employee without a chance to switch to an impactful career.

I've learned about EA 3 years ago, it took me 2 years to stop fantasizing that there is anything I can do with my career path so my "last resort" was earning to give program. I took the pledge last year and almost immediately felt the pressure to not go down with my salary but to make it as high as possible. There are million other factors at stake, personal and professional, and I need to say it, I feel completely lost at times. I only know I need to move forward, but I can get so critical about my decisions that it paralyzes me.Am I the only one? I'm sure there are other people in a similar situation. Mature in age and career, that found EA/80kh too late to adjust life road and struggle in being efficient and impactful.There is great wisdom in onboarding young people and preventing them from similar struggles later in their life.

But there is also untapped potential in mid-life and senior members of the community who are not politicians, researchers, scientists, economists, or entrepreneurs and still want to pitch in. Their, our salaries are probably much higher than when we started, and we might have a bit more influence on our positions. And yes, we still need guidance. I need guidance.

So I wanted to challenge this idea today. 40000hours sounds great. 20000hours sounds even better. I realize that it's much harder to advise senior employees, but the gain might be much higher and more immediate at the start. Worth considering I think.

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.