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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: Changes in College Admissions, published by Zvi on April 24, 2024 on LessWrong.
This post brings together various questions about the college application process, as well as practical considerations of where to apply and go. We are seeing some encouraging developments, but mostly the situation remains rather terrible for all concerned.
Application Strategy and Difficulty
Paul Graham: Colleges that weren't hard to get into when I was in HS are hard to get into now. The population has increased by 43%, but competition for elite colleges seems to have increased more. I think the reason is that there are more smart kids. If so that's fortunate for America.
Are college applications getting more competitive over time?
Yes and no.
The population size is up, but the cohort size is roughly the same.
The standard 'effort level' of putting in work and sacrificing one's childhood and gaming the process is dramatically up. So you have to do it to stay in place.
There is a shift in what is valued on several fronts.
I do not think kids are obviously smarter or dumber.
Spray and Pray and Optimal Admissions Strategy
This section covers the first two considerations.
Admission percentages are down, but additional applications per student, fueled by both lower transaction costs and lower acceptance rates, mostly explains this.
This means you have to do more work and more life distortion to stay in place in the Red Queen's Race. Everyone is gaming the system, and paying higher costs to do so.
If you match that in relative terms, for a generic value of 'you,' your ultimate success rate, in terms of where you end up, will be unchanged from these factors.
The bad news for you is that previously a lot of students really dropped the ball on the admissions process and paid a heavy price. Now 'drop the ball' means something a lot less severe.
This is distinct from considerations three and four.
It is also distinct from the question of whether the sacrifices are worthwhile. I will return to that question later on, this for now is purely the admission process itself.
The size of our age cohorts has not changed. The American population has risen, but so has its age. The number of 17-year-olds is essentially unchanged in the last 40 years.
GPT-4 says typical behavior for an applicant was to send in 1-3 applications before 1990, 4-7 in the 1990s-2000s, 7-10 in the late 2000s or later, perhaps more now. Claude said it was 3-5 in the 1990s, 5-7 in the early 200s and 7-10 in the 2010s.
In that same time period, in a high-end example, Harvard's acceptance rate has declined from 16% to 3.6%. In a middle-range example, NYU's acceptance rate in 2000 was 29% and it is now 12%. In a lower-end example, SUNY Stony Brook (where my childhood best friend ended up going) has declined from roughly 65% to roughly 44%.
The rate of return on applying to additional colleges was always crazy high. It costs on the order of hours of work and about $100 to apply to an additional college. Each college has, from the student's perspective, a high random element in its decision, and that decision includes thousands to tens of thousands in scholarship money. If you apply to a safety school, there is even the risk you get rejected for being 'too good' and thus unlikely to attend.
Yes, often there will be very clear correct fits and top choices for you, but if there is even a small chance of needing to fall back or being able to reach, or finding an unexpectedly large scholarship offer you might want, it is worth trying.
As colleges intentionally destroy the objectivity of applications (e.g. not requiring the SAT, although that is now being reversed in many places, or relying on hidden things that differ and are hard to anticipate) that further decreases predictability and correlation, so you have to apply to more places, which f

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: Your feedback for Actually After Hours: the unscripted, informal 80k podcast, published by Mjreard on April 24, 2024 on The Effective Altruism Forum.
As you may have noticed, 80k After Hours has been releasing a new show where I and some other 80k staff sit down with a guest for a very free form, informal, video(!) discussion that sometimes touches on topical themes around EA and sometimes… strays a bit further afield. We have so far called it "Actually After Hours" in part because (as listeners may be relieved to learn), I and the other hosts don't count this against work time and the actual recordings tend to take place late at night.
We've just released
episode 3 with Dwarkesh Patel and I feel like this is a good point to gather broader feedback on the early episodes. I'll give a little more background on the rationale for the show below, but if you've listened to [part of] any episode, I'm interested to know what you did or didn't enjoy or find valuable as well as specific ideas for changes.
In particular, if you have ideas for a better name than "Actually After Hours," this early point is a good time for that!
Rationales
Primarily, I have the sense that there's too much doom, gloom, and self-flagellation around EA online and this sits in strange contrast to the attitudes of the EAs I know offline. The show seemed like a low cost way to let people know that the people doing important work from an EA perspective are actually fun, interesting, and even optimistic in addition to being morally serious.
It also seemed like a way to highlight/praise individual contributors to important projects. Rob/Luisa will bring on the deep experts and leaders of orgs to talk technical details about their missions and theories of change, but I think a great outcome for more of our users will be doing things like Joel or Chana and I'd like to showcase more people like them and convey that they're still extremely valuable.
Another rationale which I haven't been great on so far is expanding the qualitative options people have for engaging with Rob Wiblin-style reasoning. The goal was (and will return to being soon) sub-1-hour, low stakes episodes where smart people ask cruxy questions and steelman alternative perspectives with some in-jokes and Twitter controversies thrown in to make it fun.
An interesting piece of feedback we've gotten from 80k plan changes is that it's rare that a single episode on some specific topic was a big driver of someone going to work on that area, but someone listening to many episodes across many topics was predictive of them often doing good work in ~any cause area.
So the hope is that shorter, less focused/formal episodes create a lower threshold to hitting play (vs 3 hours with an expert on a single, technical, weighty subject) and therefore more people picking up on both the news and the prioritization mindset.
Importantly, I don't see this as intro content. I think it only really makes sense for people already familiar with 80k and EA. And for them, it's a way of knowing more people in these spaces and absorbing the takes/conversations that never get written down. Much of what does get written down is often carefully crafted for broad consumption and that can often miss something important. Maybe this show can be a place for that.
Thanks for any and all feedback! I guess it'd be useful to write short comments that capture high level themes and let people up/down vote based on agreement. Feel free to make multiple top-level comments if you have them and DM or email me (matt at 80000hours dot org) if you'd rather not share publicly.
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: Three Reasons Early Detection Interventions Are Not Obviously Cost-Effective, published by Conrad K. on April 24, 2024 on The Effective Altruism Forum.
Summary
For pandemics that aren't 'stealth' pandemics (particularly globally catastrophic pandemics):
Reason 1: Not All 'Detections' Are Made Equal: there can be significant variation in the level of information and certainty provided by different detection modalities (e.g. wastewater surveillance vs. syndromic surveillance), and the efficacy of early detection is heavily dependent on the ability to quickly trigger an epidemiological response. Thus, the nature of the detection signal is probably an important factor affecting the time required to confirm an outbreak and take action.
There should probably be a greater prioritisation of plans for public health response to different types and levels of detection signals.
Reason 2: 'Early' Might Not Be 'Early' (or Cheap) Enough: for highly transmissible pathogens, early detection systems may only provide a lead time on the order of days to weeks compared to "naive detection" from symptomatic spread, and the costs to achieve high confidence of detection can be prohibitively expensive (on the order of billions). Improving cost-effectiveness likely requires carefully targeting surveillance to high-risk populations and locations.
Methodological uncertainties make it difficult to have high levels of confidence about how valuable early detection interventions are for a range of pathogen characteristics, particularly for GCBR-level threats.
Reason 3: Response Strategies Matter, A Lot: the cost-effectiveness of early detection is highly dependent on the feasibility and efficacy of post-detection containment measures. Factors like public compliance, strength of the detection signal, degree of pathogen spread, and contingencies around misinformation can significantly impact the success of interventions. The response strategy must be robust to uncertainty around the pathogen characteristics in the early stages of a pandemic.
More work is needed to ensure readiness plans can effectively leverage early detections.
Background
I want to start this post by making two points. Firstly, I think it is worth flagging a few wins and progress in pathogen-agnostic early detection since I began thinking about this topic roughly nine months ago:
The publication of 'Threat Net: A Metagenomic Surveillance Network for Biothreat Detection and Early Warning' by Sharma et al., 2024.
The publication of 'Towards ubiquitous metagenomic sequencing: a technology roadmap' by Whiteford et al., 2024.
The publication of 'A New Paradigm for Threat Agnostic Biodetection: Biological Intelligence (BIOINT)' by Knight and Sureka, 2024.
The publication of the preprint, 'Quantitatively assessing early detection strategies for mitigating COVID-19 and future pandemics' by Liu et al., 2023.
The Nucleic Acid Observatory continued its work, publishing several notebooks, resources, white papers, reports, and preprints and even creating a tool for simulating approaches to early detection using metagenomics.
The UK government published its biological security strategy in June 2023, which included goals such as the establishment of a National Biosurveillance Network and the expansion of wastewater surveillance.
The U.S. Department of Health and Human Services (HHS) announced actions the department will take following National Security Memorandum 15, signed by President Biden, including accelerating advanced detection technologies.
The Armed Forces Health Surveillance Division's Global Emerging Infections Surveillance branch hosted its first Next-Generation Sequencing Summit.
Various funding opportunities for improving diagnostic technology were announced, including:
The National Institute of Biomedical Imaging and Bioengineering'

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: Is there software to practice reading expressions?, published by lsusr on April 24, 2024 on LessWrong.
I took the Reading the Mind in the Eyes Test test today. I got 27/36. Jessica Livingston got 36/36.
Reading expressions is almost mind reading. Practicing reading expressions should be easy with the right software. All you need is software that shows a random photo from a large database, asks the user to guess what it is, and then informs the user what the correct answer is. I felt myself getting noticeably better just from the 36 images on the test.
Short standardized tests exist to test this skill, but is there good software for training it? It needs to have lots of examples, so the user learns to recognize expressions instead of overfitting on specific pictures.
Paul Ekman has a product, but I don't know how good it is.
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: You probably want to donate any Manifold currency this week, published by Henri Thunberg on April 24, 2024 on The Effective Altruism Forum.
In a recent announcement, Manifold Markets say they will change the exchange rate for your play-money (called "Mana") from 1:100 to 1:1000. Importantly, one of the ways to use this Mana is to do charity donations.
TLDR: The CTA here is to log in to your Manifold account and donate currency you have on your account before May 1st. It is a smooth process, and would take you 30 seconds if you know what charity you want to support.
There are multiple charities available for donations, that EAs tend to donate to, such as:
GiveWell
Rethink Priorities
EA Funds Animal Welfare Fund
EA Funds Long-Term Future Fund
The Humane League
Against Malaria Foundation
Shrimp Welfare Project
... and many more.
It is not 100% clear to what extent the donation is indeed counterfactual[1], but I believe there is reason to believe you can have positive influence through choosing which charities end up getting this money.
If you I) have an account with Mana on it, II) regularly do charity donations with your own money, then donating your balance now seems to dominate other options.
If you actually want to have some currency on Manifold to make bets with, you can buy it back next week for a cheaper rate than your current donation. I am somewhat unsure of this: it's possible that the value of one charity getting the money over another is not enough to outstrip the degree of counterfactuality discount described in the first footnote.
The reason I am still writing this post, is that I think many people have currency laying around that they never plan to use - but might be a few $10s or $100s of charity donations, and 10x more valuable than it will be next week.
Worth noting (thanks @CalebW for highlighting in comment) is that if you are locked in to positions that are hard to exit, you can get in touch with admins to help resolve your situation more satisfactorily without having to sell at crazy rates.
I apologize in advance for the possibility of:
Claims about Manifold's future that they change their mind about.
Mistaken use of terminology from my side.
Mistaken speculations about donation counterfactuality.
... other mistakes.
^
My understanding: Since the money donated to charity is from a Future Fund grant (?), it can only be used that way rather than to support other business activities. So it might be likely that the allocated funds would eventually go to some charity regardless, and your influence is which one.
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: On what research policymakers actually need, published by MondSemmel on April 24, 2024 on LessWrong.
I saw this guest post on the Slow Boring substack, by a former senior US government official, and figured it might be of interest here. The post's original title is "The economic research policymakers actually need", but it seemed to me like the post could be applied just as well to other fields.
Excerpts (totaling ~750 words vs. the original's ~1500):
I was a senior administration official, here's what was helpful
[Most] academic research isn't helpful for programmatic policymaking - and isn't designed to be. I can, of course, only speak to the policy areas I worked on at Commerce, but I believe many policymakers would benefit enormously from research that addressed today's most pressing policy problems.
... most academic papers presume familiarity with the relevant academic literature, making it difficult for anyone outside of academia to make the best possible use of them.
The most useful research often came instead from regional Federal Reserve banks, non-partisan think-tanks, the corporate sector, and from academics who had the support, freedom, or job security to prioritize policy relevance. It generally fell into three categories:
New measures of the economy
Broad literature reviews
Analyses that directly quantify or simulate policy decisions.
If you're an economic researcher and you want to do work that is actually helpful for policymakers - and increases economists' influence in government - aim for one of those three buckets.
New data and measures of the economy
The pandemic and its aftermath brought an urgent need for data at higher frequency, with greater geographic and sectoral detail, and about ways the economy suddenly changed. Some of the most useful research contributions during that period were new data and measures of the economy: they were valuable as ingredients rather than as recipes or finished meals...
These data and measures were especially useful because the authors made underlying numbers available for download. And most of them continue to be updated monthly, which means unlike analyses that are read once and then go stale, they remain fresh and can be incorporated into real-time analyses.
Broad overviews and literature reviews
Most academic journal articles introduce a new insight and assume familiarity with related academic work. But as a policymaker, I typically found it more useful to rely on overviews and reviews that summarized, organized, and framed a large academic literature. Given the breadth of Commerce's responsibilities, we had to be on top of too many different economic and policy topics to be able to read and digest dozens of academic articles on every topic...
Comprehensive, methodical overviews like these are often published by think-tanks whose primary audience is policymakers. There are also two academic journals - the Journal of Economic Perspectives and the Journal of Economic Literature - that are broad and approachable enough to be the first (or even only) stop for policymakers needing the lay of the research land.
Analysis that directly quantify or simulate policy decisions
With the Administration's focus on industrial policy and place-based economic development - and Commerce's central role - I found research that quantified policy effects or simulated policy decisions in these areas especially useful...
Another example is the work of Tim Bartik, a labor economist and expert on local economic development. In a short essay, he summarized a large academic literature and estimated how effective different local economic development policies are in terms of the cost per job created. Cleaning up contaminated sites for redevelopment creates jobs at a much lower cost per job than job training, which in turn is much more cos