DirectedEvolution

Karma: 12,463

Pandemic Prediction Checklist: H5N1

Pandemic Prediction Checklist: Monkeypox

Correlation may imply some sort of causal link.

For guessing its direction, simple models help you think.

Controlled experiments, if they are well beyond the brink

Of .05 significance will make your unknowns shrink.

Replications show there’s something new under the sun.

Did one cause the other? Did the other cause the one?

Are they both controlled by what has already begun?

Or was it their coincidence that caused it to be done?

DirectedEvolution 13 Apr 2026 22:03 UTC
2 points
0
in reply to: eniteris’s comment on: Daycare illnesses
As you know, there are immunocompromised children who are forced to live in perfectly sterile environments. That is clearly not what I am talking about.
A scientist’s job in public communications is to help separate the facts we know from the uncertainties, and the positive questions from the normative.
Strawman statements like “sure, you can life the rest of your life in a perfectly sterile environment” are the opposite of that. They encourage audiences to collapse those vital distinctions and introduces unnecessary volatility and distraction into the discussion.

DirectedEvolution 13 Apr 2026 15:57 UTC
2 points
0
on: The policy surrounding Mythos marks an irreversible power shift
I think it depends the extent to which Mythos depends on model weights vs. scaffolding. Weights are too expensive to train on a FOSS budget, but scaffolding is not. Less so now than ever before. If Mythos’s capabilities depend on a non-public Anthropic scaffold with the model weights being plug-and-play, then we may see a FOSS scaffold with Mythos-level capabilities, especially if the viability of the entire global cybercrime “industry” is imminantly threatened by newly capable defense. There’s been less pressure to build one as long as SOTA was publicly available. But governments, corporations and hackers are not going to want to have their access to the bleeding edge taken away or dependent on the good graces of an American corporation or the US government. And those institutions have a budget!

DirectedEvolution 13 Apr 2026 15:39 UTC
2 points
0
in reply to: eniteris’s comment on: Daycare illnesses
Exposure is definitely helpful in building an adaptive immune response, but what age is best is an open question.
We have to distinguish between the biological fact of establishing an adaptive immune response and the practical implication that chronic childhood infections are not worth avoiding or are net beneficial. We can’t assume that the marginal expected effect of an infection at any age is beneficial to health.

DirectedEvolution 13 Apr 2026 8:58 UTC
58 points
12
on: Daycare illnesses
It actually seems to me that daycares would be a natural setting to trial the new consumer far-UVC, air purifiers, good HEPA filters, or mandatory daily flu/COVID testing for the kids. You could potentially have a rule that kids who come in sick get semi-quarantined (only contact is with masked adults/sibs, they must wear a mask). That’s a setting where it might really make a noticeable difference and where you don’t have to convince an institution to retrofit a whole large building.
Apparently an air purifier cut sick days by a 3rd in Helsinki.
https://yle.fi/a/74-20062381
There’s also an ongoing trial called CLAIRE on using HEPA filters in schools.
https://clinicaltrials.gov/study/NCT07479420
A re-analysis of data from the 1940s found far-UVC dramatically reduced child absenteesism due to respiratory illness
https://pmc.ncbi.nlm.nih.gov/articles/PMC9915224/#:~:text=Odds%20ratios%20for%20the%20effect,and%20a%20half%20by%20GUV.
Far-UVC has been shown to be extremely effective in reducing pathogen load in the air.
https://pubmed.ncbi.nlm.nih.gov/35322064/
Maybe it’s time for somebody to open an aggressively sanitary daycare.

DirectedEvolution 13 Apr 2026 1:25 UTC
2 points
0
on: How should I judge the impact of giving $5k to a family of three kids and two mentally ill parents?
Speaking from my interest in a different field, drug trials, it’s very helpful to gauge the success of your trial on an objective metric. Avoid relying on surveys, interviews, and other fuzzy, subjective responses.
Some possibilities include:
- Days of school truancy and grades for the 7 and 12 year old children.
- BMI of the children, if they’re not getting enough to eat.
- The diet of the children.
- Changes in living space (i.e. if they move into a space with more room after getting the money)
- Changes in how well the kids are sleeping (you could give them some sort of sleep monitor)
- Consistency of medication compliance by the mother.
- If mom or kids are willing, you could measure salivary cortisol, Alpha-Amylase, sIgA, or DHEA levels as noninvasive stress and immune biomarkers. I believe there are also hair cortisol tests that would allow you to get a hair sample at the end of the trial and retroactively measure stress changes throughout the period.

DirectedEvolution 10 Apr 2026 5:13 UTC
2 points
0
in reply to: ClaireZabel’s comment on: ClaireZabel’s Shortform
We don’t know if they were on track, because Biden ultimately did not run, so we did not get a chance to find out. But the main issue I’m trying to highlight is that the Democratic Party may have a tendency to assign too much decision-making weight to polling data, despite its inaccuracies and the challenge of interpreting it. And this applies to Biden too—he should have looked himself in the mirror and said “I’m too old to run,” not assigned so much weight to his own beliefs about how he was polling.

DirectedEvolution 10 Apr 2026 5:08 UTC
2 points
0
in reply to: ClaireZabel’s comment on: ClaireZabel’s Shortform
Theranos was a conspiracy, but in the two chapters of Original Sin I’ve read so far, it seems to be describing a presidential campaign that went badly awry. It’s very interesting and maybe more important to understand, precisely because it’s much more normal. I’m just not sure I’d use the term “conspiracy“ to describe what I’ve listened to so far.

DirectedEvolution 10 Apr 2026 0:50 UTC
4 points
0
in reply to: Buck’s comment on: ClaireZabel’s Shortform
I actually wonder if the Democratic disarray stems from a particular failure mode due to their high level of education. Hypothesis:
Dems culturally believe in data-based decisions, accuracy, cooperation and the greater good. So they lean on polls, focus on the maximum likelihood outcome, and sacrifice their ambitions in service of a perceived need for unity.
But polling is notoriously unreliable. They get confused by it. Their focus on the most likely outcome leads them to underrate tail risks and opportunities. And their self-abnegation leaves them vulnerable to both inertia and to exploitation by selfish actors within their party.
The cure would be to encourage aggressive, self-serving political ambition within the Democratic party. Instead of Ds being nicey-nice with each other, they show sharp elbows and openly focus on their own political career advancement not as an exception but as a rule.

DirectedEvolution 9 Apr 2026 19:37 UTC
8 points
3
in reply to: Buck’s comment on: ClaireZabel’s Shortform
Another interesting quote from that Yglesias article:
“You could run against Biden by saying age isn’t just an electoral liability, he’s actually senile and unfit to serve, but most Democrats don’t believe that (it’s not true) and you’d lose.”
Imagine if any Democrat had had the guts to run, force Biden to get on the stage and debate them, and gotten the big reveal of his senility in the Democratic primary to lift their candidacy instead of in the national debate to lift Trump’s.
This is a pure calculus failure on Yglesias’s part. He implies total confidence that Biden wasn’t senile. Even if he thought it unlikely, which was more reasonable, the possibility that he was would have completely changed the calculus for a Democratic challenger. Probably Biden’s fine, but if he’s not, you look like a hero for saving the party from him. Anyone whose career wasn’t dependent on the Democratic Party or who just had enough ambition and guts could have taken that bet.

DirectedEvolution 9 Apr 2026 19:24 UTC
3 points
0
in reply to: Buck’s comment on: ClaireZabel’s Shortform
is this a case where many decision makers were kept in the dark, or where one key decision maker—Biden himself—was kept in the dark about his political chances by his inner circle?
Mass epistemic failure seems a lot less plausible to me than what amounts to a case of epistemic elder abuse.

DirectedEvolution 1 Apr 2026 4:32 UTC
2 points
0
on: Is Bayesianism Susceptible to the Mail-Order Prophet Scam?
The likelihood function is a modeling choice. You are free to choose one that assigns a lower P(Not Scam|N Correct Predictions) than would be warranted for an unbiased model.
For this scam, even if they mailed letters to everyone on the planet, you could calculate the maximum number of guaranteed correct predictions they could have achieved if they are running this scam and were scamming a given fraction of the population. Then you could, for example, construct a model that assigns arbitrarily low probability to that number of letters and treats the later predictions as meaningful evidence.

DirectedEvolution 1 Apr 2026 3:24 UTC
6 points
0
in reply to: Mateusz Bagiński’s comment on: Mateusz Bagiński’s Shortform
I find myself paying far more attention to the LLM replies to questions I ask than I do to other text.
I think this is for a couple reasons.
1. The questions I’m asking are extremely specific to my niche interests at that exact moment, and I’m dead certain the LLM replies I‘m reading are the best available answers to my specific question that have ever been produced.
2. The LLM is generally sycophantic, but that registers to me as a sort of egoless confirmation bias that comes with easy flags to notice and interrogate. Human authors may have a much more idiosyncratic, fixed agenda. With LLMs, I have to be cautious about a small number of fixed epistemic issues that are easy to recognize with training. With humans, the trickery is much more diverse and deliberate. I feel more on my guard with human writing than with LLMs in my own chats.
That said, the LLM writing feels purely and universally disposable to me. Good human writing does not. Interacting with LLMs feels like pure information processing, while human writing feels like communication.

DirectedEvolution 18 Mar 2026 8:11 UTC
2 points
0
on: Medical Roundup #7
I honestly would not be surprised if the high cost of medical trials is the result of lobbying by big pharma companies for more red tape to stave off competition from smaller competitors.

DirectedEvolution 5 Mar 2026 20:43 UTC
5 points
0
in reply to: Raemon’s comment on: Raemon’s Shortform Feed
I don’t know that we’ve actually lowered the bar for student tier content.
Fair! Let me rephrase. LW may have historically set its quality floor low enough to permit student-tier content, because even if it’s of minor interest, it has a beneficial side effect in promoting the author’s intellectual growth and potential to contribute in a more substantial way in the future. Most content above the current quality floor reflects enough content interest and growth value to be worth accepting.
When AI slop consistently rises above that quality floor, then including it will fill LW with minor-interest slop that has no beneficial side effect of intellectual growth for the contributor. So it will become untenable to keep the floor in the same place. But LW still wants to give student-tier contributors a way to make those contributions without getting drowned out by AI slop or filtered out by a quality bar that has to keep rising to filter out the slop. The strategies I proposed are implementation ideas for that alternate submissions channel.
Is this a fair description of the problem, as you see it?

DirectedEvolution 5 Mar 2026 18:10 UTC
10 points
0
in reply to: Raemon’s comment on: Raemon’s Shortform Feed
It seems like the problem you’re articulating has to do with the fact that LessWrong functions partly as a training ground for rational thinking and AI alignment research. In the past, you’ve lowered the epistemic bar for student-tier content, because receiving feedback on their content motivates them and provides useful feedback to them.
But soon, slop-posters may meet or exceed the epistemic bar achieved by students. Judging it on a pure minimum quality standard would admit a tidal wave of useless, inert slop. It’s not good enough to contribute to the leading edge of the conversation, and it doesn’t benefit from feedback. It just drowns out the authentic student content, which really does benefit from community input.
If that accurately reflects the problem you’re concerned about, then one possibility is to enforce an escalating minimum quality bar that exceeds both the quality of AI slop and the current minimum standards of LessWrong today. Anybody can freely post, but if moderators don’t feel the quality is excellent, then it does not get any visibility on LessWrong.
Simultaneously, create a separate submission channel for “student contributions” where quality standards are lower, but the primary rationale is that the poster would benefit from community feedback to support their intellectual growth.
This strategy would introduce the question of how to set standards and vet submissions through the student channel. This may take quite a bit of thinking to strike the right balance between effectively filtering for what you want and not taking undue amounts of moderation effort. Some possible aspects of such filtering might include:
- Making default acceptance of submissions through the student channel a benefit of participating in real-world community activities, such as Inkhaven, MATS, workshops, and so on. This functions as a costly signal of a person who’s earnestly trying to use these resources to improve their capabilities. When people register for these events, they can register their account username as well, which labels that account as being permitted to make “student submissions.”
- Posting a sufficient amount of excellent content might also enable people to post through the “student submissions” channel.
- Giving the community a way to flag posts submitted through the student channel that feel AI-corrupted, with a low threshold for those posts being taken down automatically due to being flagged.

DirectedEvolution 26 Feb 2026 3:53 UTC
4 points
0
on: AllAmericanBreakfast’s Shortform
Lazy coding is a middle ground between vibe coding and traditional. Write code that may be buggy and inelegant, but clearly shows exactly what you intend to do. Then feed it into AI to fix edge cases and gold-plate it.
Writing code is a great way to think through what you’re trying to do. Prose can be a lot harder, especially when you’ve spent vastly more time expressing concepts in code (nested for loop) than you have in language (Cartesian product). AI’s also seen these ideas in code more than it has in language.
At this point, I think it’s safe to say that AI’s just better than most people at code generation. It struggles with context, and no wonder. A lot of that context is in your head, not the training data. Writing crappy but illustrative code is just a way to provide that context. You’re in a better position to vet it, having written the shitty first draft yourself.

DirectedEvolution 25 Feb 2026 20:46 UTC
2 points
0
in reply to: gwern’s comment on: AllAmericanBreakfast’s Shortform
Haven’t explored that option, thanks for the suggestion.
I agree sycophancy is part of the problem. However, the issue I’m getting at here has more to do with biases introduced by the way ideas are selected using my current approach.
Idea viability follows a multiplicative hurdle model based on independent factors. VALUE = A x B x C x D. AI (and human operator) only have intelligence and attention to address a subset of factors at one time (A and B). The others (C and D) are ignored. This problem is compounded by iteratively sampling ideas:
PARTIAL_VALUE1 = A1 x B1, PARTIAL_VALUE2 = A2 x B2, …
The problem is finally exacerbated by picking the idea with highest PARTIAL_VALUE and optimizing the in-focus factors (A and B) while refining technical execution plan focused on these factors.
Reintroducing the selected idea by itself to the AI identifies forgotten factors C and D, which contain fatal flaws simply because most untried ideas are unviable for multiple reasons in a competitive market. Selecting for ideas where in-focus factors (A and B) are viable also selects for ideas where out-of-focus factors (C and D) are unviable.

DirectedEvolution 25 Feb 2026 19:03 UTC
2 points
0
in reply to: DirectedEvolution’s comment on: AllAmericanBreakfast’s Shortform
Also, according to Gemini pro itself in response to me pasting this post into the chat, “System recommendations for Google products during execution phases occur because they are statistically dominant in enterprise operational datasets, not due to hardcoded upselling directives.”

DirectedEvolution 25 Feb 2026 19:01 UTC
9 points
0
on: AllAmericanBreakfast’s Shortform
I’ve been spending a lot of time exploring business ideas with Gemini 3.0 pro. It is both persuasive and inconsistent.
The pattern that has emerged is this:
1. I run a set of ideas, along with my goals and context, by Gemini sequentially. It rejects most, but approves one or more of them. Some ideas are my originals, others are Gemini-proposed improvements of my ideas.
2. I use Gemini to develop the idea and produce a rough business plan. It strongly encourages me to take immediate, costly steps to execute: purchasing administrating infrastructure, taking long trips to inspect leads, contacting vendors or potential customers. It suggests Google products at times, though it’s possible these simply are the best choices for the problem it’s addressing.
3. After hitting my Gemini pro usage cap, I continue using fast mode to explore “pro-approved” business ideas. Fast mode lacks judgment to evaluate ideas, but can accurately answer many questions about practical execution details.
4. The next day, when my usage limit refreshes, I summarize the previous business idea for Gemini. It immediately identifies a profound flaw in the idea that I find compelling, causing me to drop the idea.
My explanation for this pattern is that business viability is all-or-nothing: solve every problem, or fail. There are too many problems to focus on all of them in a single conversation. When the subset I can focus on gets solved by an idea, execution of the idea becomes the new focus of the conversation. But reintroducing the idea outside the previous context allows Gemini to focus on a new set of problems. Since nearly all business ideas have at least one fatal flaw, there will almost always be one for Gemini to discover.
Here are some examples of this dynamic. As a reminder, many of these ideas are Gemini-proposed modifications of ideas I’d suggested that were subsequently immediately rejected by Gemini in a followup conversation.
- Idea: Providing business intelligence mined from government sources to rural real estate developers. Fatal flaw: tells experts what they already know or devolves into a consulting gig.
- Idea: Facilitate paying owners of unlisted property to discuss a possible sale. Fatal flaw: willingness to sell is not bottlenecked by willingness to discuss a sale.
- Idea: Pay to message CEOs. Fatal flaw: already tried and failed. CEOs won’t manage a separate inbox and won’t disclose their email.
- Idea: Biotech recruiter lead sourcing from academia based on academic data exhaust. Fatal flaw: Turned from bullish to bearish on ability to extract valuable information from this exhaust.
- Idea: Healthcare admin for allied health professionals working in home settings or remotely. Fatal flaw: Unacceptable unit economics.

DirectedEvolution 25 Feb 2026 1:13 UTC
2 points
−3
on: What is a species?
Speaking as a scientist, best practice is to provide the working definition for critical terms. It would be unwise to assume that any biological definition is universal, static, comprehensive, and known to the audience. Putting the onus on the writer to disambiguate their terms also alleviates the requirement to achieve a uniform definition. Scientific categories are basically statistical in nature. All models are wrong, some are useful.
My understanding is that in legal world, there is a different mechanism for dealing with ambiguity: we empower a judge to interpret the law. Laws, in turn, are written with the understanding that they will be interpreted by judges, enforced by police, and modified by politicians elected by voters.
Your argument implies we should use political discomfort to decide which biostatistical model the term “species” refers to, then require that judges use that model when interpreting the law. I would never accept this as a scientist, I would not want judges to adopt this heuristic, and the argument basically reads to me like a bit of motivated linguistic political theater.