Naftali Weinberger

Naftali Weinberger

@dagophile.bsky.social

Interested in all things causal modeling. Ongoing projects on causal analyses of discrimination and on causation in dynamical systems.

1.8K followers108 following308 posts

Naftali Weinberger reposted

🚨 Today (Fri 23rd) is the last chance to download the full 109 page BBS collection on 'conscious AI and biological naturalism', including target article, 50 commentaries, and my author response. Don't miss out (& do click 'save pdf')! doi.org/10.1017/S014...

Nice! My first article is grad school was a response to people who were trying to bring the levels of selection debate into the dynamical context and doing it wrong. This approach looks much better. philpapers.org/rec/WEIITA-4

philpapers.orgphilpapers.org
pierrickbourrat.bsky.social@pierrickbourrat.bsky.social

If you are a philosopher of biology, you’ve probably heard that group selection and kin selection are 'formally equivalent', just two different ways of talking about the same thing. link.springer.com/article/10.1...

Naftali Weinberger reposted

⚡ Out now: Psychological and health sciences increasingly study how phenomena dynamically influence and reinforce one another over time: for example, to understand the mechanisms maintaining mental disorders. Can we identify reliable clusters of these dynamics? A thread 🧵 osf.io/preprints/ps...

Naftali Weinberger reposted

I kinda get why people hate on fussy philosophical stuff, but on the other hand don't you think we live in a time where it'd be especially helpful to have rigorous conversations about things like personhood, meaning in life, the point of art, the point of pure mathematics, & operationalizing ethics?

I find it frustrating how philosophy journals include references in the word count. It encourages omitting all but essential citations, and reduces the citation rate for everyone in the field. Also, to everyone I didn't cite -- you were probably in the earlier draft!

Naftali Weinberger reposted

Astrophotographer Adam (AJ) Smadi caught this image a few hours ago from Washington state. A thin crescent Moon, sunlight illuminating the rugged lunar surface. And 2,400 times farther away—and on the other side of the Sun—mighty Jupiter, and its moons Io (left) and Ganymede (right).

My book diet has come to include memoirs that are not quite self-help books, but that tend a bit in that direction. About 100% of them at some point discuss Kafka in detail. I wonder if his centrality has risen recently, as he’s been glommed onto by anyone thinking about shame, writer’s block, etc.

Guy just now: what day is today? Wednesday, Thursday? Me: it’s Friday Guy: what time you got? Me: it’s a few minutes after 9 Guy: so, like 9:10? Me: 9:04 I feel like the level of desired precision really escalated quickly. I hope he’s not late for something!

Five years from now, when more people have internalized that the limits of language are the limits of LLMs, will they turn to analytic or continental philosophy? And who can better help?

Naftali Weinberger reposted

Don't forget to remind the Stephen Miller types that tomorrow is Independence Day, not Independent and Identically Distributed day.

What are the implications of the fact that social media hooked us by making us anxious and depressed, while LLMs seem optimized to make us over-confident and manic? Any good discussions of this?

Nguyen does value independence, in that what makes games non-soul-deadening is the plurality of values across games, and the book is filled with cases of independence-destroying homgenizations. Only he relates it to the incentive-stucturing implications of metrics, rather than their epistemology...

Here I'm suggesting that these insights might also have relevance to those of use who are thinking of how to design better metrics (which is not his project). Hopefully it's clear than none of this is an attack on Nguyen, but an attempt to further savor the feast his book provides.

For AI researchers reading Nguyen, the lesson they *should* take away is to become more critical about when to adopt metrics at all. But since I fear things will only become more metrified, I think the point I'm raising could potentially aid the discussion...

Namely, even from a purely knowledge-oriented perspective, one of the risks of AI is to render independent inputs dependent (e.g. linking your dating prospects to your credit score). So a fruitful question to ask is how we can try to preserve such independence...

"Objective" is a slippery word. Just because aggregation removes independent sources of error, this doesn't entail that the aggregated concept (e.g. "health") picks out something real - only that it is invariant in a certain way. & which inputs to select and how to weigh them are value judgments...

But the phenomenon here does add some nuance to the discussion of local vs aggregated information. Epistemically, local (i.e. maximally contextualized) is not always most reliable. The right move isn't always to avoid aggregation, but to find the right level of aggregation...

The arbitrariness claim is uncontestable -- we typically don't measure the complex quantity measure, but rather what is easiest to quantify and collect data on. More nefariously, our measurements can serve the interests or reflect the beliefs of those with more power...

On the other side of the scale, we have the phenomenon that aggregating a set of biased measurements of a complex quantity can provide a more reliable measure of that quantity, *provided that those measurements are independent*. (My favorite discussion of this is in Noise by Kahneman et al.'s)...

Let me be clear that I do not dispute Nguyen's main claims about why game scores and metrics play different roles, and of the dangers of adopting metrics. Among these are examples of Goodhart's law, by which metrics become gamed and cease to serve their informational purpose...

Here I want to zoom in to the Q of how metrics should be epistemically evaluated. My claim: while he correctly emphasizes how metrics can lose objectivity due to arbitrariness, he neglects ways that metrics can gain objectivity through the aggregation of independent measurement...

Naftali Weinberger reposted

Try it again with an Oxford comma?

Margaret Harris@drmlharris.bsky.social

"In the event of a nuclear holocaust, the only life remaining on Earth may be cockroaches, Keith Richards and physicists arguing about philosophical interpretations of quantum mechanics." physicsworld.com/a/experiment... 🧪⚛️

Naftali Weinberger reposted

Replying to @aschuler.bsky.social

It's a relatively new course I give called Causal Thinking where the goal is to help them reason about whether or not the causal assumptions they rely on are satisfied and also how to reason when they are not satisfied. Basically I'm trying to address this gap: bsky.app/profile/jere...

Jeremy Labrecque@jeremylabrecque.bsky.social

Causal inference courses: here's how you estimate causal effects and these are the assumptions you rely on and by the way the assumptions are always violated kthxbye

Thanks to Will for the shout out. I had a great time at the @applied-causal-graphs.de conference, which really made me optimistic about the quality of causal work being done. That I got to give a keynote there is something I still need to wrap my head around, but I'm glad it was so well received.

Will Lowe@conjugateprior.org

Should you be interested in a a less 'dude, where's my equilibrium' approach to dags, I can recommend @dagophile.bsky.social's 2020, 2021, and forthcoming on homeostasis and causal control. We just got a great keynote from him at @applied-causal-graphs.de on those topics.

Naftali Weinberger reposted

I’ve just finished this in the past week, but this is easily the best online course/series of lectures I’ve done. I’d tried it in a prior year and fallen on the wayside a bit, and I found the 2026 version with the split beginner/intermediate lectures much more approachable. Would recommend!!

Richard McElreath 🐈‍⬛@rmcelreath.bsky.social

Statistical Rethinking 2026 is done: 20 new lectures emphasizing logical and critical statistical workflow, from basics of probability theory to causal inference to reliable computation to sensitivity. It's all free, made just for you. Lecture list and links: github.com/rmcelreath/s...

Naftali Weinberger reposted

Wimsatt: Perhaps, just perhaps, the best explanation of a phenomenon at a given level is to be found at the same level. Kendler: Noone would seek to explain hypertension at the level of quarks. A few of my favourite papers on this topic of absurd biological reductionism: 🧵

Brent W. Roberts@bwroberts.bsky.social

This...the fetishizing of unreplicable neuroscience in order to legitimize our work is self-defeating. We don't need to know that depression is in the brain (as opposed to what???) to know it is problematic. How much money have we wasted proving something like depression is "real"?