cgiles

Generally, your points are well taken. The only quibble I have on a factual basis is here:

> 3. "Censorship" is when a person is not allowed to express their views. That is not what is being proposed here. This is about refusing to hand a megaphone to bad actors.

Censorship, historically, was preventing objectionable books from being published. In many forms of censorship, there was nothing theoretically preventing an author of objectionable books from handwriting them and distributing them privately, so long as they didn't draw too much attention to themselves. The historical censors could have made an identical argument to yours: they were merely depriving the objectionable view of a "megaphone" (the publisher), not eliminating the view entirely.

But given what you have asserted above about the importance of breadth of reach for views, it would seem to me that preventing a view from being widely disseminated in any practical way to people who would otherwise freely choose to read or hear the view is the very essence of censorship.

But more broadly, my real problem with your viewpoint is perfectly exemplified here:

> I also have an IQ in the 90-99th percentile range...

> Relying upon the populace to suddenly become savvy is not realistic.

Firstly, with deep respect, this is incredibly arrogant. But nevertheless, you may be right about this, that only the enlightened and educated few can discern truth from falsity and make informed choices.

But if you are right, don't you see that this undermines the bedrock assumptions and principles of democracy? The masses must be led, and shown the "right" information and protected from the "wrong" information? By whom? And what is to prevent those elite and enlightened few from acting in their own interests rather than those of the ignorant mob?

I see within your argument, which may not be factually wrong, a powerful argument in favor of authoritarianism and oligarchy. Any argument that leads to such conclusions is worth a fair amount of scrutiny, no?

Even if it is a fiction that all voters are equally intelligent and informed, sometimes we have found that certain fictions are very important prerequisites for creating a desirable society. For example, the fiction that "all men are created equal". They aren't. But we use that fiction in very important ways to create a more just and equal society. Another is the fiction of free will. Free will does not exist. But still, we treat people as if they had free will, because the alternative is disempowering and decouples people from any responsibility for their actions.

So, I think "the demos makes more-or-less informed choices that are more-or-less in its own self-interest" is another of those necessary fictions, necessary to prevent us from regressing to feudalism or worse.

> The 2016 US Election in particular showed us what happens when a platform takes a "hands off" approach - the platform is absolutely saturated with false, hateful information...

And yet, presumably you were able to employ critical thinking, see past all this, and vote the "right" way despite an alleged deluge of hateful misinformation, as were 48%, a majority, of your fellow voters.

Where is the evidence that the level of misinformation is any higher now than in the past, or that censorship (whether by public or private entities) is suddenly a desirable thing?

History has shown that we didn't need to censor and oppress Communist thought in the US during the Cold War because of its alleged threat, and even because Communist thought was being supported by foreign actors. In the upshot, communism discredited itself just fine, and in the meantime, our censorship made us intellectually and morally poorer. Why should things be any different with the new far right?

Quarantined subreddits are analogous to downweighting a particular Facebook post in the FB algorithm: both are a form of "soft" moderation stopping short of outright censorship.

Banning users who vote for Reddit's arbitrarily-defined objectionable content is a bridge even further than mere censorship. Particularly because the users are voting for the content before they know Reddit has defined it as objectionable, by definition, because once Reddit has made that determination, the original post is removed.

> There's nothing wrong with Reddit not wanting to facilitate and provide a platform for that sort of content, though.

The counterarguments to this have been presented so many times on HN.

The most fundamental problem is that there is a motte-and-bailey going on with these defenses of censorship: in theory, Reddit and others are censoring far-right hateful domestic terrorists. In reality, they are also censoring qualified medical professionals from opining on COVID in a manner contrary to the WHO, or people who are not actual climate denialists, but who merely question the precision of particular climate models.

Exactly as any defender of free speech would have predicted, these mechanisms are enacted on the pretext of defending against a bogeyman, but are actually used in practice to impose ideological uniformity and suppress legitimate dissent.

> we certainly have more confidence in the robustness of the physics-based models.

That is interesting. I don't know to what extent wind vectors are considered chaotic in the technical sense, but I would have guessed that chaotic systems would be more robustly modeled by ML instead of a physics approach. This is because I have a vague idea in my mind that ML would somehow compensate for the initial condition dependence in a way physics modeling would not. ML models tend to also have more parameters with smaller coefficients which I would identify with robustness (up to a point). I'm not gainsaying you, just expressing that I find this counterintuitive.

Of course the physics models would provide more insight into the nature of the problem.

And more generally it is my understanding that one way to define the difference between a "complex system" and a "system" is that a complex system is not predictable by physics simulations because of emergent properties and so forth.

For this reason, I interpreted OP's call for a "mathematical epistemology" not so much as a call for more physics-based modeling, or for opaque ML models, but as an expression of the need for a (currently undefined) new type of mathematical language to model, describe, and predict complex emergent systems.

> I'll bet that if you started scanning conference abstracts in your domain for "uncertainty quantification," then some more carefully-posed modeling activities would crop up.

I'm sure you're right. I let my wistful longing that there would be more of this type of thinking in biology drag me into hyperbole suggesting that there is none of it.

I appreciate the pointers to terms and books that could get me up to speed on modeling. It's not really relevant to my primary area, but I do wish these approaches well from afar. And who knows, if I learn more, maybe I can apply more of this type of approach in my work. Getting audiences to understand it would be another task entirely...

> Mere Monte Carlo state exploration is wasteful and doesn't provide much insight. Often we don't have error bars on model outputs to even know if an "improvement" in a metric is significant.

The funny thing is, I didn't check the author's name until just now. Ed Dougherty, who people below have derided as a "mere engineer", has been working on these problems forever. I'm honestly surprised he's still active or even alive: he was a graybeard when I heard his talk a decade ago. He is a bona fide systems biologist, one of the oldest ones.

At that time, his group was doing gene regulatory network inference on gene expression with ~600 genes. They were using the kind of approach (MC) you mention to infer a small subset of the overall network.

The main thing I took away from their results (at the time) is you can get multiple drastically different network topologies all with similar metrics on the objective function. This implies GRN inference was not inferring some kind of underlying reality. It also suggests you cannot accurately infer subnetworks, which in turn suggests cellular networks aren't all that modular.

Therefore, really a distinction should be drawn between models that are simply predictive and those that also model the underlying reality, which is even harder.

> We rely a lot on complex computer simulations, or complex physics-based models...we want to learn from these models, and we want to reach conclusions from them.

Not in molecular biology. There genuinely are no models like that except in very limited subfields like protein folding, and 99% of biologists would see them as mathematical mumbo-jumbo.

I see from your bio you're also in engineering research. You would not believe it if I told you how mathematically illiterate the average PhD biologist is. My PhD alma mater added a statistics course for the first time last year, a 2 week summer course. Calculus I is "recommended" for admission. This is not unusual.

It isn't seen as needed, because state of the art research is basically all qualitative, with a quantitative veneer of t-tests overlaid on top. So I'm glad to hear other fields at least recognize the problem. Biology hasn't even got that far.

> What's missing from current mathematics to make predictive models for biology?

Well, I think that, no joke, there is a Nobel Prize waiting for anyone who knows the answer to that. I think this is the next big paradigm shift needed in biology, not to mention several other fields.

Who is to say that the problem is strictly mathematical, though? It could be that the math exists, but no one knows how to fit existing data into it, or it could be that there is not enough data, or the right kind of data, to make such a model yet. It could be that both the data and algorithm exists, but we need to turn the Earth into computronium to run it. Who knows?

> So it seems that people are working on the problem of predictability

I'm sure they are. They have been for decades. The last time I did a systematic review of this area was before the resurgence of neural networks, so I can't really say what is the latest progress, or whether the progress in ANNs can inform this problem. I suspect it's very possible.

The situation right now, as far as I know is that: A) most biologists don't even know this is a problem, and B) those who do, don't have any idea what the solution is, or if one even exists (note the author of the linked article was pessimistic on that point).

I work in molecular biology research, and I think this is a great article that strikes at the heart of many problems in the field. I can't comment on the climate change stuff, although I wish he hadn't included it because it was almost certain to distract people from the overall point.

The problem is that there are no remotely comprehensive, predictive, and mathematical models of what goes on inside of cells. It is pure empiricism: you run an intervention, and see what happens. Write it up in a paper.

All well and good, except there are no viable models of what is happening inside that are predictive in the sense of being able to know what an intervention will do until you test it. We really need that if we want to develop treatments for molecular diseases that are more than marginally better.

The Santa Fe Institute, systems biology people, and others were working hard on this problem at the turn of the century, but progress has stalled. It's too hard. We don't know how to do it. A new "mathematical epistemology" that could handle this problem would be a huge step forward, if it is possible.

I can see why the author would extend this idea to things like economics or climate science. The thought in systems research was that, perhaps, different fields share similar underlying "complex systems" mechanisms, and if we can solve the problem in one area, we may have insights for how to do it elsewhere.

tl;dr: this is not an "age reduction breakthrough", it is both A) a confirmation of what we already knew, which is that parabiosis makes old mice healthier, and B) a finding that virtually proves the mAge clock has nothing useful to say about lifespan

Parabiosis does not substantially extend lifespan (possibly not at all), based on the experiments done so far. Even if it did, it definitely does not double it, which is what would be predicted by naively looking at the halved DNA mAge on the clock.

This paper is very likely intended as another validation of Horvath's mAge clock. The clock shows that there is a strong association between epigenetic state and both chronological age and morbidity risk. Exactly why that is, and what it means, is highly controversial.

Mainstream aging field does not really care about lifespan much anymore. Increasing amount of focus is on so-called "healthspan" - approximately the duration of healthy life, or incidence of morbidity. You would think that you could not affect one without the other, but in fact you can, to a frustrating and surprising degree.

Therefore when you read about "rejuvenation", we are increasingly finding that you can find treatments which improve a broad spectrum of unrelated health markers, that these treatments will affect epigenetic loci predictive of age, and all of this without any substantial effect on lifespan. (From a public policy perspective, this is considered highly desirable, as for public expenditures it would be ideal if people lived perfectly healthy until the moment they keel over, even if we cannot extend the time until they do)

One possibility to keep in mind is that rodents die almost entirely of cancer. So it is possible that some treatment which improves "everything except cancer" would not show any lifespan effect in rodents. This would be one way of reconciling this paper with Horvath's earlier finding that the clock predicts all-cause mortality in humans.

The question I am not seeing addressed in the many, many comments below is: why is there such a large pool of people willing to believe, and act on, ideas like "5G causes COVID-19 symptoms", "vaccines are harmful", anything by Alex Jones, etc?

It is because people are afraid, and because there is a breakdown of trust in Authorities and Experts to tell the truth and keep people safe. Governments and intelligence agencies lied about Iraq and beyond. Large corporations were the only ones saved in 2008. There is a reproducibility crisis in science, which it pays lip service, and lip service only, to solving. Newspapers have become partisan, ad-driven shills. Corporations buy politicians, and together they invent truths as needed to further the bottom line.

This is the context in which people are increasingly skeptical of Authorities. "5G causes coronavirus", I think, like flat-earth belief, is less an actual belief a person could actually have, than an almost symbolic statement of distrust in Experts. The specific belief is completely unfounded, but the feeling behind it is not.

In this context, I fail to see how corporate censorship could possibly be the answer. At the very best, it is a band-aid. The necessary solution is for our institutions to regain their credibility. The first in many steps will be for them to show some humility and acknowledge that they have failed us badly. Until that credibility is re-established, they need to use a softer touch, rather than doubling down on the obviously false idea that their words are the Infallible Truth.

It is also the basis of much conspiracy theorizing. The chain of logic is:

1. Group X has an incentive to do Y

2. We observe Y

3. We also observe specific instances of X doing Y

4. Therefore, all Y is caused by X

The problem, obviously, comes in step 4.

> The PhD wage situation in the US is simply shameful.

It really isn't. I recently finished my PhD, and now I'm a postdoc. My stipend was $30K, but, crucially, I wasn't living in a place like Santa Cruz. It was very easy to live on such a stipend where I was at. I paid $800/mo for a 2-bedroom apartment all to myself. This was not a CS PhD, it was biomedical, so the stipend was comparable to the normal B.S. salary in that field.

These students' problem is that they chose to do their studies in Santa Cruz. This is not a normal US PhD student situation, it is yet another dysfunctional California situation.

If someone is pursuing a PhD, yet can't do the basic math to determine, before accepting an offer, whether the stipend will allow them to live in the area, I can't summon much sympathy.

Well, for those of us who live in those flyover states, it does make us feel a little better about our own situations, as well as mightily perplexed about what people in SF, etc, are thinking.

Where I live, a mid-sized metro in the central US, I had some graduate student coworkers recently outright buy a 1500 sqft 2-bedroom house within walking distance of our university for $30K (from foreclosure). Not the best area of town, but it was a perfectly fine house and you can't beat that price. Elsewhere, one can easily get a 2000-2500 sqft home with a sub-$1000 mortgage payment. And everything else, like food and gas, is much cheaper than CA, usually about half the price. And salaries, while lower, are still high enough for this to be a beneficial trade, not to mention the lower tax brackets from having a lower nominal income.

Now if you are pulling down $250K as a tech worker, of course it makes sense to live in the Bay Area. But all these housing insecure people working service jobs? They should come to the central US where they could have a much higher quality of life. And if they did, the housing crises in coastal metros would solve itself, because of decreased demand, and because the local authorities would be forced to make the cities livable for service workers -- assuming wealthier inhabitants want to have any Starbucks, bars, etc.

Another strange paradox is that zoning restrictions are much looser, and more sprawl is allowed in these mid-sized metros, than in places like Santa Cruz, despite the lower overall demand for housing. So there is a double-whammy effect causing a huge difference in housing prices between a few select cities and, well, everywhere else in the US.

You are right, and I altered a comment below to reflect this. I was trying to make a more general point and phrased it poorly.

The general point is: the reason drug costs are high because of two things working together:

1) Pharma continually refreshes patents by making modest, but real, improvements to things that are generically available.

2) Patients will not accept any level of increased health risk, no matter how much the cost increases in exchange for decreased risk.

The result of these two things is that even though we have generics available for almost every major disease, they are rarely used, and we are always using the patented versions which are orders of magnitude more expensive. And then we have people wondering why health care is so expensive.

It would be absolutely bizarre if it were any other area of the economy. Imagine if every person insisted on owning a sports car because they go 20% faster. But because we (the public in general) continue on insisting that no price can be placed on marginal increases human life or health -- despite the huge logical contradictions that result from this -- we cannot have rational discussions about how to actually keep health care affordable.

> This would be abused like any other slush fund.

That's a Fully Generic Argument against any pool of government money for any purpose. The law could be written in such a way that the money is allocated for this specific purpose.

> Plus there’s a natural adverse selection bias as all the “really good” patents will go the private route.

A valid critique. The long-term goal, of course, would be to expand the program and phase out pharma entirely. An intermediate step would be to increasingly offer grants to academics with the stipulation "if you take this grant money, you must go the public route". In much the same way that now, any research published with NIH funds must be open-access within 1 (2? I forget) years.

> The current pharma pricing system milks the American consumer so as to subsidize the rest of the planet.

Yes, but...

> The real answer to all of this is to pass a law that drug prices in the USA have to be less than anywhere else on earth.

And you don't think this would negatively impact the rate of drug development? I do. Price controls are also a very drastic step in terms of American law. They tend to have...undesirable side-effects.

If I were pondering general solutions in the area of what you are talking about, I'd prefer laws drastically limiting the amount and type of marketing pharma can do. And ideally supplement that with FDA-provided, more objective material for consumers and MDs about the actual, objective benefits and risks of various drugs.