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.
Thanks for this generous comment. The author of TFA has articulated a genuine problem that is central to many large-scale investigations these days, across many domains. We rely a lot on complex computer simulations, or complex physics-based models, that have a lot of fiddly details that are understood by only a limited set of people.
Yet, we want to learn from these models, and we want to reach conclusions from them. This has turned into a key problem for the scientific enterprise.
There are so many linked issues, some technical, some philosophical: 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. There can be unknown unknowns that keep us from trusting our models completely.
It's a very rich and challenging problem space.
In my understanding, the Dept. of Energy was the first community to engage with these problems due to the test ban treaty. They had the mandate to ensure the nuclear stockpile works, despite not being able to fully test it. So they need models and they need to know how far to trust them.
> 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.
I also didn't care for the coarse characterizations nearby.
I take your point about the distinction between models that reproduce behavior ("simply predictive") vs. models of underlying components, and what you can learn from both.
This comes up in fields I work on with machine learning models vs. physics-based models. E.g., ML models that take a field of wind vectors at time t, and predict the wind at time t+1, vs. physical models that implement the flow equations. You can fit parameters of both flavors of models to match observations, but we certainly have more confidence in the robustness of the physics-based models.
About mathematically-challenged biologists - here's a hypothesis. 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. (As you suggest, probably in the domains where more quantitative work is done.)
> 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...
Some mathematicians have come together to investigate systematically the science of composing and decomposing systems made of systems. There is a dire need for breakthroughs in this area all across society. https://www.azimuthproject.org/azimuth/show/HomePage
I am surprised that people in biology are trying to build predictive theories.
Even in physics the moment one needs to deal with a number of strongly interactive components, we cannot calculate from the first principles. We still have no theory for high-temperature superconductors. We even cannot calculate properties of metallic hydrogen. And this is the simplest material that can exist, just a soup of electrons and protons.
Hasn't it always been like that? It took like fifteen years of sustained investigation just to identify DNA as the substrate of genetic transmission in bacteria. We've come a long way since then, but isn't it a bit much to expect that we'd already have effective, automated, catholic simulations of cellular biology?
A cell is a regulator, something that selectively responds to internal and external signals. It doesn't require new math, it requires modeling the signals and responses accurately. But it is a huge, parallel, continuous "state machine".
The problem with an empirical approach is that, when in one state it might respond to an intervention differently than when in another state. Especially if the interventions are at the same level as the signals it normally responds to. (Eg not drowning it in a chemical that inhibits a certain reaction, that will likely always yield the same outcome. But a regulatory hormone might not always yield the same outcome.)
So it seems that people are working on the problem of predictability (or at least augmenting the researcher's/experimenter's ability to do some analysis ahead of time based on simplified models).
> 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).
Cells balance right on the edge of Maxwell's Demon. Even a few thousand ions can change behavior radically. So, you are forced to track all the ions, proteins, lipids, etc. Which means you have to do a lot of atom-by-atom tracking. There are a few tricks here, but since the cell is not crystalline, you can't do a lot of fun physicsy math to get the problem to be easier.
Also, most of the time, since this is 'research' to begin with, you don't know what's in the cell. That's the point of looking. We've nearly no idea what all the proteins are in any given cell. DNA gives some guide, but a stochastic switch from coding to non-coding happens, constantly. So you don't know what all the proteins in a cell are, where they are, what they do, what they don't do, what the extracellular space is like, etc.
Cells are just really complicated. So you need a lot of flops.
How is "edge of Maxwell's Demon" related to "edge of chaos"?
Re: flops. I understand brute force is a good way to simulate dynamics but we constantly solve hard problems by approximation and have gotten pretty far with that approach. So what approximations have been tried and why have they been considered failures?
Also https://mobile.twitter.com/SteveStuWill/status/1268111230020...:
> "Scientists created fully functional mini-livers out of human skin cells, then successfully transplanted them into rats. The research is a proof-of-concept for potentially revolutionary technology and provides a glimpse of an organ donor-free future." Wow!
That's unrelated to the original points but I see plenty of innovative approaches to problems in biology. Simulating cells is just one way to figure them out and we don't need to figure them out completely through computational means to put them to good uses. Biology is already computronium and if we can understand how to "program" then we don't need to simulate everything.
Thanks for sharing your insights and experience. Are there predictive models elsewhere in your field, or in science, that inspire this search? Also I'm wondering if there are predictive models for cellular sub-systems, for example--the simpler stuff (afaik).
Is it nobody knows how to make the jump from basic physics to predicting cell behaviour (sorta like quantum/classical), or would predicting the behaviour just require too much compute power?
That's what everyone tries to do but it's fatally flawed, especially since the practitioners are often clueless about some of the soft emergent behaviours in between (like chemistry). As Sydney Brenner quipped, modern systems biology is low input, high throughput, no output science., Or as a cs person might say, GIGO principle in action.
This article meanders too much. The basic point is this:
> While stochastic models present us with numerous difficulties, an even more perplexing conundrum faces contemporary scientists and engineers who wish to model highly complex systems involving hundreds or thousands of variables and model parameters. Owing to their sheer number, many model parameters cannot be accurately estimated via experiment and are left uncertain. As a consequence of uncertainty, for each different set of possible values for the unknown parameters, there is a different model—possibly an infinite number of models.
> Confronting the problems of complexity, validation, and model uncertainty, I have previously identified four options for moving ahead: (1) dispense with modeling complex systems that cannot be validated; (2) model complex systems and pretend they are validated; (3) model complex systems, admit that the models are not validated, use them pragmatically where possible, and be extremely cautious when interpreting them; (4) strive to develop a new and perhaps weaker scientific epistemology.
At the moment I am in favor of option 3, though option 4 might be more appealing in the future.
This isn't an easy option to take. Recently I had an article accepted for publication where I basically argued that no models, including my own, were truly validated because none fit a non-naive data set well. (In another paper I argued that most data sets used for validation are too easy to match because they don't cover the parameter space well.) A reviewer recommended rejection, basically saying that because the model isn't validated, it shouldn't be published. So much for being intellectually honest!
The paper was eventually accepted after I made it more clear that none of the popular models work that well (some are absolutely terrible in my view), and that my model improves on the status quo in a few ways.
Note that this situation isn't exactly the same as that described in the link. In the case of my article, I think we can get enough data to validate a model. We just don't have that data at present.
I tend to come down on (1) in many cases, eg., social psychology.
I'd be open to more of this sort of modelling if (4) was a realistic and completed project.
The reality of (4) however is saying, to the public, things like: there may either be very limited climate change, or the entire world will be destroyed as we know it.
I don't see the current public understanding of science, esp. via journalism, as fit for such realities.
> Four conditions must be satisfied to have a valid scientific theory: (1) There is a mathematical model expressing the theory. (2) Precise relationships, known as “operational definitions,” are specified between terms in the theory and measurements of corresponding physical events. (3) There are validating data: there is a set of future quantitative predictions derived from the theory and measurements of corresponding physical events. (4) There is a statistical analysis that supports acceptance of the theory, that is, supports the concordance of the predictions with the physical measurements—including the mathematical theory justifying the application of the statistical methods.
This definition appears rigorous at a glance, but it is deficient. We cannot properly test a theory if it only predicts things which we already expect to happen. Popper said that scientific theories must instead make "risky predictions":
"Confirmations should count only if they are the result of risky predictions; that is to say, if, unenlightened by the theory in question, we should have expected an event which was incompatible with the theory–an event which would have refuted the theory."
An article by an electrical engineer, published in a political journal, about climate change... I had low hopes, which were met.
IMO few categories of professions have a harder time understanding cutting-edge science than engineers. That is because they think they know science because they use similar mathematical and technical tools, when in fact the professions do the exact opposite of one another.
Engineers use what is known and understood to construct systems that can be validated. Scientists investigate unknown systems to try to construct knowledge and understanding.
The worst thing, professionally, for an engineer is to not know or understand your work. The worst thing, for a scientist, is to spend too much time on things that are already known and understood.
Engineers love to point out how scientists don't know, and can't prove, whether their climate models are accurate. Scientists know that that is the whole point of building such models. Working without knowing whether you're correct is not epistemological conflict. It's the fundamental condition of being a scientist.
Yes, it would be better if we knew everything and only consulted systems that are provably correct. But we don't. And the only way to expand what we know is to spend a ton of time doing things that we don't know.
We haven't found any other way to do it. Writing hand-wringing articles about the state of science from the sidelines does not advance human knowledge. You can't expand the map by standing inside the border and complaining about how hard it is to see past it.
> Working without knowing whether you're correct is not epistemological conflict. It's the fundamental condition of being a scientist.
This is true, but it's also true that, when making public policy, we should be thinking like engineers, not scientists. Public policy is not about constructing new knowledge. It's about working out conflicts between different values and priorities based on our best current knowledge. To the extent the article is talking about public policy, I think it makes a valid point that the limitations of our knowledge in areas highly relevant to public policy are very often not recognized or taken into account when making public policy.
There are no sidelines in public policy. There's nowhere to wait to see how things turn out before making a decision.
So, there are no neutral decisions. "Waiting until we're more sure about something" is not actually waiting, it is an affirmative decision to disregard what we think we know right now. That can be a valid choice, and something to debate, but it's fundamentally different from the feined neutrality of the concept of waiting to see.
To make such an argument requires attacking that knowledge directly, showing specifically how it's wrong. Not just vaguely complaining that it's not good enough yet.
This is actually well-understood in public policy when it comes to other areas like economics or defense; leaders act to address needs in real time, making the best decisions they can with the information available to them. "A good plan violently executed now is better than a perfect plan executed at some indefinite time in the future.” ― General Patton
Engineering, as a discipline, cannot cover all of life. It largely constrains itself to situations that are understood, and can do so because there are other complementary disciplines that create the knowledge it uses. But in public policy there's not a complementary Earth or society that we can wait for.
> "Waiting until we're more sure about something" is not actually waiting, it is an affirmative decision to disregard what we think we know right now.
No, it's an affirmative choice to not impose a public policy on everyone based on what we think we know right now. And often that is the right choice.
> To make such an argument requires attacking that knowledge directly, showing specifically how it's wrong. Not just vaguely complaining that it's not good enough yet.
You have this backwards. Claimed knowledge doesn't get to be assumed to be right until it's shown to be wrong. It needs to demonstrate that it's right with a sufficient level of confidence before it even gets considered at all. "Not good enough yet" just means "you haven't shown your claims to be right with enough confidence to make them worth considering in this public policy debate".
> leaders act to address needs in real time, making the best decisions they can with the information available to them.
"Leaders" aren't the only ones who act to address needs in real time and make decisions. Everybody does that. Often the best thing for "leaders" to do is to not make any decisions at all as "leaders", but to simply let individual people, who have far more accurate information about their individual situations than any "leader" can possibly have, make their own decisions.
For a "leader" to make a decision and dictate a public policy, the policy needs to be based on knowledge that is strong enough to justify overriding the billions of individual decisions that people are making all the time about their individual lives, with a top-down dictated decision that everybody must follow. That's a much, much stricter requirement than most people appear to think.
>No, it's an affirmative choice to not impose a public policy on everyone based on what we think we know right now.
You're either misunderstanding his point or misunderstanding reality.
Our public policy today endorses (and heavily subsidizes) the burning of carbon on a scale never before tested.
Why should the default position be to subsidize this as opposed to alternative means?
Or are you arguing that we should be agnostic about the science and thus stop burning all carbon immediately (i.e., returning to a scientifically validated state in the past where we didn't burn carbon)?
>You have this backwards. Claimed knowledge doesn't get to be assumed to be right until it's shown to be wrong.
I'm guessing you have it backwards but I'll let you explain. Which knowledge do you think we are claiming that we shouldn't assume to be right? Give a concrete example and we can discuss. If you think we can "assume its safe to burn obscene amounts of carbon" without any evidence of that safety, then you are the one who has it backwards.
You can't make a public policy that is agnostic to the science of burning of fossil fuels at current levels. It's clearly unsustainable, but let's set that aside. If you want to argue it's sustainable and should continue, you would still need to rely on (nonexistent) science to make your case. It's not an argument to say "this is how we done things for a long time so this way doesn't need to be supported by science." That's illogical.
> Why should the default position be to subsidize this as opposed to alternative means?
I don't think the government should be subsidizing any energy sources or playing favorites with certain sources over others. That applies just as much to playing favorites in favor of "alternative" energy sources as to playing favorites in favor of oil, coal, and natural gas. (Actually the government subsidizes all of these.)
However, that has nothing to do with any beliefs about the relative risks of different energy sources. It has to do with a belief that government subsidizing anything or playing favorites in general is likely to do more harm than good. In other words, the government does not have reliable enough knowledge to justify favoring any energy source over any other, so it shouldn't.
> It's clearly unsustainable, but let's set that aside.
No, let's not. Let's ask, instead, why you apparently believe that the only way to fix anything that is "unsustainable" is government policy. Why not just let the market work? If governments would stop subsiziding fossil fuels, their prices would be higher, and there would be more market pressure to find alternatives. Just as we found alternatives to horses that saved us from the "unsustainable" practice in the late 19th century of using horses for transportation, which, if that had gone on the same way, would have us all by now, as the saying goes, knee deep in horsesxxt. (And people were predicting exactly that at the time.)
> It's not an argument to say "this is how we done things for a long time so this way doesn't need to be supported by science." That's illogical.
No, it's not, it's a fact of life. Most of the things we currently do are not "supported by science". We do not have well-supported scientific rationales for most of our current activities. That's because we don't have a good scientific understanding of the relevant domains for most of our current activities. But just stopping all of our current activities that aren't "supported by science" is not a viable alternative, never has been, and never will be. So it's you that is being illogical, not me.
>I don't think the government should be subsidizing any energy sources or playing favorites with certain sources over others. That applies just as much to playing favorites in favor of "alternative" energy sources as to playing favorites in favor of oil, coal, and natural gas. (Actually the government subsidizes all of these.)
This is not possible. You might be misunderstanding how subsidies work. Everything the government does is a subsidy of something or other. Building out roads nice new roads in every podunk town in America is an enormous subsidy to oil. Allowing people to pollute my air with carbon is an enormous subsidy to oil. Again, it is literally impossible to be agnostic.
>No, let's not. Let's ask, instead, why you apparently believe that the only way to fix anything that is "unsustainable" is government policy.
Where on earth do you get the idea that I think this?
>Why not just let the market work?
Externalities. Unless you start making all carbon users bear all costs associated with their carbon use, I have to bear that cost for them. The market can't sort that out unless I can forcibly stop other people's carbon use myself. In that case, we would have a violent solution, not a market solution. Governments is justifiable largely on the grounds that it replaces the need for these violent solutions.
>If governments would stop subsiziding fossil fuels, their prices would be higher, and there would be more market pressure to find alternatives.
Agree.
>Just as we found alternatives to horses that saved us from the "unsustainable" practice in the late 19th century of using horses for transportation, which, if that had gone on the same way, would have us all by now, as the saying goes, knee deep in horsesxxt.
This move had nothing to do with global sustainability. It had to do with scalability, user friendliness, etc.
>No, it's not, it's a fact of life. Most of the things we currently do are not "supported by science".
Agreed. So the question is why do you think some policies (like burning carbon) are justifiable without science. While others (like not burning carbon) need more science? You haven't justified this asymmetry.
>We do not have well-supported scientific rationales for most of our current activities.
Exactly. So why do you think we need well-supported scientific rationales for new activities?
>That's because we don't have a good scientific understanding of the relevant domains for most of our current activities.
Absolutely agree. You're talking common sense here.
>But just stopping all of our current activities that aren't "supported by science" is not a viable alternative,
And no one is arguing we should. But pretending that we can continue burning carbon at current rates (without any scientific support for this) is not in any way more justifiable than saying "we need to start burning less carbon".
>So it's you that is being illogical, not me.
Nope. It's you. We can't keep burning carbon the way we are and there's no reason to think we can. There are better reasons to think we can't.
> Everything the government does is a subsidy of something or other.
No, a "subsidy" is not the same as a purchase. Purchasing something at whatever the current market price is is just a purchase. Fixing prices at lower than the current market price, and making up the difference in various hidden ways, which is what the government does with fossil fuels, is a subsidy.
> Building out roads nice new roads in every podunk town in America is an enormous subsidy to oil.
No, it's an enormous investment in transportation infrastructure for the benefit of everyone. Which benefits all transportation technologies. Unless you think that hybrid or electric or fuel cell or solar powered vehicles are somehow unable to use the same roads?
> Where on earth do you get the idea that I think this?
What else do you expect me to think when the only thing you propose to fix whatever you claim is wrong is government policy?
> Externalities
Which is a non-answer unless you know, with sufficient confidence based on scientific knowledge (not somebody's beliefs or speculations or hypotheses), the amount of the externality and who can address it at the lowest cost. Which nobody knows for the case of CO2 emissions.
> Governments is justifiable largely on the grounds that it replaces the need for these violent solutions.
First, government solutions are violent: the government can dictate what everybody does only because it can back up what it says with violence if necessary.
Second, you ignore the obvious third alternative: give people a better option in the market. If the government did not subsidize fossil fuels, gasoline would be more expensive and more people would be buying cars that used less, or no, gasoline. No need to use force on anyone. And if there were more entrepreneurs figuring out how to build cars that used less, or no, gasoline, they would get cheaper. That is true even for the SUVs that you apparently abhor: a hybrid SUV can easily get double the gas mileage of a conventional one. But with gas as cheap as it is now due to government subsidies, the added cost of the hybrid simply doesn't pay for itself over the life of the vehicle.
(It's worth noting, btw, that this is even more true because the average "life of the vehicle" in the US is so short due to the availability of cheap financing and leases, which is due to government manipulation of the financial system. If people had to pay higher interest rates on car loans, they would have more incentive to keep cars longer and not buy a new one every year or two just because some shiny new thing came out. Which in turn would mean an initial investment in something like a hybrid would be more likely to pay for itself over the life of the vehicle. Another example of government meddling skewing incentives in a way that does more harm than good. And before you ask, my wife kept her last car for 19 years, and I kept my last car for 14; mine had more than 260,000 miles on it when it finally gave up the ghost. We both plan to keep our current cars as long as possible.)
> So why do you think we need well-supported scientific rationales for new activities?
I have made no such claim. I have never said individuals need well-supported scientific rationales for every new thing they decide to do.
What I have said is that dictating a public policy to everyone requires a well-supported scientific rationale, or at least a much higher standard for one than has been used.
> We can't keep burning carbon the way we are and there's no reason to think we can. There are better reasons to think we can't.
Then we simply disagree. You think this claim has a well-supported scientific rationale. I don't. I think it's a combination of ideological beliefs, speculations, and hypotheses, with no predictive track record to back it up. So I don't think dictating public policy on this basis is justified. If you want to base your own choices on it, go ahead.
Now, if you had said "we can't keep importing fossil fuels from countries like Saudi Arabia the way we are", then I would agree. But the basis for that has nothing to do with CO2 emissions, and everything to do with national security and geopolitical realities.
Or, if you had said "we can't keep burning coal the way we are", I would agree, because burning coal has a huge impact on air quality and respiratory diseases, and mining coal has a huge impact on the environment in the area where it is mined. But again, that has nothing to do with CO2 emissions.
Or we could talk about how it's stupid to burn oil when it has so many important other applications in the chemical industry, or the risk of oil spills.
> pretending that we can continue burning carbon at current rates (without any scientific support for this) is not in any way more justifiable than saying "we need to start burning less carbon".
You're misstating the alternatives. The alternatives for public policy are not "keep burning carbon at current rates" vs. "burn less carbon". The alternatives for public policy are "allow people to make their own decisions about burning carbon" vs. "dictate everyone's carbon burning activities by force". The former does not need a well-supported scientific rationale. The latter does.
>
Which is a non-answer unless you know, with sufficient confidence based on scientific knowledge (not somebody's beliefs or speculations or hypotheses), the amount of the externality and who can address it at the lowest cost. Which nobody knows for the case of CO2 emissions.
So the best course of action is to treat any uncertain number as zero?
Did I a say "subsidy" was the same as a purchase? Not sure what you're getting at.
>Purchasing something at whatever the current market price is is just a purchase.
Um. It's a purchase. It can also be a subsidy and generally is when the government does it. If the government purchases a bunch of oranges to give away (or throw in the gutter) it is subsidizing the orange market. Are you not familiar with how government subsidies work in general?
>Fixing prices at lower than the current market price, and making up the difference in various hidden ways, which is what the government does with fossil fuels, is a subsidy.
There are lots of ways to subsidize things. Almost everything (or maybe absolutely everything) the government does creates a subsidy of some kind.
>No, it's an enormous investment in transportation infrastructure for the benefit of everyone.
It's not for the benefit of everyone though. Car-centric life obviously is not very healthy, so it's not healthy for the general public to subsidize this. More importantly, it creates dangerous externalities for people that walk, bike, etc.
It heavily subsidizes unsustainable suburban modes of living, etc. Can you imagine the shitfit people in Kentucky would have if they had to fund their own roads?
>Which benefits all transportation technologies.
Wat? How does building a 20 lane highway in Houston benefit bikers? It doesn't. It actively harms them.
How does building a road to Dingleberry Alabama benefit people that ride the subway in DC (or want to ride a subway in Alabama)? It doesn't.
>Unless you think that hybrid or electric or fuel cell or solar powered vehicles are somehow unable to use the same roads?
Again, this is taking kind of an autistic view. In theory, yes my fart-powered car can use the roads. In practice, since 99% of cars are powered by carbon, we know that a road subsidy benefits carbon users 99% of the time.
>Which is a non-answer unless you know, with sufficient confidence based on scientific knowledge (not somebody's beliefs or speculations or hypotheses), the amount of the externality and who can address it at the lowest cost.
Huh? Why would we need to know this? If my neighbor is pumping carbon dioxide into my living room, I don't need to do any calculus or science to know that (a) he's wrong and (b) he needs to stop. That is a problem that markets can't solve.
Why would we need to know the person that can do it at the lowest cost? The person doing the bad behavior should bear the cost regardless of who the lowest cost avoider is.
>Which nobody knows for the case of CO2 emissions.
We know a lot about the costs of C02 emissions. It's ridiculous to say we need to have perfect solutions before we can push back on our current failed "solution".
>First, government solutions are violent: the government can dictate what everybody does only because it can back up what it says with violence if necessary.
Absolutely. That's a huge role of the government. If some asshole is pumping carbon dioxide into my living room, the government needs to correct his behavior. If a strongly worded letter doesn't do it, violent action must (ethically) be taken to correct the situation.
>Second, you ignore the obvious third alternative: give people a better option in the market.
I'm not sure why you think I ignored this. I have literally written books on market solutions. But they can't solve everything.
>If the government did not subsidize fossil fuels, gasoline would be more expensive and more people would be buying cars that used less, or no, gasoline.
But you would still have too many people buying. You need to read up on externalities.
>No need to use force on anyone.
Yes need to use force. This is econ 101. When you have an activity that forces negative externalities onto third parties without compensation, you get
sub-optimal levels of that activity. When you don't bear the full cost for polluting the air I breath (this is very well documented stuff here) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3155438/, then you'll tend to create sub-optimal levels of pollution that wouldn't exist if you had to bear those costs.
>And if there were more entrepreneurs figuring out how to build cars that used less, or no, gasoline, they would get cheaper.
This helps but doesn't fix the problem.
>That is true even for the SUVs that you apparently abhor: a hybrid SUV can easily get double the gas mileage of a conventional one. But with gas as cheap as it is now due to government subsidies, the added cost of the hybrid simply doesn't pay for itself over the life of the vehicle.
Even without government subsidies, gas is extremely cheap.
>What I have said is that dictating a public policy to everyone requires a well-supported scientific rationale, or at least a much higher standard for one than has been used.
This is the part you still aren't addressing: We are already dictating a public policy to everyone without any scientific support whatsoever.
>Then we simply disagree. You think this claim has a well-supported scientific rationale. I don't. I think it's a combination of ideological beliefs, speculations, and hypotheses, with no predictive track record to back it up. So I don't think dictating public policy on this basis is justified. If you want to base your own choices on it, go ahead.
You're misunderstanding. You are saying that the public policy you like (lets keep it simple and say "Ford Excursions") doesn't need any scientific backing whatsoever. But the public policy I like (lets say "bikes") somehow requires a "well-supported scientific rational". You need to explain this difference. Under your system, our current policies are also not justifiable (nor are the policies you are advocating for).
>You're misstating the alternatives. The alternatives for public policy are not "keep burning carbon at current rates" vs. "burn less carbon". The alternatives for public policy are "allow people to make their own decisions about burning carbon" vs. "dictate everyone's carbon burning activities by force".
You are ignoring externalities. This line of thinking is fine for activities that don't harm others. It doesn't work if there are externalities. Unless you think I should be able to forcibly go stop my neighbor from polluting, you still haven't solved the problem.
>The former does not need a well-supported scientific rationale. The latter does.
You can't just say this without justification. Or you can, and I can too:
The former needs a well-supported scientific rationale. The latter does not.
"Negative externalities" seems to be the operating pivot of this conversation. It seems to me that many laws are a system to identify entities producing negative externalities, and make them bear the cost of that externality. The purpose of the lawmaking process, then, is to be a system that discovers new externalities, or more precisely, defines what is a negative externality and what is not, as it relates to chosen policy.
At what point in the progression of scientific consensus does evidence for the consideration of a new externality require a response by passing laws that define the externality and how it can be bourne?
One argument is that there is not enough scientific basis to ground policy regarding CO2, specifically. And, separately, there is enough evidence that some forms carbon energy should be restricted, but due to other factors like pollution from coal. This argument is coming from a strong negative-rights model of government (like the US), where axiomatically people are allowed to do anything not currently restricted by law. The advantage of this system is that it allows people to act in the face of ever-changing circumstances of the world without needing to get approval from the government every time a new thing is discovered. The disadvantage is that now you have to hold externalities to higher bar of proof.
I think that we, individually and as a society, still have to act in the world, imperfect information and all. We cannot demand perfect data to base our decisions on, because always waiting for perfect data means that every decision will be too late. But, just like in science, we need to be able to change our beliefs/laws as evidence mounts that the basis for our previous belief is wrong. The problem is that politicial discourse is so deadlocked on pure narratives (all negative or all positive, no room for nuance or complexity) that we'll never be able to agree.
Yeah you're pretty much right. But this can be solved pretty simply with a pigovian tax.
It's not inconsistent with any definition of rights that I'm aware of to say that you can't pollute our air without consequences. By contrast, the current state of affairs does not mesh with any philosophical system that I've found. Under what theory can some stranger pollute my air? That's no more justifiable than me pouring perchlorate in my neighbor's well. The stronger someone believes in individual rights, the stronger they support my argument.
I think it's a misreading of philosophy (not saying you're doing this) to say that we need to justify restrictions on obvious negative externalities like air pollution. The polluter needs to justify his actions, not the neighbor whose air is being poisoned.
(The science on the deleterious effects of air pollution is of course settled regardless of what anyone thinks about global warming.)
I think the problem with your argument is that you assume there is always an a priori agreed upon definition of what constitutes negative externality, and this is just not true. Specifically, the line (emphasis mine):
> It's not inconsistent with any definition of rights that I'm aware of to say that you can't pollute our air without consequences
What exactly does pollute mean here?
I could say that my neighbor generating sawdust while sawing wood to build their deck is "polluting" the air. Or me sneezing while standing on my porch outside is "polluting" the air. Or if I'm watering my plants and some water flows downhill to my neighbor is "polluting" their lawn. Or me practicing piano in my house is polluting the soundscape of the neighborhood. Or, ..., or, ..., or, ..., or, ... See, there are limitless ways that one could construe basically any action someone takes as producing a negative externality. Almost certainly all of the examples I mentioned have been argued as negative externalities, and the answer is not to either accept every argument or reject every argument, because the details and circumstances matter.
My point is: Don't presume that negative externalities are automatically identified, and don't presume that every identified negative externality automatically justifies action to correct it, and don't presume that your chosen favorite corrective action produces no externalities of its own.
To be clear, I agree that there is enough evidence against CO2 that it is worth considering taking action to force CO2 generators to bear the cost of the externalities they produce. I don't agree that it's "obvious" that: 1. the negative externality exists, 2. the chosen remedy will actually solve the problem, 3. that the chosen remedy is known to be sufficiently free of its own negative externalities to consider forcibly changing our behavior. I think these things are true, but I don't think it's obvious that they're true, and trying to assert their truth by trying to make them axioms is dangerous and counterproductive.
No, we don't. We have a lot of ideological beliefs, hypotheses, and speculations about the costs of CO2 emissions, with no predictive track record to back them up. So we don't have the kind of knowledge that we would need to have to justify dictating public policy to everyone in this area.
> Everyone is already under the imposition of public policy, right now.
To the extent that this is true, it is, IMO, mostly a bug, not a feature.
> the public policy imposed on us today is based on what we thought we knew back when it was put in place.
If you seriously believe the public policies we have today were all based on the best current knowledge at the time they were put in place, all I can say is that you have a most charming naivete.
> I hope we can agree that is an ideal to aspire to.
It's an ideal to aspire to, but only if the ideal includes a very strict standard on what level of knowledge justifies dictating a public policy at all.
snowwrestler has repeatedly made the point that you can't not have a public policy about something; you can only accept or change the status quo.
England has a public policy that people drive on the left. America has a public policy that people drive on the right. In a society that had no codified rule about which side of the road to drive on, where people just weave back and forth, the choice to not enforce a particular convention would also be a defacto public policy.
It seems to me that in general you should only need the preponderance of evidence to suggest that a public policy change will do more good than harm in order to adopt it; otherwise you're unnecessarily biasing yourself in favour of the status quo. This of course should be done with an appropriate risk-weighting decision theory (how you weigh a small chance of large negative utility against a large chance of small positive utility, etc).
> England has a public policy that people drive on the left. America has a public policy that people drive on the right.
Neither of these policies were imposed by government. They were conventions that naturally evolved without any top-level policy being dictated, because they are obvious ways of solving an obvious coordination problem about using roads, and only got codified in law after that natural process of evolution had taken place.
> in general you should only need the preponderance of evidence to suggest that a public policy change will do more good than harm in order to adopt it
Even this standard, while it is weaker than the one I have suggested for what is needed to dictate public policy to everyone, is still strong enough to exclude many public policies that are currently in force, or suggested.
If I start putting some stuff into your tapwater, or into the air around your house, and you feel convinced it's going to cause you harm, but I insist that it's perfectly safe, what standard of proof do you think you should have to put together to stop me from doing that? And what would need to constitute harm?
If it didn't hurt humans but just insects that you're fond of, or just the ozone layer that protects you from radiation, or just the climate that you've come to enjoy, what standard of proof is needed?
Hum, I think both of you provide a good point. Ultimately though, I have to agree with snowwrestler, public policy often needs to make a decision with incomplete information. There's probably a middle ground here, I'd say having 70% of the information, something like that.
Otherwise, it is too slow to act to prevent future problems.
In that regard, I feel it's best paralleled with business. Business decisions are often made with incomplete information, to get a competitive advantage. But you always need to weigh the risk/reward potential.
Feel it's the same for public policy. Depending on the potential risk of a particular policy, pitched against the level of confidence in the information that supports it, and you can arrive at a decision. I don't think it make sense to say, always wait for the confidence level to be approaching 100%. You've got to take calculated risks sometimes.
I think only snowwrestler has a good point. "Waiting for the science" is the same as saying "we know for a fact that burning carbon is safe" if both of those result in burning unsustainable amounts of carbon.
>I'd say having 70% of the information, something like that.
This is still missing the point. This means that you need only 31% of science to continue burning carbon, but you need 70% to stop burning carbon. You have created an asymmetry that's not justified by anything (let alone science). Public policy cannot be agnostic about the science. It's not possible.
If you say "let's wait" you're saying "it's safe to keep doing what we're doing" even though you don't have any justification for that.
To put it really simply, you've created a default bias. There is no reason to assume the default is better than a given alternative. It needs to be justified. If you can justify it, fine and good, let's do it. But no one has.
> If you say "let's wait" you're saying "it's safe to keep doing what we're doing" even though you don't have any justification for that.
As I responded elsewhere in this thread, by this criterion, we should stop pretty much everything we are doing. Which is not a viable option, never has been, and never will be.
>As I responded elsewhere in this thread, by this criterion, we should stop pretty much everything we are doing. Which is not a viable option, never has been, and never will be.
No, I'm not saying this. I'm saying we need to stop pretending its sustainable to keep burning carbon at the rates we are. It's not sustainable and we need to take painful steps immediately to stop this.
It does not mean we need to "stop doing pretty much everything we're doing". Not sure where you get that idea. It means we need to stop allowing every Cletus in Kentucky to buy an F-350 and pay $1.89 for a gallon of gas to cruise 56 miles to the local Walmart to buy factory farmed beef and a bunch of plastic sh!t shipped across the globe from 12 different parts of China.
The idea is we should live in reality, not that we shouldn't live.
> It does not mean we need to "stop doing pretty much everything we're doing". Not sure where you get that idea.
Because your own stated criterion is that we need to be able to justify what we're currently doing based on scientific knowledge. We cannot justify most of the things we're currently doing on that basis.
> The idea is we should live in reality
We apparently don't agree on what "reality" actually is, at least with respect to how much of an emergency CO2 emissions are. You think they're a dire emergency. I think they're not an emergency at all. You will, I take it, claim to justify your belief that they are a dire emergency based on some kind of scientific knowledge. But it isn't. Nobody has a good enough predictive track record about the climate to make such a claim. So this claimed "knowledge" isn't actually knowledge at all; it's just people's beliefs and hypotheses and speculations. And that isn't a good enough basis to dictate public policy to everyone. Which is one of the key points the article we are discussing in this thread is making.
>We cannot justify most of the things we're currently doing on that basis.
Exactly my point. Why do you think we need to have a higher justification for good ideas (engaging in sustainable behavior) than for bad ideas (engaging in unsustainable behavior)?
>We apparently don't agree on what "reality" actually is, at least with respect to how much of an emergency CO2 emissions are. You think they're a dire emergency.
>Which is one of the key points the article we are discussing in this thread is making.
And the point you're still missing is that not doing anything to stop risky behavior is making an assumption that it is safe to continue the risky behavior. That assumption is not grounded in anything.
> Why do you think we need to have a higher justification for good ideas (engaging in sustainable behavior) than for bad ideas (engaging in unsustainable behavior)?
You're misstating the alternatives. We're talking about public policy. The alternatives for public policy are "don't dictate what everyone must do in area X" or "dictate what everyone must do in area X". The former does not need a "higher justification". The latter does.
>The alternatives for public policy are "don't dictate what everyone must do in area X" or "dictate what everyone must do in area X".
The world is not this simple. Regardless, we must dictate that people cannot inflict externalities on third parties without compensation.
>The former does not need a "higher justification". The latter does.
Where are you getting this idea? You're just stating a conclusion without any support. I can do that too:
You are wrong, I am right. You need to scientifically justify acts that have harmful externalities. You do not need to justify harmful acts that restrict harmful externalities.
Wow, this is easy! I should have been arguing like this all along!
> public policy often needs to make a decision with incomplete information
I disagree. Individual people and businesses often need to make decisions with incomplete information, but individual people's or businesses' decisions are only about their own actions, not about everybody else's. But public policy decisions affect everybody, so the criterion needs to be a lot stricter for how complete the information needs to be and how confident we need to be in our knowledge before we impose a public policy on everybody.
> You've got to take calculated risks sometimes.
The idea that public "leaders" should be able to take calculated risks with everybody else's money (and lives) is, IMO, pernicious. This is exactly the mentality that has created so much mess in the world throughout history. No, "leaders" should not take calculated risks that affect everybody.
It seems like you have an implied view that some particular policies constitute non-policies, and others constitute policies. I think that you haven't succeeded in either communicating or justifying this point, which is why all these discussions are going in circles.
There's a certain symmetry to, say, FDR in the USA suggesting "maybe we should consider a new public policy restricting certain private business activities" with his New Deal and, say, Lenin in the USSR suggesting "maybe we should consider a new public policy allowing certain private business activities" with his New Economic Plan.
It seems like most of the users here have acknowledged that both of these constitute public policy changes. In fact, we could even imagine a scenario where passing one act is equivalent to repealing the other.
To say that both changes would require extremely high confidence, just "because they affect everybody", doesn't seem rational on this regard. Either the evidence supports one thing over the other, or it doesn't.
But I think you're taking a different view -- that there's a default anarchy / state of nature policy, that should be favoured with a very high prior, and any policy to deviate from it would require a much higher certainty than doing the opposite. Is that your position?
>But public policy decisions affect everybody, so the criterion needs to be a lot stricter for how complete the information needs to be and how confident we need to be in our knowledge before we impose a public policy on everybody.
Again, your own logic destroys your argument.
We have made public policy decisions that heavily subsidize oil, cars, lowered air quality, etc.
These decisions affect everyone not just car drivers. These decisions were not based on complete information (in fact, we had very limited knowledge of global warming, air pollution, etc. when we made public policy decisions to favor air pollution).
>The idea that public "leaders" should be able to take calculated risks with everybody else's money (and lives) is, IMO, pernicious. This is exactly the mentality that has created so much mess in the world throughout history. No, "leaders" should not take calculated risks that affect everybody.
So why should Cletus who like to roll coal on Tesla drivers be able to take uncalculated risks that affect everybody?
I seriously think you don't understand what externalities are.
It's just a status quo bias to choose "act to preserve without prior of belief > 0.95". It's a novelty bias to choose "act to change without prior of belief > 0.95". But there are many modes of action in between. Knowledge is necessarily imperfect (cf. Conjectures and Refutations). The engineer/scientist difference isn't all that useful in reality. Even disciplines dominated by engineering rapidly see advances over time purely through process improvements which often manifest across the field as the hypothesis-test-apply loop of knowledge acquisition.
Public policy, like all decision making, should work with a gestalt view of utility and certainly most organizations attempt to do that irrespective of whether their spokespeople claim otherwise. It's like any intervention: if the patient will almost certainly die if you do nothing, you try the most unlikely things (assuming consent) but if the patient will most certainly recover with no ill effects you are not going to try an experimental intervention. And naturally you do account for change fatigue, etc. etc. Put simply, the science of policy is not as puerile as you think it is, but the nature of its execution is necessarily a complex interweave of the power centres available and their objectives and influence on the policymaking body.
> the hypothesis-test-apply loop of knowledge acquisition
Public policy, affecting the lives of everybody, is not the same as engineering with inanimate objects.
> It's like any intervention: if the patient will almost certainly die if you do nothing
A country of 300 million people, or a planet of 8 billion people, is not one "patient" to which one thing will or will not happen. Treating public policy as though it were just a matter of deciding "what is best for the patient" is foolish. There is no one "best" thing for everybody.
> But we don't. And the only way to expand what we know is to spend a ton of time doing things that we don't know.
That can sound great, at least from a scientist's point of view, the issue is that many times those "things the scientists don't know about" are taken as a given (i.e. closer to the "truth" compared to the things that non-scientists reason about) and they come back to bite us non-scientists, meaning the general public.
This virus has proven just that, with scientists in many countries believing lots of different things (they still do, even after ~4 months since the whole thing has reached a new level), about which things it was obvious "they didn't know that much about", but it didn't matter, because most of the time their voices were heard louder compared to everyone else's and as such the powers that be decided to follow those scientists' lead/advices.
In other words, it's ok that scientists don't know everything, but they should also be aware of that and they should let their "superiors" (government officials, people having the power to influence the lives of millions of other people) know about them not knowing everything. Going back to this virus, I've personally never heard an "expert" say out loud in the media something like: "we just don't know how this virus really works and we really don't know how best to fight it, we're really navigating in the dark here, we're asking you to stay locked-down in your houses because we think it's the right thing to do but we can't say exactly for how long because we really don't know".
That's a great example; and, the instant we got 1 (one!) report that gave evidence of a significant percentage of the population having experienced the virus with few symptoms -- thus lowering the CFR (Case Fatality Rate) for that segment of the population to something near a low multiple of the normal Flu -- the "lockdown" hypothesis should have been discarded, violently, for that segment of the population!
But, it wasn't; because these "falsifying" signals were ignored (or worse yet, stridently denied), and were certainly not actually tested -- we have yet to see even a single case of a country aggressively testing a random sampling of their entire population for immunity!
This one example is an astonishing repudiation of the global response to Coronavirus. Its insane! Wholesale self-destruction of the global supply-chain and division of labor, because the political, medical and scientific elites just don't want to be shown to be wrong.
The botched response to COVID-19 will bring the whole institution of modern science crashing down, when all is said and done. Maybe you don't want to believe that, but there it is: https://guscost.com/2020/05/12/pandemic-woo/
By the way, I still have not heard of a single serosurvey being run in South Korea. Why is that?
It's almost like people just don't want to know the CFR of this thing... Why wouldn't you want to know the CFR, in exquisite detail, down to groups refined by as many distinguishing details as you can possibly measure? But, surprise! You simply cannot know the CFR of Covid-19 without statistically valid immunity studies of the general population. Which nobody is doing! Just ... crazy.
Its stunningly clear that outcomes for this disease are wildly variable. For some, its extremely risky, for other groups, its not even a passing thought!
And yet, here we are -- global, carte-blanche "quarantining", to "flatten the curve" -- that big, ill-defined "curve" that means sudden death to our civilization if we get near it!
And yet, here I sit in a community with virtually empty hospitals, but almost no progress toward herd immunity. But, with an undercurrent of well-distributed carriers -- because nobody is interested in testing the general population (for either infection or immunity). And, we're starting to "open up". Heaven help the elderly and immune-compromised -- they are just toast.
In 2-3 weeks, a brutal "second wave" is just ... guaranteed. And, it'll be presented as a "surprise"! Who could have possibly predicted it! By golly, we'll have to really put the hammer down, this time!
I pray that this bungled, too-terrible-to-be-an-accident response to Covid-19 comes home to roost on those who should have known better. But, the truth won't be coming out from the criminally complicit "media". If only we had any "Journalists" to expose this thing! But, big-media is on the gravy-train of the politicians (here in Canada, at least), so there's no hope of that.
Independent journalists need to step up, somehow run the gauntlet of our controlled Internet media outlets (say something contrary to WHO -- ban-hammer!), and expose this thing!
Fortunately, evidence-based reality cannot be suppressed forever!
> In 2-3 weeks, a brutal "second wave" is just ... guaranteed. And, it'll be presented as a "surprise"! Who could have possibly predicted it! By golly, we'll have to really put the hammer down, this time!
I don’t think this will happen in the northern hemisphere, thankfully. There is reason to believe both that effective R drops off a
cliff in the summer months, and that we’re much closer to herd immunity (or whatever is possible with this kind of virus) than most people think. If you want references I can dig them up, but I guess we’ll see either way in a few weeks.
> I don’t think this will happen in the northern hemisphere, thankfully. There is reason to believe both that effective R drops off a cliff in the summer months, and that we’re much closer to herd immunity (or whatever is possible with this kind of virus) than most people think. If you want references I can dig them up, but I guess we’ll see either way in a few weeks.
All sources I've seen before say something different about those two things (i.e. supposedly effective R will maybe go a bit down in summer but in all likelihood not by much; and herd immunity is in most (or all) places still very far away), but I'm not following the science very close so I might very well be wrong. I'd be very grateful if you could dig up those references!
There isn't much completed research about the seasonality of COVID-19 itself (understandably), the "cliff" is mostly an educated guess based on the strong seasonal pattern we see with every other respiratory virus. Here are a couple papers:
Yes, indeed. When you talk to an epidemiologist (e.g. a doctor of physics), ask him to look at f'/f (death per day/death total) in a log-chart they'll tell you that it is 'useless' to look.
If you show him a straight line with R²>0,99 (e.g. Germany using 7 day averages) - they will tell you that it all means nothing and that the curve in fact is bending - because the 'policies' must have worked.
If you tell them that working policies should have left visible marks in the curve and that the curve is converging: They will just block you.
Science is dead - at least in the field of epidemiology.
> thus lowering the CFR (Case Fatality Rate) for that segment of the population to something near a low multiple of the normal Flu -- the "lockdown" hypothesis should have been discarded, violently, for that segment of the population!
You are deeply into "Not even wrong" territory.
The plural of anecdote is not data.
Did you forget HCQ already? Lots of "positive reports" and yet controlled studies shows that it kills more Covid patients than leaving them untreated.
We have data and hypotheses showing that it's mostly continuous exposure that matters (which means that nongroup workers are better off than we expected and casual interactions to buy groceries and such are safer than expected), and yet everybody wants to get back into groups--which is known wrong. Two Mother's Day gatherings account for more than 50% of the Covid cases in Santa Cruz county.
Data by itself is meaningless. Data leads to a "falsifiable hypothesis" which can then be tested. AFTER the test passes or fails, you modify your Bayesian priors and your working tasks. Lather, rinse, repeat.
OK, I'll bite, and ignore the dismissive "not even wrong" insult!
Remember how the scientific method works: if you make a hypothesis, it's the most fragile thing in the universe -- a single falsification is all it takes to send you packing.
So, every time someone trots out "the plural of anecdote is not data" when presented with a refutation, this tells us something important... Like it or not, that's just how it works.
A group's CFR is almost the only thing that matters to that specific group. If the CFR is low, it literally does not matter at all how, or how fast you got infected -- the statistical outcomes are now known! The "continuous exposure" vs. "casual interactions" thing? That R-0; interesting, but almost irrelevant vs. CFR. In fact, once low group-CFR has been ascertained, the "normal behavior case" R-0 defines the societal infection/immunity rate required to achieve their group's contribution to societal herd immunity, but otherwise is of little interest: the average group participant just wants to gain immunity, as quickly as possible, to provide protection for the high-risk groups.
So, the fact that multiple independent instances of (accidentally measured, statistically significant) high rates of group infection without demise, leading to a low group CFR are extremely surprising, and that should have led to a "stop work, tools down" moment for those engineering our societal response. But, it did not.
That is disappointing.
I think the most disappointing thing, to me, is how many in our society are more than ready and willing to throw the innocent, high-risk elderly and those with co-morbidities "under the bus", just to avoid their own (very low) CFR.
> A group's CFR is almost the only thing that matters to that specific group.
Except that you don't know what defines a group.
We know what makes someone at risk. That does NOT imply the reverse--that we know what makes someone NOT at risk.
Sure, maybe 18-30 year olds by and large don't drop dead. That doesn't mean they don't wind up with stroke risk or a damaged lifespan. And, maybe some 2-3% subgroup of them drops dead because they have a particular receptor--oops, sucks to be you but we can't undo it now that we let the pandemic loose.
You don't just shove a disease through a population when you don't know what the effects are. That is unethical and immoral at the level of eugenics. Good God, man.
We actually did this sort of thing with chicken pox and now those children get the joy of shingles possibly blinding them in their adult life. Oops.
And, by the way, you know what disease we achieved herd immunity to before vaccines? Oh, yeah, NONE. This one will be no different.
>You don't just shove a disease through a population when you don't know what the effects are.
Nobody is "shoving a disease"; the disease is naturally progressing, that's what diseases do. Most moral systems distinguish between actively causing harm and not doing something that could prevent harm.
Not only that, but while we may not know exactly what the effects of the disease are, we know exactly what the effects of the lockdown are, and it's causing immense misery to many many people. So it's a case of weighing up something of unknown badness (the disease passing through the population) against something with known, definite badness (the lockdowns). So we need to be reasonably sure that the unknown badness of the disease is sufficiently bad to outweigh the known badness of the lockdown.
> against something with known, definite badness (the lockdowns).
You've now left the realm of science and entered the realm of politics.
The "badness of the lockdowns", however, is not a given and varies depending upon the competence of your government and the idiocy of the people.
Yes, everybody locked WAY down at the beginning. However, governments that had universal healthcare and actually paid unemployment benefits to their people had far less "lockdown badness". People in those conditions weren't forced back to work in order to get food or healthcare.
In addition, those who actually locked down (R0 < 0.8) had cases that dropped substantially and can now reopen with contact tracing and efforts that aren't quite so invasive.
This is opposed to those who really just "sorta" locked down , whine about wearing a mask, etc. (R0 about 1.0)--see: Santa Cruz county being able to trace more than half of their cases to two Mother's Day gatherings.
"Lockdown badness" is something that is the result of poor government policy and actually has solutions--it is not immutable scientific fact.
Whether political leaders will implement those solutions is a different question.
>You've now left the realm of science and entered the realm of politics.
All badness is the realm of morality and politics, not science. Science can predict how many people will die, it can't weigh this up against other consequences. Science can't make moral judgements, only inform them.
>"Lockdown badness" is something that is the result of poor government policy and actually has solutions--it is not immutable scientific fact.
A big part of the lockdown badness is the economic damage. This is an immutable economic fact: stopping most people working will mean fewer things are produced, and more of the existing things and infrastructure will be consumed. This translates into worse standard of living and quality of life for people. Weighing this against the health damage from the virus is a matter of politics/ethics, not of science.
What we can see clearly is countries with no widespread forced closure of businesses, like Korea, Japan, Taiwan and Sweden, are having much better economic outcomes compared to countries with strong lockdowns like the US, France and Italy (expected yearly GDP growth of ~0%, vs ~-5% for the lockdown countries).
South Korea is also contemplating murder charges against a church. They also did massive contact tracing when they were still under 100 cases. South Korea took this massively seriously.
See any of those dividers in the US? Yeah, no, you can't even get people to put masks on.
Japan seems to be cooking the books because of the Olympics even though that's simply not going to happen.
Sweden is about to pass the US in cases and deaths per capita and is right about the same economics as the other European countries (which are all benefiting from their social safety net). Not sure that's counts as better than the rest of the EU--we'll see if it continues.
Your economic claims are dubious.
The primary difference is how fast a country reacted, not how hard. The problem is that if a country didn't react fast enough, it is then required that you react harder and longer.
>South Korea is also contemplating murder charges against a church. They also did massive contact tracing when they were still under 100 cases. South Korea took this massively seriously.
The HCQ thing is a complete shit show, it has been politicized to the point where everyone pretty much just says whatever they want about it. It might be helpful as a prophylactic or with milder cases (when used in smaller doses). Other studies gave higher doses to severely ill patients and it did not help. Anyway, not trying to start another argument over this, but there is a vigorous argument still going on. A couple hundred professionals signed this open letter: https://zenodo.org/record/3871094
Besides the ... weird situation where most of the “refuting” studies didn’t include Zinc, which was widely suspected to be a major component of how it attacked Covid-19!
There are no words for the level of incompetence displayed in the handling of these HCQ studies.
You make it sound as if the author is some middling engineer building smartphones for Apple.
An EE professor at a well-regarded research university deals with the scientific method all the time. Whatever opinion you may have about the article, as far as I can tell the author is certainly not on "the sidelines" and more than qualified to write about the state of science.
You claim that engineers apply known science, and consequently have a hard time understanding and doing quality science themselves. This is a common view in some groups, but it isn't true in my experience. Fact is that engineers receive training in science that is similar that of scientists as far as I can tell. (I'd argue that it's superior in some aspects!) Don't focus too much on the titles. Look at the content of the curriculum.
Full disclosure: I haven't read the linked article and am not defending or endorsing it here, just trying to correct a misconception about engineers. I have a BS and MS in engineering, and am almost done a PhD in engineering, where I have basically conducted applied scientific research. I am friends with quite a few physicists and have heard variations of your argument from them many times. When they ask why I didn't get a degree in physics, my answer is easy: "Physics" is more accurately "modern physics". Consequently, fluid dynamics (my area) is rarely studied in physics departments. Typically one gets a degree in aerospace or mechanical engineering to become a fluid "physicist".
Also, I haven't seen any clear data showing that engineers are more prone to unqualified statements that than non-engineers. I think there is something to this but I would attribute it to engineers (by title) seeming more conservative and religious than scientists, and they are willing to challenge the status-quo here. The problem in my view is not engineers being less qualified to do and understand science.
I want to be clear that I'm not intending to make a value judgment, like scientists are better than engineers or vice versa. And I agree that it's not about degrees; people with engineering degrees work in science and vice versa.
Well, the following seems fairly harsh to me, and I would recommend rephrasing it:
> IMO few categories of professions have a harder time understanding cutting-edge science than engineers. That is because they think they know science because they use similar mathematical and technical tools, when in fact the professions do the exact opposite of one another.
The wording "few categories" in particular seems hyperbolic. I think many non-STEM folks would have a much harder time understanding science than engineers.
I think the huge overlap in shared tools, techniques, technologies, language etc. can act to obscure the fundamental differences in mindset between the fields. If a research scientist doesn't know how their work is going to turn out, they're probably on the right track. If an engineer doesn't know how their work is going to turn out, they're probably on the wrong track.
Non-STEM folks might have a hard time understanding either field, but they might also be less likely to assume that they already do.
> If a research scientist doesn't know how their work is going to turn out, they're probably on the right track. If an engineer doesn't know how their work is going to turn out, they're probably on the wrong track.
Your view of engineering doesn't fit with my experience. Certain areas of engineering are quite conservative and take that approach, using primarily proven designs (e.g., structural mechanics, fire protection). Licensed professional engineers generally follow that pattern. But this is not characteristic of engineering as a whole.
E.g., a small side project of mine is a device I intend to get a patent on. I understand the physical mechanism (the science) behind the device but have only a vague idea of what the actual device itself will look like. It's going to take a fair amount of experimentation to figure that out. Experimentation and design are fairly closely linked in engineering, and this was emphasized in my education.
Add on top of that the engineers who basically just experiment with various poorly understood systems. I'm thinking in particular about the industries with chemical processes and chip fabrication here. They might not have any clear understanding at all at first about why certain changes have certain effects. Now, some of these engineers might not care and accept that something works without understanding why. But many of them will look into that.
> Non-STEM folks might have a hard time understanding either field, but they might also be less likely to assume that they already do.
You identify a real phenomena, but I don't think there's a reason to single out engineers here. My own experience with physicists is that they are typically overconfident about their own fluid dynamics knowledge, for instance.
You have a very restrictive, and frankly naive view, of what constitutes science and what is engineering. There is not a clear-cut distinction (same as with "pure" and applied mathematics), not royal line which clearly separates the disciplines.It is a continuum. Were Penzias and Wilson doing science or engineering? What about Barden or Marconi or Nakamura? What about the guys at the Manhattan project? God knows engineers suffer a lot of the "let-me-tell-you-how-your-field-should-work" syndrome, but guess what? So does physicists, so it has nothing to do with understanding what science is all about and more with overestimate our success in transferring our knowledge,experience and capability to a different field.
A primary problem with using so-called "scientific consensus" to form public policy, is that while (perhaps) the scientists that produced the research might (emphasis on might) understand that their research is falsifiable -- the rest of the body politic does not, and proceeds on the assumption that it is "settled science".
They get to reap the wind of publicly funded largesse, ... and we get to reap the whirlwind.
Both Engineers and Scientists must satisfy the Scientific Method; if your proposed hypothesis is refuted (ya, even just once, a little bit!), it must be abandoned.
Unfortunately, there appear to be a large body of both Scientists and Engineers that do not feel their research/results should be subjected to analysis and discarded when refuted -- and this insanity (ie. rejecting the Scientific Method =~= Insanity) is supported, stridently, by the political elites and public at large.
Corollary: Climate science models should be built by engineers, not by scientists. Task people who 'use what is known and understood to construct systems that can be validated'. Perhaps the whole scientist-as-faux-priest meme should be toned down a notch.
This notion seems to incorrectly represent what climate science models are and how they are developed. If there were some way to "validate" them that everyone could generally agree upon, it would be possible to move this work to the domain of engineering. But each climate model varies quite a lot in its structure, dynamical equations, what simplifications it makes for numerical tractability, use of datasets and how to map them to model parameters. The role of climate scientists is to hash out which models are more sensible than others and to push the boundaries of accuracy and certainty with new methodologies.
This is why the best we can do from a policy perspective at the moment is model aggregation: that is taking tens of models computed by various research teams and combining them into policy recommendations along with uncertainty brackets. Distilling the state of current research into a policy digest is the role of the International Panel on Climate Change, which is currently in their sixth iteration of this process. You can keep up to date and read previous reports here. https://www.ipcc.ch/
If they are anywhere nearly as sensitive to 'bayesian priors', aka parameters made up of full cloth, as the covid19 models, then I take a hard pass.
Edit: Perhaps that was a bit too much of an 'in jest' reply. I'm frustrated at the whole mathematical rituals schtick, when in reality the whole thing is at best an unfalsifiable qualitative guess. The parameter space is immense, and we only have 1 measurable trajectory through it for validation.
I've seen claims that the number of parameters in the Imperial College agent-based model was c.a. 400. Perhaps it was 40.
Compare to a quadratic or quartic fit to some log-lin data. If you have to represent your ratio of poorly-constrained model parameters (40 or 400) to apparently necessary parameters (say 4) _using Big-O notation_, then science has arguably left the premises.
> Engineers love to point out how scientists don't know, and can't prove, whether their climate models are accurate. Scientists know that that is the whole point of building such models.
I disagree, strongly. A big part of the point of building such models is to get models that are more accurate than the currently-existing models.
This comment x 1000. (Some) Engineers think they have it all figured out. And they do...because someone figured it out all for them, and they get to work with the benefit of that knowledge.
This is a polemical statement, I'm closer to an engineer than a scientists and I really like having a solid model to work from. It took me a long time to understand how useful approximations are to solving hard problems. Because I didn't want to know the approximations, I wanted a perfect model.
(Oddly, I came to understand the power of approximations in terms of electronics. Electronics are filled with rules of thumb that help you figure out how your circuit works and help to elucidate the behaviors that are important to you.
But it took me awhile to get over myself and use them. It was honestly a huge road block to progress in learning.)
My own take-aways: getting knowledge (de novo, ex nihilo) and applying knowledge are two very important things, but are separate skills. Ideally you'd like to have both.
Secondly science is difficult because you are trying to figure out true things in the face of glaring uncertainties.
My biggest pet peeve is the use of term computer science as something you get knowledge of from coding.
The latest Gödel Prize award for computer science was awarded for
"their constructive proof of the Lovász Local Lemma".
How many of the self proclaimed computer scientist would be able to read the math formulas Cynthia Dwork (latest Knuth Prize winner) wrote in her paper The Algorithmic Foundations of Differential Privacy?
That's what you get from a Computer Science degree.
How many of those theorems became understandable to someone just from coding? You have to hit the books, the CS-books.
It's like someone saying they know law because they know english, and english is what is used in the end.
You may have a good point, but this is a bad HN comment. For it to be a good HN comment you'd have to develop the point (1) in more detail, (2) with more explanation, and (3) without denunciatory rhetoric. If you were to do that, I'd be interested in reading it.
To post like this is against your interest because, to the extent that readers can tell what you're saying, they'll just be turned against it by the grandiosity and indignation. Those commodities are much too cheap on the internet.
Thank you for the feedback (and for all the moderation work). It can be hard to detach and focus on the “purely informational” substance of an argument, but you are right that that is pretty much always for the best. This is going to sound like such a HN cliche, but if there was a good way to convey emotion as independent metadata instead, I’d try to use that.
And to clarify my comment, I’m frustrated to see so much of our public discourse still rely on appeals to authority, even when we’re talking about claims that are fundamentally impossible to validate. Authority figures are proving to be nearly-as-bad at navigating these difficult topics as the rest of us, but it seems that to do any better, the public would need to understand the actual ideas being discussed, which seems a long way off (I don’t even hear this mentioned as a possible goal).
So for now we are left with a choice between trusting what is essentially an order of clerics, empowering another authority that won’t do much better (regular old politics), or mob rule. I have to go with the second option, but it is not much better than the others, at best.
We already know quite a bit about the world. Unclear why should we reach out into the fog of unknown for some cutting edge unproven state-of-the-art system, when there are quite a bit of well established and widely agreed upon known ones that can get us 90% of the way there, with much lower risks of catastrophic failure.
Case in point: covid19 policies inadvertently triggering the mega-depression of 2020 and beyond, for what appears to be relatively minor medical gains in the big picture. How bad can you botch it if it takes 5 months to come up with 'masks are good, perhaps, more research is needed' from a peer reviewed source, when even a 14 year old could have been told you that's rather a good idea all the way back in January.
Uncertainty is always going to be a problem, and (if history is any guide), allowing free peoples to self-organize and make individual/community decisions about how to live has worked out the best.
Having a few elites (or a strident majority) make decisions for everyone else has turned out ... less good.
The worst outcomes tend to arrive when these groups shield themselves from evidence-based reality (usually by forcibly stripping liberty and wealth from their unfortunate subjects and inferiors), to temporarily suspend them experiencing the results of their actions. There's alot of that going around, presently.
What an insane statement. This is literally everything that politicians do: advocate for what they think is best based on our collective lack of understanding of science/economics/relations etc
I don’t think you understand. The “people” in my statement are not politicians, they are charlatans, whining whenever their uncertain theories are not used by politicians to dictate how everyone must behave, which according to some mental gymnastics ought to happen because their theories are dressed up in an outdated notion of “science”. It is pernicious fascist bullshit, but thankfully it will fail.
I read the full article. And it feels like much ado about nothing.
One, the author for some reason is uncomfortable with a purely mathematical description of the world - without giving reasons beyond that humans cannot physically comprehend what the equations represent.
Two, the whole piece disregards the advances in emergent phenomena, complex systems and statistics. Yes, we do not understand how individual electrons look like in copper, but statistical descriptions of copper dimensions, purity and grain size are enough to get an exceptionally accurate idea of the electrical behavior of a copper wire. This extends for exceptionally complex systems like lungs (smoking will significantly increase your cancer/emphysema risk), or planetary systems (increasing CO2 concentrations will increase surface temperatures).
Three, There is also a weird bias against modeling. I am an experimental scientist, but I work closely with modellers, as all experimentalists do nowadays. Personally, I believe, that this bias against modeling often has a political component due to anthropogenic global warming. But in reality, there is a very vibrant dialogue between modellers and experimentalists, and hybrid scientists, people who are trained in both fields are the rage right now for hiring committees. Models have also improved dramatically - density functional theory is exceptionally accurate for smaller systems, while ReaxFF for dynamic systems and molecular dynamics simulations for more complex systems become better every year. The whole point about complex systems, applies to modelling too. I do not need to know the location of every molecule in the north Atlantic to predict the path of the hurricane - high pressure ridges and sea surface temperatures will give me really good results.
Feynmann: "It is whether or not the theory gives predictions that agree with experiment. It is not a question of whether a theory is philosophically delightful, or easy to understand, or perfectly reasonable from the point of view of common sense"
Then proceeds to invent string theory.
Don't get me wrong, I have a lot of admiration for Feynmann,but if he did indeed say the above, that strikes me as rather inconsistent : string theory is exactly what he preaches not to do : widely successful because of how beautiful it is, but unfalsifiable (as of yet).
A very nice article -- more pessimistic than I'd be -- about the limits of outsider understanding when predictions come from complex models that few understand.
The pessimism seems to comes from the proposition that no one really understands models beyond a certain complexity, even insiders. Even whoever created the model.
"Medicine is a form of engineering in which the physician takes some action (drug, surgery, etc.) to alter the behavior of a physical system, the patient. Its underlying science is biology. Hence our ability to characterize medical knowledge depends on our ability to represent and validate biological knowledge."
This is really very wrong. The 'physical system' is conscious and suffering, which excludes Medicine from even the most vaporous definition of engineering. Real progress in medicine is much more akin to exploration (in the Christopher Columbus sense) than engineering.
Anyway, this is a typical engineer writing about biology. They always make the same mistakes:
1. Ignoring evolution.
2. Treating mechanistic models as first class citizens with disdain for data (eventhough the latter in the form of DNA is the sine qua non of all life)
3. Failing to understand that "The simplest complete model of an organism is the organism itself."[1] as von Neumann noted.
> "The simplest complete model of an organism is the organism itself."
But that is trivially true about everything, including golf balls. The interesting question isn't if the model is complete, but if it is sufficient (or complete enough) to make predictions.
And in the case of golf balls we can make quite good predictions with a very simple model, mostly because we are only interested in if it ends up in the hole or not. But a complete model of a golf ball would need to include the exact dynamics of the different layers and the turbulence around it, which we just can't do. Luckily, we don't need to.
So what is a sufficient model of a living organism? That of course also depends on what we want to know. I am quite certain that modern egg farmers have, for their purposes, quite sufficient models of hens. They know how fast the hens grow, the effects of different amounts and types of food, how much heat they produce, and of course how many and how big eggs they lay, etc..
And it is of course possible that we need to know the exact wave function for the complete system that is the cell to model it sufficiently enough to, say, synthesise new drugs. But I don't think so.
It is definitely not trivially true about anything. A golf ball can be modelled easily. You are adding external complications to the system. An electron can be impossible to model if you inject it into a complex system.
Anyway the point is a golf ball will not evolve into a sentient being who will then fly to mars and write poetry, which is what cells have done. This shows you can’t even put a meaningful bound on what you need to model for an organism.
But that is not a complete model of a golf ball. A complete model will include the wave function of all the particles in golf ball. It won’t be simpler than the model an organism of the same size. And in this case I mean by simpler that the number of bits required would be lower.
How does the capacity of the thing you try to model affect the complexity of the model?
I think the system the author is looking for is probabilistic programming. It allows you to write programs/models with uncertain factors. Then at the end you can observe a few variables and it automatically computes the expectation and variance all the input variables. Check out psisolver.org for example.
This is a great article, but I wish it would have really dealt with the idea of complexity.
Nature is not ‘unintelligible’ it is ‘complicated’ because of the large number of discrete interactive constitutive units.
In fact, if you plot # of constitutive quarks/atoms being worked with versus uncertainty, you see an interesting layout of the sciences. e.g. take this plot by xkcd ( https://xkcd.com/435/ ) and think about how much matter is under observation (i.e. increasing complexity to the left). Somewhere on the left would be climate in this example (as in the article).
We can't easily validate models of reality that are complex/stochastic, because any experimental result could be a statistical anomaly. In particular this is a problem for models of long-term and/or global phenomena like climate or public health, since we only get one "experimental run", so to speak. Therefore:
> Confronting the problems of complexity, validation, and model uncertainty, I have previously identified four options for moving ahead: (1) dispense with modeling complex systems that cannot be validated; (2) model complex systems and pretend they are validated; (3) model complex systems, admit that the models are not validated, use them pragmatically where possible, and be extremely cautious when interpreting them; (4) strive to develop a new and perhaps weaker scientific epistemology.
I haven't read the article, but #4 is the interesting item on that list. #3 is the Goldilocks option, the one that sounds between-the-extremes like the just-right porridge that Goldilocks ate—but it's not realistic, because "be extremely cautious when interpreting them" exceeds what the human mind is capable of.
It may be possible for a person to succeed with #3, but I have a hard time believing it could work in a community of people who disagree.
For #4, the closest thing I can imagine is a utilitarian approach, which of course I’m going to prefer as an engineer, and which (I think) ultimately reduces to #1. That is a tough problem I don’t expect to see solved in my lifetime, but I’d be happy to be proven wrong.
On reflection, option 1 includes like Taleb's philosophy. At first I wrote it off as leading to arguments like "no one knows so let's do what I, an 'expert' say", which I hate. But it's written generically enough to include rules like Minimax, where you don't need model estimates to make a decision. This rule is very conservative, though.
I think there are a fair number of people who succeed at option 3. I can think of a few people who I believe do option 3 well, but as you indicated, they're individuals. I think that the US Department of Energy is probably the closest organization that I'm aware of to succeed at this. (Or at least some DOE labs; I can say from personal experience that groups at both Los Alamos and Sandia take this fairly seriously, though I don't think it has fully permeated their culture.) I'll have to think more about this.
A major problem with option 3 is that it runs the risk of people claiming to do option 3 but actually doing option 2. I think this happens regularly but it's due to ignorance, not malice. Ultimately I think we need to change scientific standards and the STEM curriculum before option 3 becomes tractable on a large scale, but even then I'm not sure it'll work because it'll always be easier to claim to do option 3 while actually doing option 2.
I agree with the writer that option 4 is more of a long-term goal. I wish the comments on this article focused more on #4 than what was discussed...
> (Or at least some DOE labs; I can say from personal experience that groups at both Los Alamos and Sandia take this fairly seriously, though I don't think it has fully permeated their culture.)
It has not. In the current funding environment it can't, given that the way research grants and contracts are awarded will always constrain organizational culture.
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.