8 ms·

All of the uncertainty around COVID-19 means that we'll build whatever complex models we want, but then just stare blankly and wonder how the hell to choose val

All of the uncertainty around COVID-19 means that we'll build whatever complex models we want, but then just stare blankly and wonder how the hell to choose values for various coefficients. You can drive the suggested analysis here as far as you want, but ultimately, you will lift your head and realize it's all useless for actual policy making wrt COVID-19. The ultimate answer is that there is no ultimate answer. Abstract mathematical modeling for these sorts of questions is therefore mostly useless. Useful answers come from going in and dealing with lots of messy particularities in a very messy reality. I like to think that this problem with quantitative rationality, where you build a model about one thing and get a very precise answer, but then realize your answer is really just another question, is exactly what's being alluded to in "42". (e: moved this sentence to the bottom of my comment because it seems to be causing some distracting hostility: "This sort of thought process is exactly how you go about convincing a wise person to just give up on the whole hard core rationality project. It's an unintentional strawman.")

solveit 6y ago
Sure, so give me a number. Eventually somebody[1] has to make a high-level decision on which strategy to pursue. And they have to give directives that can be followed by people who don't know your entire thought processes. That's what it means to be a bureaucracy. So which is it, will you choose an imperfect quantitative model where some numbers are pulled out of your ass? Or will you just let every low-level functionary pull entire decisions out of their individual asses and hope something resembling a coherent strategy will emerge?[2] [1] Well, ideally. The US response to covid was pretty much [2]. If it wasn't clear, [2] was a rhetorical question and you should not do that.
- throwawaygh 6y ago
  > Sure, so give me a number 42? I'm genuinely not sure what number you're asking for. > Eventually somebody[1] has to make a high-level decision on which strategy to pursue. Yes, of course, but that decision making doesn't require a quantitative model assigning value to life (or not). You can make a lot of very well-informed decisions without ever thinking about that particular aspect of optimization theory with any particularly high level of detail. "Aggressively avoid protracted exponential growth" doesn't require knowing anything about the value of life relative to other things. Mathematical modeling can be useful for responding to covid-19. However, mathematical modeling for this question is not useful, because your answer gets plugged into yet another model where there's too much uncertainty for your answer to the have any quantitatively justifiable impact on decision making. To give a toy example, spending a bunch of time figuring out the value of $c$ in $f(x) = x^n + c$ is a waste of time if you don't know much about the value of $n$.
  - solveit 6y ago
    Hanson explicitly disagrees, to quote: >Yet it is easy to show that we can both have more money and save more lives if we are more consistent about the price we pay for lives in the many different death-risk-versus-cost choices that we make. He's not even saying you need an accurate value, he's saying you need a consistent value.
    - throwawaygh 6y ago
      >> Yet it is easy to show that we can both have more money and save more lives if we are more consistent about the price we pay for lives in the many different death-risk-versus-cost choices that we make. TBH I'm not even quite sure what is meant by that assertion, but the article certainly doesn't contain a compelling justification and the claim is almost prime facie false. There are such completely trivial counter-examples for the plain meaning that I'm not even sure what the author's intended meaning was. E.g., "lives are worth negative infinity" is consistent, and you'll always end up with more money and save fewer lives. Ditto for "1 life = GDP of the world".
      - GavinMcG 6y ago
        If the counterexamples are so trivial and obvious, the author must be a complete idiot, right? But of course they're not a complete idiot, so maybe there's another explanation, that you could have arrived at with a dash of charitable thinking.
        throwawaygh 6y ago
        > If the counterexamples are so trivial and obvious, the author must be a complete idiot, right? No. Smart people make simple mistakes all the time. Being wrong doesn't make one an idiot. Fortunately for me :) Also, the realization that "lives vs. other-resource" is unnecessary to model because it's either dominated by other uncertainty or else results in deontology is not at all an obvious conclusion. It took me a decade to arrive there. What I was pointing out in the above comment is how choosing extreme values for one resource or the other effectively gives you a deontological ethics that strictly prioritizes one thing over the other. You can solve the optimization problem if you want, but the solution is obvious. deontological ethics is the subset of utilitarian ethics where you don't have to do some calculations because you just max out the value of certain objectives. > But of course they're not a complete idiot, so maybe there's another explanation, that you could have arrived at with a dash of charitable thinking. Maybe. Can you explain why I'm confused, then? The plain meaning of the author's quote, even taken in context, seems like it has some trivial counter-examples. What am I misunderstanding? BTW, I'm not being coy. I am being charitable. When I say "I'm not even quite sure what is meant by that assertion", I mean it. The thing I think the author means has trivial counter-examples, so the author must mean something aside from what I assume. Or else hasn't thought about the extreme values. Which would also be possible.
- deleted 6y ago
  [deleted]
donw 6y ago
Generally speaking, if you are going to offer criticism, try and offer a solution or ask a question that would address that criticism. All I see here is "The value of human life is incalculable, so just give up." Am I wrong? What am I missing? We don't have infinite resources or infinite capacity to distribute them. That means we need to make hard decisions about things like medical care. That is reality. If I get some kind of crazy-aggressive brain cancer that costs a billion dollars to treat, I fully expect to die. The best way to make those decisions is to have some kind of model that can (a) be readily understood; and (b) helps us make those decisions fairly and with as little unintentional bias as possible. There will always be bias -- it's part of the human condition -- but if we can be transparent about our biases and willing to discuss them, we open the door to improving our decision-making systems later on. With that in mind, what would you suggest as a model for determining, say, how we budget for healthcare?
- throwawaygh 6y ago
  > if you are going to offer criticism, try and offer a solution or ask a question that would address that criticism. My solution is that the solution probably isn't useful so don't bother. Seriously. The game of assigning quantitative value to lives is almost never going to help you make better decisions. > All I see here is "The value of human life is incalculable, so just give up." Nope. It's exactly not that! I don't think it's incalculable. You can definitely choose a way to calculate it. Precisely, even. But it's going to be some complex function. And you're going to plug that function into another model. And when you do that, one of two things will happen: 1. you'll ground out in a deontology because you choose extreme values, or 2. the uncertainty in the other parts of the model will dominate. In COVID-19, #2 is what happens. > With that in mind, what would you suggest as a model for determining, say, how we budget for healthcare? I suggest that any reasonable model would look more like an entire industry of mathematicians thinking about edge cases than what we would recognize as a single mathematical model. And even then the answer would probably suck. And definitely nowhere would you find it useful to have a quantitative assignment of lives vs. money, because anywhere you try to use that is going to have a ton of uncertainty that dominates the useful range of possible values. Which pretty much comports with my original comment that "there is no universal answer". I'm not an actuary or policy analyst, though.
082349872349872 6y ago
> All of the uncertainty around COVID-19 There is far less uncertainty around COVID-19 now than there was at the beginning of the year. It is possible to see which countries are successfully tracking and tracing to eliminate, which are entering second waves and need to mitigate, and which haven't even finished their first. https://www.lemonde.fr/les-decodeurs/article/2020/03/27/coronavirus-visualisez-les-pays-qui-ont-aplati-la-courbe-de-l-infection-et-ceux-qui-n-y-sont-pas-encore-parvenus_6034627_4355770.html https://www.lemonde.fr/les-decodeurs/article/2020/03/27/coro...
joe_the_user 6y ago
All of the uncertainty around COVID-19 means that we'll build whatever complex models we want, but then just stare blankly and wonder how the hell to choose values for various coefficients. References? COVID-19's behavior has been quite simple so far. Exponential growth with variations. It's more predictable than the weather next week, overall. The American curve and European curve are somewhat different, yes but the differences can be explained by the total fucked-up the American virus control efforts have been. Oh, and the American failures were accompanied by complaints about uncertainty and how much virus control efforts would cost. Now, we will find out how much a lack of control is going to cost.
- thdrdt 6y ago
  "COVID-19's behavior has been quite simple so far." What do you mean with behavior? People are still discussing how it spreads. The latest news is it travels as aerosol and that in badly ventilated rooms keeping 1.5m distance is not going to help much. And also that the amount of virus particles you inhale determines how sick you get. So it is only predictable when we know how it is growing exponential. The still ongoing discussion about how it spreads creates uncertainty.
  - joe_the_user 6y ago
    Overall behavior - how it spreads and effects people in aggregate. It spreads by air primarily (aerosol isn't "the latest news", it's what's been said from the start with any other transmission method speculative). Yes, a zillion uncertain details. Covid is a simple as any of the things we meet in daily, in aggregate, as simple as rain or dirty. The behavior in detail of any of these things is incredibly complex, of course. The forms rain takes are distinctly complex but is nearly falls roughly downwards and gets things wet (exceptions to everything, of course). People have more incentive to look at the details of Covid 'cause it's much deadly than rain or dirt. But even more, lots of people use Covid's uncertainty to argue against any mitigating measures they find inconvenient - and that part is kind of despicable.
mrow84 6y ago
With respect to specific modelling efforts policy makers sometimes act as antagonists because, without joint modelling of all the concerns they are trying to balance, they are forced to rely on their intuition. This leaves society particularly exposed to the character traits of our leaders, because it is they who end up modelling the interaction effects (in their heads). For example they might claim in the early stages of an epidemic that we actually just have linear case growth. It is then up to the modeller to try to justify why that isn't the case, which in limited-information regimes can be near impossible in any rigorous sense, for the reasons you describe. However, assumptions are important, and showing decision makers what might happen given certain assumptions (and providing a justification for those assumptions), which is part of what a model does, can cause them to reassess their current thinking, and it is for this reason that these kinds of models can remain useful, even if it is not possible to reasonably distinguish between some set of models given available data. Returning to the epidemic example, justifying why exponential rather than linear growth is likely in the early stages of spread is something that can be done on an intuitive level, and explaining this to policy makers, and showing them the range of possible outcomes that are consistent with current (at the time limited) knowledge, can be extremely powerful.
ssivark 6y ago
To add to the above, the distribution of population-level COVID outcomes has fat tails (the system evolution is non-ergodic), so computing point estimates with ensemble average “expected values” is junk modeling. And computing the (correct) time averages requires owning up to the uncertainty and fatness of tails (which is a very hard thing to model), so people have the lamppost bias of doing the easy (but useless) calculation. An alternative way to explain the meaning of “large uncertainties” is that you can fudge input numbers slightly to get whatever output decision you want, so whoever is making the policy decision is likely to keep fudging numbers till the outcome “feels right”. At that point, the modeling is just dangerously misleading pseudo-science. An intuitive approximation of the decision based on time averages is to “play it safe” in the presence of fat negative tails. “How safe tho?” is a very hard question to answer because of the uncertainties involved (as parent post explained).

solveit 6y ago

Sure, so give me a number. Eventually somebody[1] has to make a high-level decision on which strategy to pursue. And they have to give directives that can be followed by people who don't know your entire thought processes. That's what it means to be a bureaucracy. So which is it, will you choose an imperfect quantitative model where some numbers are pulled out of your ass? Or will you just let every low-level functionary pull entire decisions out of their individual asses and hope something resembling a coherent strategy will emerge?[2] [1] Well, ideally. The US response to covid was pretty much [2]. If it wasn't clear, [2] was a rhetorical question and you should not do that.

throwawaygh 6y ago
> Sure, so give me a number 42? I'm genuinely not sure what number you're asking for. > Eventually somebody[1] has to make a high-level decision on which strategy to pursue. Yes, of course, but that decision making doesn't require a quantitative model assigning value to life (or not). You can make a lot of very well-informed decisions without ever thinking about that particular aspect of optimization theory with any particularly high level of detail. "Aggressively avoid protracted exponential growth" doesn't require knowing anything about the value of life relative to other things. Mathematical modeling can be useful for responding to covid-19. However, mathematical modeling for this question is not useful, because your answer gets plugged into yet another model where there's too much uncertainty for your answer to the have any quantitatively justifiable impact on decision making. To give a toy example, spending a bunch of time figuring out the value of $c$ in $f(x) = x^n + c$ is a waste of time if you don't know much about the value of $n$.
- solveit 6y ago
  Hanson explicitly disagrees, to quote: >Yet it is easy to show that we can both have more money and save more lives if we are more consistent about the price we pay for lives in the many different death-risk-versus-cost choices that we make. He's not even saying you need an accurate value, he's saying you need a consistent value.
  - throwawaygh 6y ago
    >> Yet it is easy to show that we can both have more money and save more lives if we are more consistent about the price we pay for lives in the many different death-risk-versus-cost choices that we make. TBH I'm not even quite sure what is meant by that assertion, but the article certainly doesn't contain a compelling justification and the claim is almost prime facie false. There are such completely trivial counter-examples for the plain meaning that I'm not even sure what the author's intended meaning was. E.g., "lives are worth negative infinity" is consistent, and you'll always end up with more money and save fewer lives. Ditto for "1 life = GDP of the world".
    - GavinMcG 6y ago
      If the counterexamples are so trivial and obvious, the author must be a complete idiot, right? But of course they're not a complete idiot, so maybe there's another explanation, that you could have arrived at with a dash of charitable thinking.
      - throwawaygh 6y ago
        > If the counterexamples are so trivial and obvious, the author must be a complete idiot, right? No. Smart people make simple mistakes all the time. Being wrong doesn't make one an idiot. Fortunately for me :) Also, the realization that "lives vs. other-resource" is unnecessary to model because it's either dominated by other uncertainty or else results in deontology is not at all an obvious conclusion. It took me a decade to arrive there. What I was pointing out in the above comment is how choosing extreme values for one resource or the other effectively gives you a deontological ethics that strictly prioritizes one thing over the other. You can solve the optimization problem if you want, but the solution is obvious. deontological ethics is the subset of utilitarian ethics where you don't have to do some calculations because you just max out the value of certain objectives. > But of course they're not a complete idiot, so maybe there's another explanation, that you could have arrived at with a dash of charitable thinking. Maybe. Can you explain why I'm confused, then? The plain meaning of the author's quote, even taken in context, seems like it has some trivial counter-examples. What am I misunderstanding? BTW, I'm not being coy. I am being charitable. When I say "I'm not even quite sure what is meant by that assertion", I mean it. The thing I think the author means has trivial counter-examples, so the author must mean something aside from what I assume. Or else hasn't thought about the extreme values. Which would also be possible.
deleted 6y ago
[deleted]

donw 6y ago

Generally speaking, if you are going to offer criticism, try and offer a solution or ask a question that would address that criticism. All I see here is "The value of human life is incalculable, so just give up." Am I wrong? What am I missing? We don't have infinite resources or infinite capacity to distribute them. That means we need to make hard decisions about things like medical care. That is reality. If I get some kind of crazy-aggressive brain cancer that costs a billion dollars to treat, I fully expect to die. The best way to make those decisions is to have some kind of model that can (a) be readily understood; and (b) helps us make those decisions fairly and with as little unintentional bias as possible. There will always be bias -- it's part of the human condition -- but if we can be transparent about our biases and willing to discuss them, we open the door to improving our decision-making systems later on. With that in mind, what would you suggest as a model for determining, say, how we budget for healthcare?

throwawaygh 6y ago
> if you are going to offer criticism, try and offer a solution or ask a question that would address that criticism. My solution is that the solution probably isn't useful so don't bother. Seriously. The game of assigning quantitative value to lives is almost never going to help you make better decisions. > All I see here is "The value of human life is incalculable, so just give up." Nope. It's exactly not that! I don't think it's incalculable. You can definitely choose a way to calculate it. Precisely, even. But it's going to be some complex function. And you're going to plug that function into another model. And when you do that, one of two things will happen: 1. you'll ground out in a deontology because you choose extreme values, or 2. the uncertainty in the other parts of the model will dominate. In COVID-19, #2 is what happens. > With that in mind, what would you suggest as a model for determining, say, how we budget for healthcare? I suggest that any reasonable model would look more like an entire industry of mathematicians thinking about edge cases than what we would recognize as a single mathematical model. And even then the answer would probably suck. And definitely nowhere would you find it useful to have a quantitative assignment of lives vs. money, because anywhere you try to use that is going to have a ton of uncertainty that dominates the useful range of possible values. Which pretty much comports with my original comment that "there is no universal answer". I'm not an actuary or policy analyst, though.

082349872349872 6y ago

> All of the uncertainty around COVID-19 There is far less uncertainty around COVID-19 now than there was at the beginning of the year. It is possible to see which countries are successfully tracking and tracing to eliminate, which are entering second waves and need to mitigate, and which haven't even finished their first. https://www.lemonde.fr/les-decodeurs/article/2020/03/27/coronavirus-visualisez-les-pays-qui-ont-aplati-la-courbe-de-l-infection-et-ceux-qui-n-y-sont-pas-encore-parvenus_6034627_4355770.html https://www.lemonde.fr/les-decodeurs/article/2020/03/27/coro...

joe_the_user 6y ago

All of the uncertainty around COVID-19 means that we'll build whatever complex models we want, but then just stare blankly and wonder how the hell to choose values for various coefficients. References? COVID-19's behavior has been quite simple so far. Exponential growth with variations. It's more predictable than the weather next week, overall. The American curve and European curve are somewhat different, yes but the differences can be explained by the total fucked-up the American virus control efforts have been. Oh, and the American failures were accompanied by complaints about uncertainty and how much virus control efforts would cost. Now, we will find out how much a lack of control is going to cost.

thdrdt 6y ago
"COVID-19's behavior has been quite simple so far." What do you mean with behavior? People are still discussing how it spreads. The latest news is it travels as aerosol and that in badly ventilated rooms keeping 1.5m distance is not going to help much. And also that the amount of virus particles you inhale determines how sick you get. So it is only predictable when we know how it is growing exponential. The still ongoing discussion about how it spreads creates uncertainty.
- joe_the_user 6y ago
  Overall behavior - how it spreads and effects people in aggregate. It spreads by air primarily (aerosol isn't "the latest news", it's what's been said from the start with any other transmission method speculative). Yes, a zillion uncertain details. Covid is a simple as any of the things we meet in daily, in aggregate, as simple as rain or dirty. The behavior in detail of any of these things is incredibly complex, of course. The forms rain takes are distinctly complex but is nearly falls roughly downwards and gets things wet (exceptions to everything, of course). People have more incentive to look at the details of Covid 'cause it's much deadly than rain or dirt. But even more, lots of people use Covid's uncertainty to argue against any mitigating measures they find inconvenient - and that part is kind of despicable.

mrow84 6y ago

With respect to specific modelling efforts policy makers sometimes act as antagonists because, without joint modelling of all the concerns they are trying to balance, they are forced to rely on their intuition. This leaves society particularly exposed to the character traits of our leaders, because it is they who end up modelling the interaction effects (in their heads). For example they might claim in the early stages of an epidemic that we actually just have linear case growth. It is then up to the modeller to try to justify why that isn't the case, which in limited-information regimes can be near impossible in any rigorous sense, for the reasons you describe. However, assumptions are important, and showing decision makers what might happen given certain assumptions (and providing a justification for those assumptions), which is part of what a model does, can cause them to reassess their current thinking, and it is for this reason that these kinds of models can remain useful, even if it is not possible to reasonably distinguish between some set of models given available data. Returning to the epidemic example, justifying why exponential rather than linear growth is likely in the early stages of spread is something that can be done on an intuitive level, and explaining this to policy makers, and showing them the range of possible outcomes that are consistent with current (at the time limited) knowledge, can be extremely powerful.

ssivark 6y ago

To add to the above, the distribution of population-level COVID outcomes has fat tails (the system evolution is non-ergodic), so computing point estimates with ensemble average “expected values” is junk modeling. And computing the (correct) time averages requires owning up to the uncertainty and fatness of tails (which is a very hard thing to model), so people have the lamppost bias of doing the easy (but useless) calculation. An alternative way to explain the meaning of “large uncertainties” is that you can fudge input numbers slightly to get whatever output decision you want, so whoever is making the policy decision is likely to keep fudging numbers till the outcome “feels right”. At that point, the modeling is just dangerously misleading pseudo-science. An intuitive approximation of the decision based on time averages is to “play it safe” in the presence of fat negative tails. “How safe tho?” is a very hard question to answer because of the uncertainties involved (as parent post explained).