Comment by donw | Hacker News Reader

Comment by donw | Hacker News Reader

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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?

> 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.

Honestly, this looks to me like a word salad -- I can't pull anything out of this other than "models are useless, so don't bother", which is a non-answer.

No, models are useful.

However, models of "value of life vs. other-thing" are almost always useless.

Why? Because you don't care about that question. You care about other questions.

And, crucially, the models you use to answer those other questions tend to have so much uncertainty that your model of "value of life vs. other-thing" turns out to not really contribute anything useful to the analysis.

If I ask for a policy that maximizes the value of some function $f(x) = \theta_1 * x^n + \theta_2 * c$, for example, then will you focus your effort on modeling $n$ or $c$? What if I tell you that the best you can do is get $n$ down to an interval $[-100, 100]$? Will you then go on to think a bunch about what to do with $c$, especially if you know that whatever $c$ is it's probably between -1 and 1?

My suggestion is that, in most cases, "value of life vs. other-thing" is the $c$ in this example.

In the case of COVID, you want all your smart modelers thinking about how to minimize the chance of protected exponential growth within any region. If you do that, you have the right answer. The exact trade-off between lives and other things doesn't matter because, with covid, __preventing localized protected exponential growth is always the right answer__. The "lives vs other-thing" question is a complete distraction in the decision space.

> I can't pull anything out of this other than "models are useless, so don't bother", which is a non-answer.

Is it? Models are always modeling something! Modeling the effect of reddit meme spread on covid-19 spread is probably useless. That doesn't mean cluster SIR models are useless.

Don't model things that don't help you make decisions.

Sometimes it's fun to do things that are pointless.

> However, models of "value of life vs. other-thing" are almost always useless. > Why? Because you don't care about that question. You care about other questions.

The “value of a statistical life” (or to use a better term, “value of a prevented fatality”), is weighed against other things in a large number of public policy choices. For example: environmental pollution, biodiversity, quality of landscapes, noise pollution, economic activity. It’s also important to aim for reasonable levels of prevention of different risk categories: food safety, road safety, air transport safety, medical safety in hospitals, industrial safety, etc. If public policy (via regulatory obligations for instance) uses very different implicit values for a prevented fatality, it means that more statistical lives could have been saved by allocating prevention effort in a different way.

I think their point is something like what's discussed here:

https://news.ycombinator.com/item?id=22915837

I'd distill it as:

1. Every model should be presented alongside a discussion of whether the fitting process is well-posed[1]

2. If the fitting process is ill-posed, the model cannot be used to guide policy

3. If the fitting process is well-posed, but only via Bayesian regularization of some kind, before being used to guide policy it should be checked for sensitivity to choice of regularization, and the regularization scrutinized in exhaustive detail

4. Compositions of models must be checked for well-posedness

https://en.wikipedia.org/wiki/Well-posed_problem

Wow, thank you so much.

> game of assigning quantitative value to lives is almost never going to help you make better decisions

Quality adjusted life years is part of the analytical basis of funding of interventions in the UK's national health system (NHS).

Does this change your position?

Not really. National healthcare systems are maybe the one reason why I said "almost never" and not "never". When your problem is literally and directly, not indirectly, about assigning finite resources where lives are on the line, that's the cases where a model that relates the two is most likely to be useful. And even then most healthcare systems don't even try.

When the effects of sacrificing lives are more disconnected, the model becomes less useful. In particular, I don't think these sorts of models are useful for making decisions about how to respond to COVID, which is what this article is about.

It doesn't change mine! This came into vogue in the early 70s when they wanted to cut funding as much as possible to make the operation more efficient while still providing a high level of care, according to these "metrics" like QLAY, which are fundamentally broken (as are and have been all attempts to reduce to metrics). It managed to operate without them with a great deal of satisfaction from both the providers and the patients until this came in, and then things got really shitty while improving along the metrics so it was nobody's fault because it was just "science".

> But it's going to be some complex function.

Or you can pick a number from Wikipedia [0]. Somewhere between $50 thousand and $10 million.

I agree with what I suspect is your central point; which is when it comes to COVID-19 there are other more important uncertainties than the value cost of a life. But it is useful for an order-of-magnitude check to see if the response is overblown.

[0] https://en.wikipedia.org/wiki/Value_of_life#Estimates_of_the...

I'm not sure it's useful even as an order of magnitude check, because there's so much uncertainty about how many lives different policies cost. People who believe a response is too loose inevitably also think it kills more people than their opponents; those who believe a response is too strict inevitably think it saves fewer.

Not the OP, just wanted to add that not all problems have solutions, especially the hard problems. Believing otherwise is very "technicist" (for lack of a better word), is believing that the "world" is a closed-system that "moves"/behaves in a predictable and computable manner. It doesn't.

It's easier for us as a species to say what a star billions of light-years away has been "doing" since its "inception" and the most probable way in which it will "die" than to say what the 5-year old kid next doors is going to do in a year or two, heck, what he's going to do tomorrow.

All problems do however have outcomes, and doing nothing still results in an outcome.

But at least we're honest with ourselves that we don't know what we're doing (or what we're about not to do, in this case).

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