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.
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.
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.
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