Population density is very easy to calculate, and as a result very easy to look up. For population clustering, it isn’t even obvious how to reduce it to a number (or manageable set of numbers) given perfect knowledge. I suspect this is why it would occur to someone to compare COVID outcomes based on population density—one might expect it to at least be correlated with the thing about which one actually cares, and it’s available.
This kind of looking under the lamppost for data is I suspect the major reason for the phenomenon you’ve identified, but not the only one. For your title example, calories in and mass in (neglecting any mass (or calories!) coming from air) are both reasonably easily available, but getting precise data on calories out seems quite difficult, and not much (maybe not at all) easier than precise data on mass out. (Mass out might actually be substantially easier if it’s okay to ignore exhaled air, sweat, etc., but I have no idea whether that’s okay; we’re typically talking about quite small changes relative to the time taken.)
I don’t have a good story for what’s going on with CICO’s appeal. Maybe something like “assuming ‘weight gain’ is ‘accumulation of matter storing bioavailable energy’, and that the body is efficient at extracting bioavailable energy from food, you get CICO”. Those assumptions might be further justified (probably in most cases without ever being explicitly considered) by common sense observations like “people who eat a lot tend to be fat” and “people who exercise a lot tend to be thin”. Perhaps the tie to common sense / shared experience allows a theory like this to spread. Even among people with generally decent epistemics, not everyone has time or interest to be a nutritionist.
This feels speculative though. I’m pretty sure the phenomena I named make sense, and they probably happen, but something else could be equally or more important. I think my main takeaway from this article is just to notice choice of reference class as a decision point, and consider alternatives rather than jumping into an assessment of where the case in question falls within the reference class.
(Mass out might actually be substantially easier if it’s okay to ignore exhaled air, sweat, etc., but I have no idea whether that’s okay; we’re typically talking about quite small changes relative to the time taken.)
When you lose weight, most of it exits through the breath. In the very short term, fluctuations are dominated by the matter visibly entering at one end and departing out the other, but that is noise.
Sure, I didn’t mean it was impossible to put a useful number on it. But you (or whoever calculated the metric; I don’t know if that’s you) had to make a decision that 1⁄4 mile was the best distance around which to calculate, and it still apparently took days of computer time and required making up random positions for people within their census blocks. It’s absurdly complicated compared to population density, which could probably be calculated reasonably accurately thousands of years ago, if anyone cared to do so. So (I contend) population density is more available both in one’s mind, as a potential explanatory factor, and outside of one’s mind, as a statistic to try to explain some phenomenon (such as COVID results).
Population density is very easy to calculate, and as a result very easy to look up. For population clustering, it isn’t even obvious how to reduce it to a number (or manageable set of numbers) given perfect knowledge. I suspect this is why it would occur to someone to compare COVID outcomes based on population density—one might expect it to at least be correlated with the thing about which one actually cares, and it’s available.
This kind of looking under the lamppost for data is I suspect the major reason for the phenomenon you’ve identified, but not the only one. For your title example, calories in and mass in (neglecting any mass (or calories!) coming from air) are both reasonably easily available, but getting precise data on calories out seems quite difficult, and not much (maybe not at all) easier than precise data on mass out. (Mass out might actually be substantially easier if it’s okay to ignore exhaled air, sweat, etc., but I have no idea whether that’s okay; we’re typically talking about quite small changes relative to the time taken.)
I don’t have a good story for what’s going on with CICO’s appeal. Maybe something like “assuming ‘weight gain’ is ‘accumulation of matter storing bioavailable energy’, and that the body is efficient at extracting bioavailable energy from food, you get CICO”. Those assumptions might be further justified (probably in most cases without ever being explicitly considered) by common sense observations like “people who eat a lot tend to be fat” and “people who exercise a lot tend to be thin”. Perhaps the tie to common sense / shared experience allows a theory like this to spread. Even among people with generally decent epistemics, not everyone has time or interest to be a nutritionist.
This feels speculative though. I’m pretty sure the phenomena I named make sense, and they probably happen, but something else could be equally or more important. I think my main takeaway from this article is just to notice choice of reference class as a decision point, and consider alternatives rather than jumping into an assessment of where the case in question falls within the reference class.
When you lose weight, most of it exits through the breath. In the very short term, fluctuations are dominated by the matter visibly entering at one end and departing out the other, but that is noise.
Thanks! That’s a great example of how weird this topic is and how inadequate common sense is to the task of understanding it.
Anthropic clustering. I used to use this for forecasting at the beginning of the pandemic.
Sure, I didn’t mean it was impossible to put a useful number on it. But you (or whoever calculated the metric; I don’t know if that’s you) had to make a decision that 1⁄4 mile was the best distance around which to calculate, and it still apparently took days of computer time and required making up random positions for people within their census blocks. It’s absurdly complicated compared to population density, which could probably be calculated reasonably accurately thousands of years ago, if anyone cared to do so. So (I contend) population density is more available both in one’s mind, as a potential explanatory factor, and outside of one’s mind, as a statistic to try to explain some phenomenon (such as COVID results).