Chris Nolan's Joker is a very clever guy, almost Monroesque in his ability to identify hypocrisy and inconsistency. One of his most interesting scenes in the film has him point out how people estimate horrible things differently depending on whether they're part of what's "normal", what's "expected", rather than on how inherently horrifying they are, or how many people are involved.
Soon people extrapolated this observation to other such apparent inconsistencies in human judgment, where a behaviour that once was acceptable, with a simple tweak or change in context, becomes the subject of a much more serious reaction.
I think there's rationalist merit in giving these inconsistencies a serious look. I intuit that there's some sort of underlying pattern to them, something that makes psychological sense, in the roundabout way that most irrational things do. I think that much good could come out of figuring out what that root cause is, and how to predict this effect and manage it.
Phenomena that come to mind, are, for instance, from an Effective Altruism point of view, the expenses incurred in counter-terrorism (including some wars that were very expensive in treasure and lives), and the number of lives said expenses save, compared with the number of lives that could be saved by spending that same amount into improving road safety, increasing public helathcare expense where it would do the most good, building better lightning rods (in the USA you're four times more likely to be struck by thunder than by terrorists), or legalizing drugs.
What do y'all think? Why do people have their priorities all jumbled-up? How can we predict these effects? How can we work around them?
Good point.
True enough, though the factor-of-2 fluctuation I had in mind was more like a jump from 23 to 45 (2013's & 2007's numbers respectively), and those values are more like 2.2-sigma & 1.7-sigma events (using the observed 2006-2013 average as the parameter of a Poisson distribution). Still pretty unlikely, of course.
Yeah, you're right. (Well, I disagree about the urbanization explanation, the dip looks too sudden. But other than that.) If I take the 2006-2013 figures, subtract their mean μ from each of them and divide the results by √μ, that should give me z-scores (if the data are IID & Poisson). The sum of those z-scores' squares should then be roughly χ²-distributed with n - 1 = 7 degrees of freedom, but the actual χ² statistic I get is too far in the tail for that to be plausible (χ² = 17.9, hence p = 0.012). So the lightning deaths are unlikely to be IID from a Poisson (or, nearly equivalently, Gaussian) distribution.