There’s a Bayesian-adjacent notion of closeness to the truth: observations narrow down the set of possible worlds, and two hypotheses that heavily overlap in the possible are “close”.
But the underlying notion of closeness to the truth is underdetermined. If we were relativistic beings, we’d privilege a different part of the observation set when comparing hypotheses, and Newtonian gravity wouldn’t feel close to the truth, it would feel obviously wrong and be rejected early (or more likely, never considered at all because we aren’t actually logically-omniscient Bayesians).
The best plausible explanation I've seen is that Delta's serial interval might be much shorter, which would mean R is lower than you'd think if you assumed Delta had the same serial interval as older strains. (Roughly speaking, in the time it would take Alpha to infect R individuals, Delta has time to infect R and for each of those individuals to infect another R, leading to R + R^2 infections over the same period.) That makes it easier for behavior changes and increasing population immunity to lower R below 1.
I’ll defer to Blake if he’s done the math, but it does seem worth weighting correlated risks more strongly if they could take out all of MIRI. The inundation zone doesn’t look populated, though, so you’re probably fine.
Do you have a source for B.1.1.7 being dominant in Italy/Israel?
Assuming it’s already dominant there, that strongly suggests that it’s infectious enough to have rapidly outcompeted other strains, but that Italy/Israel were able to push down the higher R through some combination of behavioral change and vaccination.
(Note: I can’t find any sources saying B.1.1.7 is dominant in Italy or Israel, and I’d be surprised if that were already the case.)
Is this essentially just giving you leverage in PredictIt?
This process increased my "cash" on PredictIt by $117, but it looks like it will probably pay out around 15/14.75*850 - 850 = $15. If I lost my $117 on some other bet, would my PredictIt balance eventually end up negative?
I just donated $5,000 to your fund at the Society of Venturism, as promised.
Like Stephan, I really hope you make your goal.
Despite the simplicity prior, “this nontrivial program implements the behavior I want” almost always fails to be true because the program contains bugs. Even after you fix the bugs, it still almost always fails to be true because it contains even more bugs.
False scientific theories don’t seem to fail in quite the same ways, and I’m not sure how much of that is due to differences in the search processes versus the search spaces (e.g. physics seems quite different from Python).