Very cool that you posted these quantified predictions in advance!
A few thoughts re: Scott Alexander & Rob Wiblin on prediction.
Nice post. Were there any sources besides Wikipedia that you found especially helpful when researching this post?
If the U.S. kept racing in its military capacity after WW2, the U.S. may have been able to use its negotiating leverage to stop the Soviet Union from becoming a nuclear power: halting proliferation and preventing the build up of world threatening numbers of high yield weapons.
BTW, the most thorough published examination I've seen of whether the U.S. could've done this is Quester (2000). I've been digging into the question in more detail and I'm still not sure whether it's true or not (but "may" seems reasonable).
I'm very interested in this question, thanks for looking into it!
My answer from 2017 is here.
Interesting historical footnote from Louis Francini:
This issue of differing "capacities for happiness" was discussed by the classical utilitarian Francis Edgeworth in his 1881 Mathematical Psychics (pp 57-58, and especially 130-131). He doesn't go into much detail at all, but this is the earliest discussion of which I am aware. Well, there's also the Bentham-Mill debate about higher and lower pleasures ("It is better to be a human being dissatisfied than a pig satisfied"), but I think that may be a slightly different issue.
Cases where scientific knowledge was in fact lost and then rediscovered provide especially strong evidence about the discovery counterfactauls, e.g. Hero's eolipile and al-Kindi's development of relative frequency analysis for decoding messages. Probably we underestimate how common such cases are, because the knowledge of the lost discovery is itself lost — e.g. we might easily have simply not rediscovered the Antikythera mechanism.
Apparently Shelly Kagan has a book coming out soon that is (sort of?) about moral weight.
This scoring rules has some downsides from a usability standpoint. See Greenberg 2018, a whitepaper prepared as background material for a (forthcoming) calibration training app.