There's a factor not considered here: to what extent were Scalia & Ginsberg able to get along because of other material conditions?
As supreme court justices we can assume that they had a basic foundation of psychological and material security - a position of prestige, a job for life, healthcare and so on.
I believe it is a lot easier to summon the "higher thoughts" necessary to be civil when ones personal position is more secure, so to achieve a more civil society it may help to work to make more insecure people secure.
Yes - I think you're right that the actual interesting result from NFLT is not that 'optimisation is impossible', but that 'uniform priors are stupid'.
Hah, interesting - this is a reference I hadn't seen and I like the sound of it. There was me thinking I'd had an idea of my own one time!
The reference machine thing would be the next problem to argue if using 2^-K as the weight; whilst you can make the K-complexity of any particular string low by putting an instruction in your machine that is 'output the string', this is clearly cheating! So there ought to be a connection between the reference machine and some real physics, since we are perhaps not interested in building optimisers that perform well in universes whose physics is very different to ours.
Sadly even if this were cracked I think the fact that K is uncomputable would make the result likely to be useless in practise.
I think you're right to bring up the NFLT, but I don't think it is applicable, it just points at the real question.
The key assumption to get the NFLT is that each environment vote has the same weight, i.e. we are targeting a uniform distribution on objective functions / environments / problems / whatever you call it.
If you break this assumption, you get an opposite result which is that search algorithms divide into some equivalence classes determined by the sets of different outcomes (traces, if I remember the theorem's description) that you discriminate between.
A uniform distribution like this is actually a very very strong precondition; it implies (looking at results about the complexity of sets of strings, since choosing an environment is like choosing a string from 2^N given some encoding, etc) that you care equally about a very large number of environments most of which have no compressible structure or equivalently have a huge kolmogorov complexity. Most of these environments would not have a compact encoding, relative to a particular choice of machine, but we are weighing these the same as those environments which are actually implementable using less than a ridiculous amount of storage to represent the function.
The reason why I think this is too strong an assumption to use is then that we don't care about all these quadrillion problems which have no compact encoding - we know this because we literally can't encounter them as they would be too large to ever write down using ordinary matter.
Allowing for this, talking usefully about evaluating an AGI or equivalently a search strategy or optimization algorithm implies having an understanding of the distribution of environments / problems we care about. I think capturing this concept in a 'neat' way would be a significant contribution; I had a go during my PhD but failed to get anywhere. Unfortunately things like K-complexity are uncomputable, so reasoning about distributions in those terms is a dead-end.
It is being a luddite - in a good way! Don't knock the luddites, they had analogous concerns.
They didn't hate machines or novelty per se, they hated the specifics of how the machines were affecting their quality of life.
Same with facebook - networked communications might be OK (remains to be seen if you ask me), but the socio-technical-political blob that is facebook's implementation of same has side effects that lots of us find horrible.