This doesn't seem to account for property taxes, which I expect would change the story quite a bit for the US.
This seems needlessly narrow minded. Just because AI is better than humans doesn't make it uniformly better than humans in all subtasks of chess.
I don't know enough about the specifics that this guy is talking about (I am not an expert) but I do know that until the release of NN-based algorithms most top players were still comfortable talking about positions where the computer was mis-evaluating positions soon out of the opening.
To take another more concrete example - computers were much better than humans in 2004, and yet Peter Leko still managed to refute a computer prepared line OTB in a world championship game.
Agreed - as I said, the most important things are compute and dilligence. Just because a large fraction of the top games are draws doesn't really say much about whether or not there is an edge being given by the humans (A large fraction of elite chess games are draws, but no-one doubts there are differences in skill level there). Really you'd want to see Jon Edward's setup vs a completely untweaked engine being administered by a novice.
I believe the answer is potentially. The main things which matter in high-level correspondence chess are:
Although I don't think either of those are really relevant. The really relevant bit is (apparently) planning:
For me, the key is planning, which computers do not do well — Petrosian-like evaluations of where pieces belong, what exchanges are needed, and what move orders are most precise within the long-term plan.
(From this interview with Jon Edwards (reigning correspondence world champion) from New In Chess)
I would highly recommend the interview on Perpetual Chess podcast also with Jon Edwards which I would also recommend.
I'll leave you with this final quote, which has stuck with me for ages:
The most important game in the Final was my game against Osipov. I really hoped to win in order to extend my razor-thin lead, and the game’s 119 moves testify to my determination. In one middlegame sequence, to make progress, I had to find a way to force him to advance his b-pawn one square, all while avoiding the 50-move rule. I accomplished the feat in 38 moves, in a sequence that no computer would consider or find. Such is the joy of high-level correspondence chess. Sadly, I did not subsequently find a win. But happily, I won the Final without it!
Just in case anyone is struggling to find the relevant bits of the the codebase, my best guess is the link for the collections folder in github is now here.
You are looking in "views.ts" eg .../collections/comments/views.ts
The best thing to search for (I found) was ".addView(" and see what fits your requirements
I feel in all these contexts odds are better than log-odds.
Log-odds simplifies Bayesian calculations: so does odds. (The addition becomes multiplication)
Every number is meaningful: every positive number is meaningful and the numbers are clearer. I can tell you intuitively what 4:1 or 1:4 means. I can't tell you what -2.4 means quickly, especially if I have to keep specifying a base.
Certainty is infinite: same is true for odds
Negation is the complement and 0 is neutral: Inverse is the complement and 1 is neutral. 1:1 means "I don't know" and 1:x is the inverse of x:1. Both ot these are intuitive to me.
No - I think probability is the thing supposed to be a martingale, but I might be being dumb here.
So, what do you think? Does this method seem at all promising? I'm debating with myself whether I should begin using SPIES on Metaculus or elsewhere.
I'm not super impressed tbh. I don't see "give a 90% confidence interval for x" as a question which comes up frequently? (At least in the context of eliciting forecasts and estimates from humans - it comes up quite a bit in data analysis).
For example, I don't really understand how you'd use it as a method on Metaculus. Metaculus has 2 question types - binary and continuous. For binary you have to give the probability an event happens - not sure how you'd use SPIES to help here. For continuous you are effectively doing the first step of SPIES - specifying the full distribution.
If I was to make a positive case for this, it would be - forcing people to give a full distribution results in better forecasts for sub-intervals. This seems an interesting (and plausible claim) but I don't find anything beyond that insight especially valuable.
Yeah, and it doesn't adjust for taxes there either. I thought this was less of an issue when comparing rents to owning though, as the same error should affect both equally.