To me it seems like for the considerations you bring up in this post, the difference between the ideal gas and the chess game is that we have a near-exact short-timescale model for the ideal gas, but we don’t have a near-exact short-timescale model for the chess game.
If we knew the source code for both of the players in the chess game, we could simulate the game until someone wins, and get an accurate prediction of the outcome, that would be better than just using the ELO ratings.
Running the argument through, we would conclude that intelligence is characterized by us not having a reductionist model of it. Which seems not as ridiculous as it first sounds—we ascribed intelligent design to eg. rain and evolution before we understood how they worked. Also, if we could near-exactly simulate chess players (in our brains, not using computers), I doubt we would see them as very intelligent.
Another disanalogy is that for an ideal gas you want to predict the microstate (which the long-timescale model doesn’t get) but for the chess game you want to predict the macrostate (who wins).
Yeah, I think the fact that Elo only models the macrostate makes this an imperfect analogy. I think a better analogy would involve a hybrid model, which assigns a probability to a chess game based on whether each move is plausible (using a policy network), and whether the higher-rated player won.
I don’t think the distinction between near-exact and nonexact models is essential here. I bet we could introduce extra entropy into the short-term gas model and the rollout would still be superior for predicting the microstate than the Boltzmann distribution.
To me it seems like for the considerations you bring up in this post, the difference between the ideal gas and the chess game is that we have a near-exact short-timescale model for the ideal gas, but we don’t have a near-exact short-timescale model for the chess game.
If we knew the source code for both of the players in the chess game, we could simulate the game until someone wins, and get an accurate prediction of the outcome, that would be better than just using the ELO ratings.
Running the argument through, we would conclude that intelligence is characterized by us not having a reductionist model of it. Which seems not as ridiculous as it first sounds—we ascribed intelligent design to eg. rain and evolution before we understood how they worked. Also, if we could near-exactly simulate chess players (in our brains, not using computers), I doubt we would see them as very intelligent.
Another disanalogy is that for an ideal gas you want to predict the microstate (which the long-timescale model doesn’t get) but for the chess game you want to predict the macrostate (who wins).
Yeah, I think the fact that Elo only models the macrostate makes this an imperfect analogy. I think a better analogy would involve a hybrid model, which assigns a probability to a chess game based on whether each move is plausible (using a policy network), and whether the higher-rated player won.
I don’t think the distinction between near-exact and nonexact models is essential here. I bet we could introduce extra entropy into the short-term gas model and the rollout would still be superior for predicting the microstate than the Boltzmann distribution.