Apparently what works fairly well in Go is to evaluate positions based on ‘randomly’ running lots games to completion (in other words you evaluate a position as ‘good’ if in lots of random games which start from this position you win). Random sampling of the future can work in some domains. I wonder if this method is applicable to answering specific questions about the future (though naturally I don’t think science fiction novels are a good sampling method).
No, Gray_Area’s point (that I can see) was that you would only approximate the result, using cognitive heuristics, for example thinking about how an author would tell the story that starts the way your reality does. There are other, valid ways to do that. But the best known to me is simply Bayesian inference, and keeping track of probability distributions instead of sampling randomly is not that hard, since it saves you the otherwise expensive work of adjusting for biases using ad hoc methods.
Apparently what works fairly well in Go is to evaluate positions based on ‘randomly’ running lots games to completion (in other words you evaluate a position as ‘good’ if in lots of random games which start from this position you win). Random sampling of the future can work in some domains. I wonder if this method is applicable to answering specific questions about the future (though naturally I don’t think science fiction novels are a good sampling method).
We’d have to be able to randomly run reality to completion several times.
Universe seems to be doing that, only problem is that instead of us getting results we are only part of them.
No, Gray_Area’s point (that I can see) was that you would only approximate the result, using cognitive heuristics, for example thinking about how an author would tell the story that starts the way your reality does.
There are other, valid ways to do that. But the best known to me is simply Bayesian inference, and keeping track of probability distributions instead of sampling randomly is not that hard, since it saves you the otherwise expensive work of adjusting for biases using ad hoc methods.