Once it gets to enough nines, its monte carlo trees will run out of sample resolution. If it can resolve to three nines, then a 99.93% win branch has a 70% chan

Once it gets to enough nines, its monte carlo trees will run out of sample resolution. If it can resolve to three nines, then a 99.93% win branch has a 70% chance of being reported as 99.9% and a 30% chance of being reported as 100%. When all the branches here get rolled up, they report some average around 99.93% but not necessarily exactly it. This propagates upwards in the tree, adding more meaningless digits. Adding the evaluation network in increases the number of decimals, but doesn't really change the effect. It's similar to how ray tracing renderers start to return weird speckle patterns when the room is dark enough. And the policy network chooses branches to investigate, not which one to choose. It adds sample resolution to places pros might play, but doesn't add to the estimated probability of winning. Edit: Actually, since places pros might play have higher sample resolution, they're less random. So worse moves get worse evaluation, and a higher chance of leading the pack. This might actually bias AlphaGo to play some pretty bad moves - but, again, this is all assuming it's going to win anyway.