Are any bots, such as KataGo, trained with ties as a possible outcome for games played with integer komi? I know that komi can be set to 7 in KataGo, but does KataGo actually understand what a tie is? How about other bots?

If the answer is currently no, I think it would be highly interesting to experiment with this.

Maybe the old program should get to count a tie as a win, I dunno.

What do you do with ties during training? If you are trying to decide which program is stronger, then ignoring ties is preferable.

I think ties just pull the value net for the given game towards 0, the correct output. I also doubt ignoring ties is preferable in any case (in test matches they should pull the strength diff towards 0 as well - otherwise if A wins 1 and ties 9 you would think it is much stronger).

I think I would use two game matches for self play, with each player playing Black in one game and White in the other. (Since the question is which program is better

I'm not sure exactly what you mean here. Training and selfplay for NN bots serves two purposes: create targets for policy training (since search results are of higher quality than raw policy), and target data for value training (the eventual game outcome is a bit of info about the prospects of positions occurred in that particular game). How would twin matches fit here (other than two individual matches)?

And what makes you think that KataGo is not already doing some or all of these things?

One thing I have suggested for go is a two game match decided on total points. If the games are played without knowledge of the result of the other game, then we can reinforce the decisions that led to the greater win in one game, and the lesser loss in the other.

Yes. In fact, you can play O(n^2) "virtual two-game matches" with only n games if the games are played without knowledge of the other game's result; pretend games 1 and 2 are a match, games 1 and 3, games 1 and 4, etc. The "value" of a game result ends up being what fraction of the other game results it is superior to, which for those with a probability background is known as a cumulative distribution function. I have a paper about this that should go up on arXiv.org this coming week. (It learned well, but I only tried it on a simple game.)

However, to satisfy the requirement of switching sides, it should be the odd numbered games vs. the even numbered games, assuming that's how you number them. Then you get some number of virtual matches less than N*N/4, since you eliminate virtual ties when deciding which player is better.

However, to satisfy the requirement of switching sides, it should be the odd numbered games vs. the even numbered games, assuming that's how you number them. Then you get some number of virtual matches less than N*N/4, since you eliminate virtual ties when deciding which player is better.

In my setup, the games are all training games, where both players are the same bot, so the games really are all comparable. (For example, I can pretend I was White in game n and Black in game m, which really means that I "win" if my performance as White in game n is better than a clone's performance as White in game m, and this can be done for any value of m /= n.)

Yes. In fact, you can play O(n^2) "virtual two-game matches" with only n games if the games are played without knowledge of the other game's result; pretend games 1 and 2 are a match, games 1 and 3, games 1 and 4, etc. The "value" of a game result ends up being what fraction of the other game results it is superior to, which for those with a probability background is known as a cumulative distribution function. I have a paper about this that should go up on arXiv.org this coming week. (It learned well, but I only tried it on a simple game.)