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Smaller stack sizes reduce possibilities and thus reduce complexity. Pay jumps result in chips having different utility to each player which forces some situational playstyles to be more optimal. I would guess that this also reduces the complexity of the game.

Since tournaments don't often spend much time with stacks much deeper than 100bb, I would guess that tournaments would be more easily solved. Though tournaments are much more frequently run with 9-10 players rather than 6 at a table.


You're right that a single short stack hand in a vacuum has fewer game tree branches, and that factoring in chip utility is also fairly straightforward. But I strongly disagree that it reduces the overall complexity of the game. The model in the article played every single hand with 100bb; to be an effective tournament player it would have to be able to fluidly adjust strategies between big, medium and short stack play, as well as reasoning about the stack sizes of other players at the table. It's basically 4 different games at >100bb, 50-100bb, 25-50bb, and <25bb, so it would have to develop optimal strategies for each. And even if the shallower stacked games are generally simpler in isolation, there's a meta strategy of knowing which one to apply in a given hand with heterogenous stack sizes. To paraphrase Doug Polk "If cash game play is a science, tournaments are more of an art."

The bot could likely just be trained on the 4 or so different games. You’re likely increasing the complexity by a constant factor, nothing exponential here.

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