Some of the points you make don’t apply to online poker. But I imagine that the most interesting rationality lessons from poker come from studying other players and exploiting them, rather than memorizing and developing an intuition for the pure game theory of the game.
If you did want to focus on the latter goal, you can play online poker (many players can >12 tables at once) and after every session, run your hand histories through a program (e.g., “GTO Wizard”) that will tell you where you made mistakes compared to optimal strategy, and how much they would cost you against an optimal-playing opponent. Then, for any mistake, you can even input the specific spot into the trainer program and practice it with similar hands 4-tabling against the computer, with immediate feedback every time on how you played the spot.
But I imagine that the most interesting rationality lessons from poker come from studying other players and exploiting them, rather than memorizing and developing an intuition for the pure game theory of the game.
Strongly agree. I didn’t realize this when I wrote the original post, but I’m now convinced. It has been the most interesting / useful thing that I’ve learned in the working-out of Cunningham’s Law with respect to this post.
And so, there’s a reason that the curriculum for my and Max’s course shifts away from Nash equilibrium as the solution concept to optimizing winnings against an empirical (and non-Nash) field just as soon as we can manage it. For example, Practicum #3 (of 6) is “write a rock-paper-scissors bot that takes advantage of our not-exactly-random players as much as you can” without much further specification.
Some of the points you make don’t apply to online poker. But I imagine that the most interesting rationality lessons from poker come from studying other players and exploiting them, rather than memorizing and developing an intuition for the pure game theory of the game.
If you did want to focus on the latter goal, you can play online poker (many players can >12 tables at once) and after every session, run your hand histories through a program (e.g., “GTO Wizard”) that will tell you where you made mistakes compared to optimal strategy, and how much they would cost you against an optimal-playing opponent. Then, for any mistake, you can even input the specific spot into the trainer program and practice it with similar hands 4-tabling against the computer, with immediate feedback every time on how you played the spot.
Strongly agree. I didn’t realize this when I wrote the original post, but I’m now convinced. It has been the most interesting / useful thing that I’ve learned in the working-out of Cunningham’s Law with respect to this post.
And so, there’s a reason that the curriculum for my and Max’s course shifts away from Nash equilibrium as the solution concept to optimizing winnings against an empirical (and non-Nash) field just as soon as we can manage it. For example, Practicum #3 (of 6) is “write a rock-paper-scissors bot that takes advantage of our not-exactly-random players as much as you can” without much further specification.