Most poker solvers answer one question very well: given a single hand and a single decision tree, what is the equilibrium strategy? (Yes, there is subgame solving, node locking, and plenty more — but the default frame is still one hand, one equilibrium.)
I kept getting stuck on a different one. What if the same kind of spot shows up over and over, and a player can commit to a fixed strategy across those repetitions? In a few toy games I had a hunch, worked out by hand, that committing to a fixed strategy could change its value relative to the one-shot picture. I wanted a tool that could make that commitment value precise — to actually analyze it rather than just believe it. (Whether any of this rises to a repeated-game equilibrium is a much stronger claim, and one I am deliberately not making here.)
I'm still learning software engineering, so until recently I couldn't implement this — I was stuck reasoning about toy games on paper. AI tooling made the analysis feasible, so I finally started building it: repeated-poker-analysis.
It's a small research project: write one narrow model down, run small examples, and record what the model does and doesn't justify.
What repeated-poker-analysis is
