How AI Conquered Poker
Good poker players have always known that they need to maintain a balance between bluffing and playing it straight. Now they can do so perfectly. From a report: One of the earliest and most devoted adopters of what has come to be known as "game theory optimal" poker is Seth Davies's friend and poker mentor, Jason Koon. On the second day of the three-day Super High Roller tournament, I visited Koon at his multimillion-dollar house, located in a gated community inside a larger gated community next to a Jack Nicklaus-designed golf course. On Day 1, Koon paid $250,000 to play the Super High Roller, then a second $250,000 after he was knocked out four hours in, but again he lost all his chips. "Welcome to the world of nosebleed tourneys," he texted me afterward. "Just have to play your best -- it evens out." For Koon, evening out has taken the form of more than $30 million in in-person tournament winnings (and, he says, at least as much from high-stakes cash games in Las Vegas and Macau, the Asian gambling mecca). Koon began playing poker seriously in 2006 while rehabbing an injury at West Virginia Wesleyan College, where he was a sprinter on the track team. He made a good living from cards, but he struggled to win consistently in the highest-stakes games. "I was a pretty mediocre player pre-solver," he says, "but the second solvers came out, I just buried myself in this thing, and I started to improve like rapidly, rapidly, rapidly, rapidly." In a home office decorated mostly with trophies from poker tournaments he has won, Koon turned to his computer and pulled up a hand on PioSOLVER. After specifying the size of the players' chip stacks and the range of hands they would play from their particular seats at the table, he entered a random three-card flop that both players would see. A 13-by-13 grid illustrated all the possible hands one of the players could hold. Koon hovered his mouse over the square for an ace and queen of different suits. The solver indicated that Koon should check 39 percent of the time; make a bet equivalent to 30 percent the size of the pot 51 percent of the time; and bet 70 percent of the pot the rest of the time. This von Neumann-esque mixed strategy would simultaneously maximize his profit and disguise the strength of his hand. Thanks to tools like PioSOLVER, Koon has remade his approach to the game, learning what size bets work best in different situations. Sometimes tiny ones, one-fifth or even one-tenth the size of the pot, are ideal; other times, giant bets two or three times the size of the pot are correct. And, while good poker players have always known that they need to maintain a balance between bluffing and playing it straight, solvers define the precise frequency with which Koon should employ one tactic or the other and identify the (sometimes surprising) best and worst hands to bluff with, depending on the cards in play.
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