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Detecting opponents’ errors While it’s sometimes difficult to know what a specific opponent does incorrectly, many times it’s obvious. For example, many small-stakes players simply never bluff on the river. So, if a player gets to the river and an opponent with that tendency bets, fold all but the best-made hands. Other players bluff far too much, allowing a player to easily call down with all sorts of marginal made hands. Those are both examples of actively exploiting your opponent. The major problem with using the maximally exploitative strategy is that an assessment of an opponent’s strategy could simply be wrong. If a player thinks an opponent never bluffs yet that opponent actually bluffs a lot, or if the player folds to most bets, then the player will get demolished. If a player thinks an opponent bluffs a lot, and thus calls

down with lots of marginal made hands, but it turns out the opponent essentially never bluffs, a player will also get demolished. If an opponent quickly and correctly counter-adjusts to combat a maximally exploitative strategy, a player will lose much more than could have potentially been won by making the initial adjustment. Playing the GTO strategy sidesteps this dilemma, but will win less money in the long run, assuming your assessments are generally correct. So, until you are fairly certain about what your specific opponent does incorrectly against you, it is wise to play a fundamentally sound strategy. Jonathan Little, a professional poker player and WPT Player of the Year, has amassed more than $7 million in live tournament winnings, written 14 best-selling books and teaches at @jonathanlittle

BUT CAN LIBRATUS DO THE THUMB FLIP? Libratus, a poker bot created by Noam Brown and others at Carnegie Mellon University, beat four human professional players in 120,000 hands of heads-up, no-limit hold’em, in early 2017. Four distinguished professional poker players each played 30,000 hands. Each was summarily beaten by Libratus. To reduce the luck factor, special rules ensured no party could just run hot over the course of the challenge. After 20 days, Libratus convincingly beat each pro at a win rate of 14.7 big blinds per 100 hands. Despite the roughly 316,000,000,000,000,000 possible game situations, John Nash, the winner of the 1950 Nobel Prize in Economics, would deem headsup, no-limit hold’em a game with a finite number of situations. Consequently, a Nash Equilibrium exists, which ensures that players using a Nash equilibrium strategy cannot lose against any other player in the long run. A human poker player could never accurately recall, compute or apply the Nash equilibrium strategy to quadrillions of scenarios — but Libratus could. Nash equilibrium means that guts, bluffs, tells, reads and other differentiating strategies employed by the top pros, really don’t matter in the end. That has implications for the online poker industry, which posted more than $1 billion in revenue last year. The poker sites have the challenge of ensuring that no online player is using AI, while convincing players of a level playing field. For a deeper discussion of AI achievements at poker, listen to MIT’s Artificial Intelligence 12.28.18 episode podcast, which includes an interview of a Libratus co-creator, “Tuomas Sabdholm: Poker and Game Theory”. (See podcast review in Arts & Media on p. 40.)

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June 2019  

If there is a single sector that investors need to understand to navigate opportunities in the years to come, its Robotics, Automation and A...

June 2019  

If there is a single sector that investors need to understand to navigate opportunities in the years to come, its Robotics, Automation and A...

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