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Professional poker players know the optimal strategy but don't always use it

146 points by alexahn 2y ago

deleted 2y ago
[deleted]
mxwsn 2y ago
Essentially all AI work I've seen in games aims for game theory optimal play, but I think it could be really interesting to consider AI for exploitative play. Does this exist? Poker with imperfect information, human pressure and fallibility means that players will inevitably stray from Nash equilibrium. The decision on how to exploit without getting exploited back oneself seems really fascinating to consider from an AI perspective. At a glance it seems to require considering how others view you..
- raincole 2y ago
  > Imperfect information, human pressure... inevitably stray from Nash equilibrium Human pressure yes. Imperfect information no. When we talk about Nash equilibrium for a game like poker, it's already based on imperfect information.
  - mxwsn 2y ago
    Ah right, thanks
- waprin 2y ago
  Within solvers, you can do something called "node locking", which means you "lock" a tree in the game node to play a fixed strategy. You would typically lock it to play as you suspect your opponent plays. This lets the solver calculate the optimal exploitative solution against your specific oppoents. Piosolver, the first public solver and the one mentioned in the article, has this feature. However, what often happens is if you lock one node, then several other nodes in the game tree over-adjust in drastic ways, forcing you to lock all of the, which may be infeasiable. As a result, Piosolver recently introduced "incentives", which gives a player in the game an additional incentive to take a certain action . For example, you may suspect your opponent calls too much and doesn't raise enough, so you can just set that incentive and it will include that in its math equations and give you something similar to an exploitative solution with a much simpler UX. This feature was literally just introduced a few months ago so it's still very much an active area of research, both for game theory nerds, and people trying to use the game theory nerd research to make money !
  - Buttons840 2y ago
    I want to see strong AI used in video games, especially strategy games. People often retort that strong AI is not fun; it's too challenging and that's not what players want. But once we have a strong AI we can adjust its goal function in fun ways. What you're describing is effectively the same, and it's the first time I've seen this used in a strong AI.
    - currymj 2y ago
      GT Sophy is now a permanent feature in Gran Turismo and basically does this!
      - Buttons840 2y ago
        Thanks for bringing that to my attention. I still haven't seen an AI for a turn based strategy game. There's AlphaStar, but it wins via APM, not strategy.
    - dontlikeyoueith 2y ago
      In essence, you need strong (probably unbeatable) game AI in order to make more interesting weak (beatable but challenging and fun) game AI.
- asdasdsddd 2y ago
  I think it'd be interesting to see if an AI with visual input playing exploitatively can out perform AI playing GTO. In doing so, we can measure the effect of visual tells.
  - tialaramex 2y ago
    You mean, can the exploitative strategy take money from fish faster? Yes. But it doesn't need to care about visual tells. The point of the optimal strategy is that it's unexploitable so you can disregard the other player's actions (in the game or outside it) entirely. All exploitative strategies are in turn exploitable.
    - bubblyworld 2y ago
      I think you have to be careful with saying stuff like "optimal strategies are unexploitable", because it usually means "unexploitable in a particular game theory sense". Whether the assumptions of the Nash equilibrium (or any of the others) make sense for your situation in a game of poker is an empirical question, right? It's not a given that playing a NE means you'll be "perfect" in the human sense of the word, or that you'll get the best possible outcome. The best superhuman poker AIs at the moment do not play equilibriums either, for instance.
      - tialaramex 2y ago
        I agree that because of, for example rake or table fees for cash games or competition structure for tournament in practice a game theoretically optimal choice may not be the right choice in practical play. However the situation with an AI powered competitor which uses exploitative play is identical to a human, the GTO play will gradually take their chips at no risk. It's not that they're optimal but that they've chosen not to be optimal and so that's why they lose money against GTO. The AI is at least unemotional about this, humans with a "system" easily get tilted by GTO play and throw tantrums. How can it get there with KToff? What kind of idiot bluffs here with no clubs? Well the answer will usually be the one that's taking all your chips, be better. Humans used to seeing exploitable patterns in the play of other humans may mistake ordinary noise in the game for exploitable play in a GTO strategy and then get really angry when it's a mirage.
- bubblyworld 2y ago
  Yes, this exists! Look up models based on counterfactual regret minimisation - they learn to exploit regularities in their opponents play, and often stray from the GTO play when it makes sense. I believe they have beaten poker professionals in thousands-of-hands playoffs but I may be misremembering.
  - phreeza 2y ago
    The obvious follow up question: are there methods in use to bait such models into suboptimal play and then switch play style to exploit that?
  - codethief 2y ago
    > I believe they have beaten poker professionals in thousands-of-hands playoffs I really don't know anything about poker AIs but could it be you are referring to Libratus and/or Pluribus[0]? [0]: https://noambrown.github.io/ https://noambrown.github.io/
    - bubblyworld 2y ago
      To be honest I didn't have a specific AI in mind, more the technique, but it sounds like these ones also use a variant of CFRM.
    - energy123 2y ago
      I do not believe these attempt to exploit regularities.
  - currymj 2y ago
    no, CFR is mainly just a way of computing Nash equilibria and (although in some sense it is an online, iterative algorithm) would typically be used to precompute Nash strategies, not update them in real time. real poker playing systems augment the CFR strategies with some real-time solving, but just to get even closer to Nash at the end of a hand. on top of this, you could think about augmenting these systems to exploit weaknesses in opponent strategies. there is some work on this, but I don't think it's done much. The famous systems that played against professionals don't use it, they just try to get as close to GTO as possible and wait for opponents to screw up.
- genewitch 2y ago
  it took me a while to track this down last month: https://codegolf.stackexchange.com/questions/tagged/king-of-the-hill https://codegolf.stackexchange.com/questions/tagged/king-of-... there's also cops and robbers and at least one other "all AI compete against eachother" with the submitter usually making the first couple of "naive" bots.
  - nhggfu 2y ago
    very interesting share ^ !
- ggjkvcxddd 2y ago
  An AI that plays a fixed exploitative strategy will end up getting figured out relatively quickly and counter exploited pretty hard. This actually happens in real life sometimes when people attempt to deploy poker bots online. Any exploitative AI also needs the ability to adjust in real time to a different exploitative strategy, which also needs to be not easily predictable, etc.
brigadier132 2y ago
"Game theory optimal" (as poker players like to call it) is not really the optimal strategy. It's the nash equilibrium strategy assuming the other players are also playing a nash equilibrium strategy. As soon as one player deviates from the nash equilibrium it's not optimal anymore :). It's interesting how this affects play. If a player is bluffing slightly less than they should be, the adjustment is drastic, you should actually never call with hands that do not beat their value hands. If they are bluffing optimally, you are supposed to call using what is known as "minimum defense frequency". What's interesting about this is the minimum defense frequency is based on what the strongest hands you can possibly have in that situation are and the opponents possible hands do not even factor into it. It's required to prevent your opponent from bluffing with any two cards profitably. To do the math, if you are on the river and the opponent bets 100 into 100, for this to be profitable for them they need to win 50% of the time or more. If your opponent is bluffing optimally, you need to call with 50% of the strongest hands you have in that specific situation (if you don't know what hands you have in a specific situation that's a problem) and sometimes they can be dogshit like King high. But, very important to note, very few players actually bluff enough and if they bluff less than they should you should only ever call when your hand actually beats their range of possible value hands. (value vs bluff is kind of a difficult thing to communicate, generally it's value if you want your opponent to call) Most players don't bluff enough as a result of most players calling too much! When they call too much you should obviously not bluff! This leads to very boring games of poker.
- waprin 2y ago
  > What's interesting about this is the minimum defense frequency is based on what the strongest hands you can possibly have in that situation are and the opponents possible hands do not even factor into it. This is actually not quite true. MDF is purely a formula based on pot size and the bet size (pot size / pot size + bet size). The fact that it doesn't consider various ranges is why it's not really useful - it was a simplified formula used to try to understand the game before solvers existed. There are situations where your opponent can bet any two cards profitably and you do have to fold - imagine they bet the size of the pot, but have the better hand 99% of the time, you're simply forced to let them bluff the 1% of the time they're bluffing. MDF is a pre-solver concept and not an especially useful concept in the modern game.
waprin 2y ago
I'm working on a project aiming to help pro (or serious amateur) poker players learn game theory, mostly via flashcards with spaced-repetition. https://www.livepokertheory.com https://www.livepokertheory.com I do personally dislike that GTO became the nomenclature , as I prefer "theory-based", since it causes this confusion, but trying to fight it at this point is hopeless because GTO is the search term people are using. And when people say they "play GTO" they usually mean "equilibrium" rather than "optimal against my specific opponents" which is "exploitative". If you actually watch what the top players advocate for, everyone suggests you want to play exploitatively. However, there's one equilibrium solution and effectively infinite exploitative solutions, so equilibrium is a reasonble starting point to develop a baseline understanding of the mechanics of the game. It's tough to know how much "too much" bluffing is unless you know a baseline. Furthermore, if you "exploit" people by definition you are opening yourself up to being exploited so you need to be very careful your assumptions are true. Also, with solvers like piosolver, you can "node lock" (tell a node in the game tree to play like your opponent, rather than an equilibrium way plays), but there's many pitfalls, such as the solver adjusting in very unnatural ways on other nodes to adjust, and it being impractical to "lock" a strategy every node in the tree. There's new ideas called "incentives" which gives the solver an "incentive" to play more like a human would (e.g. calling too much) but these are new ideas still being actively explored. Rock paper scissors is frequently used to explain GTO but it's not the best example because equilibrium in rock paper scissors will break even against all opponents, but equilibrium poker strategy will actually beat most human poker players, albeit not as much as a maximally exploitative one. There's two other huge pieces this article glosses over: 1) It's as impossible for a human to play like a computer in poker as in chess - in fact far more impossible, because in poker you need to implement mixed strategies. In chess there's usually a best move, but in poker the optimal solution often involves doing something 30% of the time and something else 70% of the time. The problem is that, not only are there too many situations to memorize all the solutions, but actually implementing the correct frequencies is impossible for a human. Some players like to use "randomizers" like dice at the table, or looking at a clock, but I find that somewhat silly since it still so unlikely you are anywhere near equilbrium. 2) Reading someone's "tells" live is still a thing. While solvers have led to online poker to decline due to widespread "real time assistance", live poker is booming (the 2024 World Series of Poker Main Event just broke the record yet again) , and in person in live poker, people still give off various information about their hand via body language. From the 70s to the early 2000s, people were somewhat obsessed with "tells" as a way to win at poker. Since computers have advanced so much, it's fallen out of favor, but the truth is, both are useful. It's totally mistaken to think that advancement in poker AI , GTO , and solvers have rendered live reads obsolete. In fact, in 2023, Tom Dwan won the biggest pot in televised poker history (3.1 million) and credited a live read to his decision, in a spot where the solver would randomize between a call and a fold.
mckn1ght 2y ago
It’s funny they mentioned Magic: the Gathering, because the first thing I thought of when reading the headline is all the conversation I see in r/EDH, and less frequently in other related subreddits, about a reluctance or even disdain for “optimal” play, which would be trying to win as efficiently as possible. To preserve the excitement and surprise of gameplay, people discourage building decks out of “staple” cards that would quickly homogenize play. “Broken” or “busted” cards that turn out to be more powerful than anticipated by the game’s designers wind up banned in sanctioned tournament play.
- ip26 2y ago
  If you’re going to play MTG as a simple game theory optimal affair, might as well switch to poker… a Nash equilibrium surely exists all the same, but charm of the game is in the variety and checks & balances.
  - rcxdude 2y ago
    Well, from the perspective of optimal play the more complex and changing nature of MTG means that optimal play is harder to find and doesn't stay still, so there's a lot more metagame of figuring out what that play is.
- rcxdude 2y ago
  EDH is especially prone to it as a format, because it's a format which allows almost every card ever printed (with a pretty... inconsistent banlist). This makes for some pretty wild variations in power levels, and the top end tends to be both very samey and very expensive. It's very popular as a format but it only really works because there's a culture of trying to match power levels between decks (to be fair, there's also the fact that 100 card singleton does tend to mean it's hard to be completely consistent, so there's a lot more 'bleedover' between power levels in terms of chance to win in a given match, as well as the >2 player aspect levelling things out as a player obviously in the lead will tend to be ganged up on)
- willcipriano 2y ago
  EDH is the variant of the game for people who aren't that competitive. They spend a lot of time trying not to win too hard. The format is fine but I don't like it beacuse of this culture around it, it basically has this social metagame where you can get better players removed out of play by complaining about them. Draft, Standard and Modern are for people who want a real game without having to worry about playing too well.
r34 2y ago
There is a huge difference between game theoretic optimal strategy and actual profitable strategy due to the human nature of the players. I imagine a professional poker player as someone who certainly knows the odds (math of the poker game in general), but is also very good in interpreting his opponents behavior (which would minimize the information revealed by them, but could they do it completely?). There are so many biases which even professional players have to overcome, that in my opinion poker is strongly psychological game. Also because of that I think that online poker and live poker are slightly different games.
thih9 2y ago
As a person who enjoys poker recreationally, whenever I visit these submissions I soon realize that this is a very different game from the one I’m playing.
PaulRobinson 2y ago
I'm a fan of the levels of Poker thinking: https://www.blackrain79.com/2020/01/outsmart-your-opponents-in-poker.html https://www.blackrain79.com/2020/01/outsmart-your-opponents-... Basically, play one level - exactly one level - beyond where you peg your opponents at. Poker is not about playing cards. It's about playing people. Cards is just how we keep it civil and not too personal.
- midiguy 2y ago
  It used to be about playing people back when no one knew anything. Now that people study GTO, it's definitely more about the cards.
kruhft 2y ago
Is that not called 'bluffing' with strategic planning?
kkwteh 2y ago
Watching Doug Polk discuss hands on YouTube really opened my eyes as to how professional pokers think about hands.
deleted 2y ago
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leesec 2y ago
Optimal in this case means least exploitable it doesn't mean most profitable, which is the real point of poker
krukah 2y ago
One thing I haven't seen anyone mention yet is that Nash equilibria do not actually exist when you move beyond heads-up into multi-way play. There's strong empirical evidence that solving abstracted games using MCCFR and using real-time depth-limited tree search dominates humans and outperforms all other AI strategies, but these results aren't theoretically sound, unlike in heads-up play. I've actually been working slowly on https://github.com/krukah/robopoker https://github.com/krukah/robopoker, an open-source Rust implementation of Pluribus, the SOTA poker AI. What I've found interesting is the difference in how I approach actually playing poker versus how I approach building a solver. Playing the game naturally consists of reasoning about narratives and incorporating information like hand history, play style, live tells. Whereas solving the game is about evaluating tradeoffs between the guarantees of imperfect-information game theory and the constraints of Texas Hold'em, finding a balance between abstract and concrete reasoning.
- waprin 2y ago
  Looks cool I’ll keep an eye on it.
mcswell 2y ago
Sort of related: I was at a restaurant last night where a sports channel was playing a poker tournament. Paint drying would have been more interesting; it was hard to tell if one of the players was even alive.
- cheeze 2y ago
  This can be said of so many things. Like a cycling race, or NASCAR, or golf, or chess. I love it. Love cycling as well.
  - Ylpertnodi 2y ago
    In each you mentioned, you can see the participants are at least awake. Love cycling too (and have no problems with the first 280km :--)
xyst 2y ago
I vaguely recall a poker tournament where a player employed this exact strategy and it worked out for him. I think it was the circumstances and just a bit of luck that allowed him to advance to later stages.
ziofill 2y ago
This is analogous to what top chess players do. They try to take the opponent into suboptimal positions hoping that they are not familiar with the best lines, even though technically the position isn’t as good as the main line.
- betepeppers 2y ago
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- nullfrigid 2y ago
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bfeist 2y ago
https://archive.is/2024.07.20-035804/https://www.scientificamerican.com/article/the-nash-equilibrium-is-the-optimal-poker-strategy-expert-players-dont-always-use-it/ https://archive.is/2024.07.20-035804/https://www.scientifica...

146 points by alexahn 2y ago

deleted 2y ago

[deleted]

Essentially all AI work I've seen in games aims for game theory optimal play, but I think it could be really interesting to consider AI for exploitative play. Does this exist? Poker with imperfect information, human pressure and fallibility means that players will inevitably stray from Nash equilibrium. The decision on how to exploit without getting exploited back oneself seems really fascinating to consider from an AI perspective. At a glance it seems to require considering how others view you..

raincole 2y ago
> Imperfect information, human pressure... inevitably stray from Nash equilibrium Human pressure yes. Imperfect information no. When we talk about Nash equilibrium for a game like poker, it's already based on imperfect information.
- mxwsn 2y ago
  Ah right, thanks
waprin 2y ago
Within solvers, you can do something called "node locking", which means you "lock" a tree in the game node to play a fixed strategy. You would typically lock it to play as you suspect your opponent plays. This lets the solver calculate the optimal exploitative solution against your specific oppoents. Piosolver, the first public solver and the one mentioned in the article, has this feature. However, what often happens is if you lock one node, then several other nodes in the game tree over-adjust in drastic ways, forcing you to lock all of the, which may be infeasiable. As a result, Piosolver recently introduced "incentives", which gives a player in the game an additional incentive to take a certain action . For example, you may suspect your opponent calls too much and doesn't raise enough, so you can just set that incentive and it will include that in its math equations and give you something similar to an exploitative solution with a much simpler UX. This feature was literally just introduced a few months ago so it's still very much an active area of research, both for game theory nerds, and people trying to use the game theory nerd research to make money !
- Buttons840 2y ago
  I want to see strong AI used in video games, especially strategy games. People often retort that strong AI is not fun; it's too challenging and that's not what players want. But once we have a strong AI we can adjust its goal function in fun ways. What you're describing is effectively the same, and it's the first time I've seen this used in a strong AI.
  - currymj 2y ago
    GT Sophy is now a permanent feature in Gran Turismo and basically does this!
    - Buttons840 2y ago
      Thanks for bringing that to my attention. I still haven't seen an AI for a turn based strategy game. There's AlphaStar, but it wins via APM, not strategy.
  - dontlikeyoueith 2y ago
    In essence, you need strong (probably unbeatable) game AI in order to make more interesting weak (beatable but challenging and fun) game AI.
asdasdsddd 2y ago
I think it'd be interesting to see if an AI with visual input playing exploitatively can out perform AI playing GTO. In doing so, we can measure the effect of visual tells.
- tialaramex 2y ago
  You mean, can the exploitative strategy take money from fish faster? Yes. But it doesn't need to care about visual tells. The point of the optimal strategy is that it's unexploitable so you can disregard the other player's actions (in the game or outside it) entirely. All exploitative strategies are in turn exploitable.
  - bubblyworld 2y ago
    I think you have to be careful with saying stuff like "optimal strategies are unexploitable", because it usually means "unexploitable in a particular game theory sense". Whether the assumptions of the Nash equilibrium (or any of the others) make sense for your situation in a game of poker is an empirical question, right? It's not a given that playing a NE means you'll be "perfect" in the human sense of the word, or that you'll get the best possible outcome. The best superhuman poker AIs at the moment do not play equilibriums either, for instance.
    - tialaramex 2y ago
      I agree that because of, for example rake or table fees for cash games or competition structure for tournament in practice a game theoretically optimal choice may not be the right choice in practical play. However the situation with an AI powered competitor which uses exploitative play is identical to a human, the GTO play will gradually take their chips at no risk. It's not that they're optimal but that they've chosen not to be optimal and so that's why they lose money against GTO. The AI is at least unemotional about this, humans with a "system" easily get tilted by GTO play and throw tantrums. How can it get there with KToff? What kind of idiot bluffs here with no clubs? Well the answer will usually be the one that's taking all your chips, be better. Humans used to seeing exploitable patterns in the play of other humans may mistake ordinary noise in the game for exploitable play in a GTO strategy and then get really angry when it's a mirage.
bubblyworld 2y ago
Yes, this exists! Look up models based on counterfactual regret minimisation - they learn to exploit regularities in their opponents play, and often stray from the GTO play when it makes sense. I believe they have beaten poker professionals in thousands-of-hands playoffs but I may be misremembering.
- phreeza 2y ago
  The obvious follow up question: are there methods in use to bait such models into suboptimal play and then switch play style to exploit that?
- codethief 2y ago
  > I believe they have beaten poker professionals in thousands-of-hands playoffs I really don't know anything about poker AIs but could it be you are referring to Libratus and/or Pluribus[0]? [0]: https://noambrown.github.io/ https://noambrown.github.io/
  - bubblyworld 2y ago
    To be honest I didn't have a specific AI in mind, more the technique, but it sounds like these ones also use a variant of CFRM.
  - energy123 2y ago
    I do not believe these attempt to exploit regularities.
- currymj 2y ago
  no, CFR is mainly just a way of computing Nash equilibria and (although in some sense it is an online, iterative algorithm) would typically be used to precompute Nash strategies, not update them in real time. real poker playing systems augment the CFR strategies with some real-time solving, but just to get even closer to Nash at the end of a hand. on top of this, you could think about augmenting these systems to exploit weaknesses in opponent strategies. there is some work on this, but I don't think it's done much. The famous systems that played against professionals don't use it, they just try to get as close to GTO as possible and wait for opponents to screw up.
genewitch 2y ago
it took me a while to track this down last month: https://codegolf.stackexchange.com/questions/tagged/king-of-the-hill https://codegolf.stackexchange.com/questions/tagged/king-of-... there's also cops and robbers and at least one other "all AI compete against eachother" with the submitter usually making the first couple of "naive" bots.
- nhggfu 2y ago
  very interesting share ^ !
ggjkvcxddd 2y ago
An AI that plays a fixed exploitative strategy will end up getting figured out relatively quickly and counter exploited pretty hard. This actually happens in real life sometimes when people attempt to deploy poker bots online. Any exploitative AI also needs the ability to adjust in real time to a different exploitative strategy, which also needs to be not easily predictable, etc.

brigadier132 2y ago

"Game theory optimal" (as poker players like to call it) is not really the optimal strategy. It's the nash equilibrium strategy assuming the other players are also playing a nash equilibrium strategy. As soon as one player deviates from the nash equilibrium it's not optimal anymore :). It's interesting how this affects play. If a player is bluffing slightly less than they should be, the adjustment is drastic, you should actually never call with hands that do not beat their value hands. If they are bluffing optimally, you are supposed to call using what is known as "minimum defense frequency". What's interesting about this is the minimum defense frequency is based on what the strongest hands you can possibly have in that situation are and the opponents possible hands do not even factor into it. It's required to prevent your opponent from bluffing with any two cards profitably. To do the math, if you are on the river and the opponent bets 100 into 100, for this to be profitable for them they need to win 50% of the time or more. If your opponent is bluffing optimally, you need to call with 50% of the strongest hands you have in that specific situation (if you don't know what hands you have in a specific situation that's a problem) and sometimes they can be dogshit like King high. But, very important to note, very few players actually bluff enough and if they bluff less than they should you should only ever call when your hand actually beats their range of possible value hands. (value vs bluff is kind of a difficult thing to communicate, generally it's value if you want your opponent to call) Most players don't bluff enough as a result of most players calling too much! When they call too much you should obviously not bluff! This leads to very boring games of poker.

waprin 2y ago
> What's interesting about this is the minimum defense frequency is based on what the strongest hands you can possibly have in that situation are and the opponents possible hands do not even factor into it. This is actually not quite true. MDF is purely a formula based on pot size and the bet size (pot size / pot size + bet size). The fact that it doesn't consider various ranges is why it's not really useful - it was a simplified formula used to try to understand the game before solvers existed. There are situations where your opponent can bet any two cards profitably and you do have to fold - imagine they bet the size of the pot, but have the better hand 99% of the time, you're simply forced to let them bluff the 1% of the time they're bluffing. MDF is a pre-solver concept and not an especially useful concept in the modern game.

waprin 2y ago

I'm working on a project aiming to help pro (or serious amateur) poker players learn game theory, mostly via flashcards with spaced-repetition. https://www.livepokertheory.com https://www.livepokertheory.com I do personally dislike that GTO became the nomenclature , as I prefer "theory-based", since it causes this confusion, but trying to fight it at this point is hopeless because GTO is the search term people are using. And when people say they "play GTO" they usually mean "equilibrium" rather than "optimal against my specific opponents" which is "exploitative". If you actually watch what the top players advocate for, everyone suggests you want to play exploitatively. However, there's one equilibrium solution and effectively infinite exploitative solutions, so equilibrium is a reasonble starting point to develop a baseline understanding of the mechanics of the game. It's tough to know how much "too much" bluffing is unless you know a baseline. Furthermore, if you "exploit" people by definition you are opening yourself up to being exploited so you need to be very careful your assumptions are true. Also, with solvers like piosolver, you can "node lock" (tell a node in the game tree to play like your opponent, rather than an equilibrium way plays), but there's many pitfalls, such as the solver adjusting in very unnatural ways on other nodes to adjust, and it being impractical to "lock" a strategy every node in the tree. There's new ideas called "incentives" which gives the solver an "incentive" to play more like a human would (e.g. calling too much) but these are new ideas still being actively explored. Rock paper scissors is frequently used to explain GTO but it's not the best example because equilibrium in rock paper scissors will break even against all opponents, but equilibrium poker strategy will actually beat most human poker players, albeit not as much as a maximally exploitative one. There's two other huge pieces this article glosses over: 1) It's as impossible for a human to play like a computer in poker as in chess - in fact far more impossible, because in poker you need to implement mixed strategies. In chess there's usually a best move, but in poker the optimal solution often involves doing something 30% of the time and something else 70% of the time. The problem is that, not only are there too many situations to memorize all the solutions, but actually implementing the correct frequencies is impossible for a human. Some players like to use "randomizers" like dice at the table, or looking at a clock, but I find that somewhat silly since it still so unlikely you are anywhere near equilbrium. 2) Reading someone's "tells" live is still a thing. While solvers have led to online poker to decline due to widespread "real time assistance", live poker is booming (the 2024 World Series of Poker Main Event just broke the record yet again) , and in person in live poker, people still give off various information about their hand via body language. From the 70s to the early 2000s, people were somewhat obsessed with "tells" as a way to win at poker. Since computers have advanced so much, it's fallen out of favor, but the truth is, both are useful. It's totally mistaken to think that advancement in poker AI , GTO , and solvers have rendered live reads obsolete. In fact, in 2023, Tom Dwan won the biggest pot in televised poker history (3.1 million) and credited a live read to his decision, in a spot where the solver would randomize between a call and a fold.

mckn1ght 2y ago

It’s funny they mentioned Magic: the Gathering, because the first thing I thought of when reading the headline is all the conversation I see in r/EDH, and less frequently in other related subreddits, about a reluctance or even disdain for “optimal” play, which would be trying to win as efficiently as possible. To preserve the excitement and surprise of gameplay, people discourage building decks out of “staple” cards that would quickly homogenize play. “Broken” or “busted” cards that turn out to be more powerful than anticipated by the game’s designers wind up banned in sanctioned tournament play.

ip26 2y ago
If you’re going to play MTG as a simple game theory optimal affair, might as well switch to poker… a Nash equilibrium surely exists all the same, but charm of the game is in the variety and checks & balances.
- rcxdude 2y ago
  Well, from the perspective of optimal play the more complex and changing nature of MTG means that optimal play is harder to find and doesn't stay still, so there's a lot more metagame of figuring out what that play is.
rcxdude 2y ago
EDH is especially prone to it as a format, because it's a format which allows almost every card ever printed (with a pretty... inconsistent banlist). This makes for some pretty wild variations in power levels, and the top end tends to be both very samey and very expensive. It's very popular as a format but it only really works because there's a culture of trying to match power levels between decks (to be fair, there's also the fact that 100 card singleton does tend to mean it's hard to be completely consistent, so there's a lot more 'bleedover' between power levels in terms of chance to win in a given match, as well as the >2 player aspect levelling things out as a player obviously in the lead will tend to be ganged up on)
willcipriano 2y ago
EDH is the variant of the game for people who aren't that competitive. They spend a lot of time trying not to win too hard. The format is fine but I don't like it beacuse of this culture around it, it basically has this social metagame where you can get better players removed out of play by complaining about them. Draft, Standard and Modern are for people who want a real game without having to worry about playing too well.

r34 2y ago

There is a huge difference between game theoretic optimal strategy and actual profitable strategy due to the human nature of the players. I imagine a professional poker player as someone who certainly knows the odds (math of the poker game in general), but is also very good in interpreting his opponents behavior (which would minimize the information revealed by them, but could they do it completely?). There are so many biases which even professional players have to overcome, that in my opinion poker is strongly psychological game. Also because of that I think that online poker and live poker are slightly different games.

thih9 2y ago

As a person who enjoys poker recreationally, whenever I visit these submissions I soon realize that this is a very different game from the one I’m playing.

PaulRobinson 2y ago

I'm a fan of the levels of Poker thinking: https://www.blackrain79.com/2020/01/outsmart-your-opponents-in-poker.html https://www.blackrain79.com/2020/01/outsmart-your-opponents-... Basically, play one level - exactly one level - beyond where you peg your opponents at. Poker is not about playing cards. It's about playing people. Cards is just how we keep it civil and not too personal.

midiguy 2y ago
It used to be about playing people back when no one knew anything. Now that people study GTO, it's definitely more about the cards.

kruhft 2y ago

Is that not called 'bluffing' with strategic planning?

kkwteh 2y ago

Watching Doug Polk discuss hands on YouTube really opened my eyes as to how professional pokers think about hands.

deleted 2y ago

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leesec 2y ago

Optimal in this case means least exploitable it doesn't mean most profitable, which is the real point of poker

krukah 2y ago

One thing I haven't seen anyone mention yet is that Nash equilibria do not actually exist when you move beyond heads-up into multi-way play. There's strong empirical evidence that solving abstracted games using MCCFR and using real-time depth-limited tree search dominates humans and outperforms all other AI strategies, but these results aren't theoretically sound, unlike in heads-up play. I've actually been working slowly on https://github.com/krukah/robopoker https://github.com/krukah/robopoker, an open-source Rust implementation of Pluribus, the SOTA poker AI. What I've found interesting is the difference in how I approach actually playing poker versus how I approach building a solver. Playing the game naturally consists of reasoning about narratives and incorporating information like hand history, play style, live tells. Whereas solving the game is about evaluating tradeoffs between the guarantees of imperfect-information game theory and the constraints of Texas Hold'em, finding a balance between abstract and concrete reasoning.

waprin 2y ago
Looks cool I’ll keep an eye on it.

mcswell 2y ago

Sort of related: I was at a restaurant last night where a sports channel was playing a poker tournament. Paint drying would have been more interesting; it was hard to tell if one of the players was even alive.

cheeze 2y ago
This can be said of so many things. Like a cycling race, or NASCAR, or golf, or chess. I love it. Love cycling as well.
- Ylpertnodi 2y ago
  In each you mentioned, you can see the participants are at least awake. Love cycling too (and have no problems with the first 280km :--)

xyst 2y ago

I vaguely recall a poker tournament where a player employed this exact strategy and it worked out for him. I think it was the circumstances and just a bit of luck that allowed him to advance to later stages.

ziofill 2y ago

This is analogous to what top chess players do. They try to take the opponent into suboptimal positions hoping that they are not familiar with the best lines, even though technically the position isn’t as good as the main line.

betepeppers 2y ago
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nullfrigid 2y ago
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bfeist 2y ago

https://archive.is/2024.07.20-035804/https://www.scientificamerican.com/article/the-nash-equilibrium-is-the-optimal-poker-strategy-expert-players-dont-always-use-it/ https://archive.is/2024.07.20-035804/https://www.scientifica...