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I’ve been contemplating this situation. I think that what the author wants out of a Jev-like model is not at all what I want out of it. > I decided to check this on questions where the answer is well understood. For example: > A classical particle of mass m is embedded in a system at thermodynamic equilibrium with temperature T. What is its velocity v? If I feed that into a model, the answer I want is: “the combination of the model and the provided state has nothing useful to add to your prior”. If I want to know the Maxwell-Boltzmann distribution, I can look it up or I can derive it or I can ask a fancy LLM to do it for me (at the cost of some reasoning tokens and some time - unless I’m using an ultraspeed inference system, I’m not getting this answer in 50ms). [0] Similarly, if I want to know that 73% of incoming customer support requests are spam/fraud, I should measure that - it’s a property of my system, it takes some manual classification and a database query, and it will be a different percentage than your customer support system would see. I neither expect nor want my classifier to know this (unless I’m using a conventional classifier manually trained on my data, and the whole point of Jev is to avoid this). What I want out of a system like Jev is to tell me how the probabilities change as a result of the per-sample data I provide. Which, is the case of this Boltzmann distribution question, is nothing: I provided no data and the classifier can infer nothing. [0] A really good answer would observe that the answer depends on the dimension of the system (probably 3, but 2D systems are a thing) and also on whether the particles are hot enough for relativistic effects to matter (probably not). And maybe a good answer would check whether the material is a gas - the answer for a solid is not the same, but I suppose that’s not classical . Oh, and one shouldn’t forget drift: if you have a classical particle in a moving fluid or a classical charged particle in an electric field, you will again get a different answer. Yes, I’m being pedantic. But if you want good answers you should be pedantic, and the Jev-like model is not where the pedantry should go.
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There's only so many card interactions that strong players actually think about. Ex: you don't really care if the opponent plays Giant Growth or Chastise. The effect is that the opponent is playing a combat trick, and combat has moved from attackers favor into defenders favor. To defeat an instant speed combat trick requires a combat trick of your own, or a generic counter spell of some kind. Some have interactions (ex: Doom Blade beats Giant Growth but not Chastise), but the overall gist is that opponents can do things after combat is declared. You only need to keep track of how many combat tricks you think the opponent has. --------- Other situations are card advantage (ex: 2 for 1. If the opponent spends 1 cards to defeat only 2 cards of yours). The traditional card for this is Mindrot, but well placed counterspell can turn a combat trick into. 2-for-1 reversal. You don't necessarily keep track of how your opponent makes 2-for-1 opportunities. You just have vague gists of them. --------- Good spells have huge applicability. Doom blade or Murder is high because killing opponent creatures at instant speed handles the vast majority of creature buffed combat tricks, and also serves as a way to stop enemy combos and other such tricks. In contrast, chastise is very niche. If the opponent were playing like Swords to Plowshares (powerful white instant speed removal), it's pretty much always better than chastise. If the opponent plays chastise instead, you take that as a win because you know they could have had a deck of better cards. But for whatever reason decided to play with weaker cards...
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I agree in a way, but at the same time, and I think it's a bit more applicable to MtG due to the limit of cards you can have as possible plays at any given time (outside of combos), and I believe too that you can train a bot to be good, better than average - I doubt arena doesn't have bots - but I still think that without unbound compute/time it's a game where human players have much better odds to outsmart an AI if they're good players. MtG has for the past 10 or more years been re-hashing the same play patterns, while introducing some new mechanics on most cycles, but pretty much you have staples throughout most editions that are just variations on that - card advantage, denial, combat tricks, removal, curve and then the rarity enabled bombs/combos But even then (not saying I'm right) I think the depth of choices, effects and so on, on a format like modern, or legacy, would be very difficult for an AI to top against pros. If you add draft into the mix it gets worse for the AI in my view too. Because a good play in most situations can easily be a bad play under others. That doesn't happen in chess for instance, given enough decision depth to the algos to see the future game. In my own game I think those situations can occur much easier due to you always having your full deck available. Also, in MtG it's easy to get into table states that are either ahead/behind and then you kinda just have to protect your position (like with denial decks). Then you have the effects that you might remove a creature threat (graveyard) but then that enabling a combo you weren't expecting that needs a creature on the grave, or enabling delve cards or whatever have you. It's much less clear cut for a probabilistic model to make the optimal play at every single interaction. So the more you train the model on all the variations and possible follow ups, the more you dilute its certainty isn't it? In chess, or this game, or RTS such as starcraft, that doesn't really happen in my view.
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