Incorrect. Game theory predicts pollution. You just need to make sure the game has the correct predicted payoffs for each move, and the set of moves you want to measure, and their interactions...

You'll find Game Theory a lot easier to understand if you mentally replace every instance of "Math" with "AI". (So instead of saying "The Nash Equilibirum is...", just say "I trained an RNN and this is how it would play"). That's really all it is. Usually the games are designed in a simple way because the "AI" that is being run is nothing more complicated than min-cut/max-flow.

"Pollution" specifically is a case of the "Free-Rider Problem", and there are plenty of game theoretic results that explain it.

Wait, you're invoking RNNs as a simpler model than game theory?

It's not simpler, but it is bound to go over better on Hackernews.

Exactly. People here sort of already know RNNs. You could use RNNs in your game theory game (see AlphaGo, etc), but Game Theory is just a set of principals for defining a "Game", and defining incentives, etc.

If the game doesn't properly account for some incentive, it's the problem of the model designer, just like it would be the problem of the RNN designer not gathering the right type of training data.