When thinking about complex domains, having a simplistic model is helpful for a variety of reasons, one of them is to establish a shared vocabulary to communicate with others, the other is to study where/why/how the simplistic model fails to deal with the complex domain. This gives an indication for how to go on about dealing with the complex domain, namely by selectively making the simplistic model more complicated until it is sufficiently rich.
One example of the power of simplicity is the habit of theoretical computer scientists to use simplistic devices such as Turing machines as models of computing and to conflate "effective computability" with the complexity class P and polynomial reducibility (in LOGSPACE). We know that this
'looses applicability at the first contact with reality', but it's currently our only approach to have a meaningful theory of computational complexity and has lead to many deep insights already such as interactive proof systems and zero-knowledge proofs.
Hahahahaha reminds me of a saying about spherical cows.
If Game Theory was being used as an "explanation" to show why deep learning outperforms other methods then maybe it would have been fine. But then we already have Statistical Learning Theory and PAC learning which try to explain/prove bounds on Machine Learning models.
There is nothing wrong with having a theory but these days Deep Learning has become a bandwagon that any/everything gets related to. And I just don't see how Game Theory of all mathematical tools is applicable to Deep Learning.
Finally the misguided theory driven approach was exactly the reason why Deep Learning was so controversial initially. Turns out making a theory like SLT and deriving ML models like SVMs is a really bad idea. Deep Learning succeeded because the ML and Vision community adopted empiricism over the fanciest/longest proof with covex loss. So when someone goes around claiming Game Theory as a savior/future of Deep Learning I find it perplexing.
One example of the power of simplicity is the habit of theoretical computer scientists to use simplistic devices such as Turing machines as models of computing and to conflate "effective computability" with the complexity class P and polynomial reducibility (in LOGSPACE). We know that this 'looses applicability at the first contact with reality', but it's currently our only approach to have a meaningful theory of computational complexity and has lead to many deep insights already such as interactive proof systems and zero-knowledge proofs.