The articles claims (based on the paper I linked, and others) that:
What we see in these 3 players are 3 different ways game theory plays in Deep Learning. (1) As a means of describing and analyzing new DL architectures. (2) As a way to construct a learning strategy and (3) A way to predict behavior of human participants. The last application can make your skin crawl!
Of these claims, I think all are wrong.
One can certainly make the case that it is interesting to measure the performance of a system compared to the Nash equilibrium (when possible), but the author seems to think that the designers of the system are somehow using game theory to design the system.
They are in one of the cases pointed out, not the other two. The novel idea is to have two networks compete against each other in a two-player minimax game, developing progressively better strategies until something like Nash equilibrium is reached--at which point you have one well-trained classifying network and one well-trained generating network.
The articles claims (based on the paper I linked, and others) that:
What we see in these 3 players are 3 different ways game theory plays in Deep Learning. (1) As a means of describing and analyzing new DL architectures. (2) As a way to construct a learning strategy and (3) A way to predict behavior of human participants. The last application can make your skin crawl!
Of these claims, I think all are wrong.
One can certainly make the case that it is interesting to measure the performance of a system compared to the Nash equilibrium (when possible), but the author seems to think that the designers of the system are somehow using game theory to design the system.