I am trying to more clearly understand the objective of game theory.
I started off by reading papers in economics, where the main focus seems to be on finding equilibria under various behaviorial assumptions on the players (termed solution concepts, one of them being Nash equilibrium, for example). In this case, it seems a large part of the behavior is already embedded into the model and the question is how the agents reach an outcome, what is the quality of that outcome, etc. In my view, this seems descriptive, in the sense that the theory is describing what would happen if players played a certain way.
On the other hand, many of the papers I read from computer science tend to treat game theory as a more prescriptive theory. That is, how players should play given a particular objective. The most prominent example I can think of is the success of Google Deepmind's AlphaGo algorithm in which the algorithm specified high quality actions (moves) for the player to take in order to win the game. No analysis regarding the equilibrium of this (in game theory terminology, dynamic perfect information) game was ever investigated, or even desired.
Can anyone shed some light on why the two fields seemingly have such different objectives?