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Crandall, J.W. and Goodrich, M.A. (2005) Learning to Compete, Compromise, and Cooperate in Repeated General-Sum Games. Proceedings of the 22nd International Conference on Machine Learning, New York, August 2005, 161-168.
https://doi.org/10.1145/1102351.1102372

has been cited by the following article:

• TITLE: Statistical Learning in Game Theory

  AUTHORS: Luyuan Shi

  KEYWORDS: General-Sum Games, Nash Equilibrium, Minimax Theorem, Statistical Learning

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.11 No.3, March 14, 2023

  ABSTRACT: In economics, buyers and sellers are usually the main sides in a market. Game theory can perfectly model decisions behind each “player” and calculate an outcome that benefits both sides. However, the use of game theory is not lim-ited to economics. In this paper, I will introduce the mathematical model of general sum game, solutions and theorems surrounding game theory, and its real life applications in many different scenarios.