ABSTRACT
This chapter is concerned with the concept of constrained equilibrium and satisfactory solution in constrained-like games. It focuses on how to model and solve constrained games. The chapter aims to distinguish two type of constraints: Orthogonal Constraints and Coupled Constraints. It proposes learning approach in quality of service and quality of experience problems. When one imposes constraints over the payoffs that each player obtains or over the action that a player can choose in the game, the Nash equilibrium is replaced by a constrained (Nash) equilibrium. The chapter discusses finite matrix games with random entries which we call random matrix games. There are many interesting applications of zero-sum random matrix games in both wired and wireless networks. The chapter also discusses the relationship between learning outcomes, equilibria and global optima. It considers the mean-variance response and demand satisfaction to finitely many players.
