Abstract
SummaryThis article presents a tutorial on how to use repeated game theory as a framework for algorithm development in communication networks. The article starts by introducing the basis of one‐stage games and how the outcome of such games can be predicted, through iterative elimination and Nash equilibrium. In communication networks, however, not all problems can be modeled using one‐stage games. Some problems can be better modeled through multi‐stage games, as many problems in communication networks consist of several iterations or decisions that need to be made over time. Of all the multi‐stage games, the infinite‐horizon repeated games were chosen to be the focus in this tutorial, because optimal equilibrium settings can be achieved, contrarily to the suboptimal equilibria achieved in other types of game. With the theoretical concepts introduced, it is then shown how the developed game theoretical model, and devised equilibrium, can be used as a basis for the behavior of an algorithm, which is supposed to solve a particular problem and will be running at specific network devices. Copyright © 2015 John Wiley & Sons, Ltd.
Highlights
G AME theory is a mathematical tool that aims to study and predict the outcome of situations where two or more agents have conflicting interests [1]
The field of game theory has its roots in decision theory and, it can be thought as a generalization of decision theory for multiple agents [1]
The tutorial starts by introducing the basis of one-stage games and, with such knowledge, continues onto dynamic and repeated games. It is shown how optimal Nash equilibrium strategies can be obtained with infinite-horizon repeated games, while the equivalent one-stage version have suboptimal Nash equilibria and how that can be used as a support for the development of an algorithm to be run at devices in the network
Summary
G AME theory is a mathematical tool that aims to study and predict the outcome of situations where two or more agents have conflicting interests [1]. The tutorial starts by introducing the basis of one-stage games and, with such knowledge, continues onto dynamic and repeated games It is shown how optimal Nash equilibrium strategies can be obtained with infinite-horizon repeated games, while the equivalent one-stage version have suboptimal Nash equilibria and how that can be used as a support for the development of an algorithm to be run at devices in the network. This tutorial exemplifies, with a simplified model taken from [12], the use of game theory to model a problem, devise an equilibrium strategy and develop an algorithm that mimics such equilibrium strategy.
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