Risk-sensitive learners in network selection games

Manzoor Ahmed Khan, Hamidou Tembine

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider a network with finite number of users where each user observes only a numerical value of its measurement. The system is interactive in the sense that each user's payoff is affected by the environment state and the choices of all the other users. This scenario can be modeled as dynamic robust game. We examine how risk-sensitive learners influence the convergence time of such a game in a specific network selection problem. Based on imitative combined fully distributed payoff and strategy learning (CODIPAS), we provide a simple class of network selection games in which a convergence to global optimum can be obtained with a very fast convergence rate. We show that the risk-sensitive index can be used to improve the convergence time in a wide range of parameters.

Original languageEnglish
Title of host publication2012 International Conference on Wireless Communications and Signal Processing, WCSP 2012
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2012 International Conference on Wireless Communications and Signal Processing, WCSP 2012 - Huangshan, China
Duration: Oct 25 2012Oct 27 2012

Publication series

Name2012 International Conference on Wireless Communications and Signal Processing, WCSP 2012

Conference

Conference2012 International Conference on Wireless Communications and Signal Processing, WCSP 2012
Country/TerritoryChina
CityHuangshan
Period10/25/1210/27/12

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing

Fingerprint

Dive into the research topics of 'Risk-sensitive learners in network selection games'. Together they form a unique fingerprint.

Cite this