A Game Theoretic Analysis of Agent -Mediated Resource Allocation
Maheswaran, Rajiv Tharmeswaran
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/80823
Description
Title
A Game Theoretic Analysis of Agent -Mediated Resource Allocation
Author(s)
Maheswaran, Rajiv Tharmeswaran
Issue Date
2003
Doctoral Committee Chair(s)
Basar, Tamer
Department of Study
Electrical Engineering
Discipline
Electrical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Economics, Theory
Language
eng
Abstract
Developments in information technology have necessitated dynamic distributed real-time allocation of computational and network resources. We consider the use of market mechanisms to regulate a set of autonomous agents that are responsible for obtaining services. By applying game-theoretic analysis to a proportionally fair divisible auction, we show the existence of a unique Nash equilibrium in both single and multiple resource settings. Locally stable decentralized negotiation algorithms are developed for both cases. We also investigate the effects of coalition formation and show that the standard assumptions from classical cooperative game theory for determining the value of a team do not apply. Finally, we examine a larger space of mechanisms and optimize with respect to revenue generation and social welfare. This leads to the design of transparent and maximally efficient resource allocation schemes which have the minimum costs for signaling and computation.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.