טכניון מכון טכנולוגי לישראל
הטכניון מכון טכנולוגי לישראל - בית הספר ללימודי מוסמכים  
Ph.D Thesis
Ph.D StudentBlocq Gideon
SubjectCooperative Game Theoretic Models for Network
Interconnections
DepartmentDepartment of Electrical Engineering
Supervisor Professor Ariel Orda
Full Thesis textFull thesis text - English Version


Abstract

In the context of networking, research has focused on non-cooperative games, where the selfish agents cannot reach a binding agreement on the way they would share the infrastructure. Many approaches have been proposed for mitigating the typically inefficient operating points. However, in a growing number of networking scenarios selfish agents are able to communicate and reach an agreement. Hence, the degradation of performance should be considered at an operating point of a cooperative game.

Accordingly, this research aims to lay foundations for the application of cooperative game theory to fundamental problems in networking, and routing games in particular. We focus on the Nash Bargaining Scheme as an operating point and introduce the Price of Selfishness (PoS), which considers the degradation of system performance at the worst NBS. We then investigate several other solution concepts from cooperative game theory that allow for the creation of subcoalitions, such as the core and a variation of the nucleolus.

Moreover, in our research we propose an extension to the traditional setting of routing games by modeling agents to consider a combination of their own performance as well as that of their rivals. Per agent, we parameterized this trade-off, thereby allowing agents to be partially selfish and partially malicious. In this new setting we investigate solution concepts from both non-cooperative and cooperative game theory.

Finally, we deviate from the setting of routing games and focus on an influence maximization setting with budget allocation. Consequently, we introduce a game in which multiple agents place their budget in a graph so as to maximize their own influence over the nodes.

For the above scenarios we derive conclusions with regards to the behavior of the agents and the performance of the system.