Game theory and mechanism design have emerged as an important tool to model, analyze,and solve decentralized design problems involving multiple agents that interact strategically in a rational and intelligent way. Some examples of these design problems include: auctions and markets in electronic commerce; network economics; dynamic pricing; routing protocols in wireless networks; resource allocation in computational grids; algorithms for selfish agents;etc. The motivation for this doctoral work springs from the high level of current interest indesigning innovative mechanisms for solving emerging game theoretic problems in the area of electronic commerce. In this thesis, we focus on three such problems and advance the current art in mechanism design while developing new, innovative mechanisms to solve the problems. The first problem we explore is the highly strategic problem of forming a high value E-business supply chain by choosing the best mix of supply chain partners. In our research, we formulate the supply chain formation problem as a mechanism design problem in a generic wayand show that the well known VCG (Vickrey-Clarke-Groves) mechanisms provide an apt frame-work for solving this problem. We provide a compelling example of a three stage automotive distribution network to illustrate the power and e±cacy of the proposed methodology. The second problem we model and solve is that of designing a revenue maximizing sponsored search auction. This is a problem that is faced by every Internet search engine, such as Google,MSN, and Yahoo!, whenever it receives a search query. In our research, we take a comprehensivelook at existing auction mechanisms for this problem. Our work leads to an innovative new auction mechanism, which we call OPT (optimal mechanism), that exhibits a superior level ofperformance. The proposed mechanism extends, in a non-trivial way, the well known Myersonoptimal auction to the specific setting of sponsored search auctions. The proposed mechanism maximizes the revenue to the search engine and also satisfies two crucial properties, Bayesian incentive compatibility and interim individual rationality. The third contribution is in respect of Stackelberg problems, which are game theoretic problems that involve hierarchical and sequential decision making. By focusing attention on an important subclass of these problems, namely the Single-Leader-Rest-Followers (SLRF) problems, we extend all relevant aspects of classical mechanism design theory to the case of SLRF problems. We derive many important results in respect of procurement auctions with reserve prices using the developed theory. The research carried out as part of this doctoral work, we believe, advances the current art in mechanism design while developing innovative mechanisms to solve those problems.

A computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities. In the presence of grid users who are autonomous, rational, and intelligent, there is an overall degradation of the total efficiency of the computational grid in comparison to what can be achieved when the participating users are centrally coordinated . This loss in efficiency might arise due to an unwillingness on the part of some of the grid resource providers to either not perform completely or not perform to the fullest capability, the computational jobs of other users in the grid. In this thesis, our attention is focused on designing grid resource procurement mechanisms which a grid user can use for procuring resources in a computational grid based on bids submitted by autonomous, rational, and intelligent resource providers. Specifically, we follow a game theoretic and mechanism design approach to design three elegant, different incentive compatible procurement mechanisms for this purpose: G-DSIC (Grid-Dominant Strategy Incentive Compatible) mechanism which guarantees that truthful bidding is a best response for each resource provider, irrespective of what the other resource providers bid G-BIC (Grid-Bayesian Nash Incentive Compatible) mechanism which only guarantees that truthful bidding is a best response for each resource provider whenever all other resource providers also bid truthfully G-OPT (Grid-Optimal) mechanism which minimizes the cost to the grid user, satisfying at the same time, (1) Bayesian Incentive Compatibility (which guarantees that truthful bidding is a best response for each resource provider whenever all other resource providers also bid truthfully) and (2) Individual Rationality (which guarantees that the resource providers have non-negative payoffs if they participate in the bidding process). We evaluate the relative merits and demerits of the above three mechanisms using game theoretical analysis and numerical experiments. The mechanisms developed in this thesis are in the context of parameter sweep type of jobs, which consist of multiple homogeneous and independent tasks. We believe the use of the mechanisms proposed transcends beyond parameter sweep type of jobs and in general, the proposed mechanisms could be extended to provide a robust way of procuring resources in a computational grid where the resource providers exhibit rational and strategic behavior.