Global warming is currently a major challenge faced by the world. Reduction of carbon emissions is of paramount importance in the context of global warming. There are widespread ongoing efforts to find satisfactory ways of surmounting this challenge. The basic objective of all such efforts can be summarized as conception and formation of protocols to reduce the pace of global carbon levels. Countries and global companies are now engaged in understanding systematic ways of achieving well defined emission targets. In this dissertation, we explore the specific problem faced by a global industry or global company in allocating carbon emission reduction units to its different divisions and supply chain partners in achieving a required target of reductions in its carbon reduction program. The problem becomes a challenging one since the divisions and supply chain partners are often autonomous and could exhibit strategic behavior. Game theory and mechanism design provide a natural modeling tool for capturing the strategic dynamics involved in this problem. DSIC (Dominant Strategy Incentive Compatibility), AE (Allocative Efficiency), and SBB (Strict Budget Balance) are the key desirable properties for carbon reduction allocation mechanisms. But due to an impossibility result in mechanism design, DSIC, AE, and SBB can never be simultaneously achieved. Hence in this dissertation, we offer as contributions, two elegant solutions to this carbon emission reduction allocation problem. The first contribution is a mechanism which is DSIC and AE. We first propose a straightforward Vickrey-Clarke-Groves (VCG) mechanism based solution to the problem, leading to a DSIC and AE reverse auction protocol for allocating carbon reductions among the divisions. This solution, however, leads to a high level of budget imbalance. To reduce budget imbalance, we use redistribution mechanisms, without affecting the key properties of DSIC and AE. The Cavallo-Bailey redistribution mechanism, when applied to the above reverse auction protocol leads to reduced budget imbalance. To reduce the imbalance further, we propose an innovative forward auction protocol which achieves less imbalance when combined with the Cavallo-Bailey redistribution mechanism. The forward auction protocol also has the appealing feature of handsomely rewarding divisions that reduce emissions and levying appropriate penalties on divisions that do not participate in emission reductions. The second contribution is a DSIC and SBB mechanism. Even though the first mechanism tries to reduce the budget imbalance, there is always a surplus which cannot be distributed among divisions and is wasted. So, in this part, by slightly compromising on efficiency, we propose a mechanism which is DSIC and SBB. The SBB property guarantees that there is no need for any monetary support from an external agency for implementing the mechanism and there is no leakage of revenue.

本文考慮當資訊不對稱下的逆向選擇問題存在時,如何遵循Myerson(1979)提出的揭露原則 (the revelation principle)來設計一套具備誘因相容性 (incentive compatibility)的存款保險契約,契約中的自有資本比率為要保機構的自我選擇變數 (self-selection variable),而保險費則為存保公司用來控制要保機構決策行為的控制變數(control var iable),依此可以建立一套自我選擇機能(self-selection mechanism),來促使要保機構誠實揭其風險類型的私有訊息(private information),進而將要保機構依風險高低正確分類,徹底解決訊息不對稱下的逆向選擇問題。

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.