Global warming is currently a major challenge facing the world. There are widespread ongoing efforts in the form of summits, conferences, etc., 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. Game theory and mechanism design provide a natural modeling tool for capturing the strategic dynamics involved in global warming related problems. This dissertation explores for the first time the use of voting mechanisms in the context of solving the central problems, namely, allocation of emission caps and reduction quotas to strategic emitting agents (countries). The contribution of this dissertation is two-fold. The first contribution is to develop an elegant game theoretic model that accurately captures the strategic interactions among different emitting agents in a global warming setting. This model facilitates a convenient way of exploring a mechanism design approach for solving important allocation problems in the global warming context. The second contribution is to propose and explore a novel approach, based on voting mechanisms, to solve two problems: (1) allocating emission caps and (2) allocating reduction quotas to strategic agents. Our work investigates the use of voting mechanisms that satisfy four desirable properties: (1) non-dictatorship, (2) strategy-proofness, (3) efficiency, and (4) anonymity. In particular, we explore the median selection, maximum order statistic selection, and general Kth order statistic selection voting mechanisms. Our results clearly show that only trivial allocations satisfy all the above properties simultaneously. We next investigate the use of voting mechanisms for the dual problem, namely, allocation of emission reductions to emitting agents. Here, we show that non-trivial allocations are possible, however an important property, individual rationality, might be compromised. The investigations in the thesis bring out certain limitations in applying voting mechanisms that satisfy all the four properties above. Nevertheless, the insights obtained provide valuable guidelines for solving emission allocation related problems in a principled and informed way.

<p>Problem:</p><p>The problem under consideration is the World Trade Organization’s (WTO) agreement called Trade-Related Aspects of Intellectual Property Rights (TRIPS) and its impact on equal access to essential drugs in the least developed countries. Especially the countries of sub-Saharan Africa lack such access. Moreover, these countries are the ones where severe diseases like AIDS/HIV, tuberculosis and malaria are widely spread over the population. The authors focus also on patents and their obligatory length imposed through the articles of TRIPS agreement.</p><p>Purpose:</p><p>The purpose of the thesis is to describe and analyse the impact of global trade regulations (TRIPS in particular) on the accessibility of essential drugs in developing countries, and to come up with a possible solution as the way of coping with the problem is concerned. The investigation includes detailed description of solutions accomplished by Brazil and India, and their importance for the least developed countries, in terms of importing generic pharmaceuticals from these.</p><p>Method:</p><p>Qualitative method was used in order to obtain data from interviews with citizens of Botswana, Ghana, Ethiopia and South Africa for better understanding of the situation in these countries. Furthermore, the theories included in the theoretical background of this paper were gathered through deep research in the field of studies regarding Intellectual Property protection and World Trade Organization’s agreements and other legal acts.</p><p>Results:</p><p>The result of the analysis is a model developed from the Game-Theoretic Model, and called Developed Game-Theoretic Model. It is a tool which the least developed countries can use while negotiating prices of medicines with pharmaceutical companies, having the possibility of importing the pharmaceuticals from other countries manufacturing the patented product under compulsory licensing.</p>

Problem: The problem under consideration is the World Trade Organization’s (WTO) agreement called Trade-Related Aspects of Intellectual Property Rights (TRIPS) and its impact on equal access to essential drugs in the least developed countries. Especially the countries of sub-Saharan Africa lack such access. Moreover, these countries are the ones where severe diseases like AIDS/HIV, tuberculosis and malaria are widely spread over the population. The authors focus also on patents and their obligatory length imposed through the articles of TRIPS agreement. Purpose: The purpose of the thesis is to describe and analyse the impact of global trade regulations (TRIPS in particular) on the accessibility of essential drugs in developing countries, and to come up with a possible solution as the way of coping with the problem is concerned. The investigation includes detailed description of solutions accomplished by Brazil and India, and their importance for the least developed countries, in terms of importing generic pharmaceuticals from these. Method: Qualitative method was used in order to obtain data from interviews with citizens of Botswana, Ghana, Ethiopia and South Africa for better understanding of the situation in these countries. Furthermore, the theories included in the theoretical background of this paper were gathered through deep research in the field of studies regarding Intellectual Property protection and World Trade Organization’s agreements and other legal acts. Results: The result of the analysis is a model developed from the Game-Theoretic Model, and called Developed Game-Theoretic Model. It is a tool which the least developed countries can use while negotiating prices of medicines with pharmaceutical companies, having the possibility of importing the pharmaceuticals from other countries manufacturing the patented product under compulsory licensing.

We propose a novel analytic approach for the comparative statics analysis of operations management problems on the capacity investment decision and the influenza (flu) vaccine composition decision. Our approach involves exploiting the properties of the underlying mathematical models, and linking those properties to the concept of stochastic orders relationship. The use of stochastic orders allows us to establish our main results without restriction to a specific distribution. A major strength of our approach is that it is "scalable," i.e., it applies to capacity investment decision problem with any number of non-independent (i.e., demand or resource sharing) products and resources, and to the influenza vaccine composition problem with any number of candidate strains, without a corresponding increase in computational effort. This is unlike the current approaches commonly used in the operations management literature, which typically involve a parametric analysis followed by the use of the implicit function theorem. Providing a rigorous framework for comparative statics analysis, which can be applied to other problems that are not amenable to traditional parametric analysis, is our main contribution. We demonstrate this approach on two problems: (1) Capacity investment decision, and (2) influenza vaccine composition decision. A comparative statics analysis is integral to the study of these problems, as it allows answers to important questions such as, "does the firm acquire more or less of the different resources available as demand uncertainty increases? does the firm benefit from an increase in demand uncertainty? how does the vaccine composition change as the yield uncertainty increases?" Using our proposed approach, we establish comparative statics results on how the newsvendor's expected profit and optimal capacity decision change with demand risk and demand dependence in multi-product multi-resource newsvendor networks; and how the societal vaccination benefit, the manufacturer's profit, and the vaccine output change with the risk of random yield of strains. / Ph. D.

This paper introduces a novel methodology that integrates Machine Learning (ML), Operations Research (OR), and Game Theory (GT) to develop an interpretable heuristic for principal-agent models (PAM). We extract solution patterns from ensemble tree models trained on solved instances of a PAM. Using these patterns, we develop a hierarchical tree-based approach that forms an interpretable ML-based heuristic to solve the PAM. This method ensures the interpretability, feasibility, and generalizability of ML predictions for game-theoretic models. The predicted solutions from this ensemble model-based heuristic are consistently high quality and feasible, significantly reducing computational time compared to traditional optimization methods to solve PAM. Specifically, the computational results demonstrate the generalizability of the ensemble heuristic in varying problem sizes, achieving high prediction accuracy with optimality gaps between 1--2% and significant improvements in solution times. Our ensemble model-based heuristic, on average, requires only 4.5 out of the 9 input features to explain its predictions effectively for a particular application. Therefore, our ensemble heuristic enhances the interpretability of game-theoretic optimization solutions, simplifying explanations and making them accessible to those without expertise in ML or OR. Our methodology adds to the approaches for interpreting ML predictions while also improving numerical tractability of PAMs. Consequently, enhancing policy design and operational decisions, and advancing real-time decision support where understanding and justifying decisions is crucial. / Master of Science / This paper introduces a new method that combines Machine Learning (ML) with Operations Research (OR) to create a clear and understandable approach for solving a principal-agent model (PAM). We use patterns from a group of decision trees to form an ML-based strategy to predict solutions that greatly reduces the time to solve the problem compared to traditional optimization techniques. Our approach works well for different sizes of problems, maintaining high accuracy with very small differences in objective function value from the best possible solutions (1-2%). The solutions predicted are consistently high quality and practical, significantly reducing the time needed compared to traditional optimization methods. Remarkably, our heuristic typically uses only 4.5 out of 9 input features to explain its predictions, making it much simpler and more interpretable than other methods. The results show that our method is both efficient and effective, with faster solution times and better accuracy. Our method can make complex game-theoretic optimization solutions more understandable, even for those without expertise in ML or OR. By improving the interpretability making PAMs analytically explainable, our approach supports better policy design and operational decision-making, advancing real-time decision support where clarity and justification of decisions are essential.

With increasing demand for social network based activities, it is very important to understand not only the structural properties of social networks but also how social networks form, to better exploit their promise and potential. We believe the existing methods and tools for social network analysis have a major inadequacy: they do not capture the behavior (such as rationality and intelligence) of individuals nor do they model the strategic interactions that occur among these individuals. Game theory is a natural tool to overcome this inadequacy since it provides rigorous mathematical models of strategic interaction among autonomous, intelligent, and rational agents. This thesis brings out how a game theoretic approach helps analyze social networks better. In particular, we study three contemporary and pertinent problems in social networks using a game theoretic approach: determining influential individuals for viral marketing, community detection, and social network formation. The first problem deals with determining influential nodes in social networks for diffusion of information. We present an efficient heuristic algorithm (SPIN) to this problem based on cooperative game theoretic techniques. The running time of SPIN is independent of the number of influential nodes to be determined. Moreover, unlike the popular benchmark algorithms, the proposed method works well with both submodular and non-submodular objective functions for diffusion of information. In the second problem, we design a novel game theoretic approach to partition a given undirected, unweighted graph into dense subgraphs (or communities). The approach is based on determining a Nash stable partition which is a pure strategy Nash equilibrium of an appropriately defined strategic form game. In the proposed graph partitioning game, the nodes of the graph are the players and the strategy of a node is to decide to which community it ought to belong. The utility of each node is defined to depend entirely on the node’s local neighborhood. A Nash stable partition (NSP) of this game is a partition consisting of communities such that no node has incentive to defect from its community to any other community. Given any graph, we prove that an NSP always exists and we also derive a lower bound on the fraction of intra-community edges in any NSP. Our approach leads to an efficient heuristic algorithm to detect communities in social networks with the additional feature of automatically determining the number of communities. The focus of the third problem is to understand the patterns behind the evolution of social networks that helps in predicting the likely topologies of social networks. The topology of social networks plays a crucial role in determining the outcomes in several social and economic situations such as trading networks, recommendation networks. We approach the problem of topology prediction in networks by defining a game theoretic model, which we call value function -allocation rule model, that considers four determinants of network formation. This model uses techniques from both cooperative game theory and non-cooperative game theory. We characterize the topologies of networks that are in equilibrium and/or socially efficient. Finally, we study the tradeoffs between equilibrium networks and efficient networks.