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Weighted voting games are classic cooperative games which provide compact representation for coalition formation models in human societies and multiagent systems. As useful as weighted voting games are in modeling cooperation among players, they are, however, not immune from the vulnerability of manipulations (i.e., dishonest behaviors) by strategic players that may be present in the games. With the possibility of manipulations, it becomes difficult to establish or maintain trust, and, more importantly, it becomes difficult to assure fairness in such games. For these reasons, we conduct careful experimental investigations and analyses of the effects of manipulations in weighted voting games, including those of manipulation by splitting, merging, and annexation . These manipulations involve an agent or some agents misrepresenting their identities in anticipation of gaining more power or obtaining a higher portion of a coalition's profits at the expense of other agents in a game. We consider investigation of some criteria for the evaluation of game's robustness to manipulation. These criteria have been defined on the basis of theoretical and experimental analysis. For manipulation by splitting, we provide empirical evidence to show that the three prominent indices for measuring agents' power, Shapley-Shubik, Banzhaf, and Deegan-Packel, are all susceptible to manipulation when an agent splits into several false identities. We extend a previous result on manipulation by splitting in exact unanimity weighted voting games to the Deegan-Packel index, and present new results for excess unanimity weighted voting games. We partially resolve an important open problem concerning the bounds on the extent of power that a manipulator may gain when it splits into several false identities in non-unanimity weighted voting games. Specifically, we provide the first three non-trivial bounds for this problem using the Shapley-Shubik and Banzhaf indices. One of the bounds is also shown to be asymptotically tight. Furthermore, experiments on non-unanimity weighted voting games show that the three indices are highly susceptible to manipulation via annexation while they are less susceptible to manipulation via merging. Given that the problems of calculating the Shapley-Shubik and Banzhaf indices for weighted voting games are NP-complete, we show that, when the manipulators' coalitions sizes are restricted to a small constant, manipulators need to do only a polynomial amount of work to find a much improved power gain for both merging and annexation, and then present two enumeration-based pseudo-polynomial algorithms that manipulators can use. Finally, we argue and provide empirical evidence to show that despite finding the optimal beneficial merge is an NP-hard problem for both the Shapley-Shubik and Banzhaf indices, finding beneficial merge is relatively easy in practice. Also, while it appears that we may be powerless to stop manipulation by merging for a given game, we suggest a measure, termed quota ratio, that the game designer may be able to control. Thus, we deduce that a high quota ratio decreases the number of beneficial merges.

This dissertation consists of three essays on information aggregation in committees and its application. The first essay analyzes how the distribution of votes affects the accuracy of group decisions. In a weighted voting system, votes are typically assigned based on the criteria that are unrelated to the voters’ ability to make a correct judgment. I introduce an information aggregation model in which voters are identical except for voting shares. If the information is free, the optimal weight distribution is equal weighting. When acquiring information is costly, by contrast, I show that the accuracy of group decisions may be higher under some weighted majority rules than under unweighted majority rule. I characterize the equilibrium and find the optimal weight distribution to maximize the accuracy of group decisions. Asymmetric weight distributions may be optimal when the cost of improving signal is moderately high. The second essay analyzes how intergenerational family transfers can be sustained. Why are generous transfers from the younger to the older generations made in some families and not in others? My paper argues that differences in intergenerational dependence are due to variation in community networks. My analysis of the sustainability of intergenerational transfers posits game theoretical models of overlapping generations in which breadwinners make transfers to their parents and children. A novel feature of my models is that there is a local community that may supply information about its members past behaviors. I demonstrate that an efficient level of intergenerational transfers can be sustained if neighbors gossip about each other. The third essay, co-authored with Fuhito Kojima, investigates a jury decision when hung juries and retrials are possible. When jurors in subsequent trials know that previous trials resulted in hung juries, informative voting can be an equilibrium if and only if the accuracy of signals for innocence and guilt are exactly identical. Moreover, if jurors are informed of numerical split of votes in previous trials, informative voting is not an equilibrium regardless of signal accuracy. / Government

Recently implemented reform of the IMF was motivated by empowering smaller developing and emerging countries. My work has an ambition to assess the countries were empowered and whether there would be a more efficient way for IMF reform. I analyse of role and power of countries using well-established methodology of Voting Power Indices. Apart from the immediate change a longer perspective covering last 20 years would be implemented. My aim is not only assess the power of countries as isolated variables but also to assess them in context of other indicators such as economic development. Lastly, I would suggest a new way of reform proposal for the IMF. Currently countries are represented in the locus of executive power through constituencies. Constituencies are formed arbitrary often reflecting power-seeking behaviour of developed countries. The result is composition of constituencies that often further marginalize the developing countries. My proposal is to restructure these constituencies in line with regional international organizations, which can enhance the position of developing countries in two ways. By providing institutional capacity of already existing organizations the consensus within the constituency would be better facilitated. By formation constituencies in line with regional...

Differential Evolution is an optimization technique of stochastic search for a population-based vector, which is powerful and efficient over a continuous space for solving differentiable and non-linear optimization problems. Weighted voting stacking ensemble method is an important technique that combines various classifier models. However, selecting the appropriate weights of classifier models for the correct classification of transactions is a problem. This research study is therefore aimed at exploring whether the Differential Evolution optimization method is a good approach for defining the weighting function. Manual and random selection of weights for voting credit card transactions has previously been carried out. However, a large number of fraudulent transactions were not detected by the classifier models. Which means that a technique to overcome the weaknesses of the classifier models is required. Thus, the problem of selecting the appropriate weights was viewed as the problem of weights optimization in this study. The dataset was downloaded from the Kaggle competition data repository. Various machine learning algorithms were used to weight vote a class of transaction. The differential evolution optimization techniques was used as a weighting function. In addition, the Synthetic Minority Oversampling Technique (SMOTE) and Safe Level Synthetic Minority Oversampling Technique (SL-SMOTE) oversampling algorithms were modified to preserve the definition of SMOTE while improving the performance. Result generated from this research study showed that the Differential Evolution Optimization method is a good weighting function, which can be adopted as a systematic weight function for weight voting stacking ensemble method of various classification methods. / School of Computing / M. Sc. (Computing)