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La tesis de doctorado "Understanding, Evaluating and Selecting Voting Rules Through Games and Axioms" de Danilo Santa Cruz Coelho está compuesta de cuatro capítulos. La introducción es el primero y describe brevemente el contenido de los capítulos siguientes. En el Capítulo 2, en el contexto de un modelo de votación probabilística propuesto por Rae (1969), el autor investiga las consecuencias de elegir reglas de votación según el criterio de maximin. En el modelo, una regla de votación es el número mínimo de votantes favorables necesarios a una propuesta para que esta sea aceptada. El autor demuestra que la regla de votación que satisface el criterio de maximin puede ser distinta de la mayoría simple que es la que maximiza la suma de las utilidades esperadas de los votantes. El autor proporciona una caracterización de las reglas de votación que satisfacen el criterio de maximin. En el Capítulo 3, dos juegos que pueden ser inducidos por la regla de los k nombres son propuestos y analizados. El autor proporciona para cada uno de los juegos una caracterización del conjunto de los resultados del equilibrio fuerte de Nash. Estas caracterizaciones permiten al autor discutir las preferencias de los jugadores sobre diferentes variantes de la regla de los k nombres. Una parte importante de la regla de los k nombres es el procedimiento utilizado para seleccionar los k nombres que son propuestos al individuo que toma la decisión final. Seis reglas diferentes de selección que son utilizadas en la realidad por diferentes cuerpos decisorios alrededor del mundo son documentadas. En el Capítulo 4, el autor estudia si estas reglas satisfacen la propiedad de estabilidad. Una regla cumple esta propiedad si esta siempre selecciona un conjunto Weak Condorcet y cuando exista un conjunto con esta característica. El autor demuestra que todas estas seis reglas violan esta propiedad si los votantes no actúan estratégicamente. El autor entonces propone dos reglas estables. Finalmente, él proporciona dos justificaciones para el uso extensivo de las reglas inestables. / The dissertation entitled "Understanding, Evaluating and Selecting Voting Rules Through Games and Axioms" by Danilo Santa Cruz Coelho is composed of four chapters. The introduction is the first one and describes briefly the contents of the following chapters. In Chapter 2, in the context of a probabilistic voting model proposed by Rae (1969), the author investigates the consequences of choosing among threshold voting rules according to the maximin criterion. A threshold voting rule is given by the minimum number of votes needed to approve a proposal of change from the status quo. The author shows that the voting rules that satisfy the maximin criterion are different from the simple majority rule which is the one that maximizes the sum of voter's expected utilities. He provides a characterization of the threshold voting rules that satisfy this criterion as a function of the distribution of voters' probabilities to favour change from the status quo. In Chapter 3, two different game theoretical models that can be induced by the rule of k names are proposed and analysed. A characterization of the set of strong Nash Equilibrium outcomes of each of the games is provided. These characterizations enable the author to discuss the preferences of the players over different variants of the rule of k names. An important part of the rule of k names is the procedure used to screen out the k names to be proposed to the individual who takes the final decision. Six different screening rules which are used in reality by different decision bodies around the world are documented. In Chapter 4, he studies whether these screening rules satisfy stability. A screening rule is stable if it always selects a weak Condorcet set whenever such set exists. He shows that all of the six screening rules violate stability if the voters act not strategically. He then proposes two screening rules which satisfy stability. Finally, he provides two possible justifications for the widespread use of unstable screening rules.

Knowledge discovery is an important process in data analysis, data mining and machine learning. Typically knowledge is presented in the form of rules. However, knowledge discovery systems often generate a huge amount of rules. One of the challenges we face is how to automatically discover interesting and meaningful knowledge from such discovered rules. It is infeasible for human beings to select important and interesting rules manually. How to provide a measure to evaluate the qualities of rules in order to facilitate the understanding of data mining results becomes our focus. In this thesis, we present a series of rule evaluation techniques for the purpose of facilitating the knowledge understanding process. These evaluation techniques help not only to reduce the number of rules, but also to extract higher quality rules. Empirical studies on both artificial data sets and real world data sets demonstrate how such techniques can contribute to practical systems such as ones for medical diagnosis and web personalization. In the first part of this thesis, we discuss several rule evaluation techniques that are proposed towards rule postprocessing. We show how properly defined rule templates can be used as a rule evaluation approach. We propose two rough set based measures, a Rule Importance Measure, and a Rules-As-Attributes Measure, %a measure of considering rules as attributes, to rank the important and interesting rules. In the second part of this thesis, we show how data preprocessing can help with rule evaluation. Because well preprocessed data is essential for important rule generation, we propose a new approach for processing missing attribute values for enhancing the generated rules. In the third part of this thesis, a rough set based rule evaluation system is demonstrated to show the effectiveness of the measures proposed in this thesis. Furthermore, a new user-centric web personalization system is used as a case study to demonstrate how the proposed evaluation measures can be used in an actual application.

This thesis develops an algorithm for the Schubert calculus of the Grassmanian. Specifically, we state a puzzle-based, synthesis algorithm for a triple intersection of Schubert varieties. Our algorithm is a reformulation of the synthesis algorithm by Bercovici, Collins, Dykema, Li, and Timotin. We replace their combinatorial approach, based on specialized Lebesgue measures, with an approach based on the puzzles of Knutson, Tao and Woodward. The use of puzzles in our algorithm is beneficial for several reasons, foremost among them being the larger body of work exploiting puzzles. To understand the algorithm, we study the necessary Schubert calculus of the Grassmanian to define synthesis. We also discuss the puzzle-based Littlewood-Richardson rule, which connects puzzles to triple intersections of Schubert varieties. We also survey three combinatorial objects related to puzzles in which we include a puzzle-based construction, by King, Tollu, and Toumazet, of the well known Horn inequalities.

Knowledge discovery is an important process in data analysis, data mining and machine learning. Typically knowledge is presented in the form of rules. However, knowledge discovery systems often generate a huge amount of rules. One of the challenges we face is how to automatically discover interesting and meaningful knowledge from such discovered rules. It is infeasible for human beings to select important and interesting rules manually. How to provide a measure to evaluate the qualities of rules in order to facilitate the understanding of data mining results becomes our focus. In this thesis, we present a series of rule evaluation techniques for the purpose of facilitating the knowledge understanding process. These evaluation techniques help not only to reduce the number of rules, but also to extract higher quality rules. Empirical studies on both artificial data sets and real world data sets demonstrate how such techniques can contribute to practical systems such as ones for medical diagnosis and web personalization. In the first part of this thesis, we discuss several rule evaluation techniques that are proposed towards rule postprocessing. We show how properly defined rule templates can be used as a rule evaluation approach. We propose two rough set based measures, a Rule Importance Measure, and a Rules-As-Attributes Measure, %a measure of considering rules as attributes, to rank the important and interesting rules. In the second part of this thesis, we show how data preprocessing can help with rule evaluation. Because well preprocessed data is essential for important rule generation, we propose a new approach for processing missing attribute values for enhancing the generated rules. In the third part of this thesis, a rough set based rule evaluation system is demonstrated to show the effectiveness of the measures proposed in this thesis. Furthermore, a new user-centric web personalization system is used as a case study to demonstrate how the proposed evaluation measures can be used in an actual application.

This thesis develops an algorithm for the Schubert calculus of the Grassmanian. Specifically, we state a puzzle-based, synthesis algorithm for a triple intersection of Schubert varieties. Our algorithm is a reformulation of the synthesis algorithm by Bercovici, Collins, Dykema, Li, and Timotin. We replace their combinatorial approach, based on specialized Lebesgue measures, with an approach based on the puzzles of Knutson, Tao and Woodward. The use of puzzles in our algorithm is beneficial for several reasons, foremost among them being the larger body of work exploiting puzzles. To understand the algorithm, we study the necessary Schubert calculus of the Grassmanian to define synthesis. We also discuss the puzzle-based Littlewood-Richardson rule, which connects puzzles to triple intersections of Schubert varieties. We also survey three combinatorial objects related to puzzles in which we include a puzzle-based construction, by King, Tollu, and Toumazet, of the well known Horn inequalities.

The object of this thesis was to deepen the understanding of the contemporary political discourse regarding migrant women exposed to domestic violence. This was conducted by analysing propositions, motions and interpellation debates raising the issue during the years 2000-2012. The method used was inspired by Foucault’s discourse analysis and the traditional hermeneutic approach. The result showed how several different mechanisms work to both include and exclude these women from the Swedish welfare system. By being women they are included in the political debate regarding men’s violence against women, but their migrant status excludes them from it at the same time. When migrant women are exposed to domestic violence it is often seen as an individual problem even though men’s violence against women generally is seen as a structural problem. Several conflicts of interests were also found. One of them being whether migrant women are to be warned if their partners have abused women before. The man’s right to integrity stands against the woman’s right to protection. Another conflict is the fear of the migration right being abused, which is pitted against the migrant women’s rights. To summarize the analysis this thesis has shown how the portraying of migrant women as different in the political discourse plays an important role in creating conflicts of interest and to some extent exclude them from the welfare system. Women’s right seem to apply only to certain women under certain circumstances. An intersectional perspective was necessary for understanding the complexity of the situation, taking into account how different power relations interact and construct the contemporary discourse.

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Open distributed environments such as the World Wide Web facilitate information sharing but provide limited support to the protection of sensitive information and resources. Web services have become a part of components for quickly building a business process that satisfies the business goal of an organization, and access control is imperative to prevent the illegal access of sensitive information. In recent years, several researches have investigated the Web services-based workflow access control problem, and selection approaches for choosing the performer for each task so as to satisfy all access control constraints have been proposed. Based on the role-based access control model, we focus on two types of access control: separation of duties and binding of duties. Both role-level and participant-level of SoDs and of BoDs that need to be dynamically enforced are considered in this thesis. While dealing with complex and flexible business logics, we use rule engine to reasons with the business facts to get the result based on business rules. The proposed approach is evaluated by a workflow scenario and is shown to be flexible to develop new process with dynamic access control constraints at the cost of higher execution time.