Triple-acyclicity in majorities based on difference in support

In this paper we study to what extent majorities based on difference in support leads to triple-acyclic collective decisions. These majorities, which take into account voters' intensities of preference between pairs of alternatives through reciprocal preference relations, require to the winner alternative to exceed the support for the other alternative in a difference fixed before the election. Depending on that difference, i.e., on the threshold of support, and on some requirements on the individual rationality of the voters, we provide necessary and sufficient conditions for avoiding cycles of three alternatives on the collective decision

Mayorías basadas en diferencias: análisis de la consistencia y extensiones

En esta tesis se estudian las mayorías por diferencia de votos y por diferencia de apoyo. Los capítulos 1 y 2 se centran en el análisis de la transitividad y de la triple-aciclicidad de la relación de preferencia fuerte generada por las mayorías por diferencia de apoyo, al agregar relaciones de preferencia recíprocas individuales. En el capítulo 3 se estiman las probabilidades con las que se producen resultados colectivos consistentes, tanto en las mayorías por diferencia de apoyo como en las mayorías por diferencia de votos. En el capítulo 4 se extienden las mayorías por diferencia de votos al contexto de las preferencias lingüísticas, a través de conjuntos difusos y del modelo de las 2-tuplas; se justifica la equivalencia entre ambas modelizaciones bajo determinadas condiciones de regularidad y se estudian las propiedades que cumplen estas mayorías lingüísticasDepartamento de Economía Aplicad

Essays on Stochastic Choice and Welfare

Motivated by the literature on preference elicitation and welfare analysis, Chapter I studies the properties of aggregators of choice datasets into preferences. Novel normative principles and their theoretical implications are provided. I analyse numerous approaches proposed by the literature in view of the introduced principles. I also propose and characterize two counting procedures that are foundational for the analysis. Motivated by the theoretical framework of the first chapter, in Chapter II, I propose a novel experimental design to test two normative principles: (1) Informational Responsiveness guarantees that no choice data is ignored; (2) Revealed Preference constrains the preference elicitation process to a particular reorganization of data. These principles are summarized by a method denoted as Counting Reveal Preference procedure. I show that approaches founded on this procedure provide more reliable results in terms of preference relation. Motivated by the literature on stochastic choice, Chapter III studies the relation between imperfect discrimination and the transitivity of preferences. I show that the degree of transitivity depends on the degree of discrimination between pairs of alternatives. I characterize the notions of Weak, Moderate and Strong stochastic transitivity. The results allow us to organize a wide range of stochastic models in accordance with Fechnerian models and imperfect discrimination

Law, Incommensurability, and Conceptually Sequenced Argument

This Article argues that there are methods for dealing with plural values that are structurally very different from those conventionally assumed within rational choice theory under the ideas of commensurability or lexical priority. In particular it is argued that plural values can be integrated rationally into an overall all-things-considered decision as a matter of "conceptually sequenced argument". Such an argument allows for the rational possibility that one of the non-commensurable (plural) values might have a priority at one stage in the choice sequence while also allowing for that priority to be relaxed or defeated at a subsequent stage. Such a procedure, while it might appear to involve self-contradiction if we look only at the choices as revealing values embedded in the alternatives for choice, will look perfectly coherent if we understand the choice procedure as a conceptually-ordered (or partition-dependent) sequence. The Article argues that legal decision-making and the process of adjudication, in their insistence on a role for reason-based choice and the interpersonal significance of argument and reply, regularly manifest such a conceptually sequenced ordering of plural values. The different theories of Richard Epstein, George Fletcher and Ernest Weinrib are compared in this respect

Commonsense knowledge acquisition and applications

Computers are increasingly expected to make smart decisions based on what humans consider commonsense. This would require computers to understand their environment, including properties of objects in the environment (e.g., a wheel is round), relations between objects (e.g., two wheels are part of a bike, or a bike is slower than a car) and interactions of objects (e.g., a driver drives a car on the road). The goal of this dissertation is to investigate automated methods for acquisition of large-scale, semantically organized commonsense knowledge. Prior state-of-the-art methods to acquire commonsense are either not automated or based on shallow representations. Thus, they cannot produce large-scale, semantically organized commonsense knowledge. To achieve the goal, we divide the problem space into three research directions, constituting our core contributions: 1. Properties of objects: acquisition of properties like hasSize, hasShape, etc. We develop WebChild, a semi-supervised method to compile semantically organized properties. 2. Relationships between objects: acquisition of relations like largerThan, partOf, memberOf, etc. We develop CMPKB, a linear-programming based method to compile comparative relations, and, we develop PWKB, a method based on statistical and logical inference to compile part-whole relations. 3. Interactions between objects: acquisition of activities like drive a car, park a car, etc., with attributes such as temporal or spatial attributes. We develop Knowlywood, a method based on semantic parsing and probabilistic graphical models to compile activity knowledge. Together, these methods result in the construction of a large, clean and semantically organized Commonsense Knowledge Base that we call WebChild KB.Von Computern wird immer mehr erwartet, dass sie kluge Entscheidungen treffen können, basierend auf Allgemeinwissen. Dies setzt voraus, dass Computer ihre Umgebung, einschließlich der Eigenschaften von Objekten (z. B. das Rad ist rund), Beziehungen zwischen Objekten (z. B. ein Fahrrad hat zwei Räder, ein Fahrrad ist langsamer als ein Auto) und Interaktionen von Objekten (z. B. ein Fahrer fährt ein Auto auf der Straße), verstehen können. Das Ziel dieser Dissertation ist es, automatische Methoden für die Erfassung von großmaßstäblichem, semantisch organisiertem Allgemeinwissen zu schaffen. Dies ist schwierig aufgrund folgender Eigenschaften des Allgemeinwissens. Es ist: (i) implizit und spärlich, da Menschen nicht explizit das Offensichtliche ausdrücken, (ii) multimodal, da es über textuelle und visuelle Inhalte verteilt ist, (iii) beeinträchtigt vom Einfluss des Berichtenden, da ungewöhnliche Fakten disproportional häufig berichtet werden, (iv) Kontextabhängig, und hat aus diesem Grund eine eingeschränkte statistische Konfidenz. Vorherige Methoden, auf diesem Gebiet sind entweder nicht automatisiert oder basieren auf flachen Repräsentationen. Daher können sie kein großmaßstäbliches, semantisch organisiertes Allgemeinwissen erzeugen. Um unser Ziel zu erreichen, teilen wir den Problemraum in drei Forschungsrichtungen, welche den Hauptbeitrag dieser Dissertation formen: 1. Eigenschaften von Objekten: Erfassung von Eigenschaften wie hasSize, hasShape, usw. Wir entwickeln WebChild, eine halbüberwachte Methode zum Erfassen semantisch organisierter Eigenschaften. 2. Beziehungen zwischen Objekten: Erfassung von Beziehungen wie largerThan, partOf, memberOf, usw. Wir entwickeln CMPKB, eine Methode basierend auf linearer Programmierung um vergleichbare Beziehungen zu erfassen. Weiterhin entwickeln wir PWKB, eine Methode basierend auf statistischer und logischer Inferenz welche zugehörigkeits Beziehungen erfasst. 3. Interaktionen zwischen Objekten: Erfassung von Aktivitäten, wie drive a car, park a car, usw. mit temporalen und räumlichen Attributen. Wir entwickeln Knowlywood, eine Methode basierend auf semantischem Parsen und probabilistischen grafischen Modellen um Aktivitätswissen zu erfassen. Als Resultat dieser Methoden erstellen wir eine große, saubere und semantisch organisierte Allgemeinwissensbasis, welche wir WebChild KB nennen

Commonsense knowledge acquisition and applications

Computers are increasingly expected to make smart decisions based on what humans consider commonsense. This would require computers to understand their environment, including properties of objects in the environment (e.g., a wheel is round), relations between objects (e.g., two wheels are part of a bike, or a bike is slower than a car) and interactions of objects (e.g., a driver drives a car on the road). The goal of this dissertation is to investigate automated methods for acquisition of large-scale, semantically organized commonsense knowledge. Prior state-of-the-art methods to acquire commonsense are either not automated or based on shallow representations. Thus, they cannot produce large-scale, semantically organized commonsense knowledge. To achieve the goal, we divide the problem space into three research directions, constituting our core contributions: 1. Properties of objects: acquisition of properties like hasSize, hasShape, etc. We develop WebChild, a semi-supervised method to compile semantically organized properties. 2. Relationships between objects: acquisition of relations like largerThan, partOf, memberOf, etc. We develop CMPKB, a linear-programming based method to compile comparative relations, and, we develop PWKB, a method based on statistical and logical inference to compile part-whole relations. 3. Interactions between objects: acquisition of activities like drive a car, park a car, etc., with attributes such as temporal or spatial attributes. We develop Knowlywood, a method based on semantic parsing and probabilistic graphical models to compile activity knowledge. Together, these methods result in the construction of a large, clean and semantically organized Commonsense Knowledge Base that we call WebChild KB.Von Computern wird immer mehr erwartet, dass sie kluge Entscheidungen treffen können, basierend auf Allgemeinwissen. Dies setzt voraus, dass Computer ihre Umgebung, einschließlich der Eigenschaften von Objekten (z. B. das Rad ist rund), Beziehungen zwischen Objekten (z. B. ein Fahrrad hat zwei Räder, ein Fahrrad ist langsamer als ein Auto) und Interaktionen von Objekten (z. B. ein Fahrer fährt ein Auto auf der Straße), verstehen können. Das Ziel dieser Dissertation ist es, automatische Methoden für die Erfassung von großmaßstäblichem, semantisch organisiertem Allgemeinwissen zu schaffen. Dies ist schwierig aufgrund folgender Eigenschaften des Allgemeinwissens. Es ist: (i) implizit und spärlich, da Menschen nicht explizit das Offensichtliche ausdrücken, (ii) multimodal, da es über textuelle und visuelle Inhalte verteilt ist, (iii) beeinträchtigt vom Einfluss des Berichtenden, da ungewöhnliche Fakten disproportional häufig berichtet werden, (iv) Kontextabhängig, und hat aus diesem Grund eine eingeschränkte statistische Konfidenz. Vorherige Methoden, auf diesem Gebiet sind entweder nicht automatisiert oder basieren auf flachen Repräsentationen. Daher können sie kein großmaßstäbliches, semantisch organisiertes Allgemeinwissen erzeugen. Um unser Ziel zu erreichen, teilen wir den Problemraum in drei Forschungsrichtungen, welche den Hauptbeitrag dieser Dissertation formen: 1. Eigenschaften von Objekten: Erfassung von Eigenschaften wie hasSize, hasShape, usw. Wir entwickeln WebChild, eine halbüberwachte Methode zum Erfassen semantisch organisierter Eigenschaften. 2. Beziehungen zwischen Objekten: Erfassung von Beziehungen wie largerThan, partOf, memberOf, usw. Wir entwickeln CMPKB, eine Methode basierend auf linearer Programmierung um vergleichbare Beziehungen zu erfassen. Weiterhin entwickeln wir PWKB, eine Methode basierend auf statistischer und logischer Inferenz welche zugehörigkeits Beziehungen erfasst. 3. Interaktionen zwischen Objekten: Erfassung von Aktivitäten, wie drive a car, park a car, usw. mit temporalen und räumlichen Attributen. Wir entwickeln Knowlywood, eine Methode basierend auf semantischem Parsen und probabilistischen grafischen Modellen um Aktivitätswissen zu erfassen. Als Resultat dieser Methoden erstellen wir eine große, saubere und semantisch organisierte Allgemeinwissensbasis, welche wir WebChild KB nennen

The role of assisted reproduction technologies in improving cattle production under communal and emerging farming systems in Limpopo, Mpumalanga and KwaZulu-Natal, South Africa

The aim of the study was to determine the effect of assisted reproductive technologies (ARTs) in improving cattle production with the purpose of providing policy directives for the successful implementation of the ART project among communal and emerging livestock systems. The study was conducted under communal and emerging cattle farming systems in Limpopo, Mpumalanga and KwaZulu-Natal provinces. The selected districts in Limpopo were Vhembe, Capricorn, Mopani and Waterberg, in Mpumalanga were Gert Sibande and Ehlanzeni while in KwaZulu-Natal the selected districts were Zululand and Harry Gwala. A total of 282 cows were selected for the study, 38 percent in Limpopo, 32 percent in Mpumalanga and 30 percent in KwaZuluNatal. The cow parameters evaluated were breed type, parity, age, body condition score, frame size and lactation status. An ovsynch protocol which allows for fixed-time artificial insemination (FTAI) was used during the oestrous synchronisation process. A heat mount detector (Karma®) was used to detect oestrous synchronisation response. The dominant cattle breed types were the Bonsmara, Brahman and Nguni. All experimental cows that responded positively to oestrous synchronisation protocol and were inseminated with semen from a Nguni bull. Chi-Square Test of Independence were computed to determine the association among factors. Data was further modelled using the logistic regression model of SAS, establishing the probability of success. Districts, breed type, parity, age, and lactation status did not significantly influence (P > 0.05) conception rate following oestrous synchronisation and timed artificial insemination. However, conception rate was not independent (P 0.05). However, calving rate was not independent of provinces, districts and body condition score (P < 0.05). Calving rate in Mpumalanga (58 percent) and KwaZulu-Natal (54 percent) was significantly higher (P < 0.05) than that recorded in Limpopo Province (36 percent). Calving rate of Gert Sibande (61 percent) and Ehlanzeni (50 percent) districts in Mpumalanga and Zululand (50 percent) and Harry Gwala (61 percent) in KwaZulu-Natal was significantly higher (P < 0.05) than that of the Capricorn (32 percent), Mopani (23 percent and Waterberg (30 percent) in Limpopo Province with the exception of Vhembe (44 percent). Cows with body condition score of ≤ 2.5 (60 percent) had a significantly higher (P < 0.05) calving rate than those with a body condition score of 3 (43 percent). Cows in Mpumalanga had more chances to calve than those in Limpopo and KwaZulu-Natal with odds ratio of 0.076 and 0.537, respectively. Additionally, quantitative data was collected through in-depth interviews using a semi-structured questionnaire. Data collected was managed and coded into themes using the Nvivo Version 11 software programme. Themes and issues that emerged were analysed and interpreted using critical social thinking and systems thinking. The results of the study revealed many factors that could compromise the implementation and adoption of ARTs in the study areas. The general feeling amongst cattle farmers interviewed was that government should address these challenges. A shortage of bulls was the main cause of the low cattle reproduction rate. The Nguni breed type cattle was perceived as the ideal cattle breed for rural areas by respondents. Oestrous synchronisation and artificial insemination can be applied under communal and emerging farming systems with success. From the study results, breeding with small framed animals such as the Nguni type breed under communal and emerging farming systems makes a lot of sense because the breed is known for its low feed maintenance requirement. Furthermore, cattle stakeholders should co-operate and work together to address many of the constraints facing cattle productivity and the implementation and adoption of ARTs in rural areas