Poverty measurement : a theoretical contribution and application to Portugal 2007

Doutoramento em Economia.Neste trabalho apresenta-se uma história do pensamento económico na medição de pobreza - desde o que pode ser considerado o inicio da economia da pobreza ate a era de redescoberta da pobreza durante a década de 1960 - bern como uma revisão da literatura sobre as principais ferramentas de medição da pobreza apresentadas pela Ciência Económica. E ainda apresentado, no âmbito da abordagem multidimensional, uma proposta de um índice de medição de pobreza, inovadora quanto a ponderação dos diferentes atributos considerados como elementos de privação. 0 propósito do indice proposto e o de medir a pobreza na sua multidimensionalidade, sendo que cada dimensão de privação e ponderada no indice de acordo com a Hierarquia de Necessidades de Maslow. Esta forma de ponderação faz com que o indice proposto seja diferente dos ja existentes pelo facto de se incorporarem elementos de uma teoria psicológica consolidada na sua estrutura. Por fim, o indice apresentado e aplicado atraves de dados do European Union Statistics on Income and Living Conditions (EU-SILC) para Portugal em 2007 e comparado com dois outros metodos multidimensionais de medi9ao da pobreza.This work presents a history of the economic thought on poverty measurement - from what can be considered the beginning of the Poverty Economics until the "Rediscovering Poverty" era during the 1960s - as well as a review of the literature on the main poverty measurement tools presented by the Economic Science. We also present, having the multidimensional approach as background, a proposal for a poverty measurement index, somehow innovative regarding the weighting of different attributes considered as elements of deprivation. The aim of the proposed index is to measure poverty as a multidimensional phenomenon, where each dimension of deprivation is weighted in the index according to the Maslow's Hierarchy of Needs. This way of weighting makes the proposed index different from the existing indices given that it incorporates elements of a consolidated psychological theory in its structure. Finally, the index is applied using the European Union Statistics on Income and Living Conditions (EU-SILC) microdata for Portugal in 2007, and compared with two other methods of measuring multidimensional poverty

Association of Christians in the Mathematical Sciences Proceedings 2019

The conference proceedings of the Association of Christians in the Mathematical Sciences biannual conference, May 29-June 1, 2019 at Indiana Wesleyan University

The Discrete Acyclic Digraph Markov Model in Data Mining

Graphical Markov models are a powerful tool for the description of complex interactions between the variables of a domain. They provide a succinct description of the joint distribution of the variables. This feature has led to the most successful application of graphical Markov models, that is as the core component of probabilistic expert systems. The fascinating theory behind this type of models arises from three different disciplines, viz., Statistics, Graph Theory and Artificial Intelligence. This interdisciplinary origin has given rich insight from different perspectives. There are two main ways to find the qualitative structure of graphical Markov models. Either the structure is specified by a domain expert or ``structural learning'' is applied, i.e., the structure is automatically recovered from data. For structural learning, one has to compare how well different models describe the data. This is easy for, e.g., acyclic digraph Markov models. However, structural learning is still a hard problem because the number of possible models grows exponentially with the number of variables. The main contributions of this thesis are as follows. Firstly, a new class of graphical Markov models, called TCI models, is introduced. These models can be represented by labeled trees and form the intersection of two previously well-known classes. Secondly, the inclusion order of graphical Markov models is studied. From this study, two new learning algorithms are derived. One for heuristic search and the other for the Markov Chain Monte Carlo Method. Both algorithms improve the results of previous approaches without compromising the computational cost of the learning process. Finally, new diagnostics for convergence assessment of the Markov Chain Monte Carlo Method in structural learning are introduced. The results of this thesis are illustrated using both synthetic and real world datasets

Superposition modulo theory

This thesis is about the Hierarchic Superposition calculus SUP(T) and its application to reasoning in hierarchic combinations FOL(T) of the free first-order logic FOL with a background theory T where the hierarchic calculus is refutationally complete or serves as a decision procedure. Particular hierarchic combinations covered in the thesis are the combinations of FOL and linear and non-linear arithmetic, LA and NLA resp. Recent progress in automated reasoning has greatly encouraged numerous applications in soft- and hardware verification and the analysis of complex systems. The applications typically require to determine the validity/unsatisfiability of quantified formulae over the combination of the free first-order logic with some background theories. The hierarchic superposition leverages both (i) the reasoning in FOL equational clauses with universally quantified variables, like the standard superposition does, and (ii) powerful reasoning techniques in such theories as, e.g., arithmetic, which are usually not (finitely) axiomatizable by FOL formulae, like modern SMT solvers do. The thesis significantly extends previous results on SUP(T), particularly: we introduce new substantially more effective sufficient completeness and hierarchic redundancy criteria turning SUP(T) to a complete or a decision procedure for various FOL(T) fragments; instantiate and refine SUP(T) to effectively support particular combinations of FOL with the LA and NLA theories enabling a fully automatic mechanism of reasoning about systems formalized in FOL(LA) or FOL(NLA).Diese Arbeit befasst sich mit dem hierarchischen Superpositionskalkül SUP(T) und seiner Anwendung auf hierarchischen Kombinationen FOL(T) der freien Logik erste Stufe FOL und einer Hintergrundtheorie T, deren hierarchischer Kalkül widerlegungsvollständig ist oder als Entscheidungsverfahren dient. Die behandelten hierarchischen Kombinationen sind im Besonderen die Kombinationen von FOL und linearer und nichtlinearer Arithmetik, LA bzw. NLA. Die jüngsten Fortschritte in dem Bereich des automatisierten Beweisens haben zahlreiche Anwendungen in der Soft- und Hardwareverifikation und der Analyse komplexer Systeme hervorgebracht. Die Anwendungen erfordern typischerweise die Gültigkeit/Unerfüllbarkeit quantifizierter Formeln über Kombinationen der freien Logik erste Stufe mit Hintergrundtheorien zu bestimmen. Die hierarchische Superposition verbindet beides: (i) das Beweisen über FOL-Klauseln mit Gleichheit und allquantifizierten Variablen, wie in der Standardsuperposition, und (ii) mächtige Beweistechniken in Theorien, die üblicherweise nicht (endlich) axiomatisierbar durch FOL-Formeln sind (z. B. Arithmetik), wie in modernen SMT-Solvern. Diese Arbeit erweitert frühere Ergebnisse über SUP(T) signifikant, im Besonderen führen wir substantiell effektiverer Kriterien zur Bestimmung der hinreichenden Vollständigkeit und der hierarchischen Redundanz ein. Mit diesen Kriterien wird SUP(T) widerlegungsvollständig beziehungsweise ein Entscheidungsverfahren für verschiedene FOL(T)-Fragmente. Weiterhin instantiieren und verfeinern wir SUP(T), um effektiv die Kombinationen von FOL mit der LA- und der NLA-Theorie zu unterstützen, und erhalten eine vollautomatische Beweisprozedur auf Systemen, die in FOL(LA) oder FOL(NLA) formalisiert werden können

Multispace & Multistructure. Neutrosophic Transdisciplinarity (100 Collected Papers of Sciences), Vol. IV

The fourth volume, in my book series of “Collected Papers”, includes 100 published and unpublished articles, notes, (preliminary) drafts containing just ideas to be further investigated, scientific souvenirs, scientific blogs, project proposals, small experiments, solved and unsolved problems and conjectures, updated or alternative versions of previous papers, short or long humanistic essays, letters to the editors - all collected in the previous three decades (1980-2010) – but most of them are from the last decade (2000-2010), some of them being lost and found, yet others are extended, diversified, improved versions. This is an eclectic tome of 800 pages with papers in various fields of sciences, alphabetically listed, such as: astronomy, biology, calculus, chemistry, computer programming codification, economics and business and politics, education and administration, game theory, geometry, graph theory, information fusion, neutrosophic logic and set, non-Euclidean geometry, number theory, paradoxes, philosophy of science, psychology, quantum physics, scientific research methods, and statistics. It was my preoccupation and collaboration as author, co-author, translator, or cotranslator, and editor with many scientists from around the world for long time. Many topics from this book are incipient and need to be expanded in future explorations

Inheritance Reasoning: Psychological Plausibility, Proof Theory and Semantics

Institute for Communicating and Collaborative SystemsDefault inheritance reasoning is a propositional approach to non monotonic reasoning designed to model reasoning with natural language generics. Inheritance reasoners model sets of natural language generics as directed acyclicgraphs,and inference corresponds to the specification of paths through those networks. A proliferation of inheritance proof theories exist in the literature along with extensive debate about the most reasonable way to construct inferences, based on intuitions about interpretations of particular inheritance networks. There has not been an accepted semantics for inheritance which unifies the set of possible proof theories, which would help identify truly ill motivated proof theories. This thesis attempts to clarify the inheritance literature in the three ways indicated in the title: psychological plausibility, proof theory and semantics

An axiomatization of loglinear models with an application to the model-search problem

Lecture Notes in Statistics 11