This descriptive phenomenology had two purposes: first, to explore the experience of making decisions for women with advanced cancer; and second, to explore the experience for significant others and health care team members as women made their decisions. A plethora of research exists on making decisions during the cancer experience, including research regarding: 1) decision-making styles; 2) factors or determinants which play a role in decision making; 3) information: needs, seeking behaviours, and utilization; and 4) decision support technologies. However, a gap exists in the literature regarding the experience of making decisions. Conversational interviews were conducted with five women and three relevant others for each woman: her primary nurse, her oncologist, and one significant other. Women were also provided with the opportunity to journal in a diary or email their memories of decisions and the surrounding experience. Van Manens (1990) phenomenology guided the analysis of data. For the women, analysis centered on the four existentials of lived time, lived other, lived space, and lived body, revealing four themes of the lived experience of making decisions: 1) control, 2) influence, 3) normalcy, and 4) vulnerability. Phenomenological analysis on data from the significant others revealed three themes: 1) what used to be, 2) power shift, and 3) life on hold. Themes for the health care teams experience as women made decisions were: 1) emotional detachment, 2) discomfort, and 3) acquiescing. Understanding the perspectives from these lived experiences will assist the health care team to support women, and their significant others, through the experience of making decisions.

This descriptive phenomenology had two purposes: first, to explore the experience of making decisions for women with advanced cancer; and second, to explore the experience for significant others and health care team members as women made their decisions. A plethora of research exists on making decisions during the cancer experience, including research regarding: 1) decision-making styles; 2) factors or determinants which play a role in decision making; 3) information: needs, seeking behaviours, and utilization; and 4) decision support technologies. However, a gap exists in the literature regarding the experience of making decisions. Conversational interviews were conducted with five women and three relevant others for each woman: her primary nurse, her oncologist, and one significant other. Women were also provided with the opportunity to journal in a diary or email their memories of decisions and the surrounding experience. Van Manens (1990) phenomenology guided the analysis of data. For the women, analysis centered on the four existentials of lived time, lived other, lived space, and lived body, revealing four themes of the lived experience of making decisions: 1) control, 2) influence, 3) normalcy, and 4) vulnerability. Phenomenological analysis on data from the significant others revealed three themes: 1) what used to be, 2) power shift, and 3) life on hold. Themes for the health care teams experience as women made decisions were: 1) emotional detachment, 2) discomfort, and 3) acquiescing. Understanding the perspectives from these lived experiences will assist the health care team to support women, and their significant others, through the experience of making decisions.

Este trabajo busca conocer —a partir de una exhaustiva revisión de artículos de alto impacto— cuáles son las principales aplicaciones de la inteligencia artificial (IA) que han potenciado las estrategias empresariales. Se abordan los siguientes temas para responder la pregunta de investigación. En primer lugar, se define inteligencia artificial y se estudia su evolución; se alude también a las distintas posturas respecto a ella. En segundo lugar, se explica en qué consiste la inteligencia de negocios, ya que en los últimos años las empresas han demostrado interés por mejorar sus procesos con base en el manejo de grandes volúmenes de datos. En tercer lugar, se analiza el aporte de la IA a la inteligencia de negocios, su aplicación en las distintas unidades organizativas y el apoyo a la generación de estrategias empresariales. Finalmente, se incide en cómo la IA permite mejorar las estrategias de las organizaciones. Se concluye que la IA transforma la interacción de los humanos con las máquinas, ayudando a aumentar sus capacidades para la toma de decisiones, lo que es especialmente relevante para los negocios contemporáneos y, en particular, para la estrategia empresarial. / This work seeks to undestand, since an extensive review of very important articles, which are the main applications of the artificial intelligence (AI) that have improved the business strategies. The following issues are addressed in order to answer the questions of the investigative work. First, artificial intelligence is defined and its evolution is studied. It also refers to different positions abou it. Second, it is explained what the business intelligence consists, because, lately, companies have been interested in improving their processes based on the management of big data. Third, the contribution of the AI to the business intelligence is analyzed; also its application to the different organisational units and the support to the generation of business strategies are analyzed. Finally, the focus is on how the AI allows to improve the strategies of the organizations. It is concluded that the AI transforms the interaction of the human beings with machines, helping to increase their capacities of making decisions; this is very important for the contemporary business, and especially for the business strategy. / Trabajo de Suficiencia Profesional

Considérant un point de vue général de cette thèse aborde le problème de trouver, à partir d'un ensemble de blocs de construction, un sous-ensemble qui procure une solution à un problème donné. Ceci est fait en tenant compte de la compatibilité de chacun des blocs de construction par rapport au problème et l'aptitude d'interaction entre ces parties pour former ensemble une solution. Dans la perspective notamment de la thèse sont les blocs de construction de méta-modèles et le problème donné est une description d'un problème peut être résolu en utilisant un logiciel et d'être résolu en utilisant un système multi-agents. Le noyau de la proposition de thèse est un processus qui analyse un problème donné et puis il proposé une solution possible basée sur système multi-agents pour ce problème. Il peut également indiquer que le problème ne peut être résolu par ce paradigme. Le processus adressée par la thèse consiste en les étapes principales suivantes: (1) A travers un processus de caractérisation on analyse la description du problème pour localiser le domaine de solutions, puis choisissez une liste de candidats des méta-modèles. (2) Les caractérisations de méta-modèles candidats sont prises, ils sont définis dans plusieurs domaines de la solution. On fait la chois parmi le domaine trouvé dans la étape précédant. (3) On crée un système multi-agents où chaque agent représente un candidat méta-modèle. Dans cette société les agents interagissent les uns avec les autres pour trouver un groupe de méta-modèles qui est adapté pour représenter une solution donnée. Les agents utilisent des critères appropriés pour chaque méta-modèle à représenter. Il évalue également la compatibilité des groupes créés pour résoudre le problème de décider le groupe final qui est la meilleure solution. Cette thèse se concentre sur la fourniture d'un processus et un outil prototype pour résoudre plutôt la dernière étape de la liste. Par conséquent, le chemin proposé a été créé à l'aide de plusieurs concepts de la méta-analyse, l'intelligence artificielle de coopération, de la cognition bayésienne, incertitude, la probabilité et statistique. / From a general point of view this thesis addresses an automatic path to build a solution choosing a compatible set of building blocks to provide such a solution to solve a given problem. To create the solution it is considered the compatibility of each available building block with the problem and also the compatibility between each building block to be employed within a solution all together. In the particular perspective of this thesis the building blocks are meta-models and the given problem is a description of a problem that can be solved using software using a multi-agent system paradigm. The core of the thesis proposal is the creation of a process based on a multi-agent system itself. Such a process analyzes the given problem and the available meta-models then it matches both and thus it suggests one possible solution (based on meta-models) for the problem. Nevertheless if no solution is found it also indicates that the problem can not be solved through this paradigm using the available meta-models. The process addressed by the thesis consists of the following main steps: (1) Through a process of characterization the problem description is analyzed in order to locate the solution domain and therefore employ it to choose a list of most domain compatible meta-models as candidates. (2) There are required also meta-model characterization that evaluate each meta-model performance within each considered domain of solution. (3) The matching step is built over a multi-agent system where each agent represents a candidate meta-model. Within this multi-agent system each agent interact with each other in order to find a group of suitable meta-models to represent a solution. Each agent use as criteria the compatibility between their represented candidate meta-model with the other represented meta-models. When a group is found the overall compatibility with the given problem is evaluated. Finally each agent has a solution group. Then these groups are compared between them in order to find the most suitable to solve the problem and then to decide the final group. This thesis focuses on providing a process and a prototype tool to solve the last step. Therefore the proposed path has been created using several concepts from meta-analysis, cooperative artificial intelligence, Bayesian cognition, uncertainty, probability and statistics.