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Information systems and information technology are rapidly evolving, and the usage of it at the same pace. In different fields, predictive analysis is used daily. Within the area of e-retail, referring to online retailing, it is used for personalisation and as decision support. There’s a lot of research on how to increase the accuracy of the predictions and different methods for this, however, there’s a lack of research regarding the actions an organisation can take given different predictions. Hence, this master’s study researches what factors affording or constraining value are in relation to the usage of predictive tools given different organisational roles. This thesis is made by a case study with a qualitative approach, following the interpretivism paradigm. The data used in this research comes from document analysis followed by semi-structured interviews to gather additional information about what the document analysis or previous research has not covered. The empirical findings were analysed using thematic analysis and are then discussed in relation to the research questions and theoretical framework, together with what’s previously been stated in the literature. The research questions for this thesis are the following: RQ1: How do different organisational roles affect the actions taken on information from predictive analysis in e-retail? and RQ2: What are the key factors affording or constraining value generation in predictive analysis within e-retail? The empirical findings resulted in six themes, where three are relevant to each research question. The findings suggests that there are four major categories of roles that have similar affordances of predictive analysis, these are customer-, sales and financial operations-, management-, andlastly supply chain and inventory related. When several roles within an organisation use the same prediction tool, there are positive effects such as less biased decisions and improved communication through collaboration. Several factors, both constraining and affording value were found. The main constraining factors are related to technological knowledge and interpreted value as well as trustworthiness. The affording factors are instead the allowance of tying predictions to certain KPIs and the ability to be able to slice into the data to show what’s relevant for the individual. In addition to these factors, some desires for functionality were found. These were, among others, a confidence score of the predictions, prediction for certain goals, and predicted optimal send times for emails in the future. My suggestion for future research is to approach the same problem using another theoretical framework to further enhance a novel field, as well as involving participants with different backgrounds than was used in this thesis.

Through this research, a robust aircraft design methodology is developed for analysis and optimization of the Air Vehicle (AV) segment of Unmanned Aerial Vehicle (UAV) systems. The analysis functionality of the AV design is integrated with a Genetic Algorithm (GA) to form an integrated Multi-disciplinary Design Optimization (MDO) methodology for optimal AV design synthesis. This research fills the gap in integrated subsonic fixed-wing UAV AV MDO methods. No known single methodology captures all of the phenomena of interest over the wide range of UAV families considered here. Key advancements include: 1) parametric Low Reynolds Number (LRN) airfoil aerodynamics formulation, 2) UAV systems mass properties definition, 3) wing structural weight methods, 4) self-optimizing flight performance model, 5) automated geometry algorithms, and 6) optimizer integration. Multiple methods are provided for many disciplines to enable flexibility in functionality, level of detail, computational expediency, and accuracy. The AV design methods are calibrated against the High-Altitude Long-Endurance (HALE) Global Hawk, Medium-Altitude Endurance (MAE) Predator, and Tactical Shadow 200 classes, which exhibit significant variations in mission performance requirements and scale from one another. Technology impacts on the design of the three UAV classes are evaluated from a representative system technology year through 2025. Avionics, subsystems, aerodynamics, design, payloads, propulsion, and structures technology trends are assembled or derived from a variety of sources. The technology investigation serves the purposes of validating the effectiveness of the integrated AV design methods and to highlight design implications of technology insertion through future years. Flight performance, payload performance, and other attributes within a vehicle family are fixed such that the changes in the AV designs represent technology differences alone, and not requirements evolution. The optimizer seeks to minimize AV design gross weight for a given mission requirement and technology set. All three UAV families show significant design gross weight reductions as technology improves. The predicted design gross weight in 2025 for each class is: 1) 12.9% relative to the 1994 Global Hawk, 2) 6.26% relative to the 1994 Predator, and 3) 26.3% relative to the 2000 Shadow 200. The degree of technology improvement and ranking of contributing technologies differs among the vehicle families. The design gross weight is sensitive to technologies that directly affect the non-varying weights for all cases, especially payload and avionics/subsystems technologies. Additionally, the propulsion technology strongly affects the high performance Global Hawk and Predator families, which have high fuel mass fractions relative to the Tactical Shadow 200 family. The overall technology synergy experienced 10-11 years after the initial technology year is 6.68% for Global Hawk, 7.09% for Predator, and 4.22% for the Shadow 200, which means that the technology trends interact favorably in all cases. The Global Hawk and Shadow 200 families exhibited niche behavior, where some vehicles attained higher aerodynamic performance while others attained lower structural mass fractions. The high aerodynamic performance Global Hawk vehicles had high aspect ratio wings with sweep, while the low structural mass fraction vehicles had straight, relatively low aspect ratios and smaller wing spans. The high aerodynamic performance Shadow 200 vehicles had relatively low wing loadings and large wing spans, while the lower structural mass fraction counterparts sought to minimize physical size. / Ph. D.

Making good predictions is a critical feature of decision making in situations such as investing and predicting the spread of diseases. Past literature indicates that people use recent and longer-term trends while making predictions. Nonetheless, less is known about how these factors affect how well people make predictions and the timing of their predictions. Further, identifying factors underlying predictive judgments could be an important behavioral factor in manic-depression, anxiety, substance use, age effects, and understanding how income inequality affects decision making. To understand how people make predictive judgments, we conducted two experiments. In Experiment 1, we used an investment task where participants had to predict the future price of a stock based on an exponential trend of information. We found that participants generally had lower earnings with steeper exponential trends (e.g. slower starting) and delayed their decisions to sell bad stocks with steeper trends. We extended these results in Experiment 2 with an updated task with exponential and inverse exponential trends. Overall, our results suggested that people delayed longer to make their prediction with slower starting exponential trends compared to faster starting inverse exponential trends and delayed their predictions longer with more linear trends compared to more trend trends. When deciding how long to explore, participants incorporated both the average trend and recent trend, though they shifted their responses depending on the overall functional form. These choices were ultimately biased to be optimistic or pessimistic based on whether the trend started fast or slow, respectively. Additionally, we found that participants who self-reported taking more gambling risk and depressive symptoms had a greater tendency to stay with faster starting trends and to leave with slower starting trends, suggesting they were even more optimistic given initially fast starting trends. Results pointing to an optimism bias based on the trend in information available to the participant could suggest that an aspect of sunk-cost fallacy is due to errors in predicting the likelihood of future success based on past information. Our findings help understand the dynamics of how people make predictive judgments over time and could inform future research into the mechanisms people use for prospective decision making. Additionally, future research and potential interventions could account for biases in how people perceive past trends to minimize harmful effects of sunk-cost fallacy when making predictions. / Psychology

No / This paper outlines the current challenges of driver behaviour modelling for real-world applications and presents the novel method to identify the pattern of usage to predict upcoming journeys in probability sense. The primary aim is to establish similarity between observed behaviour of drivers resulting in the ability to cluster them and deploy control strategies based on contextual intelligence and datadriven approach. The proposed approach uses the probability density function (PDF) driven by kernel density estimation (KDE) as a probabilistic approach to predict the type of the upcoming journey, expressed as duration and distance. Using the proposed method, the mathematical formulation and programming algorithm procedure have been indicated in detail, while the case study examples with the data visualisation are given for algorithm validation in simulation. / aiR-FORCE project, funded as Proof of Concept by the Institute of Digital Engineering

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal.

Redes probabilísticas são modelos muito versáteis, com aplicabilidade crescente em diversas áreas. Esses modelos são capazes de estruturar e mensurar a interação entre variáveis, permitindo que sejam realizados vários tipos de análises, desde diagnósticos de causas para algum fenômeno até previsões sobre algum evento, além de permitirem a construção de modelos de tomadas de decisões automatizadas. Neste trabalho são apresentadas as etapas para a construção dessas redes e alguns métodos usados para tal, dando maior ênfase para as chamadas redes bayesianas, uma subclasse de modelos de redes probabilísticas. A modelagem de uma rede bayesiana pode ser dividida em três etapas: seleção de variáveis, construção da estrutura da rede e estimação de probabilidades. A etapa de seleção de variáveis é usualmente feita com base nos conhecimentos subjetivos sobre o assunto estudado. A construção da estrutura pode ser realizada manualmente, levando em conta relações de causalidade entre as variáveis selecionadas, ou semi-automaticamente, através do uso de algoritmos. A última etapa, de estimação de probabilidades, pode ser feita seguindo duas abordagens principais: uma frequentista, em que os parâmetros são considerados fixos, e outra bayesiana, na qual os parâmetros são tratados como variáveis aleatórias. Além da teoria contida no trabalho, mostrando as relações entre a teoria de grafos e a construção probabilística das redes, também são apresentadas algumas aplicações desses modelos, dando destaque a problemas nas áreas de marketing e finanças. / Probabilistic networks are very versatile models, with growing applicability in many areas. These models are capable of structuring and measuring the interaction among variables, making possible various types of analyses, such as diagnoses of causes for a phenomenon and predictions about some event, besides allowing the construction of automated decision-making models. This work presents the necessary steps to construct those networks and methods used to doing so, emphasizing the so called Bayesian networks, a subclass of probabilistic networks. The Bayesian network modeling is divided in three steps: variables selection, structure learning and estimation of probabilities. The variables selection step is usually based on subjective knowledge about the studied topic. The structure learning can be performed manually, taking into account the causal relations among variables, or semi-automatically, through the use of algorithms. The last step, of probabilities estimation, can be treated following two main approaches: by the frequentist approach, where parameters are considered fixed, and by the Bayesian approach, in which parameters are treated as random variables. Besides the theory contained in this work, showing the relations between graph theory and the construction of probabilistic networks, applications of these models are presented, highlighting problems in marketing and finance.

A ocorrência de inundações nos centros urbanos acarreta diferentes transtornos sócio-econômicos. É possível minimizar os problemas quando estes eventos são previstos com antecedência. A partir de ferramentas computacionais, da modelação hidrológica e modelação hidrodinâmica, podem-se obter predições de níveis d´água. O modelo hidrológico físico CSmap e o modelo de cálculo de condutos livres CLiv são adotados neste trabalho para demonstrar a aplicação de um sistema de previsões de cheias. A partir do estudo da Bacia do Rio Ribeira de Iguape, demonstra-se que o aprimoramento destes modelos matemáticos e das técnicas de calibração pode não ser relevante quando os dados de origem são imprecisos. / Differents social and economics problems affect urbanized areas when a flood happens. These problems may be minimized by predictions made by computer´s tools, hydrological models and hydraulics algorithmics. The physical hydrologic model CSmap and open channel´s model CLiv are adopted in this research to demonstrate a flood´s prediction system application. Another analysis shows that the development of math´s models and calibration´s techniques may not be relevant when inaccurate data is utilized.

Cette présente étude se propose d’actualiser la problématique des relations entre religieux et politique (dans l’espace sénégambien), en s’appuyant sur le cas des relations entre prédicateurs (saltigi) sereer et acteurs politiques. L’objet à partir duquel nous nous proposons d’analyser ces interrelations est le xoy, littéralement « l’appel » en sereer-siin. Les xoy sont des réunions de prédiction convoquées dans les localités sereer-siin à l’approche ou (plus rarement encore) pendant la saison des pluies. Les prédictions tenues (localement) par les saltigi et autres voyants sont en principe censées porter sur des questions tournant essentiellement autour de l’hivernage : pluie, récoltes, tempêtes, invasions de criquets, maladies etc. Cette forte orientation des xoy autour des questions d’hivernage, tout comme leur caractère communautaire ou local, semblent en revanche trancher d’avec le caractère associatif, « extracommunautaire », médiatique et national d’une réunion en particulier, celle organisée depuis 1981 par l’ONG PRO.ME.TRA dans la localité de Fatick et plus exactement dans un Centre de Médecine traditionnelle nommé Malango. Dans ce xoy auquel assistent des représentants locaux ou nationaux de l’Etat, les prédictions sont notoirement axées sur les questions de succession au sommet de l’Etat sénégalais. Ainsi, par une démarche de recherche associant en permanence données empiriques et éléments de littérature, nous analysons le sens et les fondements de ces prédictions axées sur le politique. Nos données nous conduisent à envisager ces prédictions comme l’expression et la conséquence d’un clientélisme discret entre acteurs politiques et saltigi du xoy de Malango. / This study is aiming to update the problem of the relationships between religion and politics in Senegambia. It is not based on the already well documented case of the relations between marabouts and modern politicians, but on the relation between sereer preachers (saltigi) and politicians. This research is based on an analysis of the xoy, meaning “the call” in sereer-siin language. Xoy are prediction reunions convened before or (more rarely) during the rainy season in sereer-siin localities. Predictions provided (locally) by saltigi and other seers are supposed to be about wintering: rain, harvests, tempests, grasshopper invasions, diseases, etc. This strong focus of xoy toward wintering questions, just as their local or community aspect, seems however to sever with the associative, extracommunal, mediatical and national dimensions of a particular meeting held by the NGO PRO.ME.TRA since 1981, that take place in Fatick (west-central Senegal) and more exactly in a traditional medicine center named Malango. This xoy is attended by local and national government representatives and predictions are notoriously focused on succession issues at the top of the Senegalese state. Thus, through empirical data (from 31 interviews and observations of xoy Malango) and elements of literature, we analyze the meaning and the basis of these political predictions. Our data show that these predictions are the expression and the consequence of a discrete clientelism between politicians and saltigi of the Malango’s xoy.

Introdução: Através da ferramenta estatística denominada análise de regressão linear múltipla se gerou equações preditivas de temperatura interna do ar de uma edificação com paredes e lajes compostas por painéis de concreto alveolar, com sistema de cobertura verde. Justificativa: Com equações preditivas é possível simular temperaturas internas de edificações utilizando uma pequena entrada de dados com uma precisão satisfatória. Utilizando tais equações é possível, também, corrigir erros de projetos antes de sua execução. Objetivos: Gerar equações preditivas para o período seco (outono e inverno) e para o período chuvoso (primavera e verão) para a edificação analisada. Metodologia: Foram selecionadas duas séries de dados, um referente ao período de característica seca e outro de característica chuvosa. Foram geradas equações preditivas de temperatura interna do ar máxima, média e mínima para os dois períodos, utilizando análise de regressão linear. Resultados: Foram geradas sete equações preditivas para o período seco e cinco para o período chuvoso. As diferenças máximas, em módulo, entre as temperaturas estimadas pelas equações e as monitoradas experimentalmente ficaram em aproximadamente 2°C. Conclusão: As equações preditivas geradas para os dois períodos considerados descrevem satisfatoriamente o comportamento térmico da edificação. / Introduction: Using a statistics tool called multiple linear regression, we created equations for predicting the indoor temperature in a building with walls and ceiling build from panels of alveolar concrete, with a green roof system. Explanation: Predictive equations enable simulations of indoor temperatures of buildings using a small number of data and with a satisfactory precision. They also allow corrections on project errors before they are put into effect. Objectives: Generate predictive equations for the building for the dry season (autumn and winter) and for the rainy season (spring and summer). Method: We selected two series of data, one for the dry and one for the rainy season. Using linear regression analysis we ran predictive equations for maximum, intermediate and minimum indoor temperatures of the air for both seasons. Results: We created seven predictive equations for the dry season and five for the wet season. The largest differences (in module) between the temperatures estimated using equations and monitored experimentally was approximately 2°C. Conclusion: The predictive equations generated for both periods described satisfactorily the thermal behavior of the building.