Influence diagrams (ID) are graphical frameworks for decision making in stochastic situations with mathematical models embedded in them. The goal of an optimal algorithm for an ID is to find a strategy that would maximize the expected utility. We will explain a few algorithms for influence diagrams in this thesis. There exists an obvious temporal ordering among decisions in an ID; and any information used in the past will always be available in the future: these two properties are respectively called the “regularity” and “noforgetting” assumptions. A limited memory influence diagram (LIMID) does not follow these two properties. The existing state-of-art depthirst-branch-and-bound (DFBnB) algorithm for solving influence diagrams does not scale very well due to the exponential increase of nodes proportional to the depth of the search (or total stages in the ID). In this paper, we propose and implement an algorithm that combines two widely used methods, depth first branch-andbound search (DFBnB) and value iteration with incremental pruning, for solving IDs and POMDPs, respectively. We describe an algorithm to convert the strategy tree to a strategy graph. Experiments show the effectiveness of these approaches. Algorithms for solving traditional influence diagrams are not easily generalized to solve LIMIDs, however, and only recently have exact algorithms for solving LIMIDs been developed. In this thesis, we provide an exact algorithm for solving LIMIDs that is based on branch-and-bound search. Our approach is related to the approach of solving an influence diagram by converting it to an equivalent decision tree, with the difference that the LIMID is converted to a much smaller decision graph that can be searched more efficiently.

O presente trabalho discute a necessidade da representação e manipulação de incertezas na resolução de problemas por sistemas baseados em conhecimento, e como isto pode ser realizado utilizando redes de crenças. Este tipo de representação do conhecimento combina a teoria das probabilidades e teoria da decisão, para representar incertezas, com a teoria dos grafos, esta última apropriada para representar as relações de dependência entre as variáveis do modelo. Os diagramas de inferência - redes de crenças que permitem representar incertezas, decisões e preferências do usuário - são discutidos e adotados neste trabalho para desenvolver um sistema normativo de apoio à decisão. A problemática da explicação em sistemas bayesianos, relativamente nova quando comparada com a dos sistemas baseados em regras, é abordada. Neste contexto dois mecanismos de explicação para diagramas de influência são propostos: análise de sensibilidade e as redes probabilísticas qualitativas. Estes mecanismos são usados para gerar conclusões genéricas bem como para entender qualitativamente as relações entre as ações e eventos que fazem parte do modelo. Uma ferramenta gráfica de apoio à decisão baseada em diagramas de influências foi implementada na linguagem Smalltalk. Este aplicativo não só permite representar e avaliar o problema do usuário como também incorpora as facilidades de explicação acima descritas. A possibilidade de observar graficamente o que acontece com o modelo quando os valores das variáveis são modificados - análise de sensibilidade - permite compreender melhor o problema descobrindo quais as variáveis que influenciam as decisões e auxilia a refinar os valores das variáveis envolvidas. Por outro lado às redes probabilísticas qualitativas permitem realizar abstrações e simplificações apropriadas do modelo, i.e., obter as relações qualitativas do modelo a partir de seu nível quantitativo. As conclusões genéricas obtidas servem tanto para limitar o espaço da estratégia ótima quanto para entender qualitativamente as relações entre as ações e eventos que fazem parte do modelo. / This work discusses the knowledge representation and uncertainty handling of knowledge based systems that use belief networks for this purpose. These sorts of networks combine the theory of probability and decision theory to represent uncertainty- with graph theory to represent the dependence relations between the model parameters. Systems that use belief networks as knowledge representation are named Bayesian or normative systems. This work investigates and adopts influence diagrams -belief networks that represent uncertainty, decisions and preferences- to develop a normative decision support system. Comprehensible explanations for probabilistic reasoning systems are a prerequisite for wider acceptance of Bayesian methods. Two schemes for explaining influence diagrams are proposed: sensitivity analysis and qualitative probabilistic networks, aiming to find general conclusions and to qualitatively understand the relations between the actions and events of the model. A graphical decision support system that represents the user problem as influence diagrams has been implemented in Smalltalk. This system allows to represent and evaluate decision problems and incorporates the explanation facilities mentioned above. The possibility to observe graphically the model as the variables change -sensitivity analysis- permits a better understanding of the problem by finding the significant variables. This process also helps to adjust the variables values. Furthermore, the qualitative probabilistic networks allow to realize model abstractions and simplifications, i.e., to obtain the qualitative relations from the quantitative level. These general conclusions limit the optimal strategy space and allow to qualitatively understanding the relations between actions and events in the model.

O presente trabalho discute a necessidade da representação e manipulação de incertezas na resolução de problemas por sistemas baseados em conhecimento, e como isto pode ser realizado utilizando redes de crenças. Este tipo de representação do conhecimento combina a teoria das probabilidades e teoria da decisão, para representar incertezas, com a teoria dos grafos, esta última apropriada para representar as relações de dependência entre as variáveis do modelo. Os diagramas de inferência - redes de crenças que permitem representar incertezas, decisões e preferências do usuário - são discutidos e adotados neste trabalho para desenvolver um sistema normativo de apoio à decisão. A problemática da explicação em sistemas bayesianos, relativamente nova quando comparada com a dos sistemas baseados em regras, é abordada. Neste contexto dois mecanismos de explicação para diagramas de influência são propostos: análise de sensibilidade e as redes probabilísticas qualitativas. Estes mecanismos são usados para gerar conclusões genéricas bem como para entender qualitativamente as relações entre as ações e eventos que fazem parte do modelo. Uma ferramenta gráfica de apoio à decisão baseada em diagramas de influências foi implementada na linguagem Smalltalk. Este aplicativo não só permite representar e avaliar o problema do usuário como também incorpora as facilidades de explicação acima descritas. A possibilidade de observar graficamente o que acontece com o modelo quando os valores das variáveis são modificados - análise de sensibilidade - permite compreender melhor o problema descobrindo quais as variáveis que influenciam as decisões e auxilia a refinar os valores das variáveis envolvidas. Por outro lado às redes probabilísticas qualitativas permitem realizar abstrações e simplificações apropriadas do modelo, i.e., obter as relações qualitativas do modelo a partir de seu nível quantitativo. As conclusões genéricas obtidas servem tanto para limitar o espaço da estratégia ótima quanto para entender qualitativamente as relações entre as ações e eventos que fazem parte do modelo. / This work discusses the knowledge representation and uncertainty handling of knowledge based systems that use belief networks for this purpose. These sorts of networks combine the theory of probability and decision theory to represent uncertainty- with graph theory to represent the dependence relations between the model parameters. Systems that use belief networks as knowledge representation are named Bayesian or normative systems. This work investigates and adopts influence diagrams -belief networks that represent uncertainty, decisions and preferences- to develop a normative decision support system. Comprehensible explanations for probabilistic reasoning systems are a prerequisite for wider acceptance of Bayesian methods. Two schemes for explaining influence diagrams are proposed: sensitivity analysis and qualitative probabilistic networks, aiming to find general conclusions and to qualitatively understand the relations between the actions and events of the model. A graphical decision support system that represents the user problem as influence diagrams has been implemented in Smalltalk. This system allows to represent and evaluate decision problems and incorporates the explanation facilities mentioned above. The possibility to observe graphically the model as the variables change -sensitivity analysis- permits a better understanding of the problem by finding the significant variables. This process also helps to adjust the variables values. Furthermore, the qualitative probabilistic networks allow to realize model abstractions and simplifications, i.e., to obtain the qualitative relations from the quantitative level. These general conclusions limit the optimal strategy space and allow to qualitatively understanding the relations between actions and events in the model.

O objetivo principal deste trabalho foi analisar o controverso conceito de informação em estatística. Para tal, primeiramente foi estudado o conceito de informação dado por Basu. A seguir, a análise foi dividida em três partes: informação nos dados, informação no experimento e diagramas de influência. Nas duas primeiras etapas, sempre se tentou definir propriedades que uma função de informação deveria satisfazer para se enquadrar ao conceito. Na primeira etapa, foi estudado como o princípio da verossimilhança é uma classe de equivalência decorrente de acreditar que experimentos triviais não trazem informação. Também foram apresentadas métricas que satisfazem o princípio da verossimilhança e estas foram usadas para avaliar um exemplo intuitivo. Na segunda etapa, passamos para o problema da informação de um experimento. Foi apresentada a relação da suficiência de Blackwell com experimentos triviais e o conceito usual de suficiência. Também foi analisada a equivalência de Blackwell e a sua relação com o Princípio da Verossimilhança anteriormente estudado. Além disso, as métricas apresentadas para medir a informação de conjuntos de dados foram adaptadas para também medir a informação de um experimento. Finalmente, observou-se que nas etapas anteriores uma série de simetrias mostraram-se como elementos essenciais do conceito de informação. Para ganhar intuição sobre elas, estas foram reescritas através da ferramenta gráfica dos diagramas de influência. Assim, definições como suficiência, suficiência de Blackwell, suficiência mínima e completude foram reapresentadas apenas usando essa ferramenta. / The main objective of this work is to analyze the controversial concept of information in Statistics. To do so, firstly the concept of information according to Basu is presented. Next, the analysis is divided in three parts: information in a data set, information in an experiment and influence diagrams. In the first two parts, we always tried to define properties an information function should satisfy in order to be in accordance to the concept of Basu. In the first part, it was studied how the likelihood principle is an equivalence class which follows from believing that trivial experiments do not bring information. Metrics which satisfy the likelihood principle were also presented and used to analyze an intuitive example. In the second part, the problem became that of determining information of a particular experiment. The relation between Blackwell\'s suciency, trivial experiments and classical suciency was presented. Blackwell\'s equivalence was also analyzed and its relationship with the Likelihood Principle was exposed. The metrics presented to evaluate the information in a data set were also adapted to do so with experiments. Finally, in the first parts a number of symmetries were shown as essencial elements of the concept of information. To gain more intuition about these elements, we tried to rewrite them using the graphic tool of influence diagrams. Therefore, definitions as sufficiency, Blackwell\'s sufficiency, minimal sufficiency and completeness were shown again, only using influence diagrams.

Predicting human behavior from a small amount of training examples is a challenging machine learning problem. In this thesis, we introduce the principle of maximum causal entropy, a general technique for applying information theory to decision-theoretic, game-theoretic, and control settings where relevant information is sequentially revealed over time. This approach guarantees decision-theoretic performance by matching purposeful measures of behavior (Abbeel & Ng, 2004), and/or enforces game-theoretic rationality constraints (Aumann, 1974), while otherwise being as uncertain as possible, which minimizes worst-case predictive log-loss (Gr¨unwald & Dawid, 2003). We derive probabilistic models for decision, control, and multi-player game settings using this approach. We then develop corresponding algorithms for efficient inference that include relaxations of the Bellman equation (Bellman, 1957), and simple learning algorithms based on convex optimization. We apply the models and algorithms to a number of behavior prediction tasks. Specifically, we present empirical evaluations of the approach in the domains of vehicle route preference modeling using over 100,000 miles of collected taxi driving data, pedestrian motion modeling from weeks of indoor movement data, and robust prediction of game play in stochastic multi-player games.

Air combat is a complex situation, training for it and analysis of possible tactics are time consuming and expensive. In order to circumvent those problems, mathematical models of air combat can be used. This thesis presents air combat as a one-on-one influence diagram game where the influence diagram allows the dynamics of the aircraft, the preferences of the pilots and the uncertainty of decision making in a structural and transparent way to be taken into account. To obtain the players’ game optimal control sequence with respect to their preferences, the influence diagram has to be solved. This is done by truncating the diagram with a moving horizon technique and determining and implementing the optimal controls for a dynamic game which only lasts a few time steps. The result is a working air combat model, where a player estimates the probability that it resides in any of four possible states. The pilot’s preferences are modeled by utility functions, one for each possible state. In each time step, the players are maximizing the cumulative sum of the utilities for each state which each possible action gives. These are weighted with the corresponding probabilities. The model is demonstrated and evaluated in a few interesting aspects. The presented model offers a way of analyzing air combat tactics and maneuvering as well as a way of making autonomous decisions in for example air combat simulators.

O objetivo principal deste trabalho foi analisar o controverso conceito de informação em estatística. Para tal, primeiramente foi estudado o conceito de informação dado por Basu. A seguir, a análise foi dividida em três partes: informação nos dados, informação no experimento e diagramas de influência. Nas duas primeiras etapas, sempre se tentou definir propriedades que uma função de informação deveria satisfazer para se enquadrar ao conceito. Na primeira etapa, foi estudado como o princípio da verossimilhança é uma classe de equivalência decorrente de acreditar que experimentos triviais não trazem informação. Também foram apresentadas métricas que satisfazem o princípio da verossimilhança e estas foram usadas para avaliar um exemplo intuitivo. Na segunda etapa, passamos para o problema da informação de um experimento. Foi apresentada a relação da suficiência de Blackwell com experimentos triviais e o conceito usual de suficiência. Também foi analisada a equivalência de Blackwell e a sua relação com o Princípio da Verossimilhança anteriormente estudado. Além disso, as métricas apresentadas para medir a informação de conjuntos de dados foram adaptadas para também medir a informação de um experimento. Finalmente, observou-se que nas etapas anteriores uma série de simetrias mostraram-se como elementos essenciais do conceito de informação. Para ganhar intuição sobre elas, estas foram reescritas através da ferramenta gráfica dos diagramas de influência. Assim, definições como suficiência, suficiência de Blackwell, suficiência mínima e completude foram reapresentadas apenas usando essa ferramenta. / The main objective of this work is to analyze the controversial concept of information in Statistics. To do so, firstly the concept of information according to Basu is presented. Next, the analysis is divided in three parts: information in a data set, information in an experiment and influence diagrams. In the first two parts, we always tried to define properties an information function should satisfy in order to be in accordance to the concept of Basu. In the first part, it was studied how the likelihood principle is an equivalence class which follows from believing that trivial experiments do not bring information. Metrics which satisfy the likelihood principle were also presented and used to analyze an intuitive example. In the second part, the problem became that of determining information of a particular experiment. The relation between Blackwell\'s suciency, trivial experiments and classical suciency was presented. Blackwell\'s equivalence was also analyzed and its relationship with the Likelihood Principle was exposed. The metrics presented to evaluate the information in a data set were also adapted to do so with experiments. Finally, in the first parts a number of symmetries were shown as essencial elements of the concept of information. To gain more intuition about these elements, we tried to rewrite them using the graphic tool of influence diagrams. Therefore, definitions as sufficiency, Blackwell\'s sufficiency, minimal sufficiency and completeness were shown again, only using influence diagrams.

Les travaux de recherche présentés dans ce document s'inscrivent dans le cadre de la gestion des connaissances appliquée à la déconstruction des avions en fin de vie avec pour objectif l'aide à la décision par l'évaluation des risques. Pour répondre à cet objectif, nous avons développé des mécanismes d'aide à la décision hybridant les retours d'expérience statistique et cognitif pour évaluer les risques sur les zones critiques d'un système. L'approche proposée permet la combinaison des avis d'experts du domaine avec des statistiques issues d'une base de données en utilisant les fonctions de croyance. L'évaluation des risques est réalisée par le traitement des connaissances combinées au moyen d'un modèle utilisant les réseaux évidentiels dirigés. Ce document s'articule en quatre chapitres.Le premier chapitre constitue un état de l'art abordant les notions liées au risque et au retour d'expérience. Il permet de définir les concepts clés concernant l'évaluation du risque, la gestion des connaissances (et en particulier le processus de retour d'expérience) ainsi que les passerelles entre ces deux concepts. Le second chapitre permet d'introduire un modèle d'évaluation des risques basé sur les méthodes bayésiennes. Cependant, les méthodes bayésiennes ont des limites, en particulier pour ce qui concerne la modélisation de l'incertitude épistémique inhérente aux avis d'experts, qui nous ont incité à proposer des alternatives, telles les fonctions de croyance et les réseaux évidentiels dirigés que nous avons finalement choisi d'utiliser. Le troisième chapitre propose une démarche permettant d'évaluer les risques en utilisant les réseaux évidentiels dirigés. L'approche proposée décrit les mécanismes utilisés pour formaliser et fusionner les connaissances expertes et statistiques, puis pour traiter ces connaissances au moyen des réseaux évidentiels dirigés. Pour finir, des indicateurs permettant la restitution des résultats au décideur sont introduits. Le dernier chapitre présente le projet DIAGNOSTAT qui a servi de cadre à ces travaux de recherche et expose un cas d'étude permettant d'appliquer la démarche introduite précédemment à la déconstruction des avions en fin de vie au moyen de deux scénarios / The research work presented in this document relates to knowledge management applied to aircraft deconstruction. The aim of this work is to provide a decision support system for risk assessment. To meet this objective, mechanisms for decision support hybridizing cognitive and statistical experience feedback to perform risk assessment on system critical areas have been developed. The proposed approach allows to combine expert opinion with statistics extracted from a database by using belief functions. The risk assessment is performed by the combined knowledge processing using a model based on directed evidential networks. This document is divided into four chapters. The first chapter is a state of the art addressing concepts related to risk and experience feedback. It defines key concepts for risk assessment, knowledge management (in particular the experience feedback process) and the links between these two concepts. The second chapter allows to introduce a risk assessment model based on Bayesian methods. However, Bayesian methods have some limitations, particularly with respect to epistemic uncertainty modelling. That is why, some alternatives have been proposed, such as belief functions and directed evidential networks that we finally chose to use. The third chapter proposes an approach for assessing the risk using directed evidential networks. The proposed approach describes the mechanisms used to formalize and combine expert and statistical knowledge, and then to process this knowledge with directed evidential networks. Finally, indicators to inform the decision maker about results are introduced. The last chapter presents the DIAGNOSTAT project which provided the framework for this research and a study case to apply the approach introduced earlier for aircraft deconstruction by using two scenarios

Dans de nombreuses applications réelles, nous sommes souvent confrontés à des problèmes de décision: de choisir des actions et de renoncer à d'autres. Les problèmes de décision deviennent complexes lorsque les connaissances disponibles sont entachées d'incertitude ou lorsque le choix établi présente un risque.L'un des principaux domaines de l'Intelligence Artificielle (IA) consiste à représenter les connaissances, à les modéliser et à raisonner sur celles-ci. Dans cette thèse, nous sommes intéressés à une discipline inhérente à l'IA portant sur les problèmes de décision. La théorie de la décision possibiliste qualitative a élaboré plusieurs critères, selon le comportement de l'agent, permettant de l'aider à faire le bon choix tout en maximisant l'un de ces critères. Dans ce contexte, la théorie des possibilités offre d'une part un cadre simple et naturel pour représenter l'incertitude et d'autre part, elle permet d'exprimer les connaissances d'une manière compacte à base de modèles logiques ou de modèles graphiques. Nous proposons dans cette thèse d'étudier la représentation et la résolution des problèmes de la décision qualitative en utilisant la théorie des possibilités. Des contreparties possibilistes des approches standards ont été proposées et chaque approche a pour objectif d'améliorer le temps de calcul des décisions optimales et d'apporter plus d'expressivité à la forme de représentation du problème. Dans le cadre logique, nous avons proposé une nouvelle méthode, pour résoudre un problème de la décision qualitative modélisé par des bases logiques possibilistes, basée sur la fusion syntaxique possibiliste. Par la suite, dans le cadre graphique, nous avons proposé un nouveau modèle graphique, basé sur les réseaux possibilistes, permettant la représentation des problèmes de décision sous incertitude. En effet, lorsque les connaissances et les préférences de l'agent sont exprimées de façon qualitative, nous avons proposé de les représenter par deux réseaux possibilistes qualitatifs distincts. Nous avons développé un algorithme pour le calcul des décisions optimales optimistes qui utilise la fusion de deux réseaux possibilistes. Nous avons montré aussi comment une approche basée sur les diagrammes d'influence peut être codée d'une manière équivalente dans notre nouveau modèle. Nous avons en particulier proposé un algorithme polynomial qui permet de décomposer le diagramme d'influence en deux réseaux possibilistes. Dans la dernière partie de la thèse, nous avons défini le concept de la négation d'un réseau possibiliste qui pourra servir au calcul des décisions optimales pessimistes. / In many applications, we are often in presence of decision making problems where the choice of appropriate actions need to be done. When the choice is clear and the risks are null, the decision becomes easy to select right actions. Decisions are more complex when available knowledge is flawed by uncertainty or when the established choice presents a risk. One of the main areas of Artificial Intelligence (AI) is to model, represent and reason about knowledge. In this thesis, we are interested in an inherent discipline in AI which concerns decision making problems.The qualitative possibility decision theory has developed several criteria, depending on the agent behavior, for helping him to make the right choice while maximizing one of these criteria. In this context, possibility theory provides a simple and natural way to encode uncertainty. It allows to express knowledge in a compact way using logical and graphical models. We propose in this thesis to study the representation and resolution of possibilistic qualitative decision problems. Possibilistic counterparts of standard approaches have been proposed and each approach aims to improve the computational complexity of computing optimal decisions and to provide more expressiveness to the representation model of the problem. In the logical framework, we proposed a new method for solving a qualitative decision problem, encoded by possibilistic bases, based on syntactic representations of data fusion problems. Subsequently, in a graphical framework, we proposed a new graphical model for decision making under uncertainty based on qualitatif possibilistic networks. Indeed, when agent's knowledge and preferences are expressed in a qualitative way, we suggest to encode them by two distinct qualitative possibilistic networks. We developed an efficient algorithm for computing optimistic optimal decisions based on syntactic counterparts of the possibilistic networks fusion. We also showed how an influence diagram can be equivalently represented in our new model. In particular, we proposed a polynomial algorithm for equivalently decomposing a given possibilistic influence diagram into two qualitatif possibilistic networks. In the last part of the thesis, we defined the concept of negated possibilistic network that can be used for computing optimal pessimistic decisions.

Purpose The purpose of the research was firstly, to determine the competencies required of risk managers and secondly, to consider the implications of such competencies in determining possible modules for inclusion in the design of a specialised undergraduate qualification in Risk Management. Methodology A qualitative research approach was followed, involving focus group interview sessions as part of an Interactive Qualitative Analysis (IQA) research study. Focus Group 1 comprised of academics teaching risk management at public universities in South Africa, and Focus Group 2 comprised of risk management practitioners in South Africa. Findings The competencies identified are business management and risk management knowledge; attributes such as assertiveness and courage; values such as ethics and integrity; as well as people, business and technical skills. Research implications The unique contribution of the current research was the innovative use of IQA for data collection, the removal of subjectivity and the rigour in analysing and presenting the results. The results are a starting point or foundation for the design of a specialised undergraduate degree in risk management that will both meet the requirements of the risk management profession and will equip learners with the best possible combination of knowledge, skills, attributes, values and attitudes to effectively manage risk in organisations. The implications for further research are that a study of the design, benchmarking and validation of a curriculum framework for a specialised undergraduate degree in risk management could be conducted. The development of a curriculum framework or curriculum did not form part of the scope of this study. / Okokuqala inhloso yocwaningo, ukuthola amakhono adingekayo kubaphathi bezinhlekelele kanti okwesibili, ukubheka imiphumela yalokho kusebenza ekunqumeni amamojuli angafakwa ekwakhiweni kweziqu ezikhethekile ezingakaphothulwa ngabafundi ku-Risk Management. Kwalandelwa indlela yocwaningo efanelekile, ebandakanya izikhathi zokuxoxisana zamaqembu njengengxenye yocwaningo lwe-Interactive Qualitative Analysis (IQA). I-Focus Group yoku-1 inabafundi abafundisa ukulawulwa kwezinhlekelele emanyuvesi vi kahulumeni aseNingizimu Afrika, kanye neFocus Group yesi-2 inabasebenzi bokulawulwa kobungozi eNingizimu Afrika. Amakhono ahlonziwe ukuphathwa kwebhizinisi nolwazi lokulawulwa kobungozi; anezimpawu ezinjengokuzethemba kanye nokuba nesibindi; ubugugu obufana nokuziphatha nobuqotho; kanye nabantu, amakhono ebhizinisi nezobuchwepheshe. / Die doel van die studie was eerstens om die bekwaamhede waaroor risikobestuurders moet beskik te bepaal, en tweedens, wat die implikasies van sodanige bekwaamhede inhou vir die modules vir insluiting in die ontwerp van ‘n gespesialiseerde voorgraadse kwalifikasie in Risikobestuur. Die studie het ‘n kwalitatiewe navorsingsbenadering gevolg deur gebruik te maak van fokusgroepsessies as deel van ‘n Interaktiewe Kwalitatiewe Ontleding (IKO) navorsingstudie. Fokusgroep 1 het bestaan uit akademici wat risikobestuur by openbare universiteite in Suid-Afrika doseer, en Fokusgroep 2 het uit risikobestuurpraktisyns in Suid-Afrika bestaan. Die bekwaamhede wat identifiseer is, is kennis van ondernemingsbestuur en risikobestuur; eienskappe soos selfgeldendheid en moed; waardes soos etiek en integriteit; asook mense, sake en tegniese vaardighede. / Finance, Risk Management and Banking / M. Com. (Risk Management)