L’inférence du modèle de préférence à partir des jugements préférentiels fournis par le décideur, Élicitation des Préférences (EP), est fondamentale au sein de l’Aide Multicritère à la Décision (AMCD), car l’élaboration des recommandations à la fois plausibles, constructives et convaincantes requiert que l’analyste construise un modèle de préférence qui rende compte fidèlement du jugement du décideur. Cependant, l’EP est une mission délicate, parce qu’il s’agit d’attribuer des valeurs aux paramètres du modèle de préférence choisi. Dans ce cadre, plusieurs aspects sont étudiés. Puisque les modèles de préférence étant de plus en plus complexes, on fait alors appel à des algorithmes sophistiqués, et il faut d’autant plus tenir compte de l’aspect computationnel.Ce travail de thèse vise à concevoir des algorithmes afin d’inférer du modèle de préférence à partir des comparaisons par paire (possiblement incohérentes), et de considérer des données de (relativement) grande taille. En particulier, nous nous sommes intéressés à un modèle de rangement multicritère récemment proposé et faisant appel à un certain nombre de points de référence. Ce modèle fait référence à la méthode intitulée “Ranking with Multiple Profiles” (RMP). Plus précisément, nous considérons une version particulière, dite S-RMP. Nos contributions sont divisées en trois parties. Du point de vue théorique, nous nous adressons sur (1) l’interprètabilité des points de référence et (2) la discriminabilité du modèle S-RMP. En termes d’algorithmes, nous présentons, d’abord, (3) un nouveau programme linéaire pour inférer du modèle S-RMP en tenant compte les incohérences et (4) une version robuste améliorée; en outre, (5) une métaheuristique qui procède avec des données massives. (6) Nous menons alors les analyses numériques. (7) Le développement de deux services web est également inclus. En termes d’application, (8) nous présentons une étude de cas. / The inference of preference model from holistic statements provided by the decision maker (DM), namely, Preference Elicitation (PE), is fundamental to Multi-Criteria Decision Aid (MCDA). In order to conduct plausible, constructive and convincing recommendations, the decision analyst should always take the DM’s preference system into account. However, PE might be tricky, as it involves setting appropriately a series of parameter values of the considered model. Various aspects should be considered. Since the preference models are becoming more and more complicated, PE usually relies on sophisticated algorithms, whereas this brings additionally the computational aspect into consideration.This PhD thesis aims at developing new elicitation algorithms dealing with (possibly inconsistent) pairwise comparisons and processing with (relatively) large input datasets. In particular, a recently introduced multi-criteria ranking method making use of a certain number of reference points is considered. It is known as RMP method as abbreviated for Ranking with Multiple reference Points. More specifically, we are interested in one of its Simplified version, namely S-RMP method. Our contributions are divided into three parts. From the theoretical perspective, we are concerned about (1) the interpretation of reference points in such models and (2) the discriminability of S-RMP model. From the algorithmic perspective, we propose firstly (3) a new linear programming formulation for eliciting S-RMP models from inconsistent pairwise comparisons and also (4) an improved robust elicitation algorithm; besides, (5) a metaheuristic for learning S-RMP models from massive data. (6) Numerical analyses are then performed. (7) The development of two web services is also included. From the practical perspective, (8) we present a realistic case study.

The first part of this doctorate dissertation examines the factors influencing the occurrence and costs of pinhole leak corrosion as well as the household decisions for corrosion prevention and plumbing material selection. Three mail surveys of households were used to elicit the experiences with leaks as well as the optimal corrosion prevention and material choices. Probability modeling (i.e. MNL) and linear regression analysis were used to analyze survey responses. Pinhole leak occurrences were found associated with pipe type installed, property age, pipe failure history, and dwelling distance from a water treatment plant. The number and location of pinhole leaks in the dwelling and the pipe type are associated with the financial costs of pinhole leaks. The corrosion prevention choices as well as the plumbing materials depended on the risk of corrosion and cost associated with each option. Previous experiences with pinhole leak impacted the decision for household choices. Faster responses to pinhole leak outbreaks by utility managers and policymakers in terms of advising homeowners on the best ways of responding to leaks would assist homeowners in reducing costs of pinhole leak repairs and associated damages. The second part of this document deals with the debt financing issues. Debt financing decisions are made simultaneously by lenders and borrowers. Since lenders are unable to observe directly the firms’ investment decisions, the banks offer contracts based up on firms’ observable characteristics (i.e. wealth and size) and the prevailing market conditions. When deciding on the financing decisions, firms also take into account the changes in macroeconomic variables in order to lower the cost of borrowing. As a result, the goal for this article is to examine empirically the hypothesis of the effect of the debt determinant as well as the macroeconomic variables on the debt maturity structure. A reduced form of the simultaneous financing decisions model is estimated by employing several OLS estimation methods. The empirical findings offer strong support for firms with few growth options, large, and of low quality having more long-term debt in their capital structure. There was, however, no clear support for the impact of macroeconomic variables on debt maturity as some variables were not statistically significant. / Ph. D.

Recentemente, várias tarefas tem sido delegadas a sistemas computacionais, principalmente quando sistemas computacionais são mais confiáveis ou quando as tarefas não são adequadas para seres humanos. O uso de extração de preferências ajuda a realizar a delegação, permitindo que mesmo pessoas leigas possam programar facilmente um sistema computacional com suas preferências. As preferências de uma pessoa são obtidas por meio de respostas para questões específicas, que são formuladas pelo próprio sistema computacional. A pessoa age como um usuário do sistema computacional, enquanto este é visto como um agente que age no lugar da pessoa. A estrutura e contexto das questões são apontadas como fonte de variações das respostas do usuário, e tais variações podem impossibilitar a factibilidade da extração de preferências. Uma forma de evitar tais variações é questionar um usuário sobre a sua preferência entre dois comportamentos observados por ele. A questão de avaliar relativamente comportamentos observados é mais simples e transparente ao usuário, diminuindo as possíveis variações, mas pode não ser fácil para o agente interpretar tais avaliações. Se existem divergências entre as percepções do agente e do usuário, o agente pode ficar impossibilitado de aprender as preferências do usuário. As avaliações são geradas com base nas percepções do usuário, mas tudo que um agente pode fazer é relacionar tais avaliações às suas próprias percepções. Um outro problema é que questões, que são expostas ao usuário por meio de comportamentos demonstrados, são agora restritas pela dinâmica do ambiente e um comportamento não pode ser escolhido arbitrariamente. O comportamento deve ser factível e uma política de ação deve ser executada no ambiente para que um comportamento seja demonstrado. Enquanto o primeiro problema influencia a inferência de como o usuário avalia comportamentos, o segundo problema influencia quão rápido e acurado o processo de aprendizado pode ser feito. Esta tese propõe o problema de Extração de Preferências com base em Comportamentos Observados utilizando o arcabouço de Processos Markovianos de Decisão, desenvolvendo propriedades teóricas em tal arcabouço que viabilizam computacionalmente tal problema. O problema de diferentes percepções é analisado e soluções restritas são desenvolvidas. O problema de demonstração de comportamentos é analisado utilizando formulação de questões com base em políticas estacionárias e replanejamento de políticas, sendo implementados algoritmos com ambas soluções para resolver a extração de preferências em um cenário sob condições restritas. / Recently, computer systems have been delegated to accomplish a variety of tasks, when the computer system can be more reliable or when the task is not suitable or not recommended for a human being. The use of preference elicitation in computational systems helps to improve such delegation, enabling lay people to program easily a computer system with their own preference. The preference of a person is elicited through his answers to specific questions, that the computer system formulates by itself. The person acts as an user of the computer system, whereas the computer system can be seen as an agent that acts in place of the person. The structure and context of the questions have been pointed as sources of variance regarding the users answers, and such variance can jeopardize the feasibility of preference elicitation. An attempt to avoid such variance is asking an user to choose between two behaviours that were observed by himself. Evaluating relatively observed behaviours turn questions more transparent and simpler for the user, decreasing the variance effect, but it might not be easier interpreting such evaluations. If divergences between agents and users perceptions occur, the agent may not be able to learn the users preference. Evaluations are generated regarding users perception, but all an agent can do is to relate such evaluation to his own perception. Another issue is that questions, which are exposed to the user through behaviours, are now constrained by the environment dynamics and a behaviour cannot be chosen arbitrarily, but the behaviour must be feasible and a policy must be executed in order to achieve a behaviour. Whereas the first issue influences the inference regarding users evaluation, the second problem influences how fast and accurate the learning process can be made. This thesis proposes the problem of Preference Elicitation under Evaluations over Observed Behaviours using the Markov Decision Process framework and theoretic properties in such framework are developed in order to turn such problem computationally feasible. The problem o different perceptions is analysed and constraint solutions are developed. The problem of demonstrating a behaviour is considered under the formulation of question based on stationary policies and non-stationary policies. Both type of questions was implemented and tested to solve the preference elicitation in a scenario with constraint conditions.

Recentemente, várias tarefas tem sido delegadas a sistemas computacionais, principalmente quando sistemas computacionais são mais confiáveis ou quando as tarefas não são adequadas para seres humanos. O uso de extração de preferências ajuda a realizar a delegação, permitindo que mesmo pessoas leigas possam programar facilmente um sistema computacional com suas preferências. As preferências de uma pessoa são obtidas por meio de respostas para questões específicas, que são formuladas pelo próprio sistema computacional. A pessoa age como um usuário do sistema computacional, enquanto este é visto como um agente que age no lugar da pessoa. A estrutura e contexto das questões são apontadas como fonte de variações das respostas do usuário, e tais variações podem impossibilitar a factibilidade da extração de preferências. Uma forma de evitar tais variações é questionar um usuário sobre a sua preferência entre dois comportamentos observados por ele. A questão de avaliar relativamente comportamentos observados é mais simples e transparente ao usuário, diminuindo as possíveis variações, mas pode não ser fácil para o agente interpretar tais avaliações. Se existem divergências entre as percepções do agente e do usuário, o agente pode ficar impossibilitado de aprender as preferências do usuário. As avaliações são geradas com base nas percepções do usuário, mas tudo que um agente pode fazer é relacionar tais avaliações às suas próprias percepções. Um outro problema é que questões, que são expostas ao usuário por meio de comportamentos demonstrados, são agora restritas pela dinâmica do ambiente e um comportamento não pode ser escolhido arbitrariamente. O comportamento deve ser factível e uma política de ação deve ser executada no ambiente para que um comportamento seja demonstrado. Enquanto o primeiro problema influencia a inferência de como o usuário avalia comportamentos, o segundo problema influencia quão rápido e acurado o processo de aprendizado pode ser feito. Esta tese propõe o problema de Extração de Preferências com base em Comportamentos Observados utilizando o arcabouço de Processos Markovianos de Decisão, desenvolvendo propriedades teóricas em tal arcabouço que viabilizam computacionalmente tal problema. O problema de diferentes percepções é analisado e soluções restritas são desenvolvidas. O problema de demonstração de comportamentos é analisado utilizando formulação de questões com base em políticas estacionárias e replanejamento de políticas, sendo implementados algoritmos com ambas soluções para resolver a extração de preferências em um cenário sob condições restritas. / Recently, computer systems have been delegated to accomplish a variety of tasks, when the computer system can be more reliable or when the task is not suitable or not recommended for a human being. The use of preference elicitation in computational systems helps to improve such delegation, enabling lay people to program easily a computer system with their own preference. The preference of a person is elicited through his answers to specific questions, that the computer system formulates by itself. The person acts as an user of the computer system, whereas the computer system can be seen as an agent that acts in place of the person. The structure and context of the questions have been pointed as sources of variance regarding the users answers, and such variance can jeopardize the feasibility of preference elicitation. An attempt to avoid such variance is asking an user to choose between two behaviours that were observed by himself. Evaluating relatively observed behaviours turn questions more transparent and simpler for the user, decreasing the variance effect, but it might not be easier interpreting such evaluations. If divergences between agents and users perceptions occur, the agent may not be able to learn the users preference. Evaluations are generated regarding users perception, but all an agent can do is to relate such evaluation to his own perception. Another issue is that questions, which are exposed to the user through behaviours, are now constrained by the environment dynamics and a behaviour cannot be chosen arbitrarily, but the behaviour must be feasible and a policy must be executed in order to achieve a behaviour. Whereas the first issue influences the inference regarding users evaluation, the second problem influences how fast and accurate the learning process can be made. This thesis proposes the problem of Preference Elicitation under Evaluations over Observed Behaviours using the Markov Decision Process framework and theoretic properties in such framework are developed in order to turn such problem computationally feasible. The problem o different perceptions is analysed and constraint solutions are developed. The problem of demonstrating a behaviour is considered under the formulation of question based on stationary policies and non-stationary policies. Both type of questions was implemented and tested to solve the preference elicitation in a scenario with constraint conditions.

In many real world situations, especially involving multiagent systems and artificial intelligence, participating agents often need to agree upon a common alternative even if they have differing preferences over the available alternatives. Voting is one of the tools of choice in these situations. Common and classic applications of voting in modern applications include collaborative filtering and recommender systems, metasearch engines, coordination and planning among multiple automated agents etc. Agents in these applications usually have computational power at their disposal. This makes the study of computational aspects of voting crucial. This thesis is devoted to a study of computational complexity of several fundamental algorithmic and complexity-theoretic problems arising in the context of voting theory. The typical setting for our work is an “election”; an election consists of a set of voters or agents, a set of alternatives, and a voting rule. The vote of any agent can be thought of as a ranking (more precisely, a complete order) of the set of alternatives. A voting profile comprises a collection of votes of all the agents. Finally, a voting rule is a mapping that takes as input a voting profile and outputs an alternative, which is called the “winner” or “outcome” of the election. Our contributions in this thesis can be categorized into three parts and are described below. Part I: Preference Elicitation. In the first part of the thesis, we study the problem of eliciting the preferences of a set of voters by asking a small number of comparison queries (such as who a voter prefers between two given alternatives) for various interesting domains of preferences. We commence with considering the domain of single peaked preferences on trees in Chapter 3. This domain is a significant generalization of the classical well studied domain of single peaked preferences. The domain of single peaked preferences and its generalizations are hugely popular among political and social scientists. We show tight dependencies between query complexity of preference elicitation and various parameters of the single peaked tree, for example, number of leaves, diameter, path width, maximum degree of a node etc. We next consider preference elicitation for the domain of single crossing preference profiles in Chapter 4. This domain has also been studied extensively by political scientists, social choice theorists, and computer scientists. We establish that the query complexity of preference elicitation in this domain crucially depends on how the votes are accessed and on whether or not any single crossing ordering is a priori known. Part II: Winner Determination. In the second part of the thesis, we undertake a study of the computational complexity of several important problems related to determining winner of an election. We begin with a study of the following problem: Given an election, predict the winners of the election under some fixed voting rule by sampling as few votes as possible. We establish optimal or almost optimal bounds on the number of votes that one needs to sample for many commonly used voting rules when the margin of victory is at least n (n is the number of voters and is a parameter). We next study efficient sampling based algorithms for estimating the margin of victory of a given election for many common voting rules. The margin of victory of an election is a useful measure that captures the robustness of an election outcome. The above two works are presented in Chapter 5. In Chapter 6, we design an optimal algorithm for determining the plurality winner of an election when the votes are arriving one-by-one in a streaming fashion. This resolves an intriguing question on finding heavy hitters in a stream of items, that has remained open for more than 35 years in the data stream literature. We also provide near optimal algorithms for determining the winner of a stream of votes for other popular voting rules, for example, veto, Borda, maximin etc. Voters’ preferences are often partial orders instead of complete orders. This is known as the incomplete information setting in computational social choice theory. In an incomplete information setting, an extension of the winner determination problem which has been studied extensively is the problem of determining possible winners. We study the kernelization complexity (under the complexity-theoretic framework of parameterized complexity) of the possible winner problem in Chapter 7. We show that there do not exist kernels of size that is polynomial in the number of alternatives for this problem for commonly used voting rules under a plausible complexity theoretic assumption. However, we also show that the problem of coalitional manipulation which is an important special case of the possible winner problem admits a kernel whose size is polynomial bounded in the number of alternatives for common voting rules. \Part III: Election Control. In the final part of the thesis, we study the computational complexity of various interesting aspects of strategic behaviour in voting. First, we consider the impact of partial information in the context of strategic manipulation in Chapter 8. We show that lack of complete information makes the computational problem of manipulation intractable for many commonly used voting rules. In Chapter 9, we initiate the study of the computational problem of detecting possible instances of election manipulation. We show that detecting manipulation may be computationally easy under certain scenarios even when manipulation is intractable. The computational problem of bribery is an extensively studied problem in computational social choice theory. We study computational complexity of bribery when the briber is “frugal” in nature. We show for many common voting rules that the bribery problem remains intractable even when the briber’s behaviour is restricted to be frugal, thereby strengthening the intractability results from the literature. This forms the subject of Chapter 10.

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The thesis provides a comprehensive overview of key issues affecting practice, policy and patients, in current efforts for colorectal cancer (CRC) disease control. The global burden of CRC is expected to increase by 60% to more than 2.2 million new cases and 1.1 million deaths by 2030. CRC incidence and mortality rates vary up to 10-fold worldwide, which is thought to reflect variation in lifestyles, especially diet. Better primary prevention, and more effective early detection, in screening and surveillance, are needed to reduce the number of patients with CRC in future1. The risk factors for CRC development include genetic, behavioural, environmental and socio-economic factors. Changes to surveillance, which offer non-invasive testing and provide primary prevention interventions represent promising opportunities to improve outcomes and personalise care in those at risk of CRC. By systematic review of the literature, I highlight the gaps in comparative effectiveness analyses of post-polypectomy surveillance. Using micro-simulation methods I assess the role of non-invasive, faecal immunochemical testing in surveillance programmes, to optimise post-polypectomy surveillance programmes, and in an accompanying sub-study, I explore the value of adding an adjunct diet and lifestyle intervention. The acceptability of such revisions is exposed to patient preference evaluation by discrete choice experiment methods. These preferences are accompanied by evidence generated from the prospective evaluation of the health literacy, numeracy, sedentary behaviour levels, body mass index (BMI) and information provision about cancer risk factors, to highlight the potential opportunities for personalisation and optimisation of surveillance. Additional analysis examines the optimisation of a screening programme facing colonoscopy constraints, highlighting the attendant potential to reduce costs and save lives within current capacity.