Optimization is a very important field with diverse applications in physical, social and biological sciences and in various areas of engineering. It appears widely in ma-chine learning, information retrieval, regression, estimation, operations research and a wide variety of computing domains. The subject is being deeply studied both theoretically and experimentally and several algorithms are available in the literature. These algorithms which can be executed (sequentially or concurrently) on a computing machine explore the space of input parameters to seek high quality solutions to the optimization problem with the search mostly guided by certain structural properties of the objective function. In certain situations, the setting might additionally demand for “absolute optimum” or solutions close to it, which makes the task even more challenging. In this thesis, we propose an optimization algorithm which is “gradient-free”, i.e., does not employ any knowledge of the gradient or higher order derivatives of the objective function, rather utilizes objective function values themselves to steer the search. The proposed algorithm is particularly effective in a black-box setting, where a closed-form expression of the objective function is unavailable and gradient or higher-order derivatives are hard to compute or estimate. Our algorithm is inspired by the well known cross entropy (CE) method. The CE method is a model based search method to solve continuous/discrete multi-extremal optimization problems, where the objective function has minimal structure. The proposed method seeks, in the statistical manifold of the parameters which identify the probability distribution/model defined over the input space to find the degenerate distribution concentrated on the global optima (assumed to be finite in quantity). In the early part of the thesis, we propose a novel stochastic approximation version of the CE method to the unconstrained optimization problem, where the objective function is real-valued and deterministic. The basis of the algorithm is a stochastic process of model parameters which is probabilistically dependent on the past history, where we reuse all the previous samples obtained in the process till the current instant based on discounted averaging. This approach can save the overall computational and storage cost. Our algorithm is incremental in nature and possesses attractive features such as stability, computational and storage efficiency and better accuracy. We further investigate, both theoretically and empirically, the asymptotic behaviour of the algorithm and find that the proposed algorithm exhibits global optimum convergence for a particular class of objective functions. Further, we extend the algorithm to solve the simulation/stochastic optimization problem. In stochastic optimization, the objective function possesses a stochastic characteristic, where the underlying probability distribution in most cases is hard to comprehend and quantify. This begets a more challenging optimization problem, where the ostentatious nature is primarily due to the hardness in computing the objective function values for various input parameters with absolute certainty. In this case, one can only hope to obtain noise corrupted objective function values for various input parameters. Settings of this kind can be found in scenarios where the objective function is evaluated using a continuously evolving dynamical system or through a simulation. We propose a multi-timescale stochastic approximation algorithm, where we integrate an additional timescale to accommodate the noisy measurements and decimate the effects of the gratuitous noise asymptotically. We found that if the objective function and the noise involved in the measurements are well behaved and the timescales are compatible, then our algorithm can generate high quality solutions. In the later part of the thesis, we propose algorithms for reinforcement learning/Markov decision processes using the optimization techniques we developed in the early stage. MDP can be considered as a generalized framework for modelling planning under uncertainty. We provide a novel algorithm for the problem of prediction in reinforcement learning, i.e., estimating the value function of a given stationary policy of a model free MDP (with large state and action spaces) using the linear function approximation architecture. Here, the value function is defined as the long-run average of the discounted transition costs. The resource requirement of the proposed method in terms of computational and storage cost scales quadratically in the size of the feature set. The algorithm is an adaptation of the multi-timescale variant of the CE method proposed in the earlier part of the thesis for simulation optimization. We also provide both theoretical and empirical evidence to corroborate the credibility and effectiveness of the approach. In the final part of the thesis, we consider a modified version of the control problem in a model free MDP with large state and action spaces. The control problem most commonly addressed in the literature is to find an optimal policy which maximizes the value function, i.e., the long-run average of the discounted transition payoffs. The contemporary methods also presume access to a generative model/simulator of the MDP with the hidden premise that observations of the system behaviour in the form of sample trajectories can be obtained with ease from the model. In this thesis, we consider a modified version, where the cost function to be optimized is a real-valued performance function (possibly non-convex) of the value function. Additionally, one has to seek the optimal policy without presuming access to the generative model. In this thesis, we propose a stochastic approximation algorithm for this peculiar control problem. The only information, we presuppose, available to the algorithm is the sample trajectory generated using a priori chosen behaviour policy. The algorithm is data (sample trajectory) efficient, stable, robust as well as computationally and storage efficient. We provide a proof of convergence of our algorithm to a high performing policy relative to the behaviour policy.

Submitted by JÚLIO HEBER SILVA (julioheber@yahoo.com.br) on 2017-04-27T18:06:17Z No. of bitstreams: 2 Tese - Edwiges Conceição Carvalho Corrêa - 2016.pdf: 5348077 bytes, checksum: 0041304c213db27078ffc050691ac1ab (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2017-05-03T11:59:58Z (GMT) No. of bitstreams: 2 Tese - Edwiges Conceição Carvalho Corrêa - 2016.pdf: 5348077 bytes, checksum: 0041304c213db27078ffc050691ac1ab (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-05-03T11:59:58Z (GMT). No. of bitstreams: 2 Tese - Edwiges Conceição Carvalho Corrêa - 2016.pdf: 5348077 bytes, checksum: 0041304c213db27078ffc050691ac1ab (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-07-28 / Among the themes that appear in the agenda of environmental policy, the water crisis, which affects a large part of the world, is an important item that arouses the interest of governments and several social, environmental and market segments. In the globalized world, public policies and modes of management of water resources can be seen as a strategic and geopolitical issue given the importance of water for productive processes and life itself. In Brazil, water is managed by the basin committee, an administrative unit that is different from the politicaladministrative division of the Federation as established by the Federal Constitution of 1988. This work aims at discussing and understanding the role of the Basin Committee of the Paranaíba River in the of creation and decision processes of policies for the management of water resources in the basin as an integration of the institutional arrangement as defined by Law 9.433/1997, which establishes the participation of the Public Power, users and the organized civil society as agents in the shared management of water resources in Brazil. The investigated problem has been synthesized into an open question: What is the political protagonism of the basin committees within the institutional arrangement of decentralization of the national policy on water resources? The work is grounded on the hypothesis that the role of political protagonist of the basin committees is secondary in the decentralized structure of management of water resources in Brazil. Multi-method quality research was chosen as it is an approach that does not constrain the researcher to any given specific method only, but it also allows a wider range of data collection. In the present study, upon the association of more than one method of qualitative research, the multi-method approach was configured in the use of more than one instrument of field research and in the performed analysis. The work seeks to analyze and understand the dynamics of the decision-making process adopted by the Paranaíba River Basin Committee, with reference to the decentralization format for the management of water resources adopted by Law 9433/1997 and by the National Policy and Water Resources.The Basin Committee of the Paranaíba River was created through a Presidential Decree on 16 July 2002 and installed in 2008. It occupies an area of 63.3% of the state of Goiás, 3.4% of Mato Grosso do Sul, 31.7% of Minas Gerais and 1.6% of the Federal District. It encompasses 197 municipalities and the Federal District4. The research analyzes the period between the installation of the Basin Committee of the Paranaíba River – namely the year 2008 – and the publishing of the Plan of Water Resources of the Basin, in 2014. / Dentre os temas que figuram na pauta da política ambiental, a crise hídrica, que afeta boa parte do mundo, é tema importante e desperta interesse de governos, de vários segmentos sociais, ambientais e de mercado. No cenário globalizado, as políticas públicas e formas de gestão dos recursos hídricos podem ser vistas como uma questão estratégica e de geopolítica, dada a importância da água para os processos produtivos e para a vida. No Brasil, a gestão da água deve ser feita pelo comitê de bacia, que constitui-se como uma unidade administrativa diferente da divisão político-administrativa da Federação, estabelecida pela Constituição Federal de 1988. Este trabalho tem o objetivo de discutir e compreender o papel do Comitê de Bacia do Rio Paranaíba no processo de elaboração e decisão de políticas para a gestão dos recursos hídricos no âmbito da bacia como integrante do arranjo institucional definido pela Lei 9.433/1997, que estabelece a participação do Poder Público, dos usuários e da sociedade civil organizada como atores na gestão compartilhada dos recursos hídricos no Brasil. O problema investigado foi sintetizado em uma pergunta aberta: qual o protagonismo político dos comitês de bacia dentro do arranjo institucional de descentralização da política nacional de recursos hídricos? Parte-se da hipótese de que o papel de protagonista político dos comitês de bacia é secundário na estrutura descentralizada da gestão dos recursos hídricos no Brasil. A opção metodológica foi pela pesquisa qualitativa multimétodo, uma abordagem que permite ao pesquisador/a não se restringir a um método específico, único, mas lançar mão de mais de um caminho para a coleta de dados. No estudo realizado, ao fazer a associação de mais de um método de pesquisa qualitativa, a abordagem multimétodos se configurou na utilização de mais de um instrumento de pesquisa de campo e na análise feita. Busca-se analisar e compreender a dinâmica do processo decisório adotado pelo Comitê de Bacia do Rio Paranaíba, tendo como referência o formato de descentralização da gestão dos recursos hídricos adotada pela Lei 9433/1997 e pela Política Nacional e Recursos Hídricos. O Comitê da Bacia do Rio Paranaíba foi criado pelo Decreto Presidencial de 16 de julho de 2002, e instalado em 2008. Ocupa uma área de 63,3% de Goiás, 3,4% de Mato Grosso do Sul, 31,7% de Minas Gerais e 1,6% do Distrito Federal. Abrange 197 municípios e o Distrito Federal. A pesquisa analisa o período da instalação do Comitê de Bacia do Rio Paranaíba - ano de 2008 - até a publicação do Plano de Recursos Hídricos da Bacia, em 2014.

Avanços recentes no tema de fronteira que exerce fascínio e curiosidade - a Neurociência - vêm explicitando conceitos sofisticados sobre um assunto emergente na Administração: o aumento do conhecimento na área da Neurociência Cognitiva e suas contribuições para a área de tomada de decisão. À luz desses avanços, a presente pesquisa possui natureza exploratória, cuja proposta contribui para integrar os conhecimentos em Neurociência Cognitiva e tomada de decisão estratégica em administração, sob a ótica comportamental. O objetivo principal do estudo foi propor contribuições ao processo de tomada de decisão estratégica a partir dos conhecimentos da Neurociência Cognitiva. Utilizou-se o método da revisão em profundidade da literatura, com o objetivo de apoiar a análise do conteúdo nas dimensões-alvo do estudo: processo de tomada de decisão estratégica, pensamento estratégico sob a ótica da racionalidade limitada, Neurociência Cognitiva e neurociência da decisão. As contribuições obtidas estão alicerçadas em três vertentes: (1) contribuições para a pesquisa, (2) contribuições para as práticas de gestão e (3) contribuições para a didática e ensino. Na perspectiva da pesquisa, a Neurociência Cognitiva possibilita evidências confirmatórias sobre fatores subjetivos, sobretudo os emocionais, que guiam o comportamento do decisor durante as fases do processo decisório, mediante o fornecimento de metodologias para testar teorias e novos conceitos. Na perspectiva das contribuições para a gestão, a ampliação da consciência dos gestores sobre as emoções, heurísticas e vieses presentes no processo decisório estratégico permite: (a) o alinhamento de expectativas sobre os resultados da decisão estratégica; (b) estimular as atitudes da liderança para uma postura mais protagonista no decorrer do processo, resultando em maior inovação nas práticas de gestão; (c) o reconhecimento da intuição associada à criatividade como competência importante para a decisão estratégica, assegurando maior precisão sobre o futuro da decisão; (d) o aceite das heurísticas da mente, possibilitando simplicidade, facilitando o entendimento de todos os envolvidos e gerando transparência no processo decisório; (e) considerar os objetivos individuais dos decisores não declarados no nível da organização, otimizando a implementação do plano estratégico; (f) o fornecimento de informações sobre a política nas decisões estratégicas, mediante a aplicação de técnicas neurocientíficas que possam trazer maior conhecimento sobre o peso da evidência na tomada de decisão estratégica. Há de se considerar, ainda, que o reforço da aprendizagem, acarretando possíveis mudanças biológicas nas sinapses cerebrais, contribui para o exercício do pensamento estratégico e, consequentemente, maior precisão nas decisões futuras. A incorporação da abordagem neurocientífica na didática do ensino sobre tomada de decisão estratégica contribui para: (a) preparo do aluno afim de superar fatores de ordem cognitiva no nível individual e em grupo que encontrarão no processo decisório estratégico; (b) facilitação do embasamento das constatações da teoria de decisão comportamental; (c) reforço da aprendizagem, sugerindo-se a inserção das técnicas de cenários e a análise ambiental com vistas à prática de avaliações prévias sobre eventos incertos que possam afetar o processo decisório estratégico; (d) incorporação do ensino de decisão das competências analíticas e intuitivas encontradas, por exemplo, nos cursos de criatividade e inovação, alinhando-se as técnicas formais de ensino com a prática da gestão. Além da relevância dos pontos citados, a pesquisa da temática é inédita, o que possibilita uma nova abordagem de pesquisas em decisão estratégica que incorpore as contribuições da Neurociência Cognitiva. / Recent advances in the pioneering theme that brings fascination and curiosity - Neuroscience - have been explaining sophisticated concepts in an Administration emergent topic: the improving knowledge in the Cognitive Neuroscience field and its contribution for the decision making studies. In light of these advances, this research has an exploratory approach, which proposal contributes to the integration of the Cognitive Neuroscience and strategic decision making in administration from the behavioral viewpoint. The main goal of this study is to propose involvement to the strategic decision making process from the Cognitive Neuroscience knowledge. The deep literature revision method was used to target the content analysis of the study dimensions: strategic decision making process, strategic thinking from the perspective of bounded rationality, Cognitive Neuroscience and decision neuroscience. There are three thresholds for the achieved contributions: (1) involvement to the research, (2) management of best practices and (3) inputs to the teaching and learning process. From the perspective of this research, the Cognitive Neuroscience provides confirmatory evidence on subjective factors especially the emotional ones, which guide the decision maker behavior in the decision process, by providing methodologies for new theories and concepts proof. From the management contribution perspective, the expansion of the manager awareness on emotions, heuristics and biases in decision-making process allows: (a) alignment of the expectations on the decision making results; (b) encourage the leadership attitude to assume a protagonist posture in the process, resulting in higher innovation in management practices; (c) recognition of the intuition associated with creativity as an important competence for the strategic decision, ensuring a better precision on the decision future; (d) acceptance of the heuristics minds allowing simplicity, facilitating the understanding of all those involved and creating transparency in the decision making process; (e) consider the non-declared decision maker individual goals at the organization level, optimizing the strategic plan implementation; (f) information provision about the strategic decision policies, by applying neuroscientific techniques that can bring better insights into the evidence relevance in the strategic decision making process. One must also consider that the learning enhancement, resulting in possible brain synapses biological changes, contributes to the strategic thinking exercise and, consequently, to more accurate future decisions. The incorporation of neuroscientific approach in didactic teaching on strategic decision-making contributes to: (a) student preparation to overcome cognitive order factors, individually and in groups, that will be found in the strategic decision process; (b) facilitate the basis of the findings of the behavioral decision theory; (c) learning improvement suggesting the insertion of scenario techniques and the environmental analysis focusing on previous assessments practices of uncertain events that may affect the strategic decision process; (d) incorporation of analytical and intuitive decision competences teaching, aligning the formal teaching techniques with the management practices. Besides the relevance of the above mentioned points, the thematic research is unprecedented, which enables a new approach in strategic decision researches to incorporate the Cognitive Neuroscience contributions

隨著資訊時代來臨、社群媒體普及，現今的消費者有能力從網路尋找多元的資訊進行購買決策，消費者也能互相交換資訊。YouTube的時尚美妝影片數量多、內容豐富，消費者在尋找產品或使用教學的影片時，會將其作為搜尋來源之一，並相信這些內容不具商業利益，客觀且公正。 美妝YouTuber樂於創作影音內容，分享產品資訊、購物經驗、和生活事物等議題，進而培養出一群訂閱的觀眾，成為具社群影響力的網路名人；然而這些YouTuber如何影響消費者、為何能吸引特定族群的消費者，目前國內外相關的研究仍十分稀少。 本研究以美妝影音部落格和美妝消費者為研究對象，探討「美妝消費者的購買決策過程，及其如何受到YouTuber之影響」，採用質性的網路民族誌 (netnography) 作為研究方法，先搜集三個YouTube熱門美妝頻道的網路資料，參與留言區的討論，再訪談十六位千禧世代的美妝消費者。本研究發現：一、創作者和觀看者透過YouTube凝聚成美妝「社群」；二、美妝YouTuber具備「發起者」和「影響者」的角色；三、美妝消費者的行為和態度可能受到人氣YouTuber影響。 / Nowadays, consumers search information about products, prices and distribution on the Internet before their purchase decisions so that social media has radically changed the communication landscape. Consumers are able to share their purchase experiences online and interact with other users. More and more users are using YouTube as a search engine as well as looking for makeup tips, tutorials, and product recommendations because consumers would believe these user-generated contents are reliable and trustable. The aim of this research is to explore how YouTubers of beauty industry in Taiwan, have influenced their viewers and to what extent this influence on the purchase decision process. This study uses a qualitative approach ‘netnography’ as the main research methods to collect data. Three YouTube beauty channels were observed and participated; additionally, 16 in-depth interviews with millennial consumers were conducted. The research finds out that YouTube beauty channels can be seen as communities where relationships between YouTubers and viewers is formed. YouTubers are the initiator and influncer on the purchase decision journey. The study also found that the popularity of YouTuber may affect consumers’ behaviors and attitudes.

Pour faire face à la croissance rapide du trafic mobile, différentes technologies d'accès radio (par exemple, HSPA, LTE, WiFi, et WiMAX) sont intégrées et gérées conjointement. Dans ce contexte, la sélection de TAR est une fonction clé pour améliorer les performances du réseau et l'expérience de l'utilisateur. Elle consiste à décider quelle TAR est la plus appropriée aux mobiles. Quand l'intelligence est poussée à la périphérie du réseau, les mobiles décident de manière autonome de leur meilleur TAR. Ils cherchent à maximiser égoïstement leur utilité. Toutefois, puisque les mobiles ne disposent d'aucune information sur les conditions de charge du réseau, leurs décisions peuvent conduire à une inefficacité de la performance. En outre, déléguer les décisions au réseau optimise la performance globale, mais au prix d'une augmentation de la complexité du réseau, des charges de signalisation et de traitement. Dans cette thèse, au lieu de favoriser une de ces deux approches décisionnelles, nous proposons un cadre de décision hybride: le réseau fournit des informations pour les mobiles pour mieux décider de leur TAR. Plus précisément, les utilisateurs mobiles choisissent leur TAR en fonction de leurs besoins et préférences individuelles, ainsi que des paramètres de coût monétaire et de QoS signalés par le réseau. En ajustant convenablement les informations du réseau, les décisions des utilisateurs répondent globalement aux objectifs de l'opérateur. Nous introduisons d'abord notre cadre de décision hybride. Afin de maximiser l'expérience de l'utilisateur, nous présentons une méthode de décision multicritère (MDMC) basée sur la satisfaction. Outre leurs conditions radio, les utilisateurs mobiles tiennent compte des paramètres de coût et de QoS, signalées par le réseau, pour évaluer les TAR disponibles. En comparaison avec les solutions existantes, notre algorithme répond aux besoins de l'utilisateur (par exemple, les demandes en débit, la tolérance de coût, la classe de trafic), et évite les décisions inadéquates. Une attention particulière est ensuite portée au réseau pour s'assurer qu'il diffuse des informations décisionnelles appropriées, afin de mieux exploiter ses ressources radio alors que les mobiles maximisent leur propre utilité. Nous présentons deux méthodes heuristiques pour dériver dynamiquement quoi signaler aux mobiles. Puisque les paramètres de QoS sont modulées en fonction des conditions de charge, l'exploitation des ressources radio s'est avérée efficace. Aussi, nous nous concentrons sur l'optimisation de l'information du réseau. La dérivation des paramètres de QoS est formulée comme un processus de décision semi-markovien, et les stratégies optimales sont calculées en utilisant l'algorithme de Policy Iteration. En outre, et puisque les paramètres du réseau ne peuvent pas être facilement obtenues, une approche par apprentissage par renforcement est introduite pour dériver quoi signaler aux mobiles. / To cope with the rapid growth of mobile broadband traffic, various radio access technologies (e.g., HSPA, LTE, WiFi, and WiMAX) are being integrated and jointly managed. Radio Access Technology (RAT) selection, devoted to decide to what RAT mobiles should connect, is a key functionality to improve network performance and user experience. When intelligence is pushed to the network edge, mobiles make autonomous decisions regarding selection of their most appropriate RAT. They aim to selfishly maximize their utility. However, because mobiles have no information on network load conditions, their decisions may lead to performance inefficiency. Moreover, delegating decisions to the network optimizes overall performance, but at the cost of increased network complexity, signaling, and processing load. In this thesis, instead of favoring either of these decision-making approaches, we propose a hybrid decision framework: the network provides information for the mobiles to make robust RAT selections. More precisely, mobile users select their RAT depending on their individual needs and preferences, as well as on the monetary cost and QoS parameters signaled by the network. By appropriately tuning network information, user decisions are globally expected to meet operator objectives, avoiding undesirable network states. We first introduce our hybrid decision framework. Decision makings, on the network and user sides, are investigated. To maximize user experience, we present a satisfaction-based Multi-Criteria Decision-Making (MCDM) method. In addition to their radio conditions, mobile users consider the cost and QoS parameters, signaled by the network, to evaluate serving RATs. In comparison with existing MCDM solutions, our algorithm meets user needs (e.g., traffic class, throughput demand, cost tolerance), avoiding inadequate decisions. A particular attention is then addressed to the network to make sure it broadcasts suitable decisional information, so as to better exploit its radio resources while mobiles maximize their own utility. We present two heuristic methods to dynamically derive what to signal to mobiles. While QoS parameters are modulated as a function of the load conditions, radio resources are shown to be efficiently exploited. Moreover, we focus on optimizing network information. Deriving QoS parameters is formulated as a semi-Markov decision process, and optimal policies are computed using the Policy Iteration algorithm. Also, and since network parameters may not be easily obtained, a reinforcement learning approach is introduced to derive what to signal to mobiles. The performances of optimal, learning-based, and heuristic policies are analyzed. When thresholds are pertinently set, our heuristic method provides performance very close to the optimal solution. Moreover, although lower performances are observed, our learning-based algorithm has the crucial advantage of requiring no prior parameterization.

This thesis analyzes Czech cosmetic company Dermacol in the on-line environment. The crucial goal was to give a compact overview of Dermacol's on-line activities and propose their possible improvement. The theoretical part describes and contrasts the purchase decision process in a traditional store and on the internet. Furthemore, it depicts the evolution of the internet and e-commerce in the Czech Republic. In the next part the situation on the market with decorative cosmetics and Dermacol's position on it is analyzed. Crucial part is dedicated to the on-line tools which are used by Dermacol for its presentation, communication and sale. The recommendations and possible proposals for improvement follow. In the end the thesis deals with a research whose goal was to find out the attitude of the Czech consumers towards decorative cosmetics. Moreover, the research examined the awareness about Dermacol's web and e-hop and the willingness of the Czech women to buy decorative cosmetics via internet.

Theme of this thesis is how to choose between agile methodologies of software development. Several agile methodologies, such as SCRUM, Kanban and eXtreme programming are described and also methods of choosing between methodologies or management techniques are discussed. New method of multi-criteria decision process on how to choose between Agile methodologies based on multi-dimensionality is presented with option of improving agile experience by adopting additional techniques. Diploma thesis can serve as managers' hand-book for those who want to change their current software development methodologies or are searching for ways of improving their agile adoption.

Since the beginning of 2000 the media have often referred to the decrease in the number of teachers in South Africa. In the light of the discrepancy between the supply and demand of teachers, I have explored some factors that possibly contribute to the career choice of teachers. I departed from a positivist as well as an interpretevist approach. The research included the implementation of a First-year Teacher Questionnaire. Some career development theories are discussed as the theoretical grounding for the career choice of first-year teaching students. First-year teaching students generally have a positive perception of teaching, based mainly on their belief that teaching offers ample opportunities for potential-facilitation, selffulfilment, self-discovery, as well as fringe benefits. It seems that where negative perceptions existed, they were based on observable hindrances and personal issues. My research complements the Social Cognitive Career Theory and confirms the concern for the fact that a relatively small percentage African language-speaking students, especially males, choose teaching as a field of study / Dissertation (MEd (Educational Psychology))--University of Pretoria, 2007. / Educational Psychology / unrestricted

<p>Forest management involves numerous stochastic elements. To sustainably manage forest resources, it is crucial to acknowledge these sources as uncertainty or risk, and incorporate them in adaptive decision-making. Here, I developed several stochastic programming models in the form of passive or active adaptive management for natural mixed-species hardwood forests in Indiana. I demonstrated how to use these tools to deal with time-invariant and time-variant natural disturbances in optimal planning of harvests.</p> <p> Markov decision process (MDP) models were first constructed based upon stochastic simulations of an empirical forest growth model for the forest type of interest. Then, they were optimized to seek the optimal or near-optimal harvesting decisions while considering risk and uncertainty in natural disturbances. In particular, a classic expected-criterion infinite-horizon MDP model was first used as a passive adaptive management tool to determine the optimal action for a specific forest state when the probabilities of forest transition remained constant over time. Next, a two-stage non-stationary MDP model combined with a rolling-horizon heuristic was developed, which allowed information update and then adjustments of decisions accordingly. It was used to determine active adaptive harvesting decisions for a three-decade planning horizon during which natural disturbance probabilities may be altered by climate change.</p> <p> The empirical results can be used to make some useful quantitative management recommendations, and shed light on the impacts of decision-making on the forests and timber yield when some stochastic elements in forest management changed. In general, the increase in the likelihood of damages by natural disturbance to forests would cause more aggressive decisions if timber production was the management objective. When windthrow did not pose a threat to mixed hardwood forests, the average optimal yield of sawtimber was estimated to be 1,376 ft³/ac/acre, while the residual basal area was 88 ft²/ac. Assuming a 10 percent per decade probability of windthrow that would reduce the stand basal area considerably, the optimal sawtimber yield per decade would decline by 17%, but the residual basal area would be lowered only by 5%. Assuming that the frequency of windthrow increased in the magnitude of 5% every decade under climate change, the average sawtimber yield would be reduced by 31%, with an average residual basal area slightly around 76 ft²/ac. For validation purpose, I compared the total sawtimber yield in three decades obtained from the heuristic approach to that of a three-decade MDP model making <i>ex post</i> decisions. The heuristic approach was proved to provide a satisfactory result which was only about 18% lower than the actual optimum.</p> These findings highlight the need for landowners, both private and public, to monitor forests frequently and use flexible planning approaches in order to anticipate for climate change impacts. They also suggest that climate change may considerably lower sawtimber yield, causing a concerning decline in the timber supply in Indiana. Future improvements of the approaches used here are recommended, including addressing the changing stumpage market condition and developing a more flexible rolling-horizon heuristic approach.

Early diagnosis is a promising strategy to reduce premature mortalities and for optimal use of resources. But the absence of mathematical models specific to the data settings in LMIC’s impedes the construction of economic analysis necessary for decision-makers in the development of cancer control programs. This thesis presents a new methodology for parameterizing the natural history model of breast cancer based on data availabilities in low and middle income countries, and formulation of a control optimization problem to find the optimal screening schedule for mammography screening, solved using dynamic programming. As harms and benefits are known to increase with the increase in the number of lifetime screens, the trade-off was modeled by formulating the immediate reward as a function of false positives and life-years saved. The method presented in thesis will provide optimal screening schedules for multiple scenarios of Willingness to Pay (numeric value assigned for each life-year lived), including the resulting total number of lifetime screens per person, which can help decision-makers evaluate current resource availabilities or plan future resource needs for implementation.