A feasibility study of an irrigation development proposal should include an analysis of the effects of water supply conditions on the degree to which development objectives are expected to be realised. A method of making this analysis was developed based on procedures for solving two problems. These were; (a) optimally allocating a property's available supply of water among competing crops, and, (b) optimally controlling an open channel distribution system to meet temporally and spatially varying water demand. The procedure developed for solving (a) was applied. A stochastic dynamic programming procedure was developed to optimally schedule the irrigation of a single crop, subject to constraints on the timing of water availability and total application depth. A second procedure was developed, employing a constrained differential dynamic programming algorithm, for determining optimal irrigation schedules for use with variable application depth systems, and when several crops compete for an intra-seasonally limited supply of water. This procedure was called, as frequently as water supply conditions allowed, to provide short-term irrigation schedules in a computer simulation of the optimal irrigation of several crops. An application system model was included in these procedures to transform a crop water-use production function into the required irrigation water-use production function. This transformation was a function of the application device type and the mean application depth. From an analysis of the on-property effects of water supply conditions, it was concluded that in order to achieve high economic and irrigation efficiencies, water supply conditions must be sufficiently flexible to allow the application system operator to vary the mean application depth but not necessarily the time periods of water availability. Additionally, irrigation scheduling procedures which seek economically optimum strategies offer the potential to achieve a maximum level of net benefit at levels of water availability significantly lower than has previously been used for design purposes.

Abstract The negative impact of invasive plant species on biodiversity and ecosystem functions, such as productivity and nutrient cycling has been deemed a global epidemic. To address this worldwide concern, information is needed on how the invasion process happens and how to control an existing invasion. The main aim of the research presented in this thesis was to develop a better understanding of the interacting role different mechanisms play in facilitating invasion and then link this understanding to the design of more effective control strategies. This aim is significant because traditional weed control strategies are not working. The estimated cost of controlling weeds in Australia is $1.4 billion per year in agricultural landscapes. Despite this substantial investment, invasive weed species are estimated to continue to cost the agricultural industry $2.2 billion per year in loss of yield. Current control strategies tend to focus on killing or removing an invasive plant species directly with the application of herbicides and/or mechanical removal. These strategies have proven ineffectual because the plant communities that assemble after management often remain dominated by the same invader or another. In this thesis, I use a combination of empirical and modelling techniques to investigate how disturbance regimes and competitive interactions between invasive plants and native plants can be manipulated to improve the efficacy of restoration efforts. To do this, I use the model scenario of the invasion of Eragrostis curvula (African lovegrass), an invasive grass species introduced into Australia in the early 1900s from South Africa. This species has now spread into every Australian state and territory (chapter 2). I specifically focus on two mechanisms: (1) disturbance, i.e. cattle grazing, and (2) competitive interactions. In chapter 3, I examine connections between dominance and competitive differences among African lovegrass and several functionally similar native grass species in a pasture community. To test the displacement hypothesis, I used a glasshouse competition trial to investigate interactions between African lovegrass and two non-persisting native grass species (Themeda australis and Bothriochloa decipiens) with manipulations of resources, neighbour density, and establishment order. To test the partitioning hypothesis, I compared in situ water use patterns among African lovegrass and two coexisting native grass species (Aristida calycina and Aristida personata) based on the assumption that water is the most limiting resource in this system. The key finding of this chapter is that competition can have important, but contingent, impacts on dominance. Competitive differences appear to partially contribute to abundance patterns after establishment, but may be relatively unimportant during the establishment phase where disturbance appears more critical. In chapter 4, I provide evidence that the identification of mechanisms that led to an invasion, while crucial for the development of effective preventative measures and understanding the invasion process, may not be necessary for the design of more effective control strategies. To examine the effects of different control strategies on African lovegrass and the resultant community, I established a large factorial field-trial with a split-plot design. I manipulated grazing, soil nutrient levels and the presence of the invader. The most common control strategy (removing the causal disturbance and killing the invasive grass), based implicitly on traditional equilibrium models, was not an effective option for restoring a desirable native community. Instead, this strategy led to the dominance of a secondary invader. The most effective control strategy was based on alternative stable states models and involved maintaining grazing, and increasing the palatability of the invader with fertilizers. The key finding of this chapter is that novel approaches for control, which consider the dynamics of the invader-dominated system, are needed. In chapter 5, I investigate the benefits of explicitly incorporating actions that manipulate disturbance (natural or imposed) into control efforts. To do this, I first developed a process model that described the dynamics of an invader whose establishment is preferentially favoured by disturbance. I then couched this model in a decision theory framework, a stochastic dynamic program, and applied a case-study of another invasive plant species, Mimosa pigra (a perennial legume shrub and pan-tropical weed). The key finding of this chapter is that strategies should not only focus on existing invader-dominated sites, but should also protect sites occupied by native species from disturbances that facilitate invasion. The research discussed in this thesis makes three key contributions to a better understanding of the invasion process and the design of more effective control strategies: 1) the search for one key mechanism is not sufficient because multiple mechanisms can interact or shift in importance to facilitate different stages of invasion, 2) a novel approach is needed to restore a more desirable native community because the dynamics of the invader-dominated system can differ from the historical native community, and 3) control efforts should be broadened in focus to include protection of the integrity of native communities from disturbances that facilitate invasion.

La production de bois intègre notamment des enjeux économiques, climatiques et énergétiques. En France, selon les données de l'Institut National de l'Information Géographique et Forestière, l'accroissement biologique de la forêt est largement supérieur aux prélèvements de bois. C'est pourquoi l'État français a fixé l'objectif de prélever 21 millions de m3 supplémentaires de bois d'ici 2020 (Grenelle de l'environnement, 2007). Cependant, la forêt française appartient majoritairement à des propriétaires forestiers privés qui ont des préférences à la fois pour le revenu issu de la vente de bois et pour les aménités non-bois. Les politiques visant à accroître la production de bois doivent donc intégrer ces aspects. L'objectif de ce travail de thèse est de comprendre les déterminants de la production jointe de bois et d'aménités non-bois en France. Pour ce faire, nous nous sommes d'abord intéressés aux déterminants individuels et régionaux de l'offre de bois. Nous montrons que le comportement d'offre de bois d'un propriétaire peut varier en fonction du comportement de production de bois constaté chez ses pairs (effets sociaux). Ensuite, nous mettons en évidence un comportement de mimétisme dans les décisions de production jointe de bois et d'aménités des propriétaires forestiers privés. Enfin, nous analysons les arbitrages inter-temporels réalisés par les propriétaires entre aménités non-bois et revenu de la vente de bois en prenant en compte explicitement les anticipations de prix et de croissance. Nous évaluons à 23e par an la valeur que les propriétaires de notre échantillon accordent à 1m3/ha de bois supplémentaire laissé sur pied par rapport au niveau de stock des propriétaires industriels afin d'avoir des aménités plus importantes.Un des enjeux de ce travail est d?offrir des pistes pour mobiliser la ressource forestière ne faisant pas l'objet d'une offre, faute d'implication des propriétaires privés, soit par manque de connaissance ou d'intérêt pour leur forêt, soit parce que d'autres aspects sont privilégiés (services d'aménités non-bois par exemple). Dans cette thèse, nous montrons que les effets de mimétisme et d'entrainement social (effets sociaux) peuvent être utilisés pour amener les propriétaires forestiers à produire plus de bois. Nous montrons également, qu'une hausse du prix du bois ou la mise en place d'une taxepeut favoriser la prise de la décision de coupe de bois et augmenter l'intensité de la récolte. / Timber production is related to economic, climate and energy issues. In France,according to data from the National Institute of Geoinformation and Forestry, thebiological growth rate of the forest is greater than the timber harvest rate. Thus, theFrench government has set a target of harvesting an additional quantity of 21 millioncubic meter of timber by 2020 ("Grenelle de l'environnement, 2007"). However, theFrench forest is majority owned by private forest owners who have preferences forboth income from timber trade and from non-timber amenities. The policies toincrease timber production must include these aspects. The objective of this thesisis to understand the determinants of joint production of timber and non-timberamenities in France.Therefore, we first analyze private forest owners' timber supply, taking into accountindividual and regional determinants. Afterwards, we investigate whether thedrivers of forest owners behavior differ within and between these different levels.We show that similar timber supply behavior can be observed when regional characteristicsor those of peers are similar. Then, we highlight a mimicry behavior injoint production decisions of timber and amenities made by private forest owners.Finally, we analyze inter-temporal trade-offs made by the owners from non-timberamenities and income from the sale of wood. We explicitly take into account theprice expectations and growth. Our estimations show that the willingness to pay fornon-timber amenities is e23 for our case study. This value is the difference betweenthe value they could have earned if they tried to maximize timber revenue and therevenue of their actual logging.Mainly beacause of a lack of involvement of private owners, either through a lackof knowledge or interest in their forest, or because other aspects are privileged (nontimberamenities, e.g.), a part of forest ressource is not subject to a commercial offer.Providing ways to mobilize this ressource is one of the challenges of this work. Weshow that the mimetic effects and the contextual effects can be used to encourageforest owners to produce more timber. An effective policy could be a combinationof these two effects. We also show that an increase in the price of timber or theadoption of a tax may be an incentive for timber harvesting.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-07-20T13:53:44Z No. of bitstreams: 1 rafaelbrunodasilvabrandi.pdf: 3504861 bytes, checksum: 82d36b1bf645c59e92876390b55e996b (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-07-22T15:19:11Z (GMT) No. of bitstreams: 1 rafaelbrunodasilvabrandi.pdf: 3504861 bytes, checksum: 82d36b1bf645c59e92876390b55e996b (MD5) / Made available in DSpace on 2016-07-22T15:19:11Z (GMT). No. of bitstreams: 1 rafaelbrunodasilvabrandi.pdf: 3504861 bytes, checksum: 82d36b1bf645c59e92876390b55e996b (MD5) Previous issue date: 2011-03-30 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O Sistema Elétrico Brasileiro (SEB) apresenta características peculiares devido às grandes dimensões do país e pelo fato da geração elétrica ser proveniente predominantemente de usinashidráulicasqueproporcionamaosistemaacapacidadedeumaregularizaçãoplurianualdos seusreservatórios. Asafluênciasnestasusinassãoestocásticasemuitasvezesapresentamcomportamentos complementares entre as diversas regiões do país, o que incentiva a existência de grandes intercâmbios energéticos entre os subsistemas através do Sistema Interligado Nacional (SIN). O planejamento da operação no horizonte de médio/longo prazo no país (que compreende a um período de 5 a 10 anos à frente com discretização mensal) é realizado por uma cadeia de modelos computacionais validados pelos principais agentes que atuam no SEB (comercialização, geração, transmissão e distribuição de energia). O principal modelo desta cadeia, a qual é desenvolvida pelo Centro de Pesquisas de Energia Elétrica/ELETROBRÁS, é o modelo NEWAVE que baseia-se na técnica de Programação Dinâmica Dual Estocástica (PDDE) para a determinação da política eletroenergética de médio prazo. O objetivo deste trabalho é implementar um modelo computacional para o planejamento da operação de médio prazo utilizando a metodologia de Programação Dinâmica Estocástica conjuntamente ao algoritmo de fechos convexos (PDE-ConvexHull) de uma forma computacionalmente eficiente (Fast-PDE-ConvexHull). Isto porque observou-se que quando utiliza-se a técnicadaPDE-ConvexHull,umnúmeroelevadodehiperplanossãoobtidosnacomposiçãodas funçõesdecustofuturoe,comisto,osdiversosproblemasdeprogramaçãolinearaseremresolvidos durante o processo iterativo podem tornar-se maiores, aumentando consideravelmente o tempodaexecuçãodocálculodapolíticaoperativa. Sendoassim,aprincipalcontribuiçãodeste trabalho é apresentar uma nova metodologia para a representação da função de custo futuro no problema de programação linear na qual o tempo computacional se torne menos sensível ao númerodehiperplanosobtidospeloalgoritmodefechosconvexos. Ressalta-sequetambémsão utilizadas técnicas de computação paralela com o objetivo de tornar o processo mais eficiente. A metodologia foi utilizada para o cálculo do planejamento de médio prazo do SEB, baseando-se em subsistemas equivalentes de energia. A metodologia Fast-PDE-ConvexHull foi incorporada a uma plataforma computacional, desenvolvida em C++/Java, capaz de considerar o mesmo conjunto de dados utilizado pelos modelos oficiais do SEB, compondo assim um modelo robusto para a resolução do problema. Primeiramente, para fins de validação da metodologia proposta, comparou-se os resultados obtidos pela metodologia da Fast-PDE-ConvexHull com os resultados obtidos pela utilização da técnica da PDE-ConvexHull com o objetivo verificar o ganho computacional e a aderência dos resultados. Por fim, como a plataforma computacional desenvolvida é capaz de utilizar o conjunto de dados oficiais disponibilizados para o SIN, fez-se o uso do Programa Mensal de Operação (PMO) de janeiro de 2011, disponibilizado pelo Operador Nacional do Sistema (ONS), como caso de estudo para comparação dos resultados obtidos pela metodologia proposta com os resultados obtidos pelo modelo NEWAVE. / The Brazilian National Grid (BNG) presents peculiar characteristics due to the huge territory dimensions and by the fact that the electricity generation is predominantly originated from hydraulic plants that provide for the system the capacity of a pluriannual regularization of the reservoirs. The water inflows to these plants are stochastic and often present complementary behavior among the regions of the country, stimulating the existence of big amounts of energy exchanges between the subsystems through the national grid. The long term operation planning problem (that includes a period of 5 to 10 years ahead with monthly discretization) is made by a chain of computational models that are validated by the main agents that act on BNG (commercialization, generation, transmition and distribution of energy). The primary model of this chain, which is developed by Electric Energy Research Center/ELETROBRÁS, is the NEWAVE model, which is based on the Stochastic Dual Dynamic Programming (SDDP) for electroenergetic policy determination on a long term horizon. Thisworkhastheobjectiveofimplementacomputationalmodelforthemid/longtermoperation planning using the Stochastic Dynamic Programming (SDP) together with the Convex Hull algorithm (PDE-ConvexHull) in a computationally efficient way (Fast-PDE-ConvexHull). This is because it was observed that when utilizing the PDE-ConvexHull technique, an elevated amount of hyperplanes are obtained for the composition of the cost-to-go function. So, the different linear programming problems to be solved during the iterative process can be turned larger, increasing the execution time for the operational policy calculus in a considerably manner. Thus, the main contribution of this work is to present a new methodology (FastPDE-ConvexHull) for the representation of the cost-to-go function on the linear programming problems where the computational time become less sensible to the number of hyperplanes obtained from the Convex Hull algorithm. It is highlighted that techniques of parallel computing was employed in order to turn the process more efficient. The methodology was utilized for the BNG’s long term planning calculus, based on the equivalent subsystems of energy. The methodology Fast-PDE-ConvexHull was incorporated to a computational platform, developed in C++/Java programming language, that is able to consider the same data set used by the official models acting on the BNG, compounding a robust model for the resolution of the problem. Firstly, in order to validate the proposed methodology, the results obtained from the FastPDE-ConvexHullarecomparedwiththoseobtainedfromtheutilizationofthePDE-ConvexHull technique aiming to verify the computational gain and the adherence between both results. Finally, as the elaborated computational platform is capable to use the official data set availablefortheNG,itwaspossibletheutilizationoftheMonthlyOperationalProgram(MOP) of January 2011, released by the Independent System Operator (ISO), as the study case for comparingtheresultsobtainedbytheproposedmethodologywiththeresultsobtainedfromthe NEWAVE model.

Efficient management of limited water resources in an irrigation reservoir system is necessary to increase crop productivity. To achieve this, a reservoir release policy should be integrated with an optimal crop water allocation. Variations in hydrologic variables such as reservoir inflow, soil moisture, reservoir storage, rainfall and evapotranspiration must be considered in the reservoir operating policy model. Uncertainties due to imprecision, subjectivity, vagueness and lack of adequate data can be handled using the fuzzy set theory. A fuzzy stochastic dynamic programming (FSDP) model with reservoir storage and soil moisture of the crops as fuzzy state variables and inflow as a stochastic variable, is developed to obtain a steady state reservoir operating policy. The model integrates the reservoir operating policy with the crop water allocation decisions by maintaining the storage continuity and the soil moisture balance. The reservoir release decisions are made in the model in 10-day periods and water is allocated to the crops on a daily basis. On comparison with the classical stochastic dynamic programming (SDP) model and a conceptual operation policy model, it is observed that the FSDP model, in general, results in lower release from the reservoir while maintaining lower soil moisture stress. However the steady state reservoir operation policy obtained using the FSDP model may not perform well in a short-term reservoir simulation. A fuzzy state short-term reservoir operation policy model with storage and soil moistures of the crops as fuzzy variables, is developed to obtain a real time release policy using forecasted inflow and forecasted rainfall. The distinguishing features of the model are accounting for (a) spatial variation of soil moisture and rainfall using gridded rainfall forecasts and (b) ponding depth requirement of the Paddy. On comparison with a conceptual operation policy model, the fuzzy state real time operation model is found most suitable for the application of the short term real time operation for irrigation. The real time operation model maintains high storage in the reservoir during most of the 10-day time periods of a year and results in a slightly lower annual releases as compared to the conceptual operation policy model. The effect of inflow forecast uncertainty is examined using different sets of forecasted inflows, and it is observed that the system performance is quite sensitive to inflow forecast uncertainties. The use of the satellite based gridded soil moisture in the real time operation model shows consideration of realistic situations. Further, three performance measures, viz., fuzzy reliability, fuzzy resiliency and fuzzy vulnerability are developed to evaluate the performance of the irrigation reservoir system under a specified operating policy. A fuzzy set with an appropriate membership function is defined to describe the working and failed states to account for the system being in partly working and partly failed state. The degree of failure of the irrigation reservoir system is defined based on the evapotranspiration deficit in a period. Inclusion of fuzziness in the performance measures enables realistic representation of uncertainties in the state of the system. A case study of Bhadra reservoir system in Karnataka, India is chosen for demonstrating the model applications.

A discrete-time stochastic optimal control problem was recently proposed to address the GLOSA (Green Light Optimal Speed Advisory) problem in cases where the next signal switching time is decided in real-time and is therefore uncertain in advance. However, there was an assumption that the traffic signal is initially red and turns to green, which means that only half traffic light cycle was considered. In this work, the aforementioned problem is extended considering a full traffic light cycle, consisting of four phases: a certain green phase, during which the vehicle can freely pass; an uncertain green phase, in which there is a probability that the traffic light will extend its duration or turn to red at any time; a certain red phase during which the vehicle cannot pass; and an uncertain red phase, in which there is a probability that the red signal may be extended or turn to green at any time. It is demonstrated, based on preliminary results, that the proposed SDP (Stochastic Dynamic Programming) approach achieves better average performance, in terms of fuel consumption, compared to the IDM (Intelligent Driver Model), which emulates human-driving behavior.

Timely access and patient-physician continuity are two key measures for a primary care practice. Timeliness refers to the ability to obtain a physician appointment as soon as possible. Patient-physician continuity is one of the hallmarks of primary care and refers to the ability to provide appointments with a patient's own physician as much as possible. In the last decade, a paradigm called "advanced access" has been adopted by many clinics, which encourages physicians "to do today's work today" rather than push appointments in the future. Advanced access necessitates the design effective capacity management policies. In this study we apply the ideas of process flexibility to capacity management in a primary care practice. This leads to a system where patients can be seen by their primary care provider or additional physicians, the latter incurring a cost due to reduction of efficiency. We model a practice with multiple physicians and their corresponding patient panels as a stochastic dynamic program. Patients call in throughout the day and a decision has to be made immediately whether to assign the patient to a specific physician or refuse her. The study consists of three parts: in the first, we investigate the general benefits and shortfalls of different implementations of physician flexibility; in the second, we develop heuristic scheduling policies that can be implemented in practice; the third part compares the benefits of the system currently used in practice with our proposed approach where physicians are chained and pairs of physician are familiar with a certain panel.

<p>In the real world, uncertainty is a common challenging problem faced by individuals, organizations, and firms. Decision quality is highly impacted by uncertainty because decision makers lack complete information and have to leverage the loss and gain in many possible outcomes or scenarios. This study explores dynamic decision making (with known distributions) and decision learning (with unknown distributions but some samples) in not-for-profit operations and supply chain management. We first study dynamic staffing for paid workers and volunteers with uncertain supply in a nonprofit operation where the optimal policy is too complex to compute and implement. Then, we consider dynamic inventory control and pricing under both supply and demand uncertainties where unmet demand is lost leading to a challenging non-concave dynamic problem. Furthermore, we explore decision learning from limited data of focal system and available data of related but different systems by transfer learning, cross learning, and co-learning utilizing the similarities among related systems.</p>

This dissertation consists of three essays in theoretical and applied microeconomics: the first essay is in cooperative game theory, and the second and third essays relate to forest economics. The first chapter studies a class of cooperative games dubbed ``r-essential games''. Cooperative game theory has proposed different notions of powerful players. For example, big-boss games (Muto et al., 1988) and clan games (Potters et al., 1989) are particular cases of veto games (Bahel, 2016). The first chapter extends these veto games by assuming that there is a given subset of powerful (or essential) players, but only a few (as opposed to all) essential players are required for a coalition to have a positive value. The resulting games, which are called r-essential games, encompass convex games (Shapley, 1971) and veto games. We show that r-essential games have a nonempty core. We give a recursive description of the core. Moreover, it is shown that the core and the bargaining set are equivalent for every r-essential game. An application to networks is provided. The second chapter employs a two-principal, one-agent model to estimate the social cost of fiscal federalism in China's northeast native forests. China's key forested region is located in the northeast and consists of state forest enterprises which manage forest harvesting and reforestation. Deforestation is a major problem there and has resulted in several central government reforms. We develop a framework for assessing the social cost of state forest enterprise deforestation. We first develop a two-principal, one-agent model that fits the federalistic organization of state forests, in that state forest managers make (potentially hidden) decisions under influence of provincial and central government policies. This model is used to quantify the social cost of these hidden actions. We then use panel data from a survey conducted by Peking University to compute social welfare losses and to formally identify the main factors in these costs. A sensitivity analysis shows that, interestingly, command and control through lower harvesting limits and a more accurate monitoring system are more important to lowering social welfare losses than conventional incentives targeting the wages of forest managers. Through regression analysis we also find that the more remote areas with a higher percentage of mature natural forests are the ones that will always have the highest social welfare losses. The third chapter studies the problem of choosing a rotation under uncertain future ecosystem values and timber prices. This problem is nearly as old as the field of forest economics itself. A forest owner faces various uncertainties caused by climate change and market shocks, due to its long-term nature of production and the joint production of interrelated timber and amenity (non-harvesting) benefit streams. The vast literature in stochastic rotation problems simply assumes a known probability distribution for whatever parameter is uncertain, but this type of assumption may lead to misspecification of a rotation decision model if a forest owner has no such information. We study a more relevant question of how to choose rotation ages when there is pure (or Knightian) uncertainty, in that the forest owner does not know distributional features of parameters and further can be averse to this type of information deficit. This chapter is the first to investigate pure uncertainty in amenity benefit streams and is also the first to analytically solve a stochastic rotation problem under pure uncertainty in either amenity streams or market prices. We use robust methods developed in macroeconomics that are particularly suited to forest capital investment problem, but with important differences owing to the nature of forest goods production. The results show that newer models suggesting rotation ages could be longer under volatile parameter distributions do not hold generally when pure uncertainty and forest owner uncertainty aversion is considered. Rather, the earlier literature showing faster or greater harvesting with increases in risk under risk neutrality may actually be a more general result than current literature supposes. In particular, we find that a landowner tends to harvest more when his degree of uncertainty aversion is higher and the model is misspecified by assumption, or when the volatility of an uncertain process is higher. These situations tend to magnify model misspecification costs, especially because the forest manager always assumes the worst case will happen when there is uncertainty. This implies the decision maker is pessimistic in the sense that he or she is always trying to maximize the utility under the worst possible state of nature (the lowest amenity benefit or the lowest timber price). Whether landowners are in fact uncertainty averse and assume the worst case in their decisions remains to be empirically investigated, but our work suggests it is an important question that must be answered. / Doctor of Philosophy / This dissertation consists of three essays in theoretical and applied microeconomics: the first essay is in cooperative game theory, and the second and third essays relate to forest economics. The first chapter studies a class of cooperative games dubbed ``r-essential games''. Cooperative game theory has proposed different notions of powerful players. For example, veto games (Bahel, 2016) have powerful players that are named veto players. Any coalition needs to include all these powerful players to achieve a positive coalition value. The first chapter extends these veto games by assuming that there is a given subset of powerful (or essential) players, but only a few (as opposed to all) essential players are required for a coalition to have a positive value. The resulting games, which are called r-essential games, encompass two classic games, convex games (Shapley, 1971) and veto games. We show that each r-essential game has at least one solution that is an allocation guaranteeing that no coalition can do better on its own. We provide a process allowing to compute this allocation in each r-essential game. An application to networks is provided. The second chapter estimates the damage of deforestation in China's northeast forests. This region consists of state forest enterprises which manage harvesting and reforestation and have represented the most important source of wood supplies since the 1950s. Deforestation is a major problem there. We develop a framework for assessing the damage to the society because of deforestation. We develop a theoretical model to describe the forest management structure, in which state forest managers make (potentially hidden) decisions under influence of provincial and central government policies. This model is used to quantify the damage. We then use data from a survey conducted by Peking University to compute the damage and confirm the main factors in these damages in practice. We find that lower harvesting limits and a more accurate monitoring system are the keys to lowering the damage. These are more important than conventional instruments used by the governments such as the wages for managers that achieve certain targets. We also find that the remote areas with a higher percentage of mature natural forests are the ones that will always have the largest damage. These areas are the hardest to monitor, but our results show they must be a critical focus moving forward. The third chapter studies when should a forest owner harvest under uncertain future ecosystem values and timber prices. A forest owner faces various uncertainties caused by climate change and market shocks, due to its long-term nature of production and the joint production of interrelated timber and non-harvesting benefit streams (such as the recreation value, the biodiversity value and the clean air supported by forests). Previous studies assume a known probability distribution for whatever parameter is uncertain, but this type of assumption may lead to a wrong decision model if a forest owner has no such information. We study a more relevant question of how to choose when to harvest with pure uncertainty, in that the forest owner does not know distributional features of parameters and further can be averse to this type of information deficit. This chapter is the first to investigate pure uncertainty and is also the first to analytically solve a harvest decision making problem under pure uncertainty in either non-harvesting benefit streams or market prices. We use macroeconomics methods that are particularly suited to forest capital investment problem. We find that a landowner tends to harvest more when there is pure uncertainty. Because the forest manager is pessimistic and always thinks the worst case will happen when there is uncertainty.

Algumas abordagens para o problema de Planejamento Ótimo da Operação de Sistemas Hidrotérmicos (POOSH) utilizam modelos estocásticos para representar as vazões afluentes dos reservatórios do sistema. Essas abordagens utilizam, em geral, técnicas de Programação Dinâmica Estocástica (PDE) para resolver o POOSH. Por outro lado, muitos autores têm defendido o uso dos modelos determinísticos ou, particularmente, a Programação Dinâmica Determinística (PDD) por representar de forma individualizada a interação entre as usinas hidroelétricas do sistema. Nesse contexto, esta dissertação tem por objetivo comparar o desempenho da solução do POOSH obtida via PDD com a solução obtida pela PDE, que emprega um modelo Markoviano periódico, com distribuição condicional Log-Normal Truncada para representar as vazões. Além disso, é realizada a análise com abordagem bayesiana, no modelo de vazões, para estimação dos parâmetros e previsões das vazões afluentes. Comparamos as performances simulando a operação das usinas hidroelétricas de Furnas e Sobradinho, considerando séries de vazões geradas artificialmente / Some approaches for problem of Optimal Operation Planning of Hydrothermal Systems (OOPHS) use stochastic models to represent the inflows in the reservoirs that compose the system. These approaches typically use the Stochastic Dynamic Programming (SDP) to solve the OOPHS. On the other hand, many authors defend the use of deterministic models and, particularly, the Deterministic Dynamic Programming (DDP) since it individually represents the interaction between the hydroelectric plants. In this context, this dissertation aims to compare the performance of the OOPHS solution obtained via DDP with the one given by SDP, which employs a periodic Markovian model with conditional Truncated Log-Normal distribution to represent the inflows. Furthermore, it is performed a bayesian approach analysis, in the inflow model, for estimating the parameters and forecasting the inflows. We have compared the performances of the DDP and SDP solutions by simulating the hydroelectric plants of Furnas and Sobradinho, employing artificially generated series