Possessing the knowledge to hedge energy price risks properly is essential and crucial for running a long-term business. In the past, many hedging instruments have been invented and widely used. By using these derivatives, decision makers reduce the price risk to a certain degree. To apply these hedging instruments to the perfect hedging strategies correctly, it is necessary to be familiar with these tools in the first place. This work introduces the financial tools widely applied in hedging, including forward contracts, futures, swaps and options. It also introduces the hedging strategies used on energy hedging. Since individuals are creating strategies according to their unique risk appetite and collected information, this work presents three risk appetites and a method of distinguishing valuable information. With the contribution of this thesis, future works can be done in the field that connect the information valuation and energy hedging by changing the behavior in each risk appetites’ hedging ratio. / text

Civil infrastructure systems form the backbone of modern civilization, providing the basic services that allow society to function. Effective management of these systems requires decision-making about the allocation of limited resources to maintain and repair infrastructure components and to replace failed or obsolete components. Making informed decisions requires an understanding of the state of the system; such an understanding can be achieved through a computational or conceptual system model combined with information gathered on the system via inspections or sensors. Gathering of this information, referred to generally as sensing, should be optimized to best support the decision-making and system management processes, in order to reduce long-term operational costs and improve infrastructure performance. In this work, an approach to optimal sensing in infrastructure systems is developed by combining probabilistic graphical models of infrastructure system behavior with the value of information (VoI) metric, which quantifies the utility of information gathering efforts (referred to generally as sensor placements) in supporting decision-making in uncertain systems. Computational methods are presented for the efficient evaluation and optimization of the VoI metric based on the probabilistic model structure. Various case studies on the application of this approach to managing infrastructure systems are presented, illustrating the flexibility of the basic method as well as various special cases for its practical implementation. Three main contributions are presented in this work. First, while the computational complexity of the VoI metric generally grows exponentially with the number of components, growth can be greatly reduced in systems with certain topologies (designated as cumulative topologies). Following from this, an efficient approach to VoI computation based on a cumulative topology and Gaussian random field model is developed and presented. Second, in systems with non-cumulative topologies, approximate techniques may be used to evaluate the VoI metric. This work presents extensive investigations of such systems and draws some general conclusions about the behavior of this metric. Third, this work presents several complete application cases for probabilistic modeling techniques and the VoI metric in supporting infrastructure system management. Case studies are presented in structural health monitoring, seismic risk mitigation, and extreme temperature response in urban areas. Other minor contributions included in this work are theoretical and empirical comparisons of the VoI with other sensor placement metrics and an extension of the developed sensor placement method to systems that evolve in time. Overall, this work illustrates how probabilistic graphical models and the VoI metric can allow for efficient sensor placement optimization to support infrastructure system management. Areas of future work to expand on the results presented here include the development of approximate, heuristic methods to support efficient sensor placement in non-cumulative system topologies, as well as further validation of the efficient sensing optimization approaches used in this work.

Background: Uncertainty in parameters is present in many risk assessment and decision making problems and leads to uncertainty in model predictions. Therefore an analysis of the degree of uncertainty around the model inputs is often needed. Importance analysis involves use of quantitative methods aiming at identifying the contribution of uncertain input model parameters to output uncertainty. Expected value of partial perfect information (EVPPI) measure is a current gold- standard technique for measuring parameters importance in health economics models. The current standard approach of estimating EVPPI through performing double Monte Carlo simulation (MCS) can be associated with a long run time. Objective: To investigate different importance analysis techniques with an aim to find alternative technique with shorter run time that will identify parameters with greatest contribution to uncertainty in model output. Methods: A health economics model was updated and served as a tool to implement various importance analysis techniques. Twelve alternative techniques were applied: rank correlation analysis, contribution to variance analysis, mutual information analysis, dominance analysis, regression analysis, analysis of elasticity, ANCOVA, maximum separation distances analysis, sequential bifurcation, double MCS EVPPI,EVPPI-quadrature and EVPPI- single method. Results: Among all these techniques, the dominance measure resulted with the closest correlated calibrated scores when compared with EVPPI calibrated scores. Performing a dominance analysis as a screening method to identify subgroup of parameters as candidates for being most important parameters and subsequently only performing EVPPI analysis on the selected parameters will reduce the overall run time.

Data collection is an integral part of hydrologic investigations; yet, hydrologic data collection is costly, particularly in subsurface environments. Consequently, it is critical to target data collection efforts toward prospective data sets that will best address the questions at hand, in the context of the study. Experimental and monitoring network designs that have been carefully planned with a specific objective in mind are likely to yield information-rich data that can address critical questions of concern. Conversely, data collection undertaken without careful planning may yield datasets that contain little information relevant to the questions of concern. This dissertation research develops and presents approaches that can be used to support careful planning of hydrologic experiments and monitoring networks. Specifically, three general types of problems are considered. Under the first problem type, the objective of the hydrologic investigation is to discriminate among rival conceptual models, or among rival predictive groupings. A Bayesian methodology is presented that can be used to rank prospective datasets during the planning phases of a hydrologic investigation. Under the second problem type, the objective is to quantify the impact of existing data on reductions in parameter uncertainty. An inverse modeling approach is presented to quantify the impact of existing data on parameter uncertainty when the hydrogeologic conceptual model is uncertain. The third and final problem type focuses on data collection in a water resource management context, with the specific goal to maximize profits without imposing adverse environmental impacts. A risk-based decision support framework is developed using detailed hydrologic simulation to evaluate probabilistic constraints. This enables direct calculation of the profit gains associated with prospective reductions in system parameter uncertainty, and the possible environmental impacts of unknown bias in the system parameters.

Economic research on climate and productivity effects of ocean phenomena has mostly focused on interannual cases such as the El Niño Southern Oscillation. Here Decadal climate variability (DCV) refers to ocean related climate influences of duration from seven to twenty years. The specific phenomena analyzed here are the Pacific Decadal Oscillation, the Tropical Atlantic Gradient and the West Pacific Warm Pool. Their positive and negative phases, occurring individually or in combination, are associated with variations in crop and water yields. This dissertation examines the value of DCV information to agriculture and water users in the Missouri river basin using a price endogenous agricultural and non-agricultural model that depicts cropping and water use. The model is used to evaluate the welfare gains and adaptations given various levels of DCV information. The analysis shows the value (for a 10-year average) for a perfect forecast is about 5.2 billion dollars, though 86% of this value, 4.55 billion dollars, can be obtained by a less perfect forecast based on already available data in the form of the prediction of DCV phase under transition probabilities. The results indicate that forecasting any DCV state is important because of differential responses in the acreage of major crops plus water use adjustments by residential, agricultural and industrial users.

Background: Uncertainty in parameters is present in many risk assessment and decision making problems and leads to uncertainty in model predictions. Therefore an analysis of the degree of uncertainty around the model inputs is often needed. Importance analysis involves use of quantitative methods aiming at identifying the contribution of uncertain input model parameters to output uncertainty. Expected value of partial perfect information (EVPPI) measure is a current gold- standard technique for measuring parameters importance in health economics models. The current standard approach of estimating EVPPI through performing double Monte Carlo simulation (MCS) can be associated with a long run time. Objective: To investigate different importance analysis techniques with an aim to find alternative technique with shorter run time that will identify parameters with greatest contribution to uncertainty in model output. Methods: A health economics model was updated and served as a tool to implement various importance analysis techniques. Twelve alternative techniques were applied: rank correlation analysis, contribution to variance analysis, mutual information analysis, dominance analysis, regression analysis, analysis of elasticity, ANCOVA, maximum separation distances analysis, sequential bifurcation, double MCS EVPPI,EVPPI-quadrature and EVPPI- single method. Results: Among all these techniques, the dominance measure resulted with the closest correlated calibrated scores when compared with EVPPI calibrated scores. Performing a dominance analysis as a screening method to identify subgroup of parameters as candidates for being most important parameters and subsequently only performing EVPPI analysis on the selected parameters will reduce the overall run time.

Background: Uncertainty in parameters is present in many risk assessment and decision making problems and leads to uncertainty in model predictions. Therefore an analysis of the degree of uncertainty around the model inputs is often needed. Importance analysis involves use of quantitative methods aiming at identifying the contribution of uncertain input model parameters to output uncertainty. Expected value of partial perfect information (EVPPI) measure is a current gold- standard technique for measuring parameters importance in health economics models. The current standard approach of estimating EVPPI through performing double Monte Carlo simulation (MCS) can be associated with a long run time. Objective: To investigate different importance analysis techniques with an aim to find alternative technique with shorter run time that will identify parameters with greatest contribution to uncertainty in model output. Methods: A health economics model was updated and served as a tool to implement various importance analysis techniques. Twelve alternative techniques were applied: rank correlation analysis, contribution to variance analysis, mutual information analysis, dominance analysis, regression analysis, analysis of elasticity, ANCOVA, maximum separation distances analysis, sequential bifurcation, double MCS EVPPI,EVPPI-quadrature and EVPPI- single method. Results: Among all these techniques, the dominance measure resulted with the closest correlated calibrated scores when compared with EVPPI calibrated scores. Performing a dominance analysis as a screening method to identify subgroup of parameters as candidates for being most important parameters and subsequently only performing EVPPI analysis on the selected parameters will reduce the overall run time.

In this study, value of quality information and the eects of centralization are investigated for a reverse supply chain consisting of a remanufacturer and a collector. Used products are collected and inspected to classify them into quality groups, then they are remanufactured to meet the demand of remanufactured products. The supply of collected products and demand of remanufactured products are both price-sensitive. The uncertain quality of the collected products is revealed by an inspection process. Two quality classes are considered, and the cost of remanufacturing depends on the quality class. The main decisions are on acquisition fee for the returns, the selling price for remanufactured products, and the transfer prices of inspected products between the collector and the remanufacturer. For this environment, centralized and decentralized settings are considered and dierent models that dier in availability of quality information when the pricing decisions are made are built. We explore the value of advance quality information and eects of centralization on the optimal prices and profits via a computational study.

Value of information is one of the key features of decision analysis. This work deals with providing a consistent and functional methodology to determine VOI on proposed well tests in the presence of uncertainties. This method strives to show that VOI analysis with the help of discretized versions of continuous probability distributions with conventional decision trees can be very accurate if the optimal method of discrete approximation is chosen rather than opting for methods such as Monte Carlo simulation to determine the VOI. This need not necessarily mean loss of accuracy at the cost of simplifying probability calculations. Both the prior and posterior probability distributions are assumed to be continuous and are discretized to find the VOI. This results in two steps of discretizations in the decision tree. Another interesting feature is that there lies a level of decision making between the two discrete approximations in the decision tree. This sets it apart from conventional discretized models since the accuracy in this case does not follow the rules and conventions that normal discrete models follow because of the decision between the two discrete approximations. The initial part of the work deals with varying the number of points chosen in the discrete model to test their accuracy against different correlation coefficients between the information and the actual values. The latter part deals more with comparing different methods of existing discretization methods and establishing conditions under which each is optimal. The problem is comprehensively dealt with in the cases of both a risk neutral and a risk averse decision maker. / text

The grain and oil seed industry plays a major role in the South African economy; therefore, having access to market information is vital for this market to remain efficient and competitive. A shortage in market information causes many inefficiencies and uncertainties. Having market information allows the playing field to be level for all role players and reduces opportunities for manipulating prices. South Africa, just like most developing countries, needs to strengthen information flows, as well as institutions governing the grain and oil seed industry. In view of the major grain producing countries in the world and the amount of money and effort spent on releasing planting progress reports, the South Africa grain and oilseed sector should to take heed. This paper considers the importance of market information and how the South African grain and oil seed industry can benefit from that, grain planting progress reports are considered to be of importance as they fill a significant gap in the production season. Taking an institutional perspective into the economics of information, the study found that actors having little financial and social resources or political influence faced high costs in accessing information and that this prevents both market development and access to existing ones. The point of discussion is on weak information flows, as well as transaction costs that come with them, and the impact they have on prices and profitability. We therefore use New Institutional Economics to emphasise the importance of information in the market and the impact thereof in the absence of perfect information. The main underlying issue for imperfect information is that the lack of perfect and freely available information leads to risk and uncertainty in transactions. When trying to analyse the importance of information in the grain and oilseed industry, it was established that accuracy, value and market effect of information for public consumption were important. In particular, information communication technology was examined as a means of information dissemination in agriculture, especially in developing countries like South Africa. The study found that the major grain and oilseed producing countries that generate planting progress reports are the USA, Brazil, Argentina and Australia. The study looked at the methods used by these countries to compile such reports. Although they have varying methodologies, the key point is timely and frequent information which is readily available for public consumption. After analysing developments and methodologies globally, the focus shifted to South Africa where current information sources in the South African grain and oilseed industry, and the kind of information provided, were analysed. A pilot study was conducted in the summer grain production area of NWK Ltd to gain some insight and experience. The source of communication comprised mobile phones and farmers were able to respond on their progress, as well as receive feedback using the same communication media. Lastly in order to re-emphasis the benefits of a planting progress report, we review the impact of price volatility and how information in the market can help stabilise it. / Dissertation (MSc (Agric))--University of Pretoria, 2017. / Agricultural Economics, Extension and Rural Development / MSc (Agric) / Unrestricted