Browsing by Subject "Decision analysis"
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Item Approximations, simulation, and accuracy of multivariate discrete probability distributions in decision analysis(2012-05) Montiel Cendejas, Luis Vicente; Bickel, J. Eric; Morton, David P.; Hasenbein, John J.; Dyer, James S.; Lake, Larry W.Many important decisions must be made without full information. For example, a woman may need to make a treatment decision regarding breast cancer without full knowledge of important uncertainties, such as how well she might respond to treatment. In the financial domain, in the wake of the housing crisis, the government may need to monitor the credit market and decide whether to intervene. A key input in this case would be a model to describe the chance that one person (or company) will default given that others have defaulted. However, such a model requires addressing the lack of knowledge regarding the correlation between groups or individuals. How to model and make decisions in cases where only partial information is available is a significant challenge. In the past, researchers have made arbitrary assumptions regarding the missing information. In this research, we developed a modeling procedure that can be used to analyze many possible scenarios subject to strict conditions. Specifically, we developed a new Monte Carlo simulation procedure to create a collection of joint probability distributions, all of which match whatever information we have. Using this collection of distributions, we analyzed the accuracy of different approximations such as maximum entropy or copula-models. In addition, we proposed several new approximations that outperform previous methods. The objective of this research is four-fold. First, provide a new framework for approximation models. In particular, we presented four new models to approximate joint probability distributions based on geometric attributes and compared their performance to existing methods. Second, develop a new joint distribution simulation procedure (JDSIM) to sample joint distributions from the set of all possible distributions that match available information. This procedure can then be applied to different scenarios to analyze the sensitivity of a decision or to test the accuracy of an approximation method. Third, test the accuracy of seven approximation methods under a variety of circumstances. Specifically, we addressed the following questions within the context of multivariate discrete distributions: Are there new approximations that should be considered? Which approximation is the most accurate, according to different measures? How accurate are the approximations as the number of random variables increases? How accurate are they as we change the underlying dependence structure? How does accuracy improve as we add lower-order assessments? What are the implications of these findings for decision analysis practice and research? While the above questions are easy to pose, they are challenging to answer. For Decision Analysis, the answers open a new avenue to address partial information, which bing us to the last contribution. Fourth, propose a new approach to decision making with partial information. The exploration of old and new approximations and the capability of creating large collections of joint distributions that match expert assessments provide new tools that extend the field of decision analysis. In particular, we presented two sample cases that illustrate the scope of this work and its impact on uncertain decision making.Item An assessment of the system costs and operational benefits of vehicle-to-grid schemes(2013-12) Harris, Chioke Bem; Webber, Michael E., 1971-With the emerging nationwide availability of plug-in electric vehicles (PEVs) at prices attainable for many consumers, electric utilities, system operators, and researchers have been investigating the impact of this new source of electricity demand. The presence of PEVs on the electric grid might offer benefits equivalent to dedicated utility-scale energy storage systems by leveraging vehicles' grid-connected energy storage through vehicle-to-grid (V2G) enabled infrastructure. Existing research, however, has not effectively examined the interactions between PEVs and the electric grid in a V2G system. To address these shortcomings in the literature, longitudinal vehicle travel data are first used to identify patterns in vehicle use. This analysis showed that vehicle use patterns are distinctly different between weekends and weekdays, seasonal interactions between vehicle charging, electric load, and wind generation might be important, and that vehicle charging might increase already high peak summer electric load in Texas. Subsequent simulations of PEV charging were performed, which revealed that unscheduled charging would increase summer peak load in Texas by approximately 1\%, and that uncertainty that arises from unscheduled charging would require only limited increases in frequency regulation procurements. To assess the market potential for the implementation of a V2G system that provides frequency regulation ancillary services, and might be able to provide financial incentives to participating PEV owners, a two-stage stochastic programming formulation of a V2G system operator was created. In addition to assessing the market potential for a V2G system, the model was also designed to determine the effect of the market power of the V2G system operator on prices for frequency regulation, the effect of uncertainty in real-time vehicle availability and state-of-charge on the aggregator's ability to provide regulation services, and the effect of different vehicle characteristics on revenues. Results from this model showed that the V2G system operator could generate revenue from participation in the frequency regulation market in Texas, even when subject to the uncertainty in real-time vehicle use. The model also showed that the V2G system operator would have a significant impact on prices, and thus as the number of PEVs participating in a V2G program in a given region increased, per-vehicle revenues, and thus compensation provided to vehicle owners, would decline dramatically. From these estimated payments to PEV owners, the decision to participate in a V2G program was analyzed. The balance between the estimated payments to PEV owners for participating in a V2G program and the increased probability of being left with a depleted battery as a result of V2G operations indicate that an owner of a range-limited battery electric vehicle (BEV) would probably not be a viable candidate for joining a V2G program, while a plug-in hybrid electric vehicle (PHEV) owner might find a V2G program worthwhile. Even for a PHEV owner, however, compensation for participating in a V2G program will provide limited incentive to join.Item Decision analysis and risk management : application to climate change and risk detection(2011-08) Agrawal, Shubham; Bickel, J. Eric; Bickel, J. Eric; Morton, DavidWe have analyzed the application of decision analysis and risk management tools to solve practical problems associated with Climate Change and Risk Detection in the financial services industry. Geoengineering, which is described as an intentional modification of earth’s environment to mitigate the harmful effects of climate change, is evaluated as a policy alternative using the aforementioned tools. We compared the performance of geoengineering with optimal emission controls and a business as usual strategy under various scenarios and found that geoengineering passes the cost benefit test for a majority of the scenarios. We modified the DICE model (Nordhaus, 2008) and used it to evaluate the performance of different environmental policies. Our results show geoengineering as a potential alternative to solve climate change problems. Through this application, and by comparing our findings against Goes et al. (2011), we showed that how framing of the decision problem can lead to completely different results. We also analyzed the application of risk management in the financial services industry. The industry faces three main types of risk: Market risk, Credit risk and Operational risk. Market risk is managed using a diversified portfolio, derivatives, insurance and contracts. More challenging is the task of preventing credit and fraud risk. Statistical models used by the industry to detect and prevent these types of risk are explained in the thesis.Item Decision analysis for climate engineering research(2015-05) Buckholtz, Michelle Carolina; Olmstead, Sheila M.; Bickel, J. EricTechnology solutions designed to manage climate change risk fall into three categories: mitigation, adaptation, and climate engineering. While mitigation and adaptation technologies are well established and have substantial public support as policy alternatives, climate engineering strategies remain mostly in the early stages of research and development. Both the further pursuit of research and eventual use of climate engineering technologies have been subject to moral and ethical objections. The intention of this report is to aid policy-makers in the decision as to whether society should pursue climate engineering research. This report identifies the unique characteristics which make climate engineering an important tool in the portfolio of strategies for managing climate change risks. Next potential benefits and costs associated with the technology are explored. The largest ethical objections to research and use of the technology are discussed and presented in a more consistent framework than found in existing literature. Finally, a model evaluating the sensitivity of the decision to pursue climate engineering research to two large ethical objections was built. Using outputs from an existing climate model, the analysis in this report adjusts the likelihood of the two ethical objections occurring across several scenarios to illustrate how the quality of the decision changes based on different assumptions about society.Item Decision impact of stochastic price models in the petroleum industry(2011-08) Hammond, Robert Kincaid; Bickel, J. Eric; Dyer, James S.; Smith, James E.Stochastic price models have proven material to decision making in the oil industry when accurate valuations are important, but little consideration is given to their impact on decisions based on relative project rankings. Traditional industry economic analysis methods do not usually consider uncertainty in oil price, although the real options literature has shown that this practice underestimates the value of projects that have flexibility. Monetary budget constraints are not always the limiting constraints in decision making; there may be other constraints that limit the number of projects a company can undertake. We consider building a portfolio of upstream petroleum development projects to determine the relevance of stochastic price models to a decision for which accurate valuations may not be important. The results provide guidelines to determine when a stochastic price model should be used in economic analysis of petroleum projects.Item Discrete approximations to continuous distributions in decision analysis(2014-05) Hammond, Robert Kincaid; Bickel, J. EricIn decision analysis, continuous uncertainties (i.e., the volume of oil in a reservoir) must be approximated by discrete distributions for use in decision trees, for example. Many methods of this process, called discretization, have been proposed and used for decades in practice. To the author’s knowledge, few studies of the methods’ accuracies exist, and were of only limited scope. This work presents a broad and systematic analysis of the accuracies of various discretization methods across large sets of distributions. The results indicate the best methods to use for approximating the moments of different types and shapes of distributions. New, more accurate, methods are also presented for a variety of distributional and practical assumptions. This first part of the work assumes perfect knowledge of the continuous distribution, which might not be the case in practice. The distributions are often elicited from subject matter experts, and because of issues such as cognitive biases, may have assessment errors. The second part of this work examines the implications of this error, and shows that differences between some discretization methods’ approximations are negligible under assessment error, whereas other methods’ errors are significantly larger than those because of imperfect assessments. The final part of this work extends the analysis of previous sections to applications to the Project Evaluation and Review Technique (PERT). The accuracies of several PERT formulae for approximating the mean and variance are analyzed, and several new formulae presented. The new formulae provide significant accuracy improvements over existing formulae.Item Efficient sequential probability assessment heuristic in decision analysis(2015-12) Huang, Tao, Ph. D.; Bickel, J. Eric; Dyer, James S; Hasenbein, John J; Caramanis, Constantine; Dimitrov, NedialkoMany decision problems involve situations where the possible outcomes are specified but the corresponding probability mass function is only partially known. In such cases, the expected utility of an alternative is not explicitly computable and decisions are made without full information. To address this problem, previous research has tried to establish dominance, by determining if one alternative has a larger expected value or utility than other alternatives for all feasible distributions. In practice, however, dominance is rarely established directly and decision maker needs to make many probability assessments. This work addresses the difficult problem of how to efficiently make probability assessments in decision analysis with complicated uncertainties. After study of the problem, we formulate the problem of achieving dominance with least probability assessments as a dynamic decision problem that shows a huge complexity. A novel heuristic called Sequential Probability Assessment Heuristic (SPAH) is proposed to offer decision analysts a practical way to solve the problem. This method iteratively selects a feasible assessment question for decision analyst to present to the decision maker or expert for assessment in their communication. The heuristic is borrowed from machine learning and, as we show, displays some desirable properties. It performs well when applied to two canonical example decision analysis problems, Eagle Airlines, a decision whether to purchase a plane, and Wildcatter’s valuation, a decision whether to explore an oil well. Our method shows a close performance when compared to the optimal strategy that is solved with the clairvoyance of the true distribution, and a dominating performance over the current standard way of doing probability assessments. The assessment strategy generated by SPAH can also give the decision analyst more insight into the structure of the decision problem they face, as finally we will see from the two examples.Item Incorporating decision theory into a virtual simulation learning platform(2010-05) Morales, Benjamin L., 1978-; Barnes, J. Wesley; Bickel, J. EricThis report describes a method of incorporating decision analysis principles to enhance a simulation being created by The University of Texas at Austin’s Institute for Advanced Technology (IAT). The simulation is called Virtual Simulation Learning Platform (VSLP) and the scenario created to test the platform is called Virtual Platoon Leader (VPL). Recommendations include a method of implementing value-focused decision making, the implementation of decision tools to build a scenario within the simulation, a dialogue process between the developer and the subject matter expert, a design for the implementation of graphical user interfaces for the decision tools used to build a scenario and a user scoring methodology.Item Multistage stochastic programming models for the portfolio optimization of oil projects(2011-08) Chen, Wei, 1974-; Dyer, James S.; Lasdon, Leon S., 1939-; Balakrishnan, Anantaram; Lake, Larry W.; Jablonowski, Christopher J.Exploration and production (E&P) involves the upstream activities from looking for promising reservoirs to extracting oil and selling it to downstream companies. E&P is the most profitable business in the oil industry. However, it is also the most capital-intensive and risky. Hence, the proper assessment of E&P projects with effective management of uncertainties is crucial to the success of any upstream business. This dissertation is concentrated on developing portfolio optimization models to manage E&P projects. The idea is not new, but it has been mostly restricted to the conceptual level due to the inherent complications to capture interactions among projects. We disentangle the complications by modeling the project portfolio optimization problem as multistage stochastic programs with mixed integer programming (MIP) techniques. Due to the disparate nature of uncertainties, we separately consider explored and unexplored oil fields. We model portfolios of real options and portfolios of decision trees for the two cases, respectively. The resulting project portfolio models provide rigorous and consistent treatments to optimally balance the total rewards and the overall risk. For explored oil fields, oil price fluctuations dominate the geologic risk. The field development process hence can be modeled and assessed as sequentially compounded options with our optimization based option pricing models. We can further model the portfolio of real options to solve the dynamic capital budgeting problem for oil projects. For unexplored oil fields, the geologic risk plays the dominating role to determine how a field is optimally explored and developed. We can model the E&P process as a decision tree in the form of an optimization model with MIP techniques. By applying the inventory-style budget constraints, we can pool multiple project-specific decision trees to get the multistage E&P project portfolio optimization (MEPPO) model. The resulting large scale MILP is efficiently solved by a decomposition-based primal heuristic algorithm. The MEPPO model requires a scenario tree to approximate the stochastic process of the geologic parameters. We apply statistical learning, Monte Carlo simulation, and scenario reduction methods to generate the scenario tree, in which prior beliefs can be progressively refined with new information.Item Optimization of production allocation under price uncertainty : relating price model assumptions to decisions(2011-08) Bukhari, Abdulwahab Abdullatif; Jablonowski, Christopher J.; Lasdon, Leon S.; Dyer, James S.Allocating production volumes across a portfolio of producing assets is a complex optimization problem. Each producing asset possesses different technical attributes (e.g. crude type), facility constraints, and costs. In addition, there are corporate objectives and constraints (e.g. contract delivery requirements). While complex, such a problem can be specified and solved using conventional deterministic optimization methods. However, there is often uncertainty in many of the inputs, and in these cases the appropriate approach is neither obvious nor straightforward. One of the major uncertainties in the oil and gas industry is the commodity price assumption(s). This paper investigates this problem in three major sections: (1) We specify an integrated stochastic optimization model that solves for the optimal production allocation for a portfolio of producing assets when there is uncertainty in commodity prices, (2) We then compare the solutions that result when different price models are used, and (3) We perform a value of information analysis to estimate the value of more accurate price models. The results show that the optimum production allocation is a function of the price model assumptions. However, the differences between models are minor, and thus the value of choosing the “correct” price model, or similarly of estimating a more accurate model, is small. This work falls in the emerging research area of decision-oriented assessments of information value.Item Risk analysis in tunneling with imprecise probabilities(2010-08) You, Xiaomin; Tonon, Fulvio; Rathje, Ellen M.; Gilbert, Robert B.; Manuel, Lance; Smirnoff, Timothy P.Due to the inherent uncertainties in ground and groundwater conditions, tunnel projects often have to face potential risks of cost overrun or schedule delay. Risk analysis has become a required tool (by insurers, Federal Transit Administration, etc.) to identify and quantify risk, as well as visualize causes and effects, and the course (chain) of events. Various efforts have been made to risk assessment and analysis by using conventional methodologies with precise probabilities. However, because of limited information or experience in similar tunnel projects, available evidence in risk assessment and analysis usually relies on judgments from experienced engineers and experts. As a result, imprecision is involved in probability evaluations. The intention of this study is to explore the use of the theory of imprecise probability as applied to risk analysis in tunneling. The goal of the methodologies proposed in this study is to deal with imprecise information without forcing the experts to commit to assessments that they do not feel comfortable with or the analyst to pick a single distribution when the available data does not warrant such precision. After a brief introduction to the theory of imprecise probability, different types of interaction between variables are studied, including unknown interaction, different types of independence, and correlated variables. Various algorithms aiming at achieving upper and lower bounds on previsions and conditional probabilities with assumed interaction type are proposed. Then, methodologies have been developed for risk registers, event trees, fault trees, and decision trees, i.e. the standard tools in risk assessment for underground projects. Corresponding algorithms are developed and illustrated by examples. Finally, several case histories of risk analysis in tunneling are revisited by using the methodologies developed in this study. All results obtained based on imprecise probabilities are compared with the results from precise probabilities.Item Risk mitigation strategies for project management, platform development and supply chain design(2010-12) Tan, Burcu; Anderson, Edward George; Feng, Annabelle (Qi); Dyer, James S.; Parker, Geoffrey G.; Seshadri, SridharThis dissertation studies strategies to mitigate the risks associated with operational and strategic decisions of a firm, particularly focusing on project management, product development and procurement decisions. In the first essay we develop two simulation-based methods to evaluate risky capital investment projects that involve managerial flexibility. Many risky projects are characterized by significant demand and operational risks (such as learning curve uncertainty) that are difficult to capture by simple stochastic processes. We propose using system dynamics simulations to estimate the cash flow resulting from these projects and build upon prior work on real options valuation in the decision analysis literature to develop two valuation algorithms. In the second essay we explore the technology investment decisions for platforms in markets that exhibit cross-network effects. We focus on the trade-off firms must make between investing new product development resources to increase a platform's core performance and functionality versus investments designed to leverage the platform's cross-network effects. Abstracting from examples drawn from multiple industries, we use a strategic model to gain intuition about how to make such trade-off decisions under competition. In the third essay, we analyze the optimal procurement strategy of a firm that faces supply and demand risk. In particular, the firm can source from two unreliable suppliers with different delivery characteristics. We study the optimal order allocation policy shaped by the trade-offs between delivery leadtime, reliability and procurement cost. Further, we discuss the value of leadtime flexibility in supply risk mitigation and highlight the role of an inferior supplier in a firm's multi-sourcing strategy. The main contribution of this dissertation to the operations management literature is two-fold. First, it illustrates the role of effective risk mitigation through operational strategies of leadtime flexibility and supply diversification as well as through recognizing managerial flexibility. Second, it highlights the importance of leveraging third-party content development while making technology investment decisions for platforms in two-sided markets.Item Shared decision-making about breast reconstruction : a decision analysis approach(2013-12) Sun, Clement Sung-Jay; Markey, Mia Kathleen; Reece, GregoryAn ongoing objective in healthcare is the development of tools to improve patient decision-making and surgical outcomes for patients with breast cancer that have undergone or plan to undergo breast reconstruction. In keeping with the bioethical concept of autonomy, these decision models are patient-oriented and expansive, covering a range of different patient decision-makers. In pursuit of these goals, this dissertation contributes to the development of a prototype shared decision support system that will guide patients with breast cancer and their physicians in making decisions about breast reconstruction. This dissertation applies principles in decision analysis to breast reconstruction decision-making. In this dissertation, we examine three important areas of decision-making: (1) the options available to decision-makers, (2) the validity of probabilistic information assessed from reconstructive surgeons, and (3) the feasibility of applying multiattribute utility theory. In addition, it discusses the influences of breast aesthetics and proposes a measure for quantifying such influences. The dissertation concludes with a fictional case study that demonstrates the integration of the findings and application of decision analysis in patient-oriented shared breast reconstruction decision-making. Through the implementation of decision analysis principles, cognitive biases and emotion may be attenuated, clearing the decision-maker’s judgment, and ostensibly leading to good decisions. While good decisions cannot guarantee good outcomes at the individual level, they can be expected to improve outcomes for patients with breast cancer as a whole. And regardless of the outcome, good decisions yield clarity of action and grant the decision-maker a measure of peace in an otherwise uncertain world.Item A software tool suite for small satellite risk management(2015-05) Gamble, Katharine Brumbaugh; Fowler, Wallace T.Risk management plans improve the likelihood of mission success by identifying potential failures early and planning mitigation methods to circumvent any issues. However, in the aerospace industry to date, risk management plans have typically only been used for larger and more expensive satellites, and have rarely been applied to satellites in the shape of 10 x 10 x 10 centimeter cubes, called CubeSats. Furthermore, existing risk management plans typically require experienced personnel and significant time to run the analysis. The purpose of this research was to develop two risk management software tools, the CubeSat Risk Analysis tool and the CubeSat Decision Advisor tool, which could be used by anyone with any level of experience. Moreover, the tools simply require the user to enter their mission-specific data; the software tools calculate the required analysis. The CubeSat Risk Analysis tool was developed for the purpose of reducing the subjectivity associated with estimating the likelihood and consequence of spacecraft mission risks. The tool estimates mission risk in terms of input characteristics, such as satellite form factor, mass, and development cycle. Using a historical database of small satellite missions, which was gathered in the course of this research, the software determines the mission risk root causes which are of the highest concern for the given mission. The CubeSat Decision Advisor tool uses components of decision theory such as decision trees, multi-attribute utility theory, and utility elicitation methods to determine the expected utility of a mitigation technique alternative. Based on the user’s value preference system, assessment of success probabilities, and resources required for a given mitigation technique, the tool suggests the course of action which will normatively yield the most value for the cost, personnel, and time resources required. The goals of this research were met in the development of two easily-accessible and free risk management software tools to assist in university satellite mission development. But more importantly, these tools will reach beyond the academic setting and allow small satellites to continue to evolve as a platform to accomplish educational, scientific, and military objectives.Item A value of information analysis of permeability data in a carbon, capture and storage project(2012-05) Puerta Ortega, Carlos Andres; Bickel, J. Eric; Hovorka, Susan; Rai, VarunCarbon dioxide capture and storage (CCS) is considered one of the key technologies for reducing atmospheric emissions of CO₂ from human activities (IPCC, 2005). The scale of potential deployment of CCS is enormous spanning manufacturing, power generation and hydrocarbon extraction worldwide. Uncertainty, cost-benefit challenges, market barriers and failures, and promotion and regulation of infrastructure are the main obstacles for deploying CCS technology in a broad scale. In a CCS project, it is the operator’s responsibility to guarantee the CO₂ containment while complying with environmental regulations and CO₂ contractual requirements with the source emitter. Acquiring new information (e.g. seismic, logs, production data, etc.) about a particular field can reduce the uncertainty about the reservoir properties and can (but not necessarily) influence the decisions affecting the deployment of a CCS project. The main objective of this study is to provide a decision-analysis framework to quantify the Value of Information (VOI) in a CCS project that faces uncertainties about permeability values in the reservoir. This uncertainty translates into risks of CO₂ migration out of the containment zone (or lease zone), non-compliance with contractual requirements on CO₂ storage capacity, and leakage of CO₂ to sources of Underground Source of Drinking Water (USDW). The field under analysis has been idealized based on a real project located in Texas. Subsurface modeling of the upper Frio Formation (injection zone) was conducted using well logs, field-specific GIS data, and other relevant published literature. The idealized model was run for different scenarios with different permeability distributions. The VOI was quantified by defining prior scenarios based on the current knowledge of a reservoir, contractual requirements, and regulatory constraints. The project operator has the option to obtain more reliable estimates of permeability, which will help to reduce the uncertainty of the CO₂ behavior and storage capacity of the formation. The accuracy of the information gathering activities is then applied to the prior probabilities (Bayesian inference) to infer the value of such data.Item Value of information and portfolio decision analysis(2013-08) Zan, Kun; Bickel, J. EricValue of information (VOI) is the amount a decision maker is willing to pay for information to better understand the uncertainty surrounding a decision, prior to making the decision. VOI is a key part of decision analysis (DA). Especially in this age of information explosion, evaluating information value is critical. VOI research tries to derive generic conclusions regarding VOI properties. However, in most cases, VOI properties rely on the specific decision context, which means that VOI properties may not be generalizable. Thus, instead, VOI properties have been derived for typical or representative decisions. In addition, VOI analysis as a method of DA has been successfully applied to practical decision problems in a variety of industries. This approach has also been adopted as the basis of a heuristic algorithm in the latest research in simulation and optimization. Portfolio Decision Analysis (PDA), rooted in DA, is a body of theories, methods, and practices that seek to help decision makers with limited budget select a subset of candidate items through mathematical modeling that accounts for relevant constraints, preferences, and uncertainties. As one of the main tools for resource allocation problems, its successful implementation, especially in capital-intensive industries such as pharmaceuticals and oil & gas, has been documented (Salo, Keisler and Morton 2011). Although VOI and PDA have been extensively researched separately, their combination has received attention only recently. Resource allocation problems are ubiquitous. Although significant attention has been directed at it, less energy has been focused on understanding the VOI within this setting, and the role of VOI analysis to solve resource allocation problems. This belief motivates the present work. We investigate VOI properties in portfolio contexts that can be modeled as a knapsack problem. By further looking at the properties, we illustrate how VOI analysis can derive portfolio management insights to facilitate PDA process. We also develop a method to evaluate the VOI of information portfolios and how the VOI will be affected by the correlations between information sources. Last, we investigate the performance of a widely implemented portfolio selection approach, the benefit-cost ratio (BCR) approach, in PDA practice.Item Value of information and the accuracy of discrete approximations(2010-08) Ramakrishnan, Arjun; Bickel, J. Eric; Lake, Larry W.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.