With businesses operating in an environment of uncertainty, questions were raised around the role of rolling forecasts in this environment and how the current financial processes have changed as a result of the uncertainty. There are currently debates regarding the role of budgeting and other planning methods, one of which is rolling forecasts. One school of thought believes that budgets are no longer relevant in current times and should be replaced with newer methods, whereas the second school of thought believes that budgets are still current and relevant. Having experienced a global financial crisis, the current time can be described as one of uncertainty. Banks were one of the industries hardest hit by this crisis. This research investigates whether the financial planning processes in banks has changed to align itself with an environment of uncertainty. It further explores whether banks have adopted rolling forecasts in their planning processes. Data was collected using expert interviews. The sample included 8 respondents across 3 different banks. The data was then analysed using content analysis. The outcome allowed for visibility into the changes made by banks in their financial planning processes. Insights into the reasons why rolling forecasts were not used was also obtained. Uncertainty in the environment resulted in banks making changes to their financial planning processes. It also emerged that none of the respondents use rolling forecasts. However some of the respondents use forecasting models which are based on some principles of rolling forecast. This research contributes to the budgeting and planning discipline. It contributes by exploring how the budget process is changing to align itself with the changing environment. It also contributes by providing insights into why rolling forecasts are not adopted. / Dissertation (MBA)--University of Pretoria, 2010. / Gordon Institute of Business Science (GIBS) / unrestricted

The EPA recently completed a major study to evaluate exposure and risk associated with a primary contaminant, radon and its progeny in drinking water (EPA, 1995). This work resulted in the development of a Monte Carlo Simulation model written in the programming language C. The model developed by the EPA has been used to estimate the cancer fatality risk from radon in water for exposed populations served by community ground water supplies, and to provide a quantitative analysis of the uncertainty associated with the calculations (EPA, 1995). This research is a continuation of the study conducted by the EPA. In this project, a Monte Carlo computer model will be developed to evaluate the risk associated with exposure to volatile compounds in drinking water. The model will be based on a computer program (developed previously by the EPA) for estimating the risks associated with exposure to radon in drinking water. The model will be re-implemented in the form of a computer program written in C. The analysis for radon will be extended to include the entire range of contaminants found in drinking water supplies. The initial focus of the project has been on extending the analysis to cover the ingestion exposure pathway for volatile compounds, but ultimately the risk via ingestion and dermal sorption will also be evaluated. The integrated model can estimate the risks associated with various levels of contaminants in drinking water and should prove valuable in establishing Maximum Contaminant Levels (MCLs) for the entire range of contaminants found in water supplies and generated in water treatment and distribution systems. / Master of Science

This dissertation extends contemporary views about uncertainty. It does so through a reading of the role of uncertainty in the political thought of two modern philosophers, Baruch Spinoza and David Hume. Despite uncertainty's notable and multi-disciplinary appeal in the academic literature, the frame in which most scholars think about social and political uncertainty is one-sided. On the whole, contemporary scholars consider uncertainty as a problem in need of a remedy. In the social sciences uncertainty is transformed into risk in order to empirically calculate risk probabilities. The hope is that risks (uncertainties) can be controlled, reduced, and in all, mitigated. In this dissertation, I argue for a conceptual rethinking of uncertainty that expands its scope and reach to include a socially and politically beneficent understanding, a constructive form of uncertainty. In particular, I explore the ways social groups experience conditions of uncertainty in different contexts through an examination of what I term future-oriented and epistemic uncertainty in Hume and Spinoza's political thought. Spinoza's arguments for liberal democracy, and Hume's arguments favoring commercial society, are highlighted as instances of constructive uncertainty. The dissertation concludes by applying a general understanding of constructive uncertainty to the ideology of the American Dream in order to illustrate suggestively how a constructive conception of uncertainty might prove useful when critically engaging contemporary matters. / Ph. D.

This study considers an expanded meaning of "uncertainty" as it affects decision-makers. The definition adopted is based on a decision-maker who is uncertain, i.e. aware of the insufficiency of her knowledge for the purpose of rationally determining which option to choose. A taxonomy of uncertainties is developed from this definition. The first stage is a Generalized Decision Model, which expands on a standard decision model often assumed in technical works by allowing uncertainty over components of the model that are assumed to be perfectly known in the standard model. These additional potential "subjects" of uncertainty include the feasibility of options, the authority of the decision-maker to effect a choice, membership of and probability distributions over the set of possible future states of the world, and considerations about how the consequences are to be valued. The taxonomy also describes possible "sources" of uncertainty, dividing them into characteristics of the world (e.g. variability), evidence the decision-maker has (e.g. ambiguity or imprecision), or characteristics of the decision-maker himself. Other important ways in which uncertainties can vary is whether they are hard (irreducible in principle) or soft, whether a decision is unique or repeatable, and the role time has in the decision and in the resolving of the uncertainties. A finding of this work is that many uncertainties in addition to the uncertainty in the standard decision model over the future state of the world can keep a procedure for implementing rational choice from being decisive, thus requiring another (nonrational) process to complete the selection of an option. Other insights: (1) Deciding is only part of being rational, and in many instances is not the most important part. (2) Uncertainty may complicate decision-making, but is by no means always bad for the decision-maker. (3) Rationality is inescapably subjective in any implementation. (4) True "decision under certainty" does not exist. (5) Uncertainties vary sufficiently that no single treatment can be prescribed; it is hoped that this work contributes to a survey of the territory of uncertainty that facilitates Smithson's (1988) "suburbanization" or subdivision into smaller tracts to be developed individually.

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To better design structures and machines, understanding of flaws and failures is essential. The body of this work has addressed numerous facets of fatigue crack propagation. The affect of crack closure, testing errors, and data scatter are a few important components of crack growth developed and investigated. It was found that the widely accepted compliance-offset technique for closure measurement may be sensitive to increases in load ratio. Opening load uncertainty was calculated to be on the order of 5%. The application of practical regression techniques and the use of DKeff were used to characterize closureree crack growth data to develop a single intrinsic da/dN curve. The best form of regression was found to be a multi-linear fit. A strip-yield model requiring the intrinsic curve was used to successfully predict crack growth at other load ratios. Uncertainties with a strong dependence on crack mouth displacement were found for da/dN, DK, and a.To better design structures and machines, understanding of flaws and failures is essential. The body of this work has addressed numerous facets of fatigue crack propagation. The affect of crack closure, testing errors, and data scatter are a few important components of crack growth developed and investigated. It was found that the widely accepted compliance-offset technique for closure measurement may be sensitive to increases in load ratio. Opening load uncertainty was calculated to be on the order of 5%. The application of practical regression techniques and the use of DKeff were used to characterize closureree crack growth data to develop a single intrinsic da/dN curve. The best form of regression was found to be a multi-linear fit. A strip-yield model requiring the intrinsic curve was used to successfully predict crack growth at other load ratios. Uncertainties with a strong dependence on crack mouth displacement were found for da/dN, deltaK, and a.

In order to identify ways to improve cost estimation, especially early in design, cost estimation needs to be viewed and represented as a process. An important activity within the cost estimation process is assessing the cost risk of a system. A decision-support tool that assesses cost risk should represent the impact of subsystem or system-level uncertainty and provide mechanisms to help select among competing designs. In order to address these problems, a generic cost estimation process was developed. It is based on an extensive review of the cost estimation literature. Also, a hierarchicial product structure, model-based approach and tool to estimate system-level cost risk was developed. This tool provides a link between cost models and cost elements for each component, mechanisms for determining the impact of risk on the cost of the design, and outputs used for selecting among alternative competing designs.

With limited resources and time available for a typical design project, it is difficult to decide how to allocate these resources and time to produce an optimum design. Also, the question arises, ?Given the design process, available resources, and available time, will the design meet the program goals?? Uncertainty analyses of design processes addresses these issues and could substantially improve design quality, cost, and cycle time. Research to examine uncertainty in the design process employs previous experience in experimental, model, and manufacturing uncertainty, in an innovative approach for analyzing the entire design process. This research was initiated with a pilot project, a 4-bar-slider mechanism. Two new theories for the research have arisen from this pilot project. First, design optimization techniques could be used to compare steps of the design process and to help determine the overall uncertainty of the final manufactured product. Second, manufacturing uncertainty can be included as an additional random uncertainty in the analysis of the final manufactured product. While more research needs to be completed to test, apply, and expand on these theories, the pilot project has been a positive step forward. It has already produced two proposals with one funded and one awaiting a decision. This research, although in its beginning stages, could substantially improve the design process.

Engineering models both for analysis and experimental data reduction include variables that have uncertainties associated with them. Analyzing these models without considering the uncertainties may provide misleading results. In this paper, several methods for evaluating uncertainty are summarized. In particular, second-order uncertainty analysis method is illustrated using Taylor series expansion. It is the intent of this paper to compare the first-order and second-order propagation methods, Monte Carlo simulation methods and sequential perturbation uncertainty analysis methods and investigate the situation that second-order propagation method is necessary through examples studies by MathCad worksheet.