Using one's success and failure experiences can be an indicator of how well risk is being managed in uncertain situations, particularly because exact probability information about outcomes is often missing. Experience-based paradigms include this more real-world aspect of a lack of information when studying risk taking behavior. This thesis builds upon experience-based paradigms to include the element of skill. A puzzle task was developed. A goal was given to participants to try to discern a pattern in each puzzle that would yield consistently positive outcomes. Participants were randomly assigned to a high or low success rate, but told that skill played a role in performance. The outcomes associated with each puzzle were chosen by the participant, and served as a measure of risk taking. After playing 41 puzzles, participants responded to scales measuring skill and chance beliefs, and motivational focus. Risk preferences were similar to experience-based paradigm predictions, though they were not well-calibrated. Those with a high success rate took more risks relative to those with a low success rate, but the results were less extreme than predicted. In addition, a closer look revealed that the pattern for those with a low success rate began by increasing their risk taking, and then did not decrease their risk taking significantly. Neither group felt that skill or chance was playing a dominant role in outcomes, though self-serving bias was observed as better performance did lead to higher ratings of skill. Overall, the results suggest that introducing the potential for skill may change how people approach risk in ways not predicted by experience-based paradigms.

This thesis is concerned with the prediction of the vibro-acoustic response of structures with uncertain properties in the mid frequency region. The motivation for this research is the growing need of engineers to understand the responses of a group of similar structures ranging from vehicles, aircraft and aerospace structures, to household whitegood appliances. These structures are complex in geometry and may possess variability in their material or geometric properties, as well as variation arising from the assembly and manufacturing processes. Small variations can have a significant effect on a dynamic response of a structure, and the effect of structural uncertainties increases as the frequency increases. Deterministic modelling techniques such as finite element analysis are only suitable to model complex structures at low frequencies. Furthermore, FEA cannot easily account for uncertainty or randomness in structural parameters. High frequency dynamic predictive techniques such as Statistical Energy Analysis can account for structural uncertainty but is limited to structures with high modal density. There exists a frequency range between the two methods in which neither technique can be applied with great confidence. The objective of this thesis is to investigate predictive techniques for mid frequency vibration analysis of dynamic systems with structural uncertainties. The first part of this work is to numerically characterise the effect of a range of uncertainties on the modal statistics of structures. The degree of uncertainty required to achieve universality of the statistical properties is investigated. This is achieved by examining the modal statistics of dynamic systems with a range of uncertainty, corresponding to uncertainty due to mass and stiffness perturbations, uncertainty at the boundaries of a structure, uncertainty in the coupling between structures, uncertainty in the material properties of a structure and uncertainty in the geometry of a structure. Several structures are examined corresponding to a plate with masses and/or linear springs added at random locations, a plate with torsional springs attached at random locations along its boundary edges, two plates coupled by linear springs at random locations, a mass-loaded coupled L-shaped plate, a mass-loaded frame-plate structure, and a plate with varying Young's modulus, density and thickness. The natural frequencies of the aforementioned structures have been derived using either the Lagrange-Rayleigh-Ritz technique, finite element analysis, or the use of interval analysis in conjunction with FEA. The natural frequency statistics of structures with uncertain properties are observed using two statistical measures; the statistical overlap factor and the probability density function of the spacing between successive natural frequencies. The statistical overlap factor is defined by the variation in a natural frequency from its mean value measured across an ensemble of nominally identical structures with uncertainty. For a single ensemble member, the probability density function of the spacing between successive natural frequencies is compared to a Rayleigh distribution of the mean frequency spacing. A Rayleigh distribution of modal spacings is a feature of the universality exhibited by structures with uncertainty. To further investigate the effect of structural uncertainty on the vibrational characteristics of structures, the interval analysis is applied to finite element models of a plate with uncertainty in its material properties and dimensions. Using this method, the Young's modulus, density and thickness of a rectangular plate were set to vary by a small amount within predefined bounds. Using finite element equations, the natural frequencies and modeshapes of the structure were then determined in terms of the Young's modulus, density and plate thickness. For the mass and spring loaded plates, the springs were shown to affect the lower order modes while the masses had a significant effect on the higher order modes. As the frequency increased, only a small amount of perturbation was sufficient to affect the natural frequencies of a structure. Using the interval analysis method, the variation of the natural frequencies from their deterministic value increased as the frequency increased. An ergodic hypothesis was used to examine the responses statistics of structures with uncertainty. Three structures have been computationally studied corresponding to two plates coupled by springs, an L-shaped plate and a frame plate structure. Uncertainty has been generated for the two coupled plates by locating the springs randomly across the surface of the two plates. For the L-shaped plate and a frame plate structure, uncertainty was generated by randomly positioning small masses across the plates. Using the ergodic hypothesis, the frequency averaged response on one member of an ensemble is compare with the ensemble averaged response. It was found that the ensemble averaged response was well predicted by a frequency averaged response of a single ensemble member. The width of the frequency averaging band was shown to have a large influence on the quality of the match between the frequency and ensemble averaged responses. Results were significantly improved using a frequency averaging bandwidth which varies proportionally to frequency. Finally, experiments have been conducted on an L-shaped plate, a frame plate structure and a vehicle to validate the computational results for the natural frequency and response statistics.

Uncertainties are inevitably part of the radiotherapy process. Uncertainty in the dose deposited in the tumour exists due to organ motion, patient positioning errors, fluctuations in machine output, delineation of regions of interest, the modality of imaging used, and treatment planning algorithm assumptions among others; there is uncertainty in the dose required to eradicate a tumour due to interpatient variations in patient-specific variables such as their sensitivity to radiation; and there is uncertainty in the dose-volume restraints that limit dose to normal tissue. This thesis involves three major streams of research including investigation of the actual dose delivered to target and normal tissue, the effect of dose uncertainty on radiobiological indices, and techniques to display the dose uncertainty in a treatment planning system. All of the analyses are performed with the dose distribution from a four-field box treatment using 6 MV photons. The treatment fields include uniform margins between the clinical target volume and planning target volume of 0.5 cm, 1.0 cm, and 1.5 cm. The major work is preceded by a thorough literature review on the size of setup and organ motion errors for various organs and setup techniques used in radiotherapy. A Monte Carlo (MC) code was written to simulate both the treatment planning and delivery phases of the radiotherapy treatment. Using MC, the mean and the variation in treatment dose are calculated for both an individual patient and across a population of patients. In particular, the possible discrepancy in tumour position located from a single CT scan and the magnitude of reduction in dose variation following multiple CT scans is investigated. A novel convolution kernel to include multiple pretreatment CT scans in the calculation of mean treatment dose is derived. Variations in dose deposited to prostate and rectal wall are assessed for each of the margins and for various magnitudes of systematic and random error, and penumbra gradients. The linear quadratic model is used to calculate prostate Tumour Control Probability (TCP) incorporating an actual (modelled) delivered prostate dose. The Kallman s-model is used to calculate the normal tissue complication probability (NTCP), incorporating actual (modelled) fraction dose in the deforming rectal wall. The impact of each treatment uncertainty on the variation in the radiobiological index is calculated for the margin sizes. / Thesis (Ph.D.)--Department of Physics and Mathematical Physics, 2002.

Thesis (M.S.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains viii, 121 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 112-121).

The aim of this Thesis is to study the relationship of investment decision with technological progress and the adoption of new technologies. In more specific, the main focus is given to some policy experiment. This dissertation is divided in 4 chapters, including the introduction and the review of the literature: · The Second Chapter deals with the impact of productive public capital on the decision to invest, taking into account irreversibility and uncertainty. Many authors have stressed the importance of the Government as ultimate risk manager. On the one hand, there is an extensive literature on how public expenditure affects growth. The purpose of the chapter is to take another perspective: to study if the productive public capital can reduce the risk faced by the private sector, in the presence of irreversibility and uncertainty. Thereby, we want to emphasize the stabilizing role for the public capital. · The Third Chapter: introduces a theoretical link between embodied technological progress, the irreversibility and uncertainty literature. In this model, Replacement is postponed in the presence irreversibility and uncertainty. The age of the oldest machine evolves stochastically. Firms can invest even if they are not using all the units, getting rid from the perfect procycle models in the irreversible literature. Furthermore, we reproduce the empirical evidence at firm level: investment is lumpy and infrequent. · The Fourth Chapter studies the impact of public policies aiming at the reduction of the adoption cost of new technologies. We assume an economy that presents AK production function with two distinct features: the adoption of new technologies is costly and embodied in new machines, and there is diffusion and learning process. Studying how technological progress, uncertainty and irreversibility affect the decision to undertake new projects and the acquisition of new equipments by private sector, we expect that this Thesis can contribute not only with academic debate but also with the efficiency of public policies.

The determination of long-term goals for climate policy, or of near-term mitigation effort, requires a shared conception among nations of what is at stake. Unfortunately, because of different attitudes to risk, problems of valuing non-market effects, and disagreements about aggregation across rich and poor nations, no single benefit measure is possible that can provide commonly accepted basis for judgment. In response to this circumstance, a portfolio of estimates is recommended, including global variables that can be represented in probabilistic terms, regional impacts expressed in natural units, and integrated monetary valuation. Development of such a portfolio is a research task, and the needed program of work suggested. / Abstract in HTML and technical report in PDF available on the Massachusetts Institute of Technology Joint Program on the Science and Policy of Global Change website (http://mit.edu/globalchange/www/). / Results cited from the MIT Joint Program on the Science and Policy of Global Change were developed with the support of the US Department of Energy, Office of Biological and Environmental Research [BER] (DE-FG02-94ER61937) the US Environmental Protection Agency (X-827703-01-0), the Electric Power Research Institute, and by a consortium of industry and foundation sponsors.

The cost of construction projects depends on their size, complexity, and duration. Construction management applies effective management techniques to the planning, design, and construction of a project from conception to completion for the purpose of controlling time, cost and quality. A real options approach in construction projects, improves strategic thinking by helping planners recognize, design and use flexible alternatives to manage dynamic uncertainty. In order to manage uncertainty using this approach, it is necessary to value the real options. Real option models assume independence of option holder and the impacts of underlying uncertainties on performance and value. The current work proposes and initially tests whether project management reduces the value of real options. The example of resource allocation is used to test this hypothesis. Based on the results, it is concluded that project management reduces the value of real options by reducing variance of the exercise signal and the difference between exercise conditions and the mean exercise signal.

Current techniques for production data integration into reservoir models can be broadly grouped into two categories: deterministic and Bayesian. The deterministic approach relies on imposing parameter smoothness constraints using spatial derivatives to ensure large-scale changes consistent with the low resolution of the production data. The Bayesian approach is based on prior estimates of model statistics such as parameter covariance and data errors and attempts to generate posterior models consistent with the static and dynamic data. Both approaches have been successful for field-scale applications although the computational costs associated with the two methods can vary widely. This is particularly the case for the Bayesian approach that utilizes a prior covariance matrix that can be large and full. To date, no systematic study has been carried out to examine the scaling properties and relative merits of the methods. The main purpose of this work is twofold. First, we systematically investigate the scaling of the computational costs for the deterministic and the Bayesian approaches for realistic field-scale applications. Our results indicate that the deterministic approach exhibits a linear increase in the CPU time with model size compared to a quadratic increase for the Bayesian approach. Second, we propose a fast and robust adaptation of the Bayesian formulation that preserves the statistical foundation of the Bayesian method and at the same time has a scaling property similar to that of the deterministic approach. This can lead to orders of magnitude savings in computation time for model sizes greater than 100,000 grid blocks. We demonstrate the power and utility of our proposed method using synthetic examples and a field example from the Goldsmith field, a carbonate reservoir in west Texas. The use of the new efficient Bayesian formulation along with the Randomized Maximum Likelihood method allows straightforward assessment of uncertainty. The former provides computational efficiency and the latter avoids rejection of expensive conditioned realizations.

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Since uncertainty and irreversibility are inherent, environmental policy involves the problems of timing of implementation. Environmental policy based on cost-benefit analysis using certainty equivalent presents values can be misleading under the combined effect of irreversibility and uncertainty. Using real options method, the thesis analyzes the timing of early action investment in Canada's Kyoto commitment. Early action investment in emission reductions is irreversible. The thesis uses a simple two-period model, and then lays out a corresponding continuous-time model to show that under technological uncertainty, early action investment should be delayed until more information - the results of R&D - is revealed. In particular, the more uncertain the outcome of research, the more the firm should delay early action investment. The thesis argues that Canada's Kyoto commitment is well intentioned but not wisely implemented: early action investment on emission reductions may not be efficient. The results suggest that a more gradual Kyoto program would be favourable.