This study assessed the associations between adult attachment, disclosure, and relational uncertainty in both cyclical and non-cyclical dating relationships using a sample of 114 participants. The analysis revealed significant relationships between relational disclosure and relational uncertainty, attachment avoidance and relational disclosure, attachment anxiety and relational uncertainty, as well as attachment avoidance and relational uncertainty. Relational status (i.e., cyclical/non-cyclical) was neither related to relational disclosure or self-disclosure, nor served as a significant moderator between relational disclosure and relational uncertainty or self-disclosure and relational uncertainty. The results are considered in terms of how individual characteristics shaped by interpersonal interaction (i.e., attachment, relational uncertainty) are associated with specific communication patterns (i.e., disclosure) in dating relationships. / text

published_or_final_version / Computer Science / Master / Master of Philosophy

This thesis consists of two essays analyzing the effect of uncertainty in macroeconomic and financial settings. Inspired by the counter-cyclical pattern of uncertainty and the role played by capital reallocation in Total Factor Productivity, we propose a theoretical viewpoint on uncertainty-driven business cycles in the first essay. Relying on the interaction between financial market and real sector, we are able to build up a transmission mechanism from uncertainty to business cycle by introducing a financial contract between firms and financial intermediaries. By setting up two types of firm with different production technology in a general equilibrium model, we show that information asymmetry leads firms with financing needs to be financially constrained. Due to information asymmetry, first best case is unachievable and production resources are allocated more to firms without financing needs. When uncertainty changes, the lending decision of financial intermediary also changes, further affecting firms’ production capacities. Production resources are reallocated between the two types of firms which generates fluctuations in TFP and other aggregates. More importantly, firms with financing needs is assumed with better production technology than the one adopted by the other type on average. Increase in uncertainty worsens the informational problem, reduces funds provided to firms with better technology, causes reallocation of resources to the other type, and further decreases productivity of the economy as a whole. This is in line with an economic downturn and also consistent with the counter-cyclicality of uncertainty. We also conduct a quantitative analysis by calibrating the model to the data and the estimated results provide corroborating evidence for the theory. Using a merged data-set of US firms during years 1971-2008, we empirically examine the impact of uncertainty on capital reallocation via financial friction in the second essay. By adopting KZ index as an indicator for firms’ financial statuses, we decompose the uncertainty-capital reallocation relation into three hypotheses. Using cross-sectional dispersion of stock return as a measure for uncertainty, we find that uncertainty is negatively associated with firms’ financial statuses. A firm with high uncertainty level is more likely to be in a low position of financial status. Second, uncertainty is in a negative relation with capital reallocation, which means capital reallocation decreases at firm level when uncertainty increases. Third, by sorting firms into different groups based on their financial statuses, we find that firms which are in worse financial situation are more responsive to uncertainty change. The finding is consistent with our prediction that uncertainty affects capital reallocation through financial friction. We employ both reduced-form and structural estimation strategies to examine our predictions, and all regression results are supportive. To further test the role of financial friction in the relation, we also sort firms into different groups by SIC code. And we find that, firms in industries relying more on financial market for external financing are more responsive to uncertainty change. / published_or_final_version / Economics and Finance / Doctoral / Doctor of Philosophy

A 1-D dynamic simulation model of a new cooling system for the upcoming Gripen E aircraft has been developed in the Modelica-based tool Dymola in order to examine the cooling performance. These types of low-dimensioned simulation models, which generally are described by ordinary differential equations or differential-algebraic equations, are often used to describe entire fluid systems. These equations are easier to solve than partial differential equations, which are used in 2-D and 3-D simulation models. Some approximations and assumptions of the physical system have to be made when developing this type of 1-D dynamic simulation model. The impact from these approximations and assumptions can be examined with an uncertainty analysis in order to increase the understanding of the simulation results. Most uncertainty analysis methods are not practically feasible when analyzing large 1-D dynamic simulation models with many uncertainties, implying the importance to simplify these methods in order to make them practically feasible. This study was aimed at finding a method that is easy to realize with low computational expense and engineering workload. The evaluated simulation model consists of several sub-models that are linked together. These sub-models run much faster when simulated as standalone models, compared to running the total simulation model as a whole. It has been found that this feature of the sub-models can be utilized in an interval-based uncertainty analysis where the uncertainty parameter settings that give the minimum and maximum simulation model response can be derived. The number of simulations needed of the total simulation model, in order to perform an uncertainty analysis, is thereby significantly reduced. The interval-based method has been found to be enough for most simulations since the control software in the simulation model controls the liquid cooling temperature to a specific reference value. The control system might be able to keep this reference value, even for the worst case uncertainty combinations, implying no need to further analyze these simulations with a more refined uncertainty propagation, such as a probabilistic propagation approach, where different uncertainty combinations are examined. While the interval-based uncertainty analysis method lacks probability information it can still increase the understanding of the simulation results. It is also computationally inexpensive and does not rely on an accurate and time-consuming characterization of the probability distribution of the uncertainties. Uncertainties from all sub-models in the evaluated simulation model have not been included in the uncertainty analysis made in this thesis. These neglected sub-model uncertainties can be included using the interval-based method, as a future work. Also, a method for combining the interval-based method with aleatory uncertainties is proposed in the end of this thesis and can be examined.

Cross-docking is an important transportation logistics strategy in supply chain management which reduces transportation costs, inventory holding costs, order-picking costs and response time. Careful planning is needed for successful cross-dock operations. Uncertainty in cross-dock problems is inevitable and needs to be addressed. Almost all research in the cross-dock area assumes determinism. This dissertation considers uncertainty in cross-dock problems and optimizes these problems under uncertainty. We consider uncertainty in processing times, using scenario-based and protection-based robust approaches. Using a heuristic method, we find a lower and upper bound and combine that with a meta-heuristic method to solve the problem. Also, we consider problems in two different industries (Goodwill and H-E-B) and address the uncertainties that happen frequently in their operations. The scenario-based robust optimization model for the unloading problem using a min max objective is presented with examples. A surrogate heuristic procedure is used to find a robust solution. Next, a two-space genetic algorithm, a meta-heuristic procedure, is applied to the unloading problem using the bounds obtained by the heuristic procedure. The results are closer to the optimal solution than those obtained by the two-space genetic algorithm without bounds. When compared with the regular genetic algorithm with bounds, the two-space algorithm performs well. The protection-based approach considers a limit on the number of coefficients allowed to change with data uncertainty, protecting against the degree of conservatism. The management of trucks and reduction of overtime pay in the cross-dock operations of Goodwill is addressed through two models and uncertainty is applied to those models. A combined cross-dock operations model together with demand is formulated and the uncertainties are discussed for H-E-B operations. This dissertation does not address the recycling operation within the cross-dock of Goodwill, or the uncertainty in H-E-B data. / text

published_or_final_version / Psychology / Doctoral / Doctor of Philosophy

No description available.

This research analyzes the sensitivity of watershed attributes, and hydrological outputs to LiDAR derived DEM uncertainty introduced through spatial resolution, and LiDAR measurement errors. Sensitivity of watershed attributes to spatial resolution was determined through a scaling analysis at three sites; Mosquito Creek, Scotty Creek and Thomas Brook, with DEMs ranging from 1 to 50 m. Results at Scotty Creek showed the highest sensitivity of watershed area to spatial resolution, due to subtle changes in elevation which were below DEM uncertainty. Validation of the stream length at Thomas Brook showed discrepancies of 3.7 to 24.1% for the 1 to 50 m DEMs, compared to independent field observations. Sensitivity of SWAT derived hydrological outputs to DEM spatial resolution were determined through a scaling analysis of DEMs (1 - 50 m) at Thomas Brook watershed, over a five year simulation period. Results indicated monthly water yield was insensitive to DEM resolution, unless a change in area was also present. Sediment yield from the 50 m DEM showed a 24% reduction compared to the 1 m DEM. The 5 - 50 m DEMs also showed a reduction in channel deposition of 45 - 90 t, compared to the 1 m DEM. Sensitivity of terrain attributes, watershed attributes and hydrological outputs to LiDAR measurement errors were determined at the Thomas Brook watershed through the propagation of LiDAR sensor measurement errors with Monte Carlo simulations. Results showed that the uncertainty in the DEM, slope, and aspect were below 0.06 cm, 1.5° and 24.1° in 97.5% of grid cells, respectively. Watershed area and stream length resulted in relative standard deviations of <1% and 1.5%, respectively. However, sensitivity of watershed area increased in regions with elevation changes below DEM uncertainty and stream length uncertainty increased with decreasing stream length. SWAT simulated flow and sediment showed minor sensitivity to LiDAR measurement error in high flow months, and increased as flow decreased. Simulated sediment showed higher sensitivity to LiDAR measurement errors than flow, due to changes in the HRU slope class, which can shift the dominant HRU (Hydrological Response Unit) if a minimum HRU threshold area is implemented.

Uncertainty has a large effect on projects in the oil and gas industry, because most aspects of project evaluation rely on estimates. Industry routinely underestimates uncertainty, often significantly. The tendency to underestimate uncertainty is nearly universal. The cost associated with underestimating uncertainty, or overconfidence, can be substantial. Studies have shown that moderate overconfidence and optimism can result in expected portfolio disappointment of more than 30%. It has been shown that uncertainty can be assessed more reliably through look-backs and calibration, i.e., comparing actual results to probabilistic predictions over time. While many recognize the importance of look-backs, calibration is seldom practiced in industry. I believe a primary reason for this is lack of systematic processes and software for calibration. The primary development of my research is a database application that provides a way to track probabilistic estimates and their reliability over time. The Brier score and its components, mainly calibration, are used for evaluating reliability. The system is general in the types of estimates and forecasts that it can monitor, including production, reserves, time, costs, and even quarterly earnings. Forecasts may be assessed visually, using calibration charts, and quantitatively, using the Brier score. The calibration information can be used to modify probabilistic estimation and forecasting processes as needed to be more reliable. Historical data may be used to externally adjust future forecasts so they are better calibrated. Three experiments with historical data sets of predicted vs. actual quantities, e.g., drilling costs and reserves, are presented and demonstrate that external adjustment of probabilistic forecasts improve future estimates. Consistent application of this approach and database application over time should improve probabilistic forecasts, resulting in improved company and industry performance.

No description available.