Causal uncertainty refers to the lack of confidence in one’s ability to understand causal relations in the social world (Weary & Edwards, 1994). Relative to people with low causal uncertainty, individuals with high causal uncertainty exhibit enhanced self-regulation performance following a social interaction (Jacobson, Papile, Passey, & Boucher, 2006). The current studies investigated the potential mechanisms underlying this relationship, and the role of self-esteem. Study 1 investigated whether the social or nonsocial nature of the depleting task and expectations about the need for future self-control could account for the relationship between causal uncertainty and self-regulation (N = 181). For the social task, high causally uncertain participants’ self-regulation performance was consistent across expectations for future self-control regardless of participant self-esteem. In contrast, low causally uncertain participants’ performance improved with increasing instructions to conserve energy for future tasks but only for participants with lower self-esteem. For low causally uncertain participants with higher self-esteem, self-regulation performance decreased with increased expectations for future self-control. In the nonsocial condition, the findings did not differ by self-esteem. Learning that the future task involved self-control and that the initial task was depleting were both associated with increases in self-regulation for high causally uncertain participants. In contrast, self-regulation abilities did not differ for low causally uncertain participants upon learning that the future task involved self-control and marginally decreased when they learned that the initial task was depleting. Study 2 examined whether or not self-presentation could account for the relationship between causal uncertainty and self-regulation abilities (N = 88). Higher causal uncertainty was associated with better self-regulation performance, but self-presentation goals did not moderate this relationship. Self-esteem did not influence self-regulation performance in this study. Study 3 investigated whether or not an accuracy goal could account for the relationship between causal uncertainty and self-regulation abilities (N = 112). For participants with lower self-esteem, high causally uncertain participants’ self-regulation performance was consistent regardless of the goal manipulation; whereas low causally uncertain participants’ performance improved with instructions to create accurate impressions of their partner. In contrast, for participants with higher self-esteem, self-regulation did not differ by causal uncertainty or goal conditions. / Thesis (Ph.D, Psychology) -- Queen's University, 2009-08-28 14:40:08.139

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The classical mean-variance model, proposed by Harry Markowitz in 1952, has been one of the most powerful tools in the field of portfolio optimization. In this model, parameters are estimated by their sample counterparts. However, this leads to estimation risk, which the model completely ignores. In addition, the mean-variance model fails to incorporate behavioral aspects of investment decisions. To remedy the problem, the notion of ambiguity aversion has been addressed by several papers where investors acknowledge uncertainty in the estimation of mean returns. We extend the idea to the variances and correlation coefficient of the portfolio, and study their impact. The performance of the portfolio is measured in terms of its Sharpe ratio. We consider different cases where one parameter is assumed to be perfectly estimated by the sample counterpart whereas the other parameters introduce ambiguity, and vice versa, and investigate which parameter has what impact on the performance of the portfolio.

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No description available.

Model uncertainty in financial markets is prevalent by the very nature of how models are constructed and used by financial practitioners. As such, a proper characterization of model uncertainty should be paramount in the eyes of every practitioner, and furthermore, a proper framework for implementing such a characterization towards financial activities should be implicit. While model uncertainty is acknowledged by practitioners, a cohesive and robust framework for determining a model uncertainty risk measure that is broadly accepted by practitioners is missing. We acknowledge this deficiency and provide a practitioner's guide for evaluating a modern characterization of model uncertainty, specifically that of Li and Kwon, as applied to a subset of derivative related calculations, with the goal of promoting its implementation by practitioners. We promote its implementation by demonstrating the utility and flexibility of such a characterization relative to another modern model uncertainty risk measure, specifically that of Cont.

Model uncertainty in financial markets is prevalent by the very nature of how models are constructed and used by financial practitioners. As such, a proper characterization of model uncertainty should be paramount in the eyes of every practitioner, and furthermore, a proper framework for implementing such a characterization towards financial activities should be implicit. While model uncertainty is acknowledged by practitioners, a cohesive and robust framework for determining a model uncertainty risk measure that is broadly accepted by practitioners is missing. We acknowledge this deficiency and provide a practitioner's guide for evaluating a modern characterization of model uncertainty, specifically that of Li and Kwon, as applied to a subset of derivative related calculations, with the goal of promoting its implementation by practitioners. We promote its implementation by demonstrating the utility and flexibility of such a characterization relative to another modern model uncertainty risk measure, specifically that of Cont.

This research presents new physics-based methods to estimate predictive uncertainty stemming from uncertainty in the material opacities in radiative transfer computations of key quantities of interest (QOIs). New methods are needed because it is infeasible to apply standard uncertainty-propagation techniques to the O(105) uncertain opacities in a realistic simulation. The new approach toward uncertainty quantification applies the uncertainty analysis to the physical parameters in the underlying model used to calculate the opacities. This set of uncertain parameters is much smaller (O(102)) than the number of opacities. To further reduce the dimension of the set of parameters to be rigorously explored, we use additional screening applied at two different levels of the calculational hierarchy: first, physics-based screening eliminates the physical parameters that are unimportant from underlying physics models a priori; then, sensitivity analysis in simplified versions of the complex problem of interest screens out parameters that are not important to the QOIs. We employ a Bayesian Multivariate Adaptive Regression Spline (BMARS) emulator for this sensitivity analysis. The high dimension of the input space and large number of samples test the efficacy of these methods on larger problems. Ultimately, we want to perform uncertainty quantification on the large, complex problem with the reduced set of parameters. Results of this research demonstrate that the QOIs for target problems agree at for different parameter screening criteria and varying sample sizes. Since the QOIs agree, we have gained confidence in our results using the multiple screening criteria and sample sizes.

A challenge in petroleum geostatistics is the application of modeling algorithms such as Gaussian simulation to unstructured grids that are being used for flow simulation. Geostatistical modeling is typically applied on a fine scale regular grid and then upscaled to the unstructured grid. This work proposes a fine scale unstructured grid. The grid is designed so that its elements align with the flow simulation grid elements, eliminating the occurrence of intersections between the two grids. Triangular and tetrahedral grids are used for the fine scale grid; however, they introduce a variety of element scales. The approach developed in this work populates the fine scale grid based on the scale of conditioning data. The resulting error due to scale discrepancy is quantified and mitigated though the upscaling process. A methodology to assess the error in upscaled properties is developed and used to control the design of the fine scale grid. Populating the fine scale grid with reservoir properties requires modification of existing geostatistical algorithms. The set of spatial locations for modeling is irregular and three differences that result from this are addressed: random path generation; spatial search; and the covariance lookup table. Results are compiled into an algorithm for sequential indicator and sequential Gaussian simulation on irregular point sets. Checking variogram reproduction on large irregular point sets is a challenge. An algorithm that efficiently computes the experimental variogram for these cases is developed. A flow based upscaling method based on the multipoint flux approximation is developed to upscale permeability models from the fine scale unstructured grid to the flow simulation grid. Triangular grids are assumed for the fine scale. Flow simulation results using the upscaled transmissibilities are very similar to results obtained using traditional flow simulation on high resolution regular grids. / Mining Engineering

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (leaves 74-83).