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- All Subjects: Clarinet and piano music
Description
There exist many facets of error and uncertainty in digital spatial information. As error or uncertainty will not likely ever be completely eliminated, a better understanding of its impacts is necessary. Spatial analytical approaches, in particular, must somehow address data quality issues. This can range from evaluating impacts of potential data uncertainty in planning processes that make use of methods to devising methods that explicitly account for error/uncertainty. To date, little has been done to structure methods accounting for error. This research focuses on developing methods to address geographic data uncertainty in spatial optimization. An integrated approach that characterizes uncertainty impacts by constructing and solving a new multi-objective model that explicitly incorporates facets of data uncertainty is developed. Empirical findings illustrate that the proposed approaches can be applied to evaluate the impacts of data uncertainty with statistical confidence, which moves beyond popular practices of simulating errors in data. Spatial uncertainty impacts are evaluated in two contexts: harvest scheduling and sex offender residency. Owing to the integration of spatial uncertainty, the detailed multi-objective models are more complex and computationally challenging to solve. As a result, a new multi-objective evolutionary algorithm is developed to address the computational challenges posed. The proposed algorithm incorporates problem-specific spatial knowledge to significantly enhance the capability of the evolutionary algorithm for solving the model.
ContributorsWei, Ran (Author) / Murray, Alan T. (Thesis advisor) / Anselin, Luc (Committee member) / Rey, Segio J (Committee member) / Mack, Elizabeth A. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Despite the wealth of folk music traditions in Portugal and the importance of the clarinet in the music of bandas filarmonicas, it is uncommon to find works featuring the clarinet using Portuguese folk music elements. In the interest of expanding this type of repertoire, three new works were commissioned from three different composers. The resulting works are Seres Imaginarios 3 by Luis Cardoso; Delirio Barroco by Tiago Derrica; and Memória by Pedro Faria Gomes. In an effort to submit these new works for inclusion into mainstream performance literature, the author has recorded these works on compact disc. This document includes interview transcripts with each composer, providing first-person discussion of each composition, as well as detailed biographical information on each composer. To provide context, the author has included a brief discussion on Portuguese folk music, and in particular, the role that the clarinet plays in Portuguese folk music culture.
ContributorsFerreira, Wesley (Contributor) / Spring, Robert S (Thesis advisor) / Bailey, Wayne (Committee member) / Gardner, Joshua (Committee member) / Hill, Gary (Committee member) / Schuring, Martin (Committee member) / Solis, Theodore (Committee member) / Arizona State University (Publisher)
Created2013
Description
It is well understood that decisions made under uncertainty differ from those made without risk in important and significant ways. Yet, there is very little research into how uncertainty manifests itself in the most ubiquitous of decision-making environments: Consumers' day-to-day decisions over where to shop, and what to buy for their daily grocery needs. Facing a choice between stores that either offer relatively stable "everyday low prices" (EDLP) or variable prices that reflect aggressive promotion strategies (HILO), consumers have to choose stores under price-uncertainty. I find that consumers' attitudes toward risk are critically important in determining store-choice, and that heterogeneity in risk attitudes explains the co-existence of EDLP and HILO stores - an equilibrium that was previously explained in somewhat unsatisfying ways. After choosing a store, consumers face another source of risk. While knowing the quality or taste of established brands, consumers have very little information about new products. Consequently, consumers tend to choose smaller package sizes for new products, which limits their exposure to the risk that the product does not meet their prior expectations. While the observation that consumers purchase small amounts of new products is not new, I show how this practice is fully consistent with optimal purchase decision-making by utility-maximizing consumers. I then use this insight to explain how manufacturers of consumer packaged goods (CPGs) respond to higher production costs. Because consumers base their purchase decisions in part on package size, manufacturers can use package size as a competitive tool in order to raise margins in the face of higher production costs. While others have argued that manufacturers reduce package sizes as a means of raising unit-prices (prices per unit of volume) in a hidden way, I show that the more important effect is a competitive one: Changes in package size can soften price competition, so manufacturers need not rely on fooling consumers in order to pass-through cost increases through changes in package size. The broader implications of consumer behavior under risk are dramatic. First, risk perceptions affect consumers' store choice and product choice patterns in ways that can be exploited by both retailers and manufacturers. Second, strategic considerations prevent manufacturers from manipulating package size in ways that seem designed to trick consumers. Third, many services are also offered as packages, and also involve uncertainty, so the effects identified here are likely to be pervasive throughout the consumer economy.
ContributorsYonezawa, Koichi (Author) / Richards, Timothy J. (Thesis advisor) / Grebitus, Carola (Committee member) / Park, Sungho (Committee member) / Arizona State University (Publisher)
Created2014
Description
In this study, I test whether firms reduce the information asymmetry stemming from the political process by investing in political connections. I expect that connected firms enjoy differential access to relevant political information, and use this information to mitigate the negative consequences of political uncertainty. I investigate this construct in the context of firm-specific investment, where prior literature has documented a negative relation between investment and uncertainty. Specifically, I regress firm investment levels on the interaction of time-varying political uncertainty and the degree of a firm's political connectedness, controlling for determinants of investment, political participation, general macroeconomic conditions, and firm and time-period fixed effects. Consistent with prior work, I first document that firm-specific investment levels are significantly lower during periods of increased uncertainty, defined as the year leading up to a national election. I then assess the extent that political connections offset the negative effect of political uncertainty. Consistent with my hypothesis, I document the mitigating effect of political connections on the negative relation between investment levels and political uncertainty. These findings are robust to controls for alternative explanations related to the pre-electoral manipulation hypothesis and industry-level political participation. These findings are also robust to alternative specifications designed to address the possibility that time-invariant firm characteristics are driving the observed results. I also examine whether investors consider time-varying political uncertainty and the mitigating effect of political connections when capitalizing current earnings news. I find support that the earnings-response coefficient is lower during periods of increased uncertainty. However, I do not find evidence that investors incorporate the value relevant information in political connections as a mitigating factor.
ContributorsWellman, Laura (Author) / Dhaliwal, Dan (Thesis advisor) / Hillegeist, Stephen (Thesis advisor) / Walther, Beverly (Committee member) / Mikhail, Mike (Committee member) / Hillman, Amy (Committee member) / Brown, Jenny (Committee member) / Arizona State University (Publisher)
Created2014
Description
This research develops heuristics for scheduling electric power production amid uncertainty. Reliability is becoming more difficult to manage due to growing uncertainty from renewable resources. This challenge is compounded by the risk of resource outages, which can occur any time and without warning. Stochastic optimization is a promising tool but remains computationally intractable for large systems. The models used in industry instead schedule for the forecast and withhold generation reserve for scenario response, but they are blind to how this reserve may be constrained by network congestion. This dissertation investigates more effective heuristics to improve economics and reliability in power systems where congestion is a concern.
Two general approaches are developed. Both approximate the effects of recourse decisions without actually solving a stochastic model. The first approach procures more reserve whenever approximate recourse policies stress the transmission network. The second approach procures reserve at prime locations by generalizing the existing practice of reserve disqualification. The latter approach is applied for feasibility and is later extended to limit scenario costs. Testing demonstrates expected cost improvements around 0.5%-1.0% for the IEEE 73-bus test case, which can translate to millions of dollars per year even for modest systems. The heuristics developed in this dissertation perform somewhere between established deterministic and stochastic models: providing an economic benefit over current practices without substantially increasing computational times.
Two general approaches are developed. Both approximate the effects of recourse decisions without actually solving a stochastic model. The first approach procures more reserve whenever approximate recourse policies stress the transmission network. The second approach procures reserve at prime locations by generalizing the existing practice of reserve disqualification. The latter approach is applied for feasibility and is later extended to limit scenario costs. Testing demonstrates expected cost improvements around 0.5%-1.0% for the IEEE 73-bus test case, which can translate to millions of dollars per year even for modest systems. The heuristics developed in this dissertation perform somewhere between established deterministic and stochastic models: providing an economic benefit over current practices without substantially increasing computational times.
ContributorsLyon, Joshua Daniel (Author) / Zhang, Muhong (Thesis advisor) / Hedman, Kory W (Thesis advisor) / Askin, Ronald G. (Committee member) / Mirchandani, Pitu (Committee member) / Arizona State University (Publisher)
Created2015
Description
The uncertainty of change inherent in issues such as climate change and regional growth has created a significant challenge for public decision makers trying to decide what adaptation actions are needed to respond to these possible changes. This challenge threatens the resiliency and thus the long term sustainability of our social-ecological systems. Using an empirical embedded case study approach to explore the application of advanced scenario analysis methods to regional growth visioning projects in two regions, this dissertation provides empirical evidence that for issues with high uncertainty, advanced scenario planning (ASP) methods are effective tools for helping decision makers to anticipate and prepare to adapt to change.
ContributorsQuay, Ray (Author) / Pijawka, David (Thesis advisor) / Shangraw, Ralph (Committee member) / Holway, James (Committee member) / Arizona State University (Publisher)
Created2011
ContributorsBurton, Charlotte (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-08
Description
As the information available to lay users through autonomous data sources continues to increase, mediators become important to ensure that the wealth of information available is tapped effectively. A key challenge that these information mediators need to handle is the varying levels of incompleteness in the underlying databases in terms of missing attribute values. Existing approaches such as Query Processing over Incomplete Autonomous Databases (QPIAD) aim to mine and use Approximate Functional Dependencies (AFDs) to predict and retrieve relevant incomplete tuples. These approaches make independence assumptions about missing values--which critically hobbles their performance when there are tuples containing missing values for multiple correlated attributes. In this thesis, I present a principled probabilis- tic alternative that views an incomplete tuple as defining a distribution over the complete tuples that it stands for. I learn this distribution in terms of Bayes networks. My approach involves min- ing/"learning" Bayes networks from a sample of the database, and using it do both imputation (predict a missing value) and query rewriting (retrieve relevant results with incompleteness on the query-constrained attributes, when the data sources are autonomous). I present empirical studies to demonstrate that (i) at higher levels of incompleteness, when multiple attribute values are missing, Bayes networks do provide a significantly higher classification accuracy and (ii) the relevant possible answers retrieved by the queries reformulated using Bayes networks provide higher precision and recall than AFDs while keeping query processing costs manageable.
ContributorsRaghunathan, Rohit (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Lee, Joohyung (Committee member) / Arizona State University (Publisher)
Created2011
ContributorsDruesedow, Elizabeth (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-07
Description
This dissertation is to address product design optimization including reliability-based design optimization (RBDO) and robust design with epistemic uncertainty. It is divided into four major components as outlined below. Firstly, a comprehensive study of uncertainties is performed, in which sources of uncertainty are listed, categorized and the impacts are discussed. Epistemic uncertainty is of interest, which is due to lack of knowledge and can be reduced by taking more observations. In particular, the strategies to address epistemic uncertainties due to implicit constraint function are discussed. Secondly, a sequential sampling strategy to improve RBDO under implicit constraint function is developed. In modern engineering design, an RBDO task is often performed by a computer simulation program, which can be treated as a black box, as its analytical function is implicit. An efficient sampling strategy on learning the probabilistic constraint function under the design optimization framework is presented. The method is a sequential experimentation around the approximate most probable point (MPP) at each step of optimization process. It is compared with the methods of MPP-based sampling, lifted surrogate function, and non-sequential random sampling. Thirdly, a particle splitting-based reliability analysis approach is developed in design optimization. In reliability analysis, traditional simulation methods such as Monte Carlo simulation may provide accurate results, but are often accompanied with high computational cost. To increase the efficiency, particle splitting is integrated into RBDO. It is an improvement of subset simulation with multiple particles to enhance the diversity and stability of simulation samples. This method is further extended to address problems with multiple probabilistic constraints and compared with the MPP-based methods. Finally, a reliability-based robust design optimization (RBRDO) framework is provided to integrate the consideration of design reliability and design robustness simultaneously. The quality loss objective in robust design, considered together with the production cost in RBDO, are used formulate a multi-objective optimization problem. With the epistemic uncertainty from implicit performance function, the sequential sampling strategy is extended to RBRDO, and a combined metamodel is proposed to tackle both controllable variables and uncontrollable variables. The solution is a Pareto frontier, compared with a single optimal solution in RBDO.
ContributorsZhuang, Xiaotian (Author) / Pan, Rong (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Zhang, Muhong (Committee member) / Du, Xiaoping (Committee member) / Arizona State University (Publisher)
Created2012