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In Joyce's Myth of Morality, Joyce proposes an error theory about morality. He then argues that, once we accept an error theory, we have three options: we can be abolitionists and jettison moral discourse, be conservationists and maintain our false moral beliefs, or be revolutionary fictionalists and assent to and act in accordance with moral discourse while believing it's false. In this paper, I argue that Joyce has ignored a fourth option—limited revisionism, or slightly changing our moral terms to avoid problematic commitments—and that this option is superior to the three aforementioned possibilities. Along the way, I show that Joyce has unfairly ignored limited revisionism because of faulty views about what makes a concept or term normative, and that limited revisionism ignores some expected pitfalls, such as overgeneralizing to legitimately error-theoretic discourses. / Master of Arts / In this paper, I argue against Joyce’s assertion that if our moral terms centrally implicate falsehoods, we should act as though morality is true (because it’s useful) while also believing that it’s false (because doing otherwise would be epistemically irresponsible). I show Joyce’s position rests on a specific misunderstanding of how language operates, and argue instead that, if we accept that our current moral talk centrally implicates falsehoods, we should be open to slightly revising our moral terms to avoid implicating such falsehoods instead. I also show that my position, limited revisionism, avoids a few expected pitfalls—most importantly, it doesn’t overgeneralize and apply to terms like “witch” and “phlogiston,” which also centrally implicate falsehoods but seem like terms that shouldn’t be open to conceptual change.

Computational Fluid Dynamics (CFD) has been pivotal in scientific computing, providing critical insights into complex fluid dynamics unattainable through traditional experimental methods. Despite its widespread use, the accuracy of CFD results remains contingent upon the underlying modeling and numerical errors. A key aspect of ensuring simulation reliability is the accurate quantification of discretization error (DE), which is the difference between the simulation solution and the exact solution in the physical world. This study addresses quantifying DE through Error Transport Equations (ETE), which are an additional set of equations capable of quantifying the local DE in a solution. Historically, Richardson extrapolation has been a mainstay for DE estimation due to its simplicity and effectiveness. However, the method's feasibility diminishes with increasing computational demands, particularly in large-scale and high-dimensional problems. The integration of ETE into existing CFD frameworks is facilitated by their compatibility with existing numerical codes, minimizing the need for extensive code modification. By incorporating techniques developed for managing discontinuities, the study broadens ETE applicability to a wider range of scientific computing applications, particularly those involving complex, unsteady flows. The culmination of this research is demonstrated on unsteady discontinuous problems, such as Sod's problem. / Master of Science / In the ever-evolving field of Computational Fluid Dynamics (CFD), the quest for accuracy is paramount. This thesis focuses on discretization error estimation within CFD simulations, specifically on the challenge of predicting fluid behavior in scenarios marked by sudden changes, such as shock waves. At the core of this work lies an error estimation tool known as Error Transport Equations (ETE) to improve the numerical accuracy of simulations involving unsteady flows and discontinuities. Traditionally, the accuracy of CFD simulations has been limited by discretization errors, generally the largest numerical error, which is the difference between the numerical solution and the exact solution. With ETE, this research identifies these errors to enhance the simulation's overall accuracy. The implications of ETE research are far-reaching. Improved error estimation and correction methods can lead to more reliable predictions in a wide range of applications, from aeronautical engineering, where the aerodynamics of aircraft is critical, to plasma science, with applications in fusion and deep space propulsion.

Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(工学) / 甲第16841号 / 工博第3562号 / 新制||工||1538(附属図書館) / 29516 / 京都大学大学院工学研究科マイクロエンジニアリング専攻 / (主査)教授 松原 厚, 教授 松久 寛, 教授 西脇 眞二 / 学位規則第4条第1項該当

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / Radio frequency power margins in well planned line-of-sight (LOS) air-to-ground digital data transmission systems usually produce signal to noise ratios (SNR) that can deliver error free service. Sometimes field performance falls short of design and customer expectations. Recent flight tests conducted by the tri-service Advanced Range Telemetry (ARTM) project confirm that the dominant source of bit errors and short term link failures are “clusters” of severe error burst activity produced by flat fading, dispersive fading and poor antenna patterns on airborne vehicles. This paper introduces the techniques used by ARTM to measure bit error performance of aeronautical telemetry links.

This dissertation develops methods to minimize recommendation error costs when inputs to a rule-based expert system are prone to errors. The problem often arises in web-based applications where data are inherently noisy or provided by users who perceive some benefit from falsifying inputs. Prior studies proposed methods that attempted to minimize the probability of recommendation error, but did not take into account the relative costs of different types of errors. In situations where these differences are significant, an approach that minimizes the expected misclassification error costs has advantages over extant methods that ignore these costs. Building on the existing literature, two new techniques - Cost-Based Input Modification (CBIM) and Cost-Based Knowledge-Base Modification (CBKM) were developed and evaluated. Each method takes as inputs (1) the joint probability distribution of a set of rules, (2) the distortion matrix for input noise as characterized by the probability distribution of the observed input vectors conditioned on their true values, and (3) the misclassification cost for each type of recommendation error. Under CBIM, for any observed input vector v, the recommendation is based on a modified input vector v' such that the expected error costs are minimized. Under CBKM the rule base itself is modified to minimize the expected cost of error. The proposed methods were investigated as follows: as a control, in the special case where the costs associated with different types of errors are identical, the recommendations under these methods were compared for consistency with those obtained under extant methods. Next, the relative advantages of CBIM and CBKM were compared as (1) the noise level changed, and (2) the structure of the cost matrix varied. As expected, CBKM and CBIM outperformed the extant Knowledge Base Modification (KM) and Input Modification (IM) methods over a wide range of input distortion and cost matrices, with some restrictions. Under the control, with constant misclassification costs, the new methods performed equally with the extant methods. As misclassification costs increased, CBKM outperformed KM and CBIM outperformed IM. Using different cost matrices to increase misclassification cost asymmetry and order, CBKM and CBIM performance increased. At very low distortion levels, CBKM and CBIM underperformed as error probability became more significant in each method's estimation. Additionally, CBKM outperformed CBIM over a wide range of input distortion as its technique of modifying an original knowledge base outperformed the technique of modifying inputs to an unmodified decision tree.

É natural considerar o erro como algo que deve ser evitado, um indicador de mau desempenho. Desde pequenas, as crianças são habituadas a buscar os acertos, de forma que, quando o raciocínio está errado, elas devem refazê-lo. Tal resultado é cobrado em casa pela família e na escola pelos educadores. Porém, o erro é o mais antigo elemento no processo de aprendizagem, e, além de ser um indicador de desempenho, o erro também mostra aquilo que o aluno sabe ou pensa ter compreendido. É possível notar que alguns alunos do Ensino Médio mantêm erros e dúvidas que deveriam ter sido sanados ao longo do Ensino Fundamental. Neste trabalho, será analisado o porquê de essas dúvidas ainda se apresentarem, pois a análise desses erros pode auxiliar tanto o aluno como o professor. O aluno, com uma devolutiva do que foi feito para tentar aprimorar o seu saber e o professor, levando-o a elaborar novas estratégias didáticas e planos de ensino que melhor se adaptem ao seu público alvo. / It is expected to consider the error as something that must be avoided, a non-satisfactory performance indicator. Since childhood, the human being is used to seek the right answers, so that, when the reasoning is wrong, he/she should remake it. Such outcome is charged at home by the family and at school by the teachers. However, the error is the oldest element in the learning process and, in addition to being a performance indicator, the error also shows something that the student knows or thinks he/she has understood. It is possible to notice that some high school students make some mistakes or has some doubts that were supposed to be clarified during the elementary school. In this paper, it will be analyzed the reason why these doubts are still present, because the analysis of these errors can help both students and teachers. The students, with a feedback of what has been done to try to improve their knowledge and the teacher, leading him to design new teaching strategies and lesson plans to best suit his/her target audience.