Science benefits from substantial cognitive diversity because cognitive diversity promotes scientific progress toward greater accuracy. Without diversity of goals, beliefs, and methods, science would neither generate novel discoveries nor certify representations with its present effectiveness. The revolution in geosciences is a principal case study.The role of cognitive diversity in discovery is explored with attention to computational results. Discovery and certification are inseparable. Moreover, diverse scientific groups agree convergently, and their agreements manifest an explanatory defense akin to the explanatory defense of realism. Scientists accept representations as a matter of their instrumental success in individual scientific research. Because scientists are diverse, this standard of acceptance means that widespread acceptance involves widespread instrumental success. This success is best explained through the accuracy of topics of agreement.The pessimistic induction is addressed; it fails to undermine the explanatory defense because past scientific successes don't resemble present ones in their degree of instrumental success; to make this point, instrumental success of representations of caloric and of oxygen are compared.Cognitive diversity challenges the methodological uniformity of scientific practice. Science lacks uniform methods and aims, and it ought to. It is argued that there is no sound basis for thinking that science aims. Moreover, the growth of science itself is not the growth of knowledge. Scientific communities rather than individual scientists are the main certifiers of scientific results. Hence, since knowledge requires a certifying belief formation process but the process relevant to science is not realized individually, science does not progress toward knowledge. The epistemology of science is socialized, but remains broadly realist because, even without a method of inquiry, science develops accurate representations of unobservable nature.

Most traits of medical or economic importance are quantitative, i.e. they can be measured on a continuous scale. Strong biological evidence indicates that quantitative traits are governed by a complex interplay between the environment and multiple quantitative trait loci, QTL, in the genome. Nonlinear interactions make it necessary to search for several QTL simultaneously. This thesis concerns numerical methods for QTL search in experimental populations. The core computational problem of a statistical analysis of such a population is a multidimensional global optimization problem with many local optima. Simultaneous search for d QTL involves solving a d-dimensional problem, where each evaluation of the objective function involves solving one or several least squares problems with special structure. Using standard software, already a two-dimensional search is costly, and searches in higher dimensions are prohibitively slow. Three efficient algorithms for evaluation of the most common forms of the objective function are presented. The computing time for the linear regression method is reduced by up to one order of magnitude for real data examples by using a new scheme based on updated QR factorizations. Secondly, the objective function for the interval mapping method is evaluated using an updating technique and an efficient iterative method, which results in a 50 percent reduction in computing time. Finally, a third algorithm, applicable to the imputation and weighted linear mixture model methods, is presented. It reduces the computing time by between one and two orders of magnitude. The global search problem is also investigated. Standard software techniques for finding the global optimum of the objective function are compared with a new approach based on the DIRECT algorithm. The new method is more accurate than the previously fastest scheme and locates the optimum in 1-2 orders of magnitude less time. The method is further developed by coupling DIRECT to a local optimization algorithm for accelerated convergence, leading to additional time savings of up to eight times. A parallel grid computing implementation of exhaustive search is also presented, and is suitable e.g for verifying global optima when developing efficient optimization algorithms tailored for the QTL mapping problem. Using the algorithms presented in this thesis, simultaneous search for at least six QTL can be performed routinely. The decrease in overall computing time is several orders of magnitude. The results imply that computations which were earlier considered impossible are no longer difficult, and that genetic researchers thus are free to focus on model selection and other central genetical issues.

Scientific software, by which we mean application software that has a large computational component, models physical phenomena and provides data for decision support. This can be software that calculates loads on bridges, provides predictions for weather systems, images bone structures for surgical procedures, models subsystems at nuclear generating stations, or processes images from ground-based telescopes. There is no consensus on what the best practices for the development of scientific software are. We carried out a study at two Canadian universities in which we interviewed scientists and engineers who develop or use scientific software to identify characteristics of current development and usage. Through qualitative analysis, I identified key characteristics of scientific software development and usage and observed correlations between these characteristics. The results are a collection of observations about how scientific software is documented and designed; the nature of the scientific software lifecycle; the selection of development languages; approaches to testing, especially validation testing; and sources of risk. I also examine concerns scientists have with commercial software they use to determine what quality factors are of interest to them and also which seem to require special trade-offs. I find that scientific software development and use differs fundamentally from development in most other domains. / Thesis (Master, Computing) -- Queen's University, 2008-04-27 11:54:00.268

We present an algorithm for overlaying polygonal data with regular grids and calculating the percentage overlap for each cell in the regular grid.  Our algorithm is able to support self-intersecting polygons, meaning that some spatial regions may be covered by two or more polygons.  Our algorithm is able to identify these cases and eliminate redundant polygons, preventing erroneous results.  We also present an optimized version of our algorithm that uses spatial sorting through interval trees, and provide a performance comparison between the optimized and unoptimized versions. Finally, we apply our algorithm to geography data, specifically of bark beetle infestation

This dissertation is an attempt to understand the role of instruments in the process of knowledge production in science. I ask: how can we trust scientific instruments and what do we learn about when we use them? The dissertation has four parts. First, I construct a novel account of “epistemic possibility,” the possibility of knowing, that captures the dependency of knowledge on action, and I introduce the notion of “technological possibility,” which depends on the availability of material and conceptual means to bring about a desired state of affairs. I argue that, under certain circumstances, technological possibility is a condition for epistemic possibility. Second, I ask how instruments become reliable. I argue that when the material capacities and conceptual functions of a scientific instrument correspond, the instrument is a reliable component of the process of knowledge production. I then describe how the instrument design process can result in just such a correspondence. Instrument design produces the material device, a functional concept of the device revised in light of experience, a measure of the closeness of fit between material and function, and practices of trust such as calibration routines. ii Third, I ask what we learn from instruments such as those used for experimentation and simulation. I argue that in experiments, instruments function to inform us about the material capacities of the object of investigation, while in simulations, instruments function to inform us about the conceptual model of the object of investigation. Fourth, I put these philosophical distinctions into historical context through a case study of Monte Carlo simulations run on digital electronic computers in the 1940s-70s. I argue that digital electronic computers made the practice of Monte Carlo simulation technologically possible, but that the new method did not meet existing scientific standards. Consequently, Monte Carlo design practices were revised to address the worries of potential practitioners.

In this thesis, I present a novel account of scientific modeling that achieves the stability and generalizability of static approaches with the flexibility and practical relevance of diachronic approaches. In this account, modeling is characterized by the use of a similarity relation for the purpose of surrogate reasoning. Many criticisms of similarity are based on the fact that there is no way to objectively assess similarity between two things that share some, but not all, features. This account does not rely on the inherently flawed notion of objectively assessing similarity. Instead, the focus is on subjective assessment of similarity, within the specific context of an agent using the similarity for surrogate reasoning. This account captures the diversity of models while providing coherence among common features and functions, as evidenced by application to a series of interrelated examples in a case study from mid-twentieth century cognitive psychology. The similarity/difference account advocated in this thesis is particularly significant because its demonstrated success, evidenced by the case study, dispels several misconceptions about the study of scientific models. Advocates of static approaches claim that a diachronic approach cannot provide the generalizability necessary for a unified account, but the functional and agent-oriented similarity/difference account proves otherwise. Advocates of practice-based approaches often suggest that imilarity is too restrictive to capture the diversity of scientific models, but the similarity/difference account demonstrates that this concern only applies to a radically naturalized concept of similarity. As part of an agent-oriented account, a non-naturalized concept of similarity can be flexible enough to capture the full range of scientific models. Combining a diachronic approach with the similarity relation usually associated with static approaches results in an account that can circumvent the issues usually associated with either diachronic approaches or similarity alone.

No description available.

This dissertation is an attempt to understand the role of instruments in the process of knowledge production in science. I ask: how can we trust scientific instruments and what do we learn about when we use them? The dissertation has four parts. First, I construct a novel account of “epistemic possibility,” the possibility of knowing, that captures the dependency of knowledge on action, and I introduce the notion of “technological possibility,” which depends on the availability of material and conceptual means to bring about a desired state of affairs. I argue that, under certain circumstances, technological possibility is a condition for epistemic possibility. Second, I ask how instruments become reliable. I argue that when the material capacities and conceptual functions of a scientific instrument correspond, the instrument is a reliable component of the process of knowledge production. I then describe how the instrument design process can result in just such a correspondence. Instrument design produces the material device, a functional concept of the device revised in light of experience, a measure of the closeness of fit between material and function, and practices of trust such as calibration routines. ii Third, I ask what we learn from instruments such as those used for experimentation and simulation. I argue that in experiments, instruments function to inform us about the material capacities of the object of investigation, while in simulations, instruments function to inform us about the conceptual model of the object of investigation. Fourth, I put these philosophical distinctions into historical context through a case study of Monte Carlo simulations run on digital electronic computers in the 1940s-70s. I argue that digital electronic computers made the practice of Monte Carlo simulation technologically possible, but that the new method did not meet existing scientific standards. Consequently, Monte Carlo design practices were revised to address the worries of potential practitioners.

In this thesis, I present a novel account of scientific modeling that achieves the stability and generalizability of static approaches with the flexibility and practical relevance of diachronic approaches. In this account, modeling is characterized by the use of a similarity relation for the purpose of surrogate reasoning. Many criticisms of similarity are based on the fact that there is no way to objectively assess similarity between two things that share some, but not all, features. This account does not rely on the inherently flawed notion of objectively assessing similarity. Instead, the focus is on subjective assessment of similarity, within the specific context of an agent using the similarity for surrogate reasoning. This account captures the diversity of models while providing coherence among common features and functions, as evidenced by application to a series of interrelated examples in a case study from mid-twentieth century cognitive psychology. The similarity/difference account advocated in this thesis is particularly significant because its demonstrated success, evidenced by the case study, dispels several misconceptions about the study of scientific models. Advocates of static approaches claim that a diachronic approach cannot provide the generalizability necessary for a unified account, but the functional and agent-oriented similarity/difference account proves otherwise. Advocates of practice-based approaches often suggest that imilarity is too restrictive to capture the diversity of scientific models, but the similarity/difference account demonstrates that this concern only applies to a radically naturalized concept of similarity. As part of an agent-oriented account, a non-naturalized concept of similarity can be flexible enough to capture the full range of scientific models. Combining a diachronic approach with the similarity relation usually associated with static approaches results in an account that can circumvent the issues usually associated with either diachronic approaches or similarity alone.

Indiana University-Purdue University Indianapolis (IUPUI) / Scientific realism and antirealism are two main views in the philosophy of science regarding the status of unobservable entities in science and whether we have good epistemic reasons to believe that our current successful scientific theories are (approximately) true. Briefly, the former claims that our scientific theories are (approximately) true and unobservable entities these scientific theories postulate exist. On the other hand, the latter claims that we do not have good epistemic reasons to believe that our scientific theories are (approximately) true and that unobservable entities our scientific theories postulate exist. The scientific realism has two primary tenets, one axiological (i.e., science should seek truth) and the other epistemological (namely, our current successful theories are (approximately) true). In this thesis, the issue has been examined from standpoint of the account of Peirce’s philosophy of science, more accurately based on his understanding of reality, truth and basic idealism. In the first chapter, I outline the main points of the debate from the perspectives of both sides. In the second chapter, I give reasons why the scientific realists’ argument is not convincing. In the third chapter, I attempt to draw an accurate picture of the account of Peirce’s views on the nature of scientific theories. In the last chapter, I make a case for scientific realism from the Peircean account of philosophy of science. I have claimed why the current debate cannot be settled without accepting a kind of Peirce's basic idealism and his understanding of reality. I think both scientific realists and antirealists accept a kind of naïve realism. This is the main reason why it is not possible to settle the debate from their standpoints. In order to overcome this issue, I attempt to develop a more sophisticated realism based on Peirce’s understanding of reality, truth and basic idealism.