Varying the Explanatory Span: Scientific Explanation for Computer Simulations

This article aims to develop a new account of scientific explanation for computer simulations. To this end, two questions are answered: what is the explanatory relation for computer simulations? And what kind of epistemic gain should be expected? For several reasons tailored to the benefits and needs of computer simulations, these questions are better answered within the unificationist model of scientific explanation. Unlike previous efforts in the literature, I submit that the explanatory relation is between the simulation model and the results of the simulation. I also argue that our epistemic gain goes beyond the unificationist account, encompassing a practical dimension as well

Conceptions of computer simulation: simulations and scientific models

El uso masivo y ubicuo de simulaciones computacionales en la práctica científica actual hace que su estudio filosófico cobre centralidad para la comprensión general de estos métodos. En este trabajo analizo el modo en que el concepto de simulación computacional ha tenido tratamiento en la literatura filosófica contemporánea. Concretamente, analizo tres conceptualizaciones de simulación computacional que han servido de base fundante para toda la literatura posterior. Muestro, asimismo, cuáles son las dificultades que estas conceptualizaciones deben afrontar. Finalmente presento críticas que, a mi ver, resultan insalvables y que sugieren un cambio en el modo de conceptualizar simulaciones computacionales.The massive and ubiquitous use of computer simulations in current scientific practice makes their philosophical study a necessity for the general understanding of such methods. In this work I analyze the different ways in which the concept of computer simulation has been conceptualized in the contemporary philosophical literature. Concretely, I analyze three conceptualizations of the notion of computer simulation that have grounded the succeeding philosophical literature. I show, in addition, the difficulties that these conceptualizations run into. Finally, I present some criticisms that, to my mind, are insurmountable and therefore suggest a change in the way of conceptualizing computer simulations

Ciencia de la computación y filosofía: unidades de análisis del software

Una imagen muy generalizada a la hora de entender el software de computador es la que lo representa como una “caja negra”: no importa realmente saber qué partes lo componen internamente, sino qué resultados se obtienen de él según ciertos valores de entrada. Al hacer esto, muchos problemas filosóficos son ocultados, negados o simplemente mal entendidos. Este artículo discute tres unidades de análisis del software de computador, esto es, las especificaciones, los algoritmos y los procesos computacionales. El objetivo central es entender las prácticas cientficas e ingenieriles detrás de cada unidad de software, así como analizar su metodología, ontología y epistemología

Computer Simulations in Science and Engineering. Concept, Practices, Perspectives

This book addresses key conceptual issues relating to the modern scientific and engineering use of computer simulations. It analyses a broad set of questions, from the nature of computer simulations to their epistemological power, including the many scientific, social and ethics implications of using computer simulations. The book is written in an easily accessible narrative, one that weaves together philosophical questions and scientific technicalities. It will thus appeal equally to all academic scientists, engineers, and researchers in industry interested in questions related to the general practice of computer simulations

Thomas Reid on Causation and Scientific Explanation

We argue that there is no tension between Reid's description of science and his claim that science is based on the principles of common sense. For Reid, science is rooted in common sense since it is based on the (common sense) idea that fixed laws govern nature. This, however, does not contradict his view that the scientific notions of causation and explanation are fundamentally different from their common sense counterparts. After discussing these points, we dispute with Cobb's ( Cobb 2010 ) and Benbaji's ( Benbaji 2003 ) interpretations of Reid's views on causation and explanation. Finally, we present Reid's views from the perspective of the contemporary debate on scientific explanation. </jats:p

Curso de innovación docente y presentación HTML en Bellas Artes

Presentación del Curso Curso de innovación docente y presentación HTML en Bellas Artes. Convocatoria pública para el profesorado, becarios de investigación, y alumnos de 3º y 2º ciclo de la Universidad de Sevilla. Organizado por el Instituto de Ciencias de la Educación de la Universidad de Sevilla

Retratos al fresco

Lo que vamos a mostrar en este artículo es el proceso metodológico seguido para realizar al fresco una cabeza naturalista, desde su dibujo, la preparación del soporte, y su ejecución pictórica, que además se ilustra con un inédito video. Corresponde a un trabajo al fresco que como alumno desarrollamos en el curso 1986-1987, en la asignatura Pintura Mural, y cuya grabación en video betacam por nuestro profesor Juan Manuel Calle, que también la ha digitalizado y editado para la ocasión, ha permitido poder rescatarla de un deterioro inevitable tras 15 años, casi en el límite de la vida media de una cinta magnética. Otros ejemplos de retratos se acompañan, para resaltar el valor pedagógico del proceso de la pintura al fresco

Sobre figuración infográfica

El objetivo principal de este artículo es mostrar las posibilidades plásticas y educativas de la Infografía figurativa, y dar rienda suelta a la imaginación y creatividad que cada cual puede desarrollar

Réplica anatómica de disecciones para la enseñanza

El propósito de este proyecto es mejorar los medios de enseñanza artística y científica de la Anatomía Humana, realizando nuevos materiales didácticos totalmente objetivos de disecciones corporales de la Facultad de Medicina de Sevilla mediante reproducciones o Réplicas tridimensionales producidas por moldes de silicona, realizadas en la Facultad de Bellas Artes de Sevilla. Dada la escasez de cuerpos donados para la ciencia, este estudio visual anatómico está actualmente limitado a la consulta de manuales bidimensionales y modelos subjetivos, por lo que la Réplica cobra valor de autenticidad para el estudio visual tanto artístico como científico, es aséptica y su número de copias puede solventar dicha escasez, permitiendo al alumnado un acercamiento a las estructuras totalmente fiel.The purpose of this project is to improve the means of artistic and scientific teaching of the Human Anatomy, accomplishing new totally objective didactic materials of corporal dissections of the Medical Faculty of Seville through reproductions or three-dimensional Replies produced by molds of silicone, accomplished in the Fine Arts Faculty of Seville. Given the bodies shortage donated for the science, this anatomical visual study is currently limited to the two-dimensional handbooks conference and subjective models, therefore the Reply acquires value of genuineness for the visual study artistic as well as scientific, it is aseptic and its number of copies can settle said shortage, permitting student body an approximation to the totally staunch structures

Machine learning, justification, and computational reliabilism

This article asks the question, ``what is reliable machine learning?'' As I intend to answer it, this is a question about epistemic justification. Reliable machine learning gives justification for believing its output. Current approaches to reliability (e.g., transparency) involve showing the inner workings of an algorithm (functions, variables, etc.) and how they render outputs. We then have justification for believing the output because we know how it was computed. Thus, justification is contingent on what can be shown about the algorithm, its properties, and its behavior. In this paper, I defend computational reliabilism (CR). CR is a computationally-inspired off-shoot of process reliabilism that does not require showing the inner workings of an algorithm. CR credits reliability to machine learning by identifying reliability indicators external to the algorithm (validation methods, knowledge-based integration, etc.). Thus, we have justification for believing the output of machine learning when we have identified the appropriate reliability indicators. CR is advanced as a more suitable epistemology for machine learning. The main goal of this article is to lay the groundwork for CR, how it works, and what we can expect as a justificatory framework for reliable machine learning