Search results for 'computer modeling and simulation' (try it on Scholar)

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  1.  11
    Vitaly Pronskikh, Computer Modeling and Simulation: Towards Epistemic Distinction Between Verification and Validation.
    Verification and validation of computer codes and models used in simulation are two aspects of the scientific practice of high importance and have recently been discussed by philosophers of science. While verification is predominantly associated with the correctness of the way a model is represented by a computer code or algorithm, validation more often refers to model’s relation to the real world and its intended use. It has been argued that because complex simulations are generally not transparent (...)
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  2. Franck Varenne (2001). What Does a Computer Simulation Prove? The Case of Plant Modeling at CIRAD. In N. Giambiasi & C. Frydman (eds.), Simulation in industry - ESS 2001, Proc. of the 13th European Simulation Symposium. Society for Computer Simulation (SCS)
    The credibility of digital computer simulations has always been a problem. Today, through the debate on verification and validation, it has become a key issue. I will review the existing theses on that question. I will show that, due to the role of epistemological beliefs in science, no general agreement can be found on this matter. Hence, the complexity of the construction of sciences must be acknowledged. I illustrate these claims with a recent historical example. Finally I temperate this (...)
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  3.  94
    Johannes Lenhard (2007). Computer Simulation: The Cooperation Between Experimenting and Modeling. Philosophy of Science 74 (2):176-194.
    The goal of the present article is to contribute to the epistemology and methodology of computer simulations. The central thesis is that the process of simulation modeling takes the form of an explorative cooperation between experimenting and modeling. This characteristic mode of modeling turns simulations into autonomous mediators in a specific way; namely, it makes it possible for the phenomena and the data to exert a direct influence on the model. The argumentation will be illustrated (...)
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  4.  6
    Carole J. Clem & Jean Paul Rigaut (1995). Computer Simulation Modelling and Visualization of 3d Architecture of Biological Tissues. Acta Biotheoretica 43 (4):425-442.
    Recent technical improvements, such as 3D microscopy imaging, have shown the necessity of studying 3D biological tissue architecture during carcinogenesis. In the present paper a computer simulation model is developed allowing the visualization of the microscopic biological tissue architecture during the development of metaplastic and dysplastic lesions.The static part of the model allows the simulation of the normal, metaplastic and dysplastic architecture of an external epithelium. This model is associated to a knowledge base which contains only data (...)
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  5.  3
    Graeme Earl (forthcoming). Archaeological Computer Graphic Modelling, Simulation and Spatial Interpretation. Perspectives on Science.
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  6.  14
    Fabio Boschetti, David McDonald & Randall Gray (2008). Complexity of a Modelling Exercise: A Discussion of the Role of Computer Simulation in Complex System Science. Complexity 13 (6):21-28.
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  7.  4
    Yu-Hsiang Yang, Huimin Bhikshu & Rua-Huan Tsaih (2015). The Power of One Sentient Being: The Computer Simulation of a Bodhisattva's Altruism Using Agent-Based Modelling. Contemporary Buddhism 16 (2):330-354.
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  8. Catharina Landström & Sarah J. Whatmore (2014). Virtually Expert: Modes of Environmental Computer Simulation Modeling. Science in Context 27 (4):579-603.
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  9.  15
    Jean‐Christophe Chiêm, Thérèse Van Durme, Florence Vandendorpe, Olivier Schmitz, Niko Speybroeck, Sophie Cès & Jean Macq (2013). Expert Knowledge Elicitation Using Computer Simulation: The Organization of Frail Elderly Case Management as an Illustration. Journal of Evaluation in Clinical Practice 20 (4):534-543.
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  10.  5
    Rodrigo B. Ferreira, Fernando C. Coelli, Wagner C. A. Pereira & Renan M. V. R. Almeida (2008). Optimizing Patient Flow in a Large Hospital Surgical Centre by Means of Discrete‐Event Computer Simulation Models. Journal of Evaluation in Clinical Practice 14 (6):1031-1037.
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  11. Margaret Morrison (2009). Models, Measurement and Computer Simulation: The Changing Face of Experimentation. Philosophical Studies 143 (1):33-57.
    The paper presents an argument for treating certain types of computer simulation as having the same epistemic status as experimental measurement. While this may seem a rather counterintuitive view it becomes less so when one looks carefully at the role that models play in experimental activity, particularly measurement. I begin by discussing how models function as “measuring instruments” and go on to examine the ways in which simulation can be said to constitute an experimental activity. By focussing (...)
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  12. Wendy S. Parker (2003). Computer Modeling in Climate Science: Experiment, Explanation, Pluralism. Dissertation, University of Pittsburgh
    Computer simulation modeling is an important part of contemporary scientific practice but has not yet received much attention from philosophers. The present project helps to fill this lacuna in the philosophical literature by addressing three questions that arise in the context of computer simulation of Earth's climate. Computer simulation experimentation commonly is viewed as a suspect methodology, in contrast to the trusted mainstay of material experimentation. Are the results of computer simulation (...)
     
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  13.  68
    John A. Barker (2002). Computer Modeling and the Fate of Folk Psychology. Metaphilosophy 33 (1-2):30-48.
  14.  14
    M. Füllsack (2013). Constructivism and Computation: Can Computer-Based Modeling Add to the Case for Constructivism? Constructivist Foundations 9 (1):7-16.
    Problem: Is constructivism contradicted by the reductionist determinism inherent in digital computation? Method: Review of examples from dynamical systems sciences, agent-based modeling and artificial intelligence. Results: Recent scientific insights seem to give reason to consider constructivism in line with what computation is adding to our knowledge of interacting dynamics and the functioning of our brains. Implications: Constructivism is not necessarily contradictory to digital computation, in particular to computer-based modeling and simulation. Constructivist content: When viewed through the (...)
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  15.  55
    Alexandre Muzy, Franck Varenne, Bernard P. Zeigler, Jonathan Caux, Patrick Coquillard, Luc Touraille, Dominique Prunetti, Philippe Caillou, Olivier Michel & David R. C. Hill (2013). Refounding of the Activity Concept? Towards a Federative Paradigm for Modeling and Simulation. Simulation - Transactions of the Society for Modeling and Simulation International 89 (2):156-177.
    Currently, the widely used notion of activity is increasingly present in computer science. However, because this notion is used in specific contexts, it becomes vague. Here, the notion of activity is scrutinized in various contexts and, accordingly, put in perspective. It is discussed through four scientific disciplines: computer science, biology, economics, and epistemology. The definition of activity usually used in simulation is extended to new qualitative and quantitative definitions. In computer science, biology and economics disciplines, the (...)
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  16.  5
    Johannes Kästner & Eckhart Arnold, When Can a Computer Simulation Act as Substitute for an Experiment? A Case-Study From Chemisty.
    In this paper we investigate with a case study from chemistry under what conditions a simulation can serve as a surrogate for an experiment. The case-study concerns a simulation of H2-formation in outer space. We find that in this case the simulation can act as a surrogate for an experiment, because there exists comprehensive theoretical background knowledge in form of quantum mechanics about the range of phenomena to which the investigated process belongs and because any particular modelling (...)
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  17.  23
    Roger Stanev (2012). Modelling and Simulating Early Stopping of RCTs: A Case Study of Early Stop Due to Harm. Journal of Experimental and Theoretical Artificial Intelligence 24 (4):513-526.
    Despite efforts from regulatory agencies (e.g. NIH, FDA), recent systematic reviews of randomised controlled trials (RCTs) show that top medical journals continue to publish trials without requiring authors to report details for readers to evaluate early stopping decisions carefully. This article presents a systematic way of modelling and simulating interim monitoring decisions of RCTs. By taking an approach that is both general and rigorous, the proposed framework models and evaluates early stopping decisions of RCTs based on a clear and consistent (...)
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  18.  48
    James L. McClelland (2009). The Place of Modeling in Cognitive Science. Topics in Cognitive Science 1 (1):11-38.
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  19. Günter Küppers & Johannes Lenhard (2005). Computersimulationen: Modellierungen 2. Ordnung. [REVIEW] Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 36 (2):305 - 329.
    Es soll ein Beitrag zur epistemischen Charakterisierung von Computersimulationen als jenseits von Experiment und Theorie geleistet werden. Es wird argumentiert, dass die in der Simulationstechnik eingesetzten Verfahren nicht numerische Lösungen liefern, sondern deren Dynamik mittels generativer Mechanismen imitieren. Die Computersimulationen in der Klimatologie werden als systematisches wie historisches Fallbeispiel behandelt. Erst "Simulationsexperimente" gestatten es, mittels Modellen eine Dynamik zu imitieren, ohne deren Grundgleichungen zu "lösen". /// Computer simulations will be characterized in epistemic respect as a method between experiment and (...)
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  20.  14
    Philip Garnett, Arno Steinacher, Susan Stepney, Richard Clayton & Ottoline Leyser (2010). Computer Simulation: The Imaginary Friend of Auxin Transport Biology. Bioessays 32 (9):828-835.
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  21.  92
    Ronald N. Giere (2009). Is Computer Simulation Changing the Face of Experimentation? Philosophical Studies 143 (1):59 - 62.
    Morrison points out many similarities between the roles of simulation models and other sorts of models in science. On the basis of these similarities she claims that running a simulation is epistemologically on a par with doing a traditional experiment and that the output of a simulation therefore counts as a measurement. I agree with her premises but reject the inference. The epistemological payoff of a traditional experiment is greater (or less) confidence in the fit between a (...)
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  22.  59
    Tibor Bosse, Martijn C. Schut & Jan Treur (2009). Formal Analysis of Dynamics Within Philosophy of Mind by Computer Simulation. Minds and Machines 19 (4):543-555.
    Computer simulations can be useful tools to support philosophers in validating their theories, especially when these theories concern phenomena showing nontrivial dynamics. Such theories are usually informal, whilst for computer simulation a formally described model is needed. In this paper, a methodology is proposed to gradually formalise philosophical theories in terms of logically formalised dynamic properties. One outcome of this process is an executable logic-based temporal specification, which within a dedicated software environment can be used as a (...)
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  23.  35
    Markus F. Peschl & Chris Stary (1998). The Role of Cognitive Modeling for User Interface Design Representations: An Epistemological Analysis of Knowledge Engineering in the Context of Human-Computer Interaction. [REVIEW] Minds and Machines 8 (2):203-236.
    In this paper we review some problems with traditional approaches for acquiring and representing knowledge in the context of developing user interfaces. Methodological implications for knowledge engineering and for human-computer interaction are studied. It turns out that in order to achieve the goal of developing human-oriented (in contrast to technology-oriented) human-computer interfaces developers have to develop sound knowledge of the structure and the representational dynamics of the cognitive system which is interacting with the computer.We show that in (...)
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  24.  1
    Eric B. Winsberg (2010). Science in the Age of Computer Simulation. The University of Chicago Press.
    Introduction -- Sanctioning models : theories and their scope -- Methodology for a virtual world -- A tale of two methods -- When theories shake hands -- Models of climate : values and uncertainties -- Reliability without truth -- Conclusion.
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  25.  16
    Beckett Sterner (2012). Agent-Based Computer Simulation and Ethics. [REVIEW] Metascience 21 (2):403-407.
    Agent-based computer simulation and ethics Content Type Journal Article Category Book Review Pages 1-5 DOI 10.1007/s11016-012-9660-7 Authors Beckett Sterner, Conceptual and Historical Studies of Science, The University of Chicago, Social Sciences Building 205, 1126 E 59th St, Chicago, IL 60637, USA Journal Metascience Online ISSN 1467-9981 Print ISSN 0815-0796.
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  26. N. S. Sutherland (1974). Computer Simulation of Brain Function. In Philosophy Of Psychology. Macmillan
     
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  27. Carlos Zednik (forthcoming). Heuristics, Descriptions and the Scope of Mechanistic Explanation. In P. Braillard & C. Malaterre (eds.), Explanation in Biology. An Enquiry into the Diversity of Explanatory Patterns in the Life Sciences. Springer
    The philosophical conception of mechanistic explanation is grounded on a limited number of canonical examples. These examples provide an overly narrow view of contemporary scientific practice, because they do not reflect the extent to which the heuristic strategies and descriptive practices that contribute to mechanistic explanation have evolved beyond the well-known methods of decomposition, localization, and pictorial representation. Recent examples from evolutionary robotics and network approaches to biology and neuroscience demonstrate the increasingly important role played by computer simulations and (...)
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  28.  12
    Saikou Y. Diallo, Jose J. Padilla, Ross Gore, Heber Herencia‐Zapana & Andreas Tolk (2014). Toward a Formalism of Modeling and Simulation Using Model Theory. Complexity 19 (3):56-63.
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  29.  7
    Leonard Uhr, Charles Vossler & James Uleman (1962). Pattern Recognition Over Distortions, by Human Subjects and by a Computer Simulation of a Model for Human Form Perception. Journal of Experimental Psychology 63 (3):227.
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  30.  3
    Ivo F. Sbalzarini (2013). Modeling and Simulation of Biological Systems From Image Data. Bioessays 35 (5):482-490.
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  31.  10
    John Hale (2006). Uncertainty About the Rest of the Sentence. Cognitive Science 30 (4):643-672.
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  32.  53
    W. S. Parker (2006). Understanding Pluralism in Climate Modeling. Foundations of Science 11 (4):349-368.
    To study Earth’s climate, scientists now use a variety of computer simulation models. These models disagree in some of their assumptions about the climate system, yet they are used together as complementary resources for investigating future climatic change. This paper examines and defends this use of incompatible models. I argue that climate model pluralism results both from uncertainty concerning how to best represent the climate system and from difficulties faced in evaluating the relative merits of complex models. I (...)
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  33.  45
    Sashank Varma (2011). Criteria for the Design and Evaluation of Cognitive Architectures. Cognitive Science 35 (7):1329-1351.
    Cognitive architectures are unified theories of cognition that take the form of computational formalisms. They support computational models that collectively account for large numbers of empirical regularities using small numbers of computational mechanisms. Empirical coverage and parsimony are the most prominent criteria by which architectures are designed and evaluated, but they are not the only ones. This paper considers three additional criteria that have been comparatively undertheorized. (a) Successful architectures possess subjective and intersubjective meaning, making cognition comprehensible to individual cognitive (...)
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  34.  10
    Louise Connell & Mark T. Keane (2006). A Model of Plausibility. Cognitive Science 30 (1):95-120.
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  35. Johan E. Gustafsson & Martin Peterson (2012). A Computer Simulation of the Argument From Disagreement. Synthese 184 (3):387–405.
    In this paper we shed new light on the Argument from Disagreement by putting it to test in a computer simulation. According to this argument widespread and persistent disagreement on ethical issues indicates that our moral opinions are not influenced by any moral facts, either because no such facts exist or because they are epistemically inaccessible or inefficacious for some other reason. Our simulation shows that if our moral opinions were influenced at least a little bit by (...)
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  36.  59
    Jordi Fernández (2003). Explanation by Computer Simulation in Cognitive Science. Minds and Machines 13 (2):269-284.
    My purpose in this essay is to clarify the notion of explanation by computer simulation in artificial intelligence and cognitive science. My contention is that computer simulation may be understood as providing two different kinds of explanation, which makes the notion of explanation by computer simulation ambiguous. In order to show this, I shall draw a distinction between two possible ways of understanding the notion of simulation, depending on how one views the relation (...)
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  37.  67
    Eckhart Arnold, Tools or Toys? On Specific Challenges for Modeling and the Epistemology of Models and Computer Simulations in the Social Sciences.
    Mathematical models are a well established tool in most natural sciences. Although models have been neglected by the philosophy of science for a long time, their epistemological status as a link between theory and reality is now fairly well understood. However, regarding the epistemological status of mathematical models in the social sciences, there still exists a considerable unclarity. In my paper I argue that this results from specific challenges that mathematical models and especially computer simulations face in the social (...)
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  38.  7
    Carlo Martini & Manuela Fernández Pinto (forthcoming). Modeling the Social Organization of Science. European Journal for Philosophy of Science:1-18.
    At least since Kuhn’s Structure, philosophers have studied the influence of social factors in science’s pursuit of truth and knowledge. More recently, formal models and computer simulations have allowed philosophers of science and social epistemologists to dig deeper into the detailed dynamics of scientific research and experimentation, and to develop very seemingly realistic models of the social organization of science. These models purport to be predictive of the optimal allocations of factors, such as diversity of methods used in science, (...)
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  39. Isabelle Peschard, Is Simulation a Substitute for Experimentation?
    It is sometimes said that simulation can serve as epistemic substitute for experimentation. Such a claim might be suggested by the fast-spreading use of computer simulation to investigate phenomena not accessible to experimentation (in astrophysics, ecology, economics, climatology, etc.). But what does that mean? The paper starts with a clarification of the terms of the issue and then focuses on two powerful arguments for the view that simulation and experimentation are ‘epistemically on a par’. One is (...)
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  40.  57
    Franck Varenne (2009). Models and Simulations in the Historical Emergence of the Science of Complexity. In Ma Aziz-Alaoui & C. Bertelle (eds.), From System Complexity to Emergent Properties. Springer 3--21.
    As brightly shown by Mainzer [24], the science of complexity has many distinct origins in many disciplines. Those various origins has led to “an interdisciplinary methodology to explain the emergence of certain macroscopic phenomena via the nonlinear interactions of microscopic elements” (ibid.). This paper suggests that the parallel and strong expansion of modeling and simulation - especially after the Second World War and the subsequent development of computers - is a rationale which also can be counted as an (...)
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  41.  17
    Kevin J. S. Zollman (2015). Modeling the Social Consequences of Testimonial Norms. Philosophical Studies 172 (9):2371-2383.
    This paper approaches the problem of testimony from a new direction. Rather than focusing on the epistemic grounds for testimony, it considers the problem from the perspective of an individual who must choose whom to trust from a population of many would-be testifiers. A computer simulation is presented which illustrates that in many plausible situations, those who trust without attempting to judge the reliability of testifiers outperform those who attempt to seek out the more reliable members of the (...)
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  42. Eric Winsberg (2009). Computer Simulation and the Philosophy of Science. Philosophy Compass 4 (5):835-845.
    There are a variety of topics in the philosophy of science that need to be rethought, in varying degrees, after one pays careful attention to the ways in which computer simulations are used in the sciences. There are a number of conceptual issues internal to the practice of computer simulation that can benefit from the attention of philosophers. This essay surveys some of the recent literature on simulation from the perspective of the philosophy of science and (...)
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  43.  71
    Wendy Parker (2008). Computer Simulation Through an Error-Statistical Lens. Synthese 163 (3):371-384.
    After showing how Deborah Mayo’s error-statistical philosophy of science might be applied to address important questions about the evidential status of computer simulation results, I argue that an error-statistical perspective offers an interesting new way of thinking about computer simulation models and has the potential to significantly improve the practice of simulation model evaluation. Though intended primarily as a contribution to the epistemology of simulation, the analysis also serves to fill in details of Mayo’s (...)
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  44. By Nick Bostrom (2003). Are We Living in a Computer Simulation? Philosophical Quarterly 53 (211):243–255.
    This paper argues that at least one of the following propositions is true: (1) the human species is very likely to go extinct before reaching a “posthuman” stage; (2) any posthuman civilization is extremely unlikely to run a significant number of simulations of their evolutionary history (or variations thereof); (3) we are almost certainly living in a computer simulation. It follows that the belief that there is a significant chance that we will one day become posthumans who run (...)
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  45.  70
    Nick Bostrom (2003). Are We Living in a Computer Simulation? Philosophical Quarterly 53 (211):243 - 255.
    I argue that at least one of the following propositions is true: (1) the human species is very likely to become extinct before reaching a 'posthuman' stage; (2) any posthuman civilization is extremely unlikely to run a significant number of simulations of its evolutionary history (or variations thereof); (3) we are almost certainly living in a computer simulation. It follows that the belief that there is a significant chance that we shall one day become posthumans who run ancestor-simulations (...)
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  46.  9
    David Dunnett, Anthony Goodbody & Martin Stanisstreet (1991). Computer Modelling of Neural Tube Defects. Acta Biotheoretica 39 (1):63-79.
    Neurulation, the curling of the neuroepithelium to form the neural tube, is an essential component of the development of animal embryos. Defects of neural tube formation, which occur with an overall frequency of one in 500 human births, are the cause of severe and distressing congenital abnormalities. However, despite the fact that there is increasing information from animal experiments about the mechanisms which effect neural tube formation, much less is known about the fundamental causes of neural tube defects (NTD). The (...)
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  47.  57
    Wendy S. Parker (2008). Franklin, Holmes, and the Epistemology of Computer Simulation. International Studies in the Philosophy of Science 22 (2):165 – 183.
    Allan Franklin has identified a number of strategies that scientists use to build confidence in experimental results. This paper shows that Franklin's strategies have direct analogues in the context of computer simulation and then suggests that one of his strategies—the so-called 'Sherlock Holmes' strategy—deserves a privileged place within the epistemologies of experiment and simulation. In particular, it is argued that while the successful application of even several of Franklin's other strategies (or their analogues in simulation) may (...)
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  48.  22
    Fritz Rohrlich (1990). Computer Simulation in the Physical Sciences. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1990:507-518.
    Computer simulation is shown to be philosophically interesting because it introduces a qualitatively new methodology for theory construction in science different from the conventional two components of "theory" and "experiment and/or observation". This component is "experimentation with theoretical models." Two examples from the physical sciences are presented for the purpose of demonstration but it is claimed that the biological and social sciences permit similar theoretical model experiments. Furthermore, computer simulation permits theoretical models for the evolution of (...)
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  49.  10
    F. H. D. van Batenburg (1982). Modeling, Simulation, and Embryology. Acta Biotheoretica 31 (4):245-248.
    A delightful book to get the whole picture of simulation applications in developmental biology and easily readable for those who would like to overlook the field in one quick sweep. If you would like to get a more detailed perspective, a great number of literature references provides this opportunity.
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  50.  4
    Nicole J. Saam (forthcoming). What is a Computer Simulation? A Review of a Passionate Debate. Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie:1-17.
    Where should computer simulations be located on the ‘usual methodological map’ which distinguishes experiment from theory? Specifically, do simulations ultimately qualify as experiments or as thought experiments? Ever since Galison raised that question, a passionate debate has developed, pushing many issues to the forefront of discussions concerning the epistemology and methodology of computer simulation. This review article illuminates the positions in that debate, evaluates the discourse and gives an outlook on questions that have not yet been addressed.
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