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  1. Annamaria Carusi (2012). Making the Visual Visible in Philosophy of Science. Spontaneous Generations 6 (1):106-114.
    As data-intensive and computational science become increasingly established as the dominant mode of conducting scientific research, visualisations of data and of the outcomes of science become increasingly prominent in mediating knowledge in the scientific arena. This position piece advocates that more attention should be paid to the epistemological role of visualisations beyond their being a cognitive aid to understanding, but as playing a crucial role in the formation of evidence for scientific claims. The new generation of computational and informational visualisations (...)
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  2. Brian Epstein (2011). Agent-Based Modeling and the Fallacies of Individualism. In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. Routledge. 115444.
    Agent-​​based modeling is showing great promise in the social sciences. However, two misconceptions about the relation between social macroproperties and microproperties afflict agent-based models. These lead current models to systematically ignore factors relevant to the properties they intend to model, and to overlook a wide range of model designs. Correcting for these brings painful trade-​​offs, but has the potential to transform the utility of such models.
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  3. 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 in which a computing system that performs (...)
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  4. Axel Gelfert (2011). Scientific Models, Simulation, and the Experimenter's Regress. In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. Routledge.
    According to the "experimenter's regress", disputes about the validity of experimental results cannot be closed by objective facts because no conclusive criteria other than the outcome of the experiment itself exist for deciding whether the experimental apparatus was functioning properly or not. Given the frequent characterization of simulations as "computer experiments", one might worry that an analogous regress arises for computer simulations. The present paper analyzes the most likely scenarios where one might expect such a "simulationist's regress" to surface, and, (...)
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  5. Patrick Grim, Robert Rosenberger, Adam Rosenfeld, Brian Anderson & Robb E. Eason (2013). How Simulations Fail. Synthese 190 (12):2367-2390.
    ‘The problem with simulations is that they are doomed to succeed.’ So runs a common criticism of simulations—that they can be used to ‘prove’ anything and are thus of little or no scientific value. While this particular objection represents a minority view, especially among those who work with simulations in a scientific context, it raises a difficult question: what standards should we use to differentiate a simulation that fails from one that succeeds? In this paper we build on a structural (...)
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  6. Marcin Miłkowski (2013). Reverse-Engineering in Cognitive-Science. In Marcin Miłkowski & Konrad Talmont-Kaminski (eds.), Regarding the Mind, Naturally: Naturalist Approaches to the Sciences of the Mental. Cambridge Scholars Publishing. 12-29.
    I discuss whether there are some lessons for philosophical inquiry over the nature of simulation to be learnt from the practical methodology of reengineering. I will argue that reengineering serves a similar purpose as simulations in theoretical science such as computational neuroscience or neurorobotics, and that the procedures and heuristics of reengineering help to develop solutions to outstanding problems of simulation.
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  7. 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 new simulation activity definition (...)
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  8. Pauline Sargent (1996). On the Use of Visualizations in the Practice of Science. Philosophy of Science 63 (3):238.
    Visualizations used in the practice of neuroscience, as one example of a scientific practice, can be sorted according to whether they represent (A) actual things, (B) theoretical models, or (C) some integration of these two. In this paper I hypothesize that an assessment of a chain of visual representations from (A) through (C) to (B) (and back again) is used, as part of the practice of scientific judgment, to assess the adequacy of the "working fit" between the theoretical model and (...)
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  9. Eran Tal (2011). From Data to Phenomena and Back Again: Computer-Simulated Signatures. Synthese 182 (1):117-129.
    This paper draws attention to an increasingly common method of using computer simulations to establish evidential standards in physics. By simulating an actual detection procedure on a computer, physicists produce patterns of data (‘signatures’) that are expected to be observed if a sought-after phenomenon is present. Claims to detect the phenomenon are evaluated by comparing such simulated signatures with actual data. Here I provide a justification for this practice by showing how computer simulations establish the reliability of detection procedures. I (...)
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  10. Franck Varenne (2013). Chains of Reference in Computer Simulations. FMSH Working Papers 51:1-32.
    This paper proposes an extensionalist analysis of computer simulations (CSs). It puts the emphasis not on languages nor on models, but on symbols, on their extensions, and on their various ways of referring. It shows that chains of reference of symbols in CSs are multiple and of different kinds. As they are distinct and diverse, these chains enable different kinds of remoteness of reference and different kinds of validation for CSs. Although some methodological papers have already underlined the role of (...)
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  11. Franck Varenne (2010). Framework for M&S with Agents in Regard to Agent Simulations in Social Sciences: Emulation and Simulation. In Alexandre Muzy, David R. C. Hill & Bernard P. Zeigler (eds.), Activity-Based Modeling and Simulation. Presses Universitaires Blaise-Pascal.
    The aim of this paper is to discuss the “Framework for M&S with Agents” (FMSA) proposed by Zeigler et al. [2000, 2009] in regard to the diverse epistemological aims of agent simulations in social sciences. We first show that there surely are great similitudes, hence that the aim to emulate a universal “automated modeler agent” opens new ways of interactions between these two domains of M&S with agents. E.g., it can be shown that the multi-level conception at the core of (...)
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  12. 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 explanation of (...)
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  13. Franck Varenne (2009). Simulation informatique et pluriformalisation des objets composites. Philosophia Scientiae 13 (1):135-154.
    A recent evolution of computer simulations has led to the emergence of complex computer simulations. In particular, the need to formalize composite objects (those objects that are composed of other objects) has led to what the author suggests to call pluriformalizations, i.e. formalizations that are based on distinct sub-models which are expressed in a variety of heterogeneous symbolic languages. With the help of four case-studies, he shows that such pluriformalizations enable to formalize distinctly but simultaneously either different aspects or different (...)
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  14. Franck Varenne (2008). Émergences par les règles sans « formes de vie » une relecture de Kripke (1982) pour la simulation informatique du vivant. Noesis 14:201-236.
    Cet article ne se veut pas un commentaire suivi de la réflexion de Wittgenstein sur les règles. Ce ne sera pas non plus un commentaire de l’interprétation que Kripke fait du « suivi de la règle » chez Wittgenstein. Il ne sera pas davantage une application des thèses de Wittgenstein ni une tentative d’application directe d’une interprétation de ces thèses à l’épistémologie de la simulation du vivant ; ce qui serait, en soi, d’ailleurs contestable. Ce travail vise seulement à approfondir (...)
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  15. Franck Varenne (2003). La simulation conçue comme expérience concrète. In Jean-Pierre Müller (ed.), Le statut épistémologique de la simulation. Editions de l'ENST.
    Par un procédé d'objections/réponses, nous passons d'abord en revue certains des arguments en faveur ou en défaveur du caractère empirique de la simulation informatique. A l'issue de ce chemin clarificateur, nous proposons des arguments en faveur du caractère concret des objets simulés en science, ce qui légitime le fait que l'on parle à leur sujet d'une expérience, plus spécifiquement d'une expérience concrète du second genre.
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  16. Franck Varenne (2003). La simulation informatique face à la « méthode des modèles ». Le cas de la croissance des plantes. Natures Sciences Sociétés 11 (1):16-28.
    The paper deals with an intellectual and historical approach to the changing meanings of the term “model” in life sciences. The author 1st tries to understand how modeling has gradually spread over life sciences then he particularly focus on the birth of mathematical modeling in this field. This quite new practice offers new insights on the old debate concerning the mathematization of life sciences. Nowadays, through computers, mathematics not only analyze or quantify but model things: what does it mean? The (...)
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  17. Marcel Weber (forthcoming). Experimental Modeling in Biology: In Vivo Representation and Stand-Ins As Modeling Strategies. Philosophy of Science.
    Experimental modeling in biology involves the use of living organisms (not necessarily so-called "model organisms") in order to model or simulate biological processes. I argue here that experimental modeling is a bona fide form of scientific modeling that plays an epistemic role that is distinct from that of ordinary biological experiments. What distinguishes them from ordinary experiments is that they use what I call "in vivo representations" where one kind of causal process is used to stand in for a physically (...)
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  18. Sylvia Wenmackers & Danny E. P. Vanpoucke (2012). Models and Simulations in Material Science: Two Cases Without Error Bars. Statistica Neerlandica 66 (3):339–355.
    We discuss two research projects in material science in which the results cannot be stated with an estimation of the error: a spectroscopic ellipsometry study aimed at determining the orientation of DNA molecules on diamond and a scanning tunneling microscopy study of platinum-induced nanowires on germanium. To investigate the reliability of the results, we apply ideas from the philosophy of models in science. Even if the studies had reported an error value, the trustworthiness of the result would not depend on (...)
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