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  1. Multiagent-Based Simulation in Biology.Francesco Amigoni & Viola Schiaffonati - 2007 - In L. Magnani & P. Li (eds.), Model-Based Reasoning in Science, Technology, and Medicine. Springer. pp. 179--191.
  2. Learning in a Landscape: Simulation-Building as Reflexive Intervention.Anne Beaulieu, Matt Ratto & Andrea Scharnhorst - 2013 - Mind and Society 12 (1):91-112.
    This article makes a dual contribution to scholarship in science and technology studies on simulation-building. It both documents a specific simulation-building project, and demonstrates a concrete contribution of STS insights to interdisciplinary work. The article analyses the struggles that arise in the course of determining what counts as theory, as model and even as a simulation. Such debates are especially decisive when working across disciplinary boundaries, and their resolution is an important part of the work involved in building simulations. In (...)
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  3. Review of Sherry Turkle’s ‘Simulation and Its Discontents’. [REVIEW]Olle Blomberg - 2009 - Metapsychology Online Reviews 13 (47).
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  4. Making the Visual Visible in Philosophy of Science.Annamaria Carusi - 2012 - 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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  5. Why Build a Virtual Brain? Large-Scale Neural Simulations as Test-Bed for Artificial Computing Systems.Matteo Colombo - 2015 - In D. C. Noelle, R. Dale, A. S. Warlaumont, J. Yoshimi, T. Matlock, C. D. Jennings & P. P. Maglio (eds.), Proceedings of the 37th Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 429-434.
    Despite the impressive amount of financial resources invested in carrying out large-scale brain simulations, it is controversial what the payoffs are of pursuing this project. The present paper argues that in some cases, from designing, building, and running a large-scale neural simulation, scientists acquire useful knowledge about the computational performance of the simulating system, rather than about the neurobiological system represented in the simulation. What this means, why it is not a trivial lesson, and how it advances the literature on (...)
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  6. BML Revisited: Statistical Physics, Computer Simulation, and Probability.Raissa M. D'Souza - 2006 - Complexity 12 (2):30-39.
  7. Confirmation Via Analogue Simulation: What Dumb Holes Could Tell Us About Gravity.Radin Dardashti, Karim P. Y. Thébault & Eric Winsberg - 2017 - British Journal for the Philosophy of Science 68 (1).
    In this article we argue for the existence of ‘analogue simulation’ as a novel form of scientific inference with the potential to be confirmatory. This notion is distinct from the modes of analogical reasoning detailed in the literature, and draws inspiration from fluid dynamical ‘dumb hole’ analogues to gravitational black holes. For that case, which is considered in detail, we defend the claim that the phenomena of gravitational Hawking radiation could be confirmed in the case that its counterpart is detected (...)
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  8. A Brief Overview of the Philosophical Study of Computer Simulations.Juan M. Durán - 2013 - American Philosophical Association Newsletter on Philosophy and Computers 13 (1):38-46.
  9. Computer Simulations in Science and Engineering. Concept, Practices, Perspectives.Juan Manuel Durán - 2018 - Springer.
  10. Combining Qualitative and Quantitative Techniques in the Simulation of Chemical Reaction Mechanisms.Michael Eisenberg - forthcoming - Ai and Simulation: Theory and Applications (Simulation Series Vol. 22, No. 3.). Society for Computer Simulation, San Diego. Ca.
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  11. Agent-Based Modeling and the Fallacies of Individualism.Brian Epstein - 2011 - In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. Routledge. pp. 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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  12. The Role of Computers in Scientific Research: A Cognitive Approach.Roberto Feltrero - 2005 - In L. Magnani & R. Dossena (eds.), Computing, Philosophy and Cognition. pp. 87--98.
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  13. Explanation by Computer Simulation in Cognitive Science.Jordi Fernández - 2003 - 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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  14. Computer Simulations as Experiments.Sara Franceschelli - 2009 - Synthese 169 (3):557 - 574.
    Whereas computer simulations involve no direct physical interaction between the machine they are run on and the physical systems they are used to investigate, they are often used as experiments and yield data about these systems. It is commonly argued that they do so because they are implemented on physical machines. We claim that physicality is not necessary for their representational and predictive capacities and that the explanation of why computer simulations generate desired information about their target system is only (...)
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  15. A Critical Look at the Philosophy of Simulation.Roman Frigg & Julian Reiss - 2009 - Synthese 169 (3).
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  16. The Philosophy of Simulation: Hot New Issues or Same Old Stew?Roman Frigg & Julian Reiss - 2009 - Synthese 169 (3):593-613.
    Computer simulations are an exciting tool that plays important roles in many scientific disciplines. This has attracted the attention of a number of philosophers of science. The main tenor in this literature is that computer simulations not only constitute interesting and powerful new science , but that they also raise a host of new philosophical issues. The protagonists in this debate claim no less than that simulations call into question our philosophical understanding of scientific ontology, the epistemology and semantics of (...)
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  17. Combining Causal Bayes Nets and Cellular Automata: A Hybrid Modelling Approach to Mechanisms.Alexander Gebharter & Daniel Koch - forthcoming - British Journal for the Philosophy of Science.
    Causal Bayes nets (CBNs) can be used to model causal relationships up to whole mechanisms. Though modelling mechanisms with CBNs comes with many advantages, CBNs might fail to adequately represent some biological mechanisms because—as Kaiser (2016) pointed out—they have problems with capturing relevant spatial and structural information. In this paper we propose a hybrid approach for modelling mechanisms that combines CBNs and cellular automata. Our approach can incorporate spatial and structural information while, at the same time, it comes with all (...)
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  18. Scientific Models, Simulation, and the Experimenter's Regress.Axel Gelfert - 2011 - 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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  19. How Simulations Fail.Patrick Grim, Robert Rosenberger, Adam Rosenfeld, Brian Anderson & Robb E. Eason - 2013 - 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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  20. Order Out of Chaos? A Case Study in High Energy Physics.Rafaela Hillerbrand - 2012 - Studia Philosophica Estonica 5 (2):61-78.
    In recent years, computational sciences such as computational hydrodynamics or computational field theory have supplemented theoretical and experimental investigations in many scientific fields. Often, there is a seemingly fruitful overlap between theory, experiment, and numerics. The computational sciences are highly dynamic and seem a fairly successful endeavor---at least if success is measured in terms of publications or engineering applications. However, for theories, success in application and correctness are two very different things; and just the same may hold for "methodologies" like (...)
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  21. Extending Ourselves: Computational Science, Empiricism, and Scientific Method.Paul Humphreys - 2004 - Oxford University Press.
    Computational methods have become the dominant technique in many areas of science. This book contains the first systematic philosophical account of these new methods and their consequences for scientific method. This book will be of interest to philosophers of science and to anyone interested in the role played by computers in modern science.
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  22. Computational Science and Scientific Method.Paul Humphreys - 1995 - Minds and Machines 5 (4):499-512.
    The process of constructing mathematical models is examined and a case made that the construction process is an integral part of the justification for the model. The role of heuristics in testing and modifying models is described and some consequences for scientific methodology are drawn out. Three different ways of constructing the same model are detailed to demonstrate the claims made here.
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  23. Computer Simulations, Idealizations and Approximations.Ronald Laymon - 1990 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1990:519 - 534.
    It's uncontroversial that notions of idealization and approximation are central to understanding computer simulations and their rationale. What's not so clear is what exactly these notions come to. Two distinct forms of approximation will be distinguished and their features contrasted with those of idealizations. These distinctions will be refined and closely tied to computer simulations by means of Scott-Strachey denotational programming semantics. The use of this sort of semantics also provides a convenient format for argumentation in favor of several theses (...)
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  24. The Emergence of Symbol-Based Communication in a Complex System of Artificial Creatures.Angelo Loula, Ricardo Gudwin, Charbel El-Hani & João Queiroz - unknown
    We present here a digital scenario to simulate the emergence of self-organized symbol-based communication among artificial creatures inhabiting a virtual world of predatory events. In order to design the environment and creatures, we seek theoretical and empirical constraints from C.S.Peirce Semiotics and an ethological case study of communication among animals. Our results show that the creatures, assuming the role of sign users and learners, behave collectively as a complex system, where self-organization of communicative interactions plays a major role in the (...)
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  25. Humanities’ Metaphysical Underpinnings of Late Frontier Scientific Research.Alcibiades Malapi-Nelson - 2014 - Humanities 214 (3):740-765.
    The behavior/structure methodological dichotomy as locus of scientific inquiry is closely related to the issue of modeling and theory change in scientific explanation. Given that the traditional tension between structure and behavior in scientific modeling is likely here to stay, considering the relevant precedents in the history of ideas could help us better understand this theoretical struggle. This better understanding might open up unforeseen possibilities and new instantiations, particularly in what concerns the proposed technological modification of the human condition. The (...)
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  26. Evaluating Artificial Models of Cognition.Marcin Miłkowski - 2015 - Studies in Logic, Grammar and Rhetoric 40 (1):43-62.
    Artificial models of cognition serve different purposes, and their use determines the way they should be evaluated. There are also models that do not represent any particular biological agents, and there is controversy as to how they should be assessed. At the same time, modelers do evaluate such models as better or worse. There is also a widespread tendency to call for publicly available standards of replicability and benchmarking for such models. In this paper, I argue that proper evaluation ofmodels (...)
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  27. Reverse-Engineering in Cognitive-Science.Marcin Miłkowski - 2013 - In Marcin Miłkowski & Konrad Talmont-Kaminski (eds.), Regarding Mind, Naturally. Cambridge Scholars Press. pp. 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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  28. Epistemic Opacity, Confirmation Holism and Technical Debt: Computer Simulation in the Light of Empirical Software Engineering.Julian Newman - 2016 - In History and Philosophy of Computing (IFIP AICT 487). Cham, Switzerland: Springer. pp. 256-272.
    Epistemic opacity vis a vis human agents has been presented as an essential, ineliminable characteristic of computer simulation models resulting from the characteristics of the human cognitive agent. This paper argues, on the contrary, that such epistemic opacity as does occur in computer simulations is not a consequence of human limitations but of a failure on the part of model developers to adopt good software engineering practice for managing human error and ensuring the software artefact is maintainable. One consequence of (...)
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  29. Computing the Uncomputable; or, The Discrete Charm of Second-Order Simulacra.Matthew W. Parker - 2009 - Synthese 169 (3):447-463.
    We examine a case in which non-computable behavior in a model is revealed by computer simulation. This is possible due to differing notions of computability for sets in a continuous space. The argument originally given for the validity of the simulation involves a simpler simulation of the simulation, still further simulations thereof, and a universality conjecture. There are difficulties with that argument, but there are other, heuristic arguments supporting the qualitative results. It is urged, using this example, that absolute validation, (...)
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  30. Does Matter Really Matter? Computer Simulations, Experiments, and Materiality.Wendy Parker - 2009 - Synthese 169 (3):483-496.
    A number of recent discussions comparing computer simulation and traditional experimentation have focused on the significance of “materiality.” I challenge several claims emerging from this work and suggest that computer simulation studies are material experiments in a straightforward sense. After discussing some of the implications of this material status for the epistemology of computer simulation, I consider the extent to which materiality (in a particular sense) is important when it comes to making justified inferences about target systems on the basis (...)
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  31. Computer Simulation, Measurement, and Data Assimilation.Wendy S. Parker - 2017 - British Journal for the Philosophy of Science 68 (1):273-304.
    This article explores some of the roles of computer simulation in measurement. A model-based view of measurement is adopted and three types of measurement—direct, derived, and complex—are distinguished. It is argued that while computer simulations on their own are not measurement processes, in principle they can be embedded in direct, derived, and complex measurement practices in such a way that simulation results constitute measurement outcomes. Atmospheric data assimilation is then considered as a case study. This practice, which involves combining information (...)
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  32. Network Representation and Complex Systems.Charles Rathkopf - 2018 - Synthese (1).
    In this article, network science is discussed from a methodological perspective, and two central theses are defended. The first is that network science exploits the very properties that make a system complex. Rather than using idealization techniques to strip those properties away, as is standard practice in other areas of science, network science brings them to the fore, and uses them to furnish new forms of explanation. The second thesis is that network representations are particularly helpful in explaining the properties (...)
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  33. Computer Simulation in the Physical Sciences.Fritz Rohrlich - 1990 - 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 physical systems which use (...)
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  34. From Instructional Social Computer Simulation to Heidegger's Aesthetics.Ron Shiro Saito - 2000 - Dissertation, Indiana University
    Using Schon's conception of reflection-in-action as an organizing structure, the author examines instructional social computer simulation by designing and reflecting upon computer prototypes and linking this analysis to appropriate literature. ;The author begins his study by examining the theoretical antecedents of model and location simulations. However, eventually agreeing with Dilthey's critique that society cannot be represented via scientific, law-like generalizations, he decides that model/location simulation reflects the "standard view of science" approach to the representation of society. ;Drawing from the interpretivist (...)
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  35. On the Use of Visualizations in the Practice of Science.Pauline Sargent - 1996 - 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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  36. Social Explanation and Computational Simulation.R. Keith Sawyer - 2004 - Philosophical Explorations 7 (3):219-231.
    I explore a type of computational social simulation known as artificial societies. Artificial society simulations are dynamic models of real-world social phenomena. I explore the role that these simulations play in social explanation, by situating these simulations within contemporary philosophical work on explanation and on models. Many contemporary philosophers have argued that models provide causal explanations in science, and that models are necessary mediators between theory and data. I argue that artificial society simulations provide causal mechanistic explanations. I conclude that (...)
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  37. Imagination: A Sine Qua Non of Science.Michael T. Stuart - 2017 - Croatian Journal of Philosophy (49):9-32.
    What role does the imagination play in scientific progress? After examining several studies in cognitive science, I argue that one thing the imagination does is help to increase scientific understanding, which is itself indispensable for scientific progress. Then, I sketch a transcendental justification of the role of imagination in this process.
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  38. From Data to Phenomena and Back Again: Computer-Simulated Signatures.Eran Tal - 2011 - 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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  39. From Models to Simulations.Franck Varenne - 2018 - London, UK: Routledge.
    This book analyses the impact computerization has had on contemporary science and explains the origins, technical nature and epistemological consequences of the current decisive interplay between technology and science: an intertwining of formalism, computation, data acquisition, data and visualization and how these factors have led to the spread of simulation models since the 1950s. -/- Using historical, comparative and interpretative case studies from a range of disciplines, with a particular emphasis on the case of plant studies, the author shows how (...)
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  40. Théories et modèles en sciences humaines. Le cas de la géographie.Franck Varenne - 2017 - Paris, France: Editions Matériologiques.
    Face à la diversité et à la complexification des modes de formalisation, une épistémologie des méthodes scientifiques doit confronter directement ses analyses à une pluralité d’études de cas comparatives. C’est l’objectif de cet ouvrage. -/- Aussi, dans une première partie, propose-t-il d’abord une classification large et raisonnée des différentes fonctions de connaissance des théories, des modèles et des simulations (de fait, cette partie constitue un panorama d’épistémologie générale particulièrement poussé). C’est ensuite à la lumière de cette classification que les deux (...)
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  41. The Nature of Computational Things.Franck Varenne - 2013 - In Frédéric Migayrou Brayer & Marie-Ange (eds.), Naturalizing Architecture. Orléans: HYX Editions. pp. 96-105.
    Architecture often relies on mathematical models, if only to anticipate the physical behavior of structures. Accordingly, mathematical modeling serves to find an optimal form given certain constraints, constraints themselves translated into a language which must be homogeneous to that of the model in order for resolution to be possible. Traditional modeling tied to design and architecture thus appears linked to a topdown vision of creation, of the modernist, voluntarist and uniformly normative type, because usually (mono)functionalist. One available instrument of calculation/representation/prescription (...)
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  42. Chains of Reference in Computer Simulations.Franck Varenne - 2013 - 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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  43. Théorie, Réalité, Modèle.Franck Varenne - 2012 - Paris, France: Editions Matériologiques.
    Dans cet ouvrage, Franck Varenne pose la question du réalisme scientifique, essentiellement dans sa forme contemporaine, et ce jusqu’aux années 1980. Il s’est donné pour cela la contrainte de focaliser l’attention sur ce que devenaient sa formulation et les réponses diverses qu’on a pu lui apporter en réaction spécifique à l’évolution parallèle qu’ont subie les notions de théories et surtout de modèles dans les sciences, à la même époque. Même si, bien sûr, on ne peut pas attribuer le considérable essor (...)
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  44. Modéliser le social. Méthodes fondatrices et évolutions récentes.Franck Varenne - 2011 - Paris, France: Dunod.
    Cet ouvrage très pédagogique informe les étudiants sur les méthodes quantitatives les plus classiques comme les plus récentes en sciences sociales, et notamment sur les différentes pratiques de modélisation et de simulation informatique des systèmes sociaux (sciences sociales computationnelles ou modèles informatiques).
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  45. Framework for Models and Simulations with Agents in Regard to Agent Simulations in Social Sciences: Emulation and Simulation.Franck Varenne - 2010 - 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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  46. Simulation informatique et pluriformalisation des objets composites.Franck Varenne - 2009 - Philosophia Scientae 13:135-154.
    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 calling 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 parts (...)
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  47. Models and Simulations in the Historical Emergence of the Science of Complexity.Franck Varenne - 2009 - In Ma Aziz-Alaoui & C. Bertelle (eds.), From System Complexity to Emergent Properties. Springer. pp. 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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  48. Simulation informatique et pluriformalisation des objets composites.Franck Varenne - 2009 - 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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  49. Émergences par les règles sans « formes de vie » une relecture de Kripke (1982) pour la simulation informatique du vivant.Franck Varenne - 2008 - 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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  50. Du Modèle à la Simulation Informatique.Franck Varenne - 2007 - Vrin.
    Avec l’évolution récente des modèles mathématiques vers des simulations informatiques, les formalisations du vivant sont de plus en plus intégratives, mixtes et, en un sens, réalistes. Plus généralement, les formalisations d’objets complexes deviennent assises sur et non plus seulement traitées par l’infrastructure informatique.Quelle est la véritable portée épistémologique de cette empirie simulée? Comment la distinguer de la créativité proprement interne aux mathématiques dont la philosophie des sciences a déjà su rendre compte?En se penchant sur les modèles de plantes, cette enquête (...)
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