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  1. added 2018-12-02
    The Dark Side of the Force: When Computer Simulations Lead Us Astray and ``Model Think'' Narrows Our Imagination.Eckhart Arnold - manuscript
    This paper is intended as a critical examination of the question of when the use of computer simulations is beneficial to scientific explanations. This objective is pursued in two steps: First, I try to establish clear criteria that simulations must meet in order to be explanatory. Basically, a simulation has explanatory power only if it includes all causally relevant factors of a given empirical configuration and if the simulation delivers stable results within the measurement inaccuracies of the input parameters. If (...)
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  2. added 2018-11-26
    Peeking Inside the Black Box: A New Kind of Scientific Visualization.Michael T. Stuart & Nancy J. Nersessian - 2018 - Minds and Machines:1-21.
    Computational systems biologists create and manipulate computational models of biological systems, but they do not always have straightforward epistemic access to the content and behavioural profile of such models because of their length, coding idiosyncrasies, and formal complexity. This creates difficulties both for modellers in their research groups and for their bioscience collaborators who rely on these models. In this paper we introduce a new kind of visualization that was developed to address just this sort of epistemic opacity. The visualization (...)
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  3. added 2018-10-21
    Computer Simulations in Science and Engineering. Concept, Practices, Perspectives.Juan Manuel Durán - 2018 - Springer.
  4. added 2018-09-08
    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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  5. added 2018-09-04
    What Does a Computer Simulation Prove? The Case of Plant Modeling at CIRAD.Franck Varenne - 2001 - 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 diversity (...)
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  6. added 2018-08-29
    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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  7. added 2018-08-29
    Les notions de métaphore et d'analogie dans les épistémologies des modèles et des simulations.Franck Varenne - 2006 - Paris, France: Editions Petra.
    Le développement considérable des simulations informatiques dans les sciences contemporaines impose une remise à plat des épistémologies des modèles. Franck Varenne propose de revenir sur les limites des notions de métaphore et d'analogie pour penser les modèles, en particulier quand il s'agit des modèles composés, des pluri-modèles et des modèles de simulation (à objets ou à agents), tels qu'ils se développent depuis une dizaine d'années. Il suggère que le paradigme linguistique, à l'oeuvre aussi bien dans la pensée analytique anglo-saxonne que (...)
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  8. added 2018-08-28
    Models and Mechanisms in Network Neuroscience.Carlos Zednik - 2018 - Philosophical Psychology 32 (1):23-51.
    This paper considers the way mathematical and computational models are used in network neuroscience to deliver mechanistic explanations. Two case studies are considered: Recent work on klinotaxis by Caenorhabditis elegans, and a longstanding research effort on the network basis of schizophrenia in humans. These case studies illustrate the various ways in which network, simulation and dynamical models contribute to the aim of representing and understanding network mechanisms in the brain, and thus, of delivering mechanistic explanations. After outlining this mechanistic construal (...)
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  9. added 2018-08-28
    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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  10. added 2018-08-28
    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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  11. added 2018-08-19
    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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  12. added 2018-06-16
    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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  13. added 2018-06-12
    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.
  14. added 2018-04-23
    Theoretical Technologies in an “Experimental” Setting: Empirical Modeling of Proteinic Objects and Simulation of Their Dynamics Within Scientific Collaborations Around a Supercomputer.Frederic Wieber - unknown
    This paper examines, as a case study, some modeling and simulating practices in protein chemistry. In this field, theorists try to grasp proteinic objects by constructing models of their structures and by simulating their dynamical properties. The kind of models they construct and the necessity of performing simulations are linked with the molecular complexity of proteins. Two main types of problems emerge from this complexity. First, experimental problems arise when scientists want to perform on (and to adapt to) proteins some (...)
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  15. added 2018-04-10
    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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  16. added 2018-04-10
    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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  17. added 2018-03-05
    Review of Sherry Turkle’s ‘Simulation and Its Discontents’. [REVIEW]Olle Blomberg - 2009 - Metapsychology Online Reviews 13 (47).
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  18. added 2018-01-08
    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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  19. added 2018-01-06
    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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  20. added 2017-10-02
    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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  21. added 2017-10-01
    BML Revisited: Statistical Physics, Computer Simulation, and Probability.Raissa M. D'Souza - 2006 - Complexity 12 (2):30-39.
  22. added 2017-09-21
    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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  23. added 2017-06-14
    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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  24. added 2016-12-08
    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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  25. added 2016-12-08
    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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  26. added 2016-12-08
    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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  27. added 2016-12-05
    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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  28. added 2016-11-28
    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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  29. added 2016-11-04
    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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  30. added 2016-10-24
    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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  31. added 2016-10-08
    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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  32. added 2016-10-05
    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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  33. added 2016-10-04
    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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  34. added 2016-10-03
    Science in the Age of Computer Simulation.Eric B. Winsberg - 2010 - 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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  35. added 2016-10-03
    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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  36. added 2016-10-02
    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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  37. added 2016-10-01
    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.
  38. added 2016-08-30
    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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  39. added 2016-03-17
    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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  40. added 2016-03-11
    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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  41. added 2015-10-14
    Heuristics, Descriptions, and the Scope of Mechanistic Explanation.Carlos Zednik - 2015 - In P. Braillard & C. Malaterre (eds.), Explanation in Biology. An Enquiry into the Diversity of Explanatory Patterns in the Life Sciences. Dordrecht: Springer. pp. 295-318.
    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 mathematical (...)
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  42. added 2015-08-20
    A Critical Look at the Philosophy of Simulation.Roman Frigg & Julian Reiss - 2009 - Synthese 169 (3).
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  43. added 2015-06-06
    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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  44. added 2015-05-24
    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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  45. added 2015-04-27
    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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  46. added 2014-09-16
    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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  47. added 2014-03-26
    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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  48. added 2014-03-26
    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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  49. added 2014-03-26
    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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  50. added 2014-03-23
    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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