Search results for 'Simulations' (try it on Scholar)

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  1.  57
    Claus Beisbart (2012). How Can Computer Simulations Produce New Knowledge? European Journal for Philosophy of Science 2 (3):395-434.
    It is often claimed that scientists can obtain new knowledge about nature by running computer simulations. How is this possible? I answer this question by arguing that computer simulations are arguments. This view parallels Norton’s argument view about thought experiments. I show that computer simulations can be reconstructed as arguments that fully capture the epistemic power of the simulations. Assuming the extended mind hypothesis, I furthermore argue that running the computer simulation is to execute the reconstructing (...)
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  2.  31
    Matteo Colombo (2015). Why Build a Virtual Brain? Large-Scale Neural Simulations as Test-Bed for Artificial Computing Systems. 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 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 (...)
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  3.  45
    Dirk Schlimm (2009). Learning From the Existence of Models: On Psychic Machines, Tortoises, and Computer Simulations. Synthese 169 (3):521 - 538.
    Using four examples of models and computer simulations from the history of psychology, I discuss some of the methodological aspects involved in their construction and use, and I illustrate how the existence of a model can demonstrate the viability of a hypothesis that had previously been deemed impossible on a priori grounds. This shows a new way in which scientists can learn from models that extends the analysis of Morgan (1999), who has identified the construction and manipulation of models (...)
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  4.  58
    Denis Phan & Franck Varenne (2010). Agent-Based Models and Simulations in Economics and Social Sciences: From Conceptual Exploration to Distinct Ways of Experimenting. Journal of Artificial Societies and Social Simulation 13 (1).
    Now that complex Agent-Based Models and computer simulations spread over economics and social sciences - as in most sciences of complex systems -, epistemological puzzles (re)emerge. We introduce new epistemological concepts so as to show to what extent authors are right when they focus on some empirical, instrumental or conceptual significance of their model or simulation. By distinguishing between models and simulations, between types of models, between types of computer simulations and between types of empiricity obtained through (...)
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  5.  45
    Erich Rast (2009). What Simulations Can't Do. The Reasoner 3 (10):5-6.
    Simulations can only simulate knowledge.
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  6.  1
    Pio Garcia (2015). Computer Simulations and Experiments: In Vivo–in Vitro Conditions in Biochemistry. Foundations of Chemistry 17 (1):49-65.
    Scientific practices have been changed by the increasing use of computer simulations. A central question for philosophers is how to characterize computer simulations. In this paper, we address this question by analyzing simulations in biochemistry. We propose that simulations have been used in biochemistry long before computers arrived. Simulation can be described as a surrogate relationship between models. Moreover, a simulative aspect is implicit in the classical dichotomy between in vivo–in vitro conditions. Based on a discussion (...)
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  7. Matteo Colombo (2016). Why Build a Virtual Brain? Large-Scale Neural Simulations as Jump Start for Cognitive Computing. Journal of Experimental and Theoretical Artificial Intelligence.
    Despite the impressive amount of financial resources recently invested in carrying out large-scale brain simulations, it is controversial what the pay-offs are of pursuing this project. One idea is that from designing, building, and running a large-scale neural simulation, scientists acquire knowledge about the computational performance of the simulating system, rather than about the neurobiological system represented in the simulation. It has been claimed that this knowledge may usher in a new era of neuromorphic, cognitive computing systems. This study (...)
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  8. 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 (...)
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  9. Franck Varenne (2010). Les simulations computationnelles dans les sciences sociales. Nouvelles Perspectives En Sciences Sociales 5 (2):17-49.
    Since the 1990’s, social sciences are living their computational turn. This paper aims to clarify the epistemological meaning of this turn. To do this, we have to discriminate between different epistemic functions of computation among the diverse uses of computers for modeling and simulating in the social sciences. Because of the introduction of a new – and often more user-friendly – way of formalizing and computing, the question of realism of formalisms and of proof value of computational treatments reemerges. Facing (...)
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  10. Anouk Barberousse, Sara Franceschelli & Cyrille Imbert (2009). Computer Simulations as Experiments. 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 (...)
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  11. Cyrille Imbert, Can Simulations Be Explanatory an Why Do They Seem Not to Be?
    Computer simulations are usually considered to be non-explanatory because, when a simulation reveals that a property is instantiated in a system, it does not enable the exact identification of what it is that brings this property out (relevance requirement). Conversely, analytical deductions are widely considered to yield explanations and understanding. In this paper, I emphasize that explanations should satisfy the relevance requirement and argue that the more they do so, the more they have explanatory value. Finally, I show that (...)
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  12. Stephan Hartmann (1996). The World as a Process: Simulations in the Natural and Social Sciences. In Rainer Hegselmann (ed.), Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View.
    Simulation techniques, especially those implemented on a computer, are frequently employed in natural as well as in social sciences with considerable success. There is mounting evidence that the "model-building era" (J. Niehans) that dominated the theoretical activities of the sciences for a long time is about to be succeeded or at least lastingly supplemented by the "simulation era". But what exactly are models? What is a simulation and what is the difference and the relation between a model and a simulation? (...)
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  13.  44
    Emily C. Parke (2014). Experiments, Simulations, and Epistemic Privilege. Philosophy of Science 81 (4):516-536.
    Experiments are commonly thought to have epistemic privilege over simulations. Two ideas underpin this belief: first, experiments generate greater inferential power than simulations, and second, simulations cannot surprise us the way experiments can. In this article I argue that neither of these claims is true of experiments versus simulations in general. We should give up the common practice of resting in-principle judgments about the epistemic value of cases of scientific inquiry on whether we classify those cases (...)
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  14.  59
    Marcin Miłkowski (2016). Explanatory Completeness and Idealization in Large Brain Simulations: A Mechanistic Perspective. Synthese 193 (5):1457-1478.
    The claim defended in the paper is that the mechanistic account of explanation can easily embrace idealization in big-scale brain simulations, and that only causally relevant detail should be present in explanatory models. The claim is illustrated with two methodologically different models: Blue Brain, used for particular simulations of the cortical column in hybrid models, and Eliasmith’s SPAUN model that is both biologically realistic and able to explain eight different tasks. By drawing on the mechanistic theory of computational (...)
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  15.  36
    Ryan Muldoon (2007). Robust Simulations. Philosophy of Science 74 (5):873-883.
    As scientists begin to study increasingly complex questions, many have turned to computer simulation to assist in their inquiry. This methodology has been challenged by both analytic modelers and experimentalists. A primary objection of analytic modelers is that simulations are simply too complicated to perform model verification. From the experimentalist perspective it is that there is no means to demonstrate the reality of simulation. The aim of this paper is to consider objections from both of these perspectives, and to (...)
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  16.  48
    Gerhard Schurz & Paul D. Thorn (2012). REWARD VERSUS RISK IN UNCERTAIN INFERENCE: THEOREMS AND SIMULATIONS. Review of Symbolic Logic 5 (4):574-612.
    Systems of logico-probabilistic (LP) reasoning characterize inference from conditional assertions that express high conditional probabilities. In this paper we investigate four prominent LP systems, the systems O, P, Z, and QC. These systems differ in the number of inferences they licence (O ⊂ P ⊂ Z ⊂ QC). LP systems that license more inferences enjoy the possible reward of deriving more true and informative conclusions, but with this possible reward comes the risk of drawing more false or uninformative conclusions. In (...)
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  17.  33
    Claus Beisbart & John D. Norton (2012). Why Monte Carlo Simulations Are Inferences and Not Experiments. International Studies in the Philosophy of Science 26 (4):403-422.
    Monte Carlo simulations arrive at their results by introducing randomness, sometimes derived from a physical randomizing device. Nonetheless, we argue, they open no new epistemic channels beyond that already employed by traditional simulations: the inference by ordinary argumentation of conclusions from assumptions built into the simulations. We show that Monte Carlo simulations cannot produce knowledge other than by inference, and that they resemble other computer simulations in the manner in which they derive their conclusions. Simple (...)
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  18. 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 (...)
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  19.  38
    Thomas Boyer-Kassem (2014). Layers of Models in Computer Simulations. International Studies in the Philosophy of Science 28 (4):417-436.
    I discuss here the definition of computer simulations, and more specifically the views of Humphreys, who considers that an object is simulated when a computer provides a solution to a computational model, which in turn represents the object of interest. I argue that Humphreys's concepts are not able to analyse fully successfully a case of contemporary simulation in physics, which is more complex than the examples considered so far in the philosophical literature. I therefore modify Humphreys's definition of simulation. (...)
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  20. John Zeimbekis (2011). Thought Experiments and Mental Simulations. In Katerina Ierodiakonou & Sophie Roux (eds.), Thought Experiments in Methodological and Historical Contexts. Brill
    Thought experiments have a mysterious way of informing us about the world, apparently without examining it, yet with a great degree of certainty. It is tempting to try to explain this capacity by making use of the idea that in thought experiments, the mind somehow simulates the processes about which it reaches conclusions. Here, I test this idea. I argue that when they predict the outcomes of hypothetical physical situations, thought experiments cannot simulate physical processes. They use mental models, which (...)
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  21.  11
    Edoardo Datteri & Federico Laudisa, Large-Scale Simulations of Brain Mechanisms: Beyond the Synthetic Method.
    In recent years, a number of research projects have been proposed whose goal is to build large-scale simulations of brain mechanisms at unprecedented levels of biological accuracy. Here it is argued that the roles these simulations are expected to play in neuroscientific research go beyond the “synthetic method” extensively adopted in Artificial Intelligence and biorobotics. In addition we show that, over and above the common goal of simulating brain mechanisms, these projects pursue various modelling ambitions that can be (...)
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  22.  37
    Paul Humphreys (1990). Computer Simulations. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1990:497 - 506.
    This article provides a survey of some of the reasons why computational approaches have become a permanent addition to the set of scientific methods. The reasons for this require us to represent the relation between theories and their applications in a different way than do the traditional logical accounts extant in the philosophical literature. A working definition of computer simulations is provided and some properties of simulations are explored by considering an example from quantum chemistry.
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  23.  42
    Gerhard Schurz (2009). Meta-Induction and Social Epistemology: Computer Simulations of Prediction Games. Episteme 6 (2):200-220.
    The justification of induction is of central significance for cross-cultural social epistemology. Different ‘epistemological cultures’ do not only differ in their beliefs, but also in their belief-forming methods and evaluation standards. For an objective comparison of different methods and standards, one needs (meta-)induction over past successes. A notorious obstacle to the problem of justifying induction lies in the fact that the success of object-inductive prediction methods (i.e., methods applied at the level of events) can neither be shown to be universally (...)
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  24.  31
    Charles Lenay, John Stewart, Marieke Rohde & Amal Ali Amar (2011). “You Never Fail to Surprise Me”: The Hallmark of the Other: Experimental Study and Simulations of Perceptual Crossing. Interaction Studies 12 (3):373-396.
    Classically, the question of recognizing another subject is posed unilaterally, in terms of the observed behaviour of the other entity. Here, we propose an alternative, based on the emergent patterns of activity resulting from the interaction of both partners. We employ a minimalist device which forces the subjects to externalize their perceptual activity as trajectories which can be observed and recorded; the results show that subjects do identify the situation of perceptual crossing with their partner. The interpretation of the results (...)
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  25. Ulrich Krohs (2008). How Digital Computer Simulations Explain Real-World Processes. International Studies in the Philosophy of Science 22 (3):277 – 292.
    Scientists of many disciplines use theoretical models to explain and predict the dynamics of the world. They often have to rely on digital computer simulations to draw predictions fromthe model. But to deliver phenomenologically adequate results, simulations deviate from the assumptions of the theoretical model. Therefore the role of simulations in scientific explanation demands itself an explanation. This paper analyzes the relation between real-world system, theoretical model, and simulation. It is argued that simulations do not explain (...)
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  26.  74
    Aron Vallinder & Erik J. Olsson (2013). Do Computer Simulations Support the Argument From Disagreement? Synthese 190 (8):1437-1454.
    According to the Argument from Disagreement (AD) widespread and persistent disagreement on ethical issues indicates that our moral opinions are not influenced by moral facts, either because there are no such facts or because there are such facts but they fail to influence our moral opinions. In an innovative paper, Gustafsson and Peterson (Synthese, published online 16 October, 2010) study the argument by means of computer simulation of opinion dynamics, relying on the well-known model of Hegselmann and Krause (J Artif (...)
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  27.  56
    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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  28.  41
    Igor Douven (2009). Introduction: Computer Simulations in Social Epistemology. Episteme 6 (2):107-109.
    Over recent decades, computer simulations have become a common tool among practitioners of the social sciences. They have been utilized to study such diverse phenomena as the integration and segregation of different racial groups, the emergence and evolution of friendship networks, the spread of gossip, fluctuations of housing prices in an area, the transmission of social norms, and many more. Philosophers of science and others interested in the methodological status of these studies have identified a number of distinctive virtues (...)
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  29.  13
    Ji Y. Son & Robert L. Goldstone (2009). Fostering General Transfer with Specific Simulations. Pragmatics and Cognition 17 (1):1-42.
    Science education faces the difficult task of helping students understand and appropriately generalize scientific principles across a variety of superficially dissimilar specific phenomena. Can cognitive technologies be adapted to benefit both learning specific domains and generalizable transfer? This issue is examined by teaching students complex adaptive systems with computer-based simulations. With a particular emphasis on fostering understanding that transfers to dissimilar phenomena, the studies reported here examine the influence of different descriptions and perceptual instantiations of the scientific principle of (...)
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  30.  7
    Jordi Vallverdú, What Are Simulations? An Epistemological Approach.
    Contemporary sciences use a wide and diverse range of computational simulations, including in the areas of aeronautics, chemistry, bioinformatics, social sciences, AI, the physics of elementary particles and most other scientific fields. A simulation is a mathematical model that describes or creates computationally a system process. Simulations are our best cognitive representation of complex reality, that is, our deepest conception of what reality is. In this paper we defend that a simulation is equivalent epistemologically and ontologically with all (...)
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  31.  67
    Eckhart Arnold, Tools of 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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  32.  17
    Ronald Laymon (1990). Computer Simulations, Idealizations and Approximations. 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 (...)
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  33.  46
    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 (...)
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  34.  18
    Maria Luisa Bonet & Samuel R. Buss (1993). The Deduction Rule and Linear and Near-Linear Proof Simulations. Journal of Symbolic Logic 58 (2):688-709.
    We introduce new proof systems for propositional logic, simple deduction Frege systems, general deduction Frege systems, and nested deduction Frege systems, which augment Frege systems with variants of the deduction rule. We give upper bounds on the lengths of proofs in Frege proof systems compared to lengths in these new systems. As applications we give near-linear simulations of the propositional Gentzen sequent calculus and the natural deduction calculus by Frege proofs. The length of a proof is the number of (...)
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  35.  16
    John Schatzel & Claus Dierksmeier (2013). Teaching Business Ethics Through Social Audit Simulations. Journal of Business Ethics Education 10:305-326.
    This paper reports on a preliminary investigation of the pedagogical uses and possibilities of interactive ethics audit simulations. We want to foster experience-based learning in business ethics and examine how simulated social audits of corporations can be useful supplements to traditional textbook-oriented pedagogy. We argue that social audit simulations may offer many benefits for business ethics instruction, especially when it comes to developing ethical literacy for institutionally complex and morally complicated multi-stakeholder scenarios. We conclude that ethics education based (...)
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  36.  7
    Anouk Barberousse (2008). Les simulations numériques de l'évolution du climat : de nouveaux problèmes philosophiques ? Revue Philosophique de la France Et de l'Etranger 3 (3):299-308.
    L’étude de l’évolution du climat passe nécessairement par des simulations numériques. Leur utilisation a été remise en cause en raison des incertitudes qui affectent leurs résultats. Cet article présente les différentes composantes des simulations numériques utilisées en climatologie et en propose une analyse épistémologique. La conclusion en est que ces simulations numériques procèdent des exigences scientifiques plus généralement à l’œuvre dans la science contemporaine.In order to study the evolution of global climate, computer simulation is required. The use (...)
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  37.  25
    Rawad El Skaf & Cyrille Imbert (2013). Unfolding in the Empirical Sciences: Experiments, Thought Experiments and Computer Simulations. Synthese 190 (16):3451-3474.
    Experiments (E), computer simulations (CS) and thought experiments (TE) are usually seen as playing different roles in science and as having different epistemologies. Accordingly, they are usually analyzed separately. We argue in this paper that these activities can contribute to answering the same questions by playing the same epistemic role when they are used to unfold the content of a well-described scenario. We emphasize that in such cases, these three activities can be described by means of the same conceptual (...)
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  38.  34
    Sara Franceschelli (2009). Computer Simulations as Experiments. 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 (...)
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  39.  39
    Eckhart Arnold, The Dark Side of the Force: When Computer Simulations Lead Us Astray and ``Model Think'' Narrows Our Imagination --- Pre Conference Draft for the Models and Simulation Conference, Paris, June 12-14 ---. [REVIEW]
    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 (...)
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  40.  8
    P. Auger, R. Cardinal, A. Bril, L. Rochette & A. Bardou (1992). Interpretation of Epicardial Mapping by Means of Computer Simulations: Applications to Calcium, Lidocaine and to BRL 34915. Acta Biotheoretica 40 (2-3):161-168.
    The aim of this work was to compare experimental investigations on effects of lidocaine, calcium and, BRL 34915 on reentries to simulated data obtained by use of a model of propagation based on the Huygens' constriction method already described in previous works. Calcium and lidocaine effects are investigated on anisotropic conduction conditions. In both cases, reduction in conduction velocities are observed. In lidocaine case, a refractory area is located along the longitudinal axis. In agreement with experimental electrical mapping, the (...) show that the stabilization of reentrant excitation is mainly due to the existence of this refractory area around which the reentrant circuit can develop. The experimental study shows that BRL 34915 has both arrhythmogenic and antiarrhythmic effects. A detailed electrophysiological analysis has shown that drug infusion act on normal cardiac cells by decreasing the relative and absolute refractory period. BRL 34915 action is simulated by a decrease in the refractory period showing that the time frequency of the reentrant activity is increased and that the spatial size where the reentry is developing is becoming smaller. These two effects are arrhythmogenic, the simulated data being so in good agreement with the experimental ones. (shrink)
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  41.  6
    Claus Beisbart (2014). Are We Sims? How Computer Simulations Represent and What This Means for the Simulation Argument. The Monist 97 (3):399-417.
    N. Bostrom’s simulation argument and two additional assumptions imply that we likely live in a computer simulation. The argument is based upon the following assumption about the workings of realistic brain simulations: The hardware of a computer on which a brain simulation is run bears a close analogy to the brain itself. To inquire whether this is so, I analyze how computer simulations trace processes in their targets. I describe simulations as fictional, mathematical, pictorial, and material models. (...)
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  42.  5
    Juan M. Durán (2013). The Use of the ‘Materiality Argument’ in the Literature on Computer Simulations. In Juan M. Durán & Eckhart Arnold (eds.), Computer simulations and the changing face of scientific experimentation. 76-98.
  43.  11
    S. Franchi (2013). On Models, Simulations, and the Relevancy of Biochemistry to Cognitive Functions. Constructivist Foundations 9 (1):141-142.
    Open peer commentary on the article “A Cybernetic Computational Model for Learning and Skill Acquisition” by Bernard Scott & Abhinav Bansal. Upshot: Scott and Bansal’s assessment of the limitations of their work relies on a concept of simulation that I find problematic. It assumes that the ultimate goal of a model is a replication of the phenomena it applies, whereas a limited model produces only simulations. I argue that this position leads to unfortunate epistemological results, and it ends up (...)
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  44.  18
    Peter Krebs (2007). Virtual Models and Simulations. Techne 11 (1):42-54.
    The personal computer has become the primary research tool in many scientific and engineering disciplines. The role of the computer has been extended to be an experimental and modelling tool both for convenience and sometimes necessity. In this paper some of the relationships between real models and virtual models, i.e. models that exist only as programs and data structures, areexplored. It is argued that the shift from experimenting with real objects to experimentation with computer models and simulations may also (...)
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  45.  28
    Raymond W. Gibbs & Marcus Perlman (2010). Language Understanding is Grounded in Experiential Simulations: A Response to Weiskopf. Studies in History and Philosophy of Science Part A 41 (3):305-308.
    Several disciplines within the cognitive sciences have advanced the idea that people comprehend the actions of others, including the linguistic meanings they communicate, through embodied simulations where they imaginatively recreate the actions they observe or hear about. This claim has important consequences for theories of mind and meaning, such as that people’s use and interpretation of language emerges as a kind of bodily activity that is an essential part of ordinary cognition. Daniel Weiskopf presents several arguments against the idea (...)
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  46.  35
    Paul Weirich, Computer Simulations in Game Theory.
    A computer simulation runs a model generating a phenomenon under investigation. For the simulation to be explanatory, the model has to be explanatory. The model must be isomorphic to the natural system that realizes the phenomenon. This paper elaborates the method of assessing a simulation's explanatory power. Then it illustrates the method by applying it to two simulations in game theory. The first is Brian Skyrms's (1990) simulation of interactive deliberations. It is intended to explain the emergence of a (...)
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  47.  4
    Otávio Bueno (2014). Computer Simulations: An Inferential Conception. The Monist 97 (3):378-398.
    In this paper, I offer an inferential conception of computer simulations, emphasizing the role that simulations play as inferential devices to represent empirical phenomena. Three steps are involved in a simulation: an immersion step , a derivation step , and an interpretation and correction step . After presenting the view, I mention some cases, such as simulations of the current flow between silicon atoms and buckyballs as well as of genetic regulatory systems. I argue that the inferential (...)
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  48.  4
    Robert W. Mitchell & Catherine A. Clement (1999). Simulations, Simulators, Amodality, and Abstract Terms. Behavioral and Brain Sciences 22 (4):628-629.
    Barsalou's interesting model might benefit from defining simulation and clarifying the implications of prior critiques for simulations (and not just for perceptual symbols). Contrary to claims, simulators (or frames) appear, in the limit, to be amodal. In addition, the account of abstract terms seems extremely limited.
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    Nicolas Glade, Jacques Demongeot & James Tabony (2002). Numerical Simulations of Microtubule Self-Organisation by Reaction and Diffusion. Acta Biotheoretica 50 (4):239-268.
    This article addresses the physical chemical processes underlying biological self-organisation by which a homogenous solution of reacting chemicals spontaneously self-organises. Theoreticians have predicted that self-organisation can arise from a coupling of reactive processes with molecular diffusion. In addition, the presence of an external field, such as gravity, at a critical moment early in the process may determine the morphology that subsequently develops. The formation, in-vitro, of microtubules, a constituent of the cellular skeleton, shows this type of behaviour. The preparations spontaneously (...)
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    Richard P. Cooper (2002). Two Closely Related Simulations Provide Weak Limits on Residual Normality. Behavioral and Brain Sciences 25 (6):754-755.
    Thomas & Karmiloff- Smith correctly identify Residual Normality as a critical assumption of some theorising about mental structure within developmental psychology. However, their simulations provide only weak support for the conditions under which RN may occur because they explore closely related architectures that share a learning algorithm. It is suggested that more work is required to establish the limits of RN.
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