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

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  1. 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).score: 25.0
    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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  2. Erich Rast (2009). What Simulations Can't Do. The Reasoner 3 (10):5-6.score: 24.0
    Simulations can only simulate knowledge.
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  3. Claus Beisbart (2012). How Can Computer Simulations Produce New Knowledge? European Journal for Philosophy of Science 2 (3):395-434.score: 24.0
    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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  4. Dirk Schlimm (2009). Learning From the Existence of Models: On Psychic Machines, Tortoises, and Computer Simulations. Synthese 169 (3):521 - 538.score: 24.0
    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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  5. 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.score: 21.0
    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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  6. Franck Varenne (2010). Les simulations computationnelles dans les sciences sociales. Nouvelles Perspectives En Sciences Sociales 5 (2):17-49.score: 20.0
    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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  7. Franck Varenne (2013). Chains of Reference in Computer Simulations. FMSH Working Papers 51:1-32.score: 20.0
    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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  8. Aron Vallinder & Erik J. Olsson (2013). Do Computer Simulations Support the Argument From Disagreement? Synthese 190 (8):1437-1454.score: 20.0
    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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  9. Patrick Grim, Robert Rosenberger, Adam Rosenfeld, Brian Anderson & Robb E. Eason (2013). How Simulations Fail. Synthese 190 (12):2367-2390.score: 20.0
    ‘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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  10. 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.score: 20.0
    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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  11. Ryan Muldoon (2007). Robust Simulations. Philosophy of Science 74 (5):873-883.score: 20.0
    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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  12. Rawad El Skaf & Cyrille Imbert (2013). Unfolding in the Empirical Sciences: Experiments, Thought Experiments and Computer Simulations. Synthese 190 (16):3451-3474.score: 20.0
    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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  13. Esther Katharina Papies (2013). Tempting Food Words Activate Eating Simulations. Frontiers in Psychology 4.score: 20.0
    This study shows that tempting food words activate simulations of eating the food, including simulations of the taste and texture of the food, simulations of eating situations, and simulations of hedonic enjoyment. In a feature listing task, participants generated features that are typically true of four tempting foods (e.g., chips) and four neutral foods (e.g., rice). The resulting features were coded as features of eating simulations if they referred to the taste, texture and temperature of (...)
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  14. 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.score: 19.0
    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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  15. John Zeimbekis (2011). Thought Experiments and Mental Simulations. In Katerina Ierodiakonou & Sophie Roux (eds.), Thought Experiments in Methodological and Historical Contexts. Brill.score: 18.0
    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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  16. Ulrich Krohs (2008). How Digital Computer Simulations Explain Real-World Processes. International Studies in the Philosophy of Science 22 (3):277 – 292.score: 18.0
    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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  17. Wendy S. Parker (2009). Does Matter Really Matter? Computer Simulations, Experiments, and Materiality. Synthese 169 (3):483 - 496.score: 18.0
    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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  18. Anouk Barberousse, Sara Franceschelli & Cyrille Imbert (2009). Computer Simulations as Experiments. Synthese 169 (3):557 - 574.score: 18.0
    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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  19. Eckhart Arnold, Tools of Toys? On Specific Challenges for Modeling and the Epistemology of Models and Computer Simulations in the Social Sciences.score: 18.0
    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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  20. Gerhard Schurz (2009). Meta-Induction and Social Epistemology: Computer Simulations of Prediction Games. Episteme 6 (2):200-220.score: 18.0
    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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  21. Gerhard Schurz & Paul D. Thorn (2012). REWARD VERSUS RISK IN UNCERTAIN INFERENCE: THEOREMS AND SIMULATIONS. Review of Symbolic Logic 5 (4):574-612.score: 18.0
    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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  22. Paul Humphreys (1990). Computer Simulations. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1990:497 - 506.score: 18.0
    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. Paul Weirich, Computer Simulations in Game Theory.score: 18.0
    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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  24. Cyrille Imbert, Can Simulations Be Explanatory an Why Do They Seem Not to Be?score: 18.0
    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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  25. Sara Franceschelli (2009). Computer Simulations as Experiments. Synthese 169 (3):557 - 574.score: 18.0
    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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  26. Charles Lenay, John Stewart, Marieke Rohde & Amal Ali Amar (2012). You Never Fail to Surprise Me: The Hallmark of the Other: Experimental Study and Simulations of Perceptual Crossing. Interaction Studies 12 (3):373-396.score: 18.0
    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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  27. 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]score: 18.0
    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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  28. Richard P. Cooper (2002). Two Closely Related Simulations Provide Weak Limits on Residual Normality. Behavioral and Brain Sciences 25 (6):754-755.score: 18.0
    Thomas & Karmiloff-Smith (T&K-S) correctly identify Residual Normality (RN) 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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  29. Ronald Laymon (1990). Computer Simulations, Idealizations and Approximations. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1990:519 - 534.score: 18.0
    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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  30. David J. Kijowski, Harry Dankowicz & Michael C. Loui (2013). Observations on the Responsible Development and Use of Computational Models and Simulations. Science and Engineering Ethics 19 (1):63-81.score: 18.0
    Most previous works on responsible conduct of research have focused on good practices in laboratory experiments. Because computation now rivals experimentation as a mode of scientific research, we sought to identify the responsibilities of researchers who develop or use computational modeling and simulation. We interviewed nineteen experts to collect examples of ethical issues from their experiences in conducting research with computational models. We gathered their recommendations for guidelines for computational research. Informed by these interviews, we describe the respective professional responsibilities (...)
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  31. Whit Schonbein (2010). Can Computational Simulations of Language Emergence Support a 'Use' Theory of Meaning? Philosophical Psychology 23 (1):59-74.score: 18.0
    Some researchers claim that simulations of the emergence of communication in populations of autonomous agents provide empirical support for 'use' theories of meaning. I argue that this claim faces at least two major challenges. First, the empirical adequacy of such simulations must be justified, or the inference from simulation results to real-world linguistic behavior must be dropped; and second, the proffered simulations are in fact compatible with all of the competing theories of meaning surveyed, suggesting that theories (...)
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  32. 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.score: 18.0
    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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  33. Peter Krebs (2007). Virtual Models and Simulations. Techne 11 (1):42-54.score: 18.0
    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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  34. William Spees (2001). Ethical Responsibilities of Software Developers in Developing Simulations. International Journal of Applied Philosophy 15 (1):59-64.score: 18.0
    Recent innovations in computer software development have produced a new breed of pet, AIBO 2, a robotic pet that simulates the behavior of real pets. This paper argues that software developers who create such simulations have ethical responsibilities to product users and to society. The paper concludes with some general ethical guidelines for software developers to follow when engaged in projects involving real-world simulations.
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  35. 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.score: 18.0
    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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  36. Donald R. Franceschetti (2001). Biorobotic Simulations Might Offer Some Advantages Over Purely Computational Ones. Behavioral and Brain Sciences 24 (6):1058-1059.score: 18.0
    A slight modification of Webb's diagrammatic representation of the dimensions for describing models is proposed which extends it to cover a range of theoretical models as well as material models. It is also argued that beyond a certain level robotic simulations could offer a number of real advantages over computer simulations of organisms interacting with their environment.
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  37. Nicolas Glade, Jacques Demongeot & James Tabony (2002). Numerical Simulations of Microtubule Self-Organisation by Reaction and Diffusion. Acta Biotheoretica 50 (4).score: 18.0
    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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  38. Paul Humphreys & Cyrille Imbert (eds.) (2011). Models, Simulations, and Representations. Routledge.score: 18.0
    Although scientific models and simulations differ in numerous ways, they are similar in so far as they are posing essentially philosophical problems about the nature of representation. This collection is designed to bring together some of the best work on the nature of representation being done by both established senior philosophers of science and younger researchers. Most of the pieces, while appealing to existing traditions of scientific representation, explore new types of questions, such as: how understanding can be developed (...)
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  39. Stan Franklin (2001). Models as Implementations of a Theory, Rather Than Simulations: Dancing to a Different Drummer. Behavioral and Brain Sciences 24 (6):1059-1059.score: 18.0
    Robots, as well as software agents, can be of use in biology as implementations of a theory rather than as simulations of specific real world target systems. Such implementations generate hypotheses rather than representing them. Their behavior is not predicted, but rather observed, and is not expected to duplicate that of a target system. Scientific knowledge is gained through the testing of generated hypotheses.
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  40. S. Franchi (2013). On Models, Simulations, and the Relevancy of Biochemistry to Cognitive Functions. Constructivist Foundations 9 (1):141-142.score: 18.0
    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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  41. William J. McKinney (1997). The Educational Use of Computer Based Science Simulations: Some Lessons From the Philosophy of Science. Science and Education 6 (6):591-603.score: 18.0
    Examines some of the potential and some of the problems inherent in using computerized simulations in science and science studies classes by applying lessons from the epistemology of science. While computer simulations are useful pedagogical tools, they are not experiments and thus are of only limited utility as substitutes for actual laboratories. Contains 20 references. (Author/PVD).
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  42. Robert W. Mitchell & Catherine A. Clement (1999). Simulations, Simulators, Amodality, and Abstract Terms. Behavioral and Brain Sciences 22 (4):628-629.score: 18.0
    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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  43. 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).score: 18.0
    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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  44. Emily C. Parke (forthcoming). Experiments, Simulations, and Epistemic Privilege. 81 (4):516-536,.score: 18.0
    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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  45. Rainer Reisenzein (2009). On Literary Works as Simulations That Run on Minds. Emotion Review 1 (1):35-36.score: 18.0
    This commentary discusses Oatley's proposal that literary works considered as simulations that run on minds can fulfill similar epistemic functions as computer simulations of mental processes. Whereas in computer simulation, both the input data and the computations to be performed on these data are explicit, only the input is explicitly known in the case of mental simulation. For this reason, literary simulations cannot play exactly the same epistemic role as computer simulations. Still, literary simulations can (...)
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  46. Patrick Suppes (2011). Models and Simulations in Brain Experiments. In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. Routledge.score: 18.0
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  47. Katja Valli & Antti Revonsuo (2006). Recurrent Dreams: Recurring Threat Simulations? Consciousness and Cognition 15 (2):464-469.score: 17.0
  48. Barry Dainton (2012). On Singularities and Simulations. Journal of Consciousness Studies 19 (1):42.score: 17.0
  49. Rafaela Hillerbrand (2014). Climate Simulations: Uncertain Projections for an Uncertain World. Journal for General Philosophy of Science 45 (1):17-32.score: 17.0
    Between the fourth and the recent fifth IPCC report, science as well as policy making have made great advances in dealing with uncertainties in global climate models. However, the uncertainties public decision making has to deal with go well beyond what is currently addressed by policy makers and climatologists alike. It is shown in this paper that within an anthropocentric framework, a whole hierarchy of models from various scientific disciplines is needed for political decisions as regards climate change. Via what (...)
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  50. Frank Moss & P. V. E. McClintock (eds.) (1989). Experiments and Simulations. Cambridge University Press.score: 17.0
    The three volumes that make up Noise in Nonlinear Dynamical Systems comprise a collection of specially written authoritative reviews on all aspects of the subject, representative of all the major practitioners in the field.
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