Search results for 'computer modeling and simulation' (try it on Scholar)

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  1. Franck Varenne (2001). What Does a Computer Simulation Prove? The Case of Plant Modeling at CIRAD. In N. Giambiasi & C. Frydman (eds.), Simulation in industry - ESS 2001, Proc. of the 13th European Simulation Symposium. Society for Computer Simulation (SCS).score: 504.0
    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 (...)
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  2. Johannes Lenhard (2007). Computer Simulation: The Cooperation Between Experimenting and Modeling. Philosophy of Science 74 (2):176-194.score: 486.0
    The goal of the present article is to contribute to the epistemology and methodology of computer simulations. The central thesis is that the process of simulation modeling takes the form of an explorative cooperation between experimenting and modeling. This characteristic mode of modeling turns simulations into autonomous mediators in a specific way; namely, it makes it possible for the phenomena and the data to exert a direct influence on the model. The argumentation will be illustrated (...)
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  3. Carole J. Clem & Jean Paul Rigaut (1995). Computer Simulation Modelling and Visualization of 3d Architecture of Biological Tissues. Acta Biotheoretica 43 (4).score: 342.0
    Recent technical improvements, such as 3D microscopy imaging, have shown the necessity of studying 3D biological tissue architecture during carcinogenesis. In the present paper a computer simulation model is developed allowing the visualization of the microscopic biological tissue architecture during the development of metaplastic and dysplastic lesions.The static part of the model allows the simulation of the normal, metaplastic and dysplastic architecture of an external epithelium. This model is associated to a knowledge base which contains only data (...)
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  4. Graeme Earl (forthcoming). Archaeological Computer Graphic Modelling, Simulation and Spatial Interpretation. Perspectives on Science.score: 330.0
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  5. Fabio Boschetti, David McDonald & Randall Gray (2008). Complexity of a Modelling Exercise: A Discussion of the Role of Computer Simulation in Complex System Science. Complexity 13 (6):21-28.score: 310.0
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  6. Rodrigo B. Ferreira, Fernando C. Coelli, Wagner C. A. Pereira & Renan M. V. R. Almeida (2008). Optimizing Patient Flow in a Large Hospital Surgical Centre by Means of Discrete‐Event Computer Simulation Models. Journal of Evaluation in Clinical Practice 14 (6):1031-1037.score: 284.0
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  7. Jean‐Christophe Chiêm, Thérèse Van Durme, Florence Vandendorpe, Olivier Schmitz, Niko Speybroeck, Sophie Cès & Jean Macq (2013). Expert Knowledge Elicitation Using Computer Simulation: The Organization of Frail Elderly Case Management as an Illustration. Journal of Evaluation in Clinical Practice:n/a-n/a.score: 284.0
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  8. John A. Barker (2002). Computer Modeling and the Fate of Folk Psychology. Metaphilosophy 33 (1-2):30-48.score: 282.0
  9. Margaret Morrison (2009). Models, Measurement and Computer Simulation: The Changing Face of Experimentation. Philosophical Studies 143 (1):33 - 57.score: 274.0
    The paper presents an argument for treating certain types of computer simulation as having the same epistemic status as experimental measurement. While this may seem a rather counterintuitive view it becomes less so when one looks carefully at the role that models play in experimental activity, particularly measurement. I begin by discussing how models function as “measuring instruments” and go on to examine the ways in which simulation can be said to constitute an experimental activity. By focussing (...)
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  10. M. Füllsack (2013). Constructivism and Computation: Can Computer-Based Modeling Add to the Case for Constructivism? Constructivist Foundations 9 (1):7-16.score: 261.0
    Problem: Is constructivism contradicted by the reductionist determinism inherent in digital computation? Method: Review of examples from dynamical systems sciences, agent-based modeling and artificial intelligence. Results: Recent scientific insights seem to give reason to consider constructivism in line with what computation is adding to our knowledge of interacting dynamics and the functioning of our brains. Implications: Constructivism is not necessarily contradictory to digital computation, in particular to computer-based modeling and simulation. Constructivist content: When viewed through the (...)
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  11. Eckhart Arnold, Tools of Toys? On Specific Challenges for Modeling and the Epistemology of Models and Computer Simulations in the Social Sciences.score: 258.0
    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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  12. Alexandre Muzy, Franck Varenne, Bernard P. Zeigler, Jonathan Caux, Patrick Coquillard, Luc Touraille, Dominique Prunetti, Philippe Caillou, Olivier Michel & David R. C. Hill (2013). Refounding of the Activity Concept? Towards a Federative Paradigm for Modeling and Simulation. Simulation - Transactions of the Society for Modeling and Simulation International 89 (2):156-177.score: 252.0
    Currently, the widely used notion of activity is increasingly present in computer science. However, because this notion is used in specific contexts, it becomes vague. Here, the notion of activity is scrutinized in various contexts and, accordingly, put in perspective. It is discussed through four scientific disciplines: computer science, biology, economics, and epistemology. The definition of activity usually used in simulation is extended to new qualitative and quantitative definitions. In computer science, biology and economics disciplines, the (...)
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  13. Hélène Guillemot (2010). Connections Between Simulations and Observation in Climate Computer Modeling. Scientist's Practices and “Bottom-Up Epistemology” Lessons. Studies in History and Philosophy of Science Part B 41 (3):242-252.score: 238.3
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  14. Ivo F. Sbalzarini (2013). Modeling and Simulation of Biological Systems From Image Data. Bioessays 35 (5):482-490.score: 230.0
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  15. Roger Stanev (2012). Modelling and Simulating Early Stopping of RCTs: A Case Study of Early Stop Due to Harm. Journal of Experimental and Theoretical Artificial Intelligence 24 (4):513-526.score: 228.0
    Despite efforts from regulatory agencies (e.g. NIH, FDA), recent systematic reviews of randomised controlled trials (RCTs) show that top medical journals continue to publish trials without requiring authors to report details for readers to evaluate early stopping decisions carefully. This article presents a systematic way of modelling and simulating interim monitoring decisions of RCTs. By taking an approach that is both general and rigorous, the proposed framework models and evaluates early stopping decisions of RCTs based on a clear and consistent (...)
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  16. Ç Gülçiçek & B. Güneş (2004). Materializing the Concepts During Science Instruction: Modeling Strategy, Computer Simulations and Analogies. Science and Education 29 (134):36-48.score: 215.0
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  17. Günter Küppers & Johannes Lenhard (2005). Computersimulationen: Modellierungen 2. Ordnung. [REVIEW] Journal for General Philosophy of Science 36 (2):305 - 329.score: 210.0
    Es soll ein Beitrag zur epistemischen Charakterisierung von Computersimulationen als jenseits von Experiment und Theorie geleistet werden. Es wird argumentiert, dass die in der Simulationstechnik eingesetzten Verfahren nicht numerische Lösungen liefern, sondern deren Dynamik mittels generativer Mechanismen imitieren. Die Computersimulationen in der Klimatologie werden als systematisches wie historisches Fallbeispiel behandelt. Erst "Simulationsexperimente" gestatten es, mittels Modellen eine Dynamik zu imitieren, ohne deren Grundgleichungen zu "lösen". /// Computer simulations will be characterized in epistemic respect as a method between experiment and (...)
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  18. James L. McClelland (2009). The Place of Modeling in Cognitive Science. Topics in Cognitive Science 1 (1):11-38.score: 207.0
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  19. Ronald N. Giere (2009). Is Computer Simulation Changing the Face of Experimentation? Philosophical Studies 143 (1):59 - 62.score: 192.0
    Morrison points out many similarities between the roles of simulation models and other sorts of models in science. On the basis of these similarities she claims that running a simulation is epistemologically on a par with doing a traditional experiment and that the output of a simulation therefore counts as a measurement. I agree with her premises but reject the inference. The epistemological payoff of a traditional experiment is greater (or less) confidence in the fit between a (...)
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  20. Wendy S. Parker (2008). Computer Simulation Through an Error-Statistical Lens. Synthese 163 (3):371 - 384.score: 192.0
    After showing how Deborah Mayo’s error-statistical philosophy of science might be applied to address important questions about the evidential status of computer simulation results, I argue that an error-statistical perspective offers an interesting new way of thinking about computer simulation models and has the potential to significantly improve the practice of simulation model evaluation. Though intended primarily as a contribution to the epistemology of simulation, the analysis also serves to fill in details of Mayo’s (...)
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  21. Tibor Bosse, Martijn C. Schut & Jan Treur (2009). Formal Analysis of Dynamics Within Philosophy of Mind by Computer Simulation. Minds and Machines 19 (4):543-555.score: 192.0
    Computer simulations can be useful tools to support philosophers in validating their theories, especially when these theories concern phenomena showing nontrivial dynamics. Such theories are usually informal, whilst for computer simulation a formally described model is needed. In this paper, a methodology is proposed to gradually formalise philosophical theories in terms of logically formalised dynamic properties. One outcome of this process is an executable logic-based temporal specification, which within a dedicated software environment can be used as a (...)
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  22. Markus F. Peschl & Chris Stary (1998). The Role of Cognitive Modeling for User Interface Design Representations: An Epistemological Analysis of Knowledge Engineering in the Context of Human-Computer Interaction. [REVIEW] Minds and Machines 8 (2):203-236.score: 192.0
    In this paper we review some problems with traditional approaches for acquiring and representing knowledge in the context of developing user interfaces. Methodological implications for knowledge engineering and for human-computer interaction are studied. It turns out that in order to achieve the goal of developing human-oriented (in contrast to technology-oriented) human-computer interfaces developers have to develop sound knowledge of the structure and the representational dynamics of the cognitive system which is interacting with the computer.We show that in (...)
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  23. Beckett Sterner (2012). Agent-Based Computer Simulation and Ethics. [REVIEW] Metascience 21 (2):403-407.score: 192.0
    Agent-based computer simulation and ethics Content Type Journal Article Category Book Review Pages 1-5 DOI 10.1007/s11016-012-9660-7 Authors Beckett Sterner, Conceptual and Historical Studies of Science, The University of Chicago, Social Sciences Building 205, 1126 E 59th St, Chicago, IL 60637, USA Journal Metascience Online ISSN 1467-9981 Print ISSN 0815-0796.
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  24. N. S. Sutherland (1974). Computer Simulation of Brain Function. In Philosophy Of Psychology. Macmillan.score: 176.0
  25. Philip Garnett, Arno Steinacher, Susan Stepney, Richard Clayton & Ottoline Leyser (2010). Computer Simulation: The Imaginary Friend of Auxin Transport Biology. Bioessays 32 (9):828-835.score: 172.0
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  26. Sashank Varma (2011). Criteria for the Design and Evaluation of Cognitive Architectures. Cognitive Science 35 (7):1329-1351.score: 169.0
    Cognitive architectures are unified theories of cognition that take the form of computational formalisms. They support computational models that collectively account for large numbers of empirical regularities using small numbers of computational mechanisms. Empirical coverage and parsimony are the most prominent criteria by which architectures are designed and evaluated, but they are not the only ones. This paper considers three additional criteria that have been comparatively undertheorized. (a) Successful architectures possess subjective and intersubjective meaning, making cognition comprehensible to individual cognitive (...)
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  27. Jordi Fernández (2003). Explanation by Computer Simulation in Cognitive Science. Minds And Machines 13 (2):269-284.score: 168.0
    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 (...)
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  28. 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: 168.0
    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 (...)
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  29. Leonard Uhr, Charles Vossler & James Uleman (1962). Pattern Recognition Over Distortions, by Human Subjects and by a Computer Simulation of a Model for Human Form Perception. Journal of Experimental Psychology 63 (3):227.score: 168.0
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  30. Saikou Y. Diallo, Jose J. Padilla, Ross Gore, Heber Herencia‐Zapana & Andreas Tolk (2014). Toward a Formalism of Modeling and Simulation Using Model Theory. Complexity 19 (3):56-63.score: 168.0
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  31. Eric B. Winsberg (2010). Science in the Age of Computer Simulation. The University of Chicago Press.score: 168.0
    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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  32. Louise Connell & Mark T. Keane (2006). A Model of Plausibility. Cognitive Science 30 (1):95-120.score: 166.0
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  33. Giovanni Pezzulo, Lawrence W. Barsalou, Angelo Cangelosi, Martin H. Fischer, Michael Spivey & Ken McRae (2011). The Mechanics of Embodiment: A Dialog on Embodiment and Computational Modeling. Frontiers in Psychology 2.score: 164.0
    Embodied theories are increasingly challenging traditional views of cognition by arguing that conceptual representations that constitute our knowledge are grounded in sensory and motor experiences, and processed at this sensorimotor level, rather than being represented and processed abstractly in an amodal conceptual system. Given the established empirical foundation, and the relatively underspecified theories to date, many researchers are extremely interested in embodied cognition but are clamouring for more mechanistic implementations. What is needed at this stage is a push toward explicit (...)
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  34. John Hale (2006). Uncertainty About the Rest of the Sentence. Cognitive Science 30 (4):643-672.score: 162.0
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  35. Paul Humphreys (2009). The Philosophical Novelty of Computer Simulation Methods. Synthese 169 (3):615 - 626.score: 156.0
    Reasons are given to justify the claim that computer simulations and computational science constitute a distinctively new set of scientific methods and that these methods introduce new issues in the philosophy of science. These issues are both epistemological and methodological in kind.
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  36. Johan E. Gustafsson & Martin Peterson (2012). A Computer Simulation of the Argument From Disagreement. Synthese 184 (3):387–405.score: 156.0
    In this paper we shed new light on the Argument from Disagreement by putting it to test in a computer simulation. According to this argument widespread and persistent disagreement on ethical issues indicates that our moral opinions are not influenced by any moral facts, either because no such facts exist or because they are epistemically inaccessible or inefficacious for some other reason. Our simulation shows that if our moral opinions were influenced at least a little bit by (...)
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  37. Kenneth F. Schaffner (1981). Modeling Medical Diagnosis: Logical and Computer Approaches. Synthese 47 (1):163 - 199.score: 156.0
    In the present article I have surveyed several approaches to modeling the clinical diagnostic process. I have argued that at this point of the field's development, logics which simulate the reasoning patterns and knowledge base of expert clinicians represent research programs that are most likely to succeed. No logic of diagnosis has yet attained the status of being definitive; in spite of striking progress much more research and testing is required. On the basis of various existing logics, I have (...)
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  38. Isabelle Peschard, Is Simulation a Substitute for Experimentation?score: 153.0
    It is sometimes said that simulation can serve as epistemic substitute for experimentation. Such a claim might be suggested by the fast-spreading use of computer simulation to investigate phenomena not accessible to experimentation (in astrophysics, ecology, economics, climatology, etc.). But what does that mean? The paper starts with a clarification of the terms of the issue and then focuses on two powerful arguments for the view that simulation and experimentation are ‘epistemically on a par’. One is (...)
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  39. W. S. Parker (2006). Understanding Pluralism in Climate Modeling. Foundations of Science 11 (4):349-368.score: 153.0
    To study Earth’s climate, scientists now use a variety of computer simulation models. These models disagree in some of their assumptions about the climate system, yet they are used together as complementary resources for investigating future climatic change. This paper examines and defends this use of incompatible models. I argue that climate model pluralism results both from uncertainty concerning how to best represent the climate system and from difficulties faced in evaluating the relative merits of complex models. I (...)
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  40. By Nick Bostrom (2003). Are We Living in a Computer Simulation? Philosophical Quarterly 53 (211):243–255.score: 144.0
    This paper argues that at least one of the following propositions is true: (1) the human species is very likely to go extinct before reaching a “posthuman” stage; (2) any posthuman civilization is extremely unlikely to run a significant number of simulations of their evolutionary history (or variations thereof); (3) we are almost certainly living in a computer simulation. It follows that the belief that there is a significant chance that we will one day become posthumans who run (...)
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  41. Eric Winsberg (2009). Computer Simulation and the Philosophy of Science. Philosophy Compass 4 (5):835-845.score: 144.0
    There are a variety of topics in the philosophy of science that need to be rethought, in varying degrees, after one pays careful attention to the ways in which computer simulations are used in the sciences. There are a number of conceptual issues internal to the practice of computer simulation that can benefit from the attention of philosophers. This essay surveys some of the recent literature on simulation from the perspective of the philosophy of science and (...)
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  42. Wendy S. Parker (2008). Franklin, Holmes, and the Epistemology of Computer Simulation. International Studies in the Philosophy of Science 22 (2):165 – 183.score: 144.0
    Allan Franklin has identified a number of strategies that scientists use to build confidence in experimental results. This paper shows that Franklin's strategies have direct analogues in the context of computer simulation and then suggests that one of his strategies—the so-called 'Sherlock Holmes' strategy—deserves a privileged place within the epistemologies of experiment and simulation. In particular, it is argued that while the successful application of even several of Franklin's other strategies (or their analogues in simulation) may (...)
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  43. Wendy Parker (2012). Computer Simulation and Philosophy of Science. Metascience 21 (1):111-114.score: 144.0
    Computer simulation and philosophy of science Content Type Journal Article Pages 1-4 DOI 10.1007/s11016-011-9567-8 Authors Wendy S. Parker, Department of Philosophy, Ellis Hall 202, Ohio University, Athens, OH 45701, USA Journal Metascience Online ISSN 1467-9981 Print ISSN 0815-0796.
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  44. 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: 144.0
    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 (...)
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  45. Fritz Rohrlich (1990). Computer Simulation in the Physical Sciences. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1990:507 - 518.score: 144.0
    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 (...)
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  46. Franck Varenne (2003). La simulation informatique face à la « méthode des modèles ». Le cas de la croissance des plantes. Natures Sciences Sociétés 11 (1):16-28.score: 144.0
    The paper deals with an intellectual and historical approach to the changing meanings of the term “model” in life sciences. The author 1st tries to understand how modeling has gradually spread over life sciences then he particularly focus on the birth of mathematical modeling in this field. This quite new practice offers new insights on the old debate concerning the mathematization of life sciences. Nowadays, through computers, mathematics not only analyze or quantify but model things: what does it (...)
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  47. Nick Bostrom (2003). Are We Living in a Computer Simulation? Philosophical Quarterly 53 (211):243 - 255.score: 144.0
    I argue that at least one of the following propositions is true: (1) the human species is very likely to become extinct before reaching a 'posthuman' stage; (2) any posthuman civilization is extremely unlikely to run a significant number of simulations of its evolutionary history (or variations thereof); (3) we are almost certainly living in a computer simulation. It follows that the belief that there is a significant chance that we shall one day become posthumans who run ancestor-simulations (...)
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  48. Graeme Earl (2013). Modeling in Archaeology: Computer Graphic and Other Digital Pasts. Perspectives on Science 21 (2):226-244.score: 144.0
    Computer graphic modeling forms an increasing part of archaeological practice, implicated in modes of recording objects and spaces, interpretation of types, management of three-dimensional information, creation of artificial experiences of place for interpretation, and representation of archaeological ideas to a broader public. In all spheres of life computer graphics are increasingly influential—by some estimates computed visions constitute the "dominant medium of thought" (Gooding 2008, p. 1). Archaeological computer graphics build on a long tradition of physical model (...)
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  49. Berni Alder (1979). Review of Computer Modeling of Matter by Peter Lykos. American Chemical Society. [REVIEW] Acm Sigcas Computers and Society 9 (3-4):32.score: 142.0
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  50. 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: 141.0
    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 (...)
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