Results for 'Classification of models and simulations'

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  1. From Models to Simulations.Franck Varenne - 2018 - London, UK: Routledge.
    This book analyses the impact computerization has had on contemporary science and explains the origins, technical nature and epistemological consequences of the current decisive interplay between technology and science: an intertwining of formalism, computation, data acquisition, data and visualization and how these factors have led to the spread of simulation models since the 1950s. -/- Using historical, comparative and interpretative case studies from a range of disciplines, with a particular emphasis on the case of plant studies, the author shows (...)
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  2.  9
    Classification of Sign-Language Using Deep Learning - A Comparison Between Inception and Xception Models.Tanseem N. Abu-Jamie & Samy S. Abu-Naser - 2022 - International Journal of Academic Engineering Research (IJAER) 6 (8):9-19.
    there is a communication gap between hearing-impaired people and those with normal hearing, sign language is the main means of communication in the hearing-impaired population. Continuous sign language recognition, which can close the communication gap, is a difficult task since the ordered annotations are weakly supervised and there is no frame-level label. To solve this issue, we compare the accuracy of each model using two deep learning models, Inception and Xception . To that end, the purpose of this paper (...)
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  3. Agent-Based Models and Simulations in Economics and Social Sciences: From Conceptual Exploration to Distinct Ways of Experimenting.Franck Varenne & Denis Phan - 2008 - In Nuno David, José Castro Caldas & Helder Coelho (eds.), Proceedings of the 3rd EPOS congress (Epistemological Perspectives On Simulations). Lisbon: pp. 51-69.
    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 tools so as to show to what precise extent each author is right when he focuses on some empirical, instrumental or conceptual significance of his model or simulation. By distinguishing between models and simulations, between types of models, between types of computer simulations and between (...)
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  4. Classification of Real and Fake Human Faces Using Deep Learning.Fatima Maher Salman & Samy S. Abu-Naser - 2022 - International Journal of Academic Engineering Research (IJAER) 6 (3):1-14.
    Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques used to solve many real-world problems. Artificial intelligence is the branch of computer sciences that emphasizes the development of intelligent machines, thinking and working like humans. For example, recognition, problem-solving, learning, visual perception, decision-making and planning. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Deep learning (...)
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  5. Modelling and Simulation of Vehicle Windshield Wiper System Using H Infinity Loop Shaping and Robust Pole Placement Controllers.Mustefa Jibril, Messay Tadese & Eliyas Alemayehu Tadese - manuscript
    Vehicle windshield wiper system increases the driving safety by contributing a clear shot viewing to the driver. In this paper, modelling, designing and simulation of a vehicle windshield wiper system with robust control theory is done successfully. H  loop shaping and robust pole placement controllers are used to improve the wiping speed by tracking a reference speed signals. The reference speed signals used in this paper are step and sine wave signals. Comparison of the H  loop shaping and (...)
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  6. Framework for Models and Simulations with Agents in Regard to Agent Simulations in Social Sciences: Emulation and Simulation.Franck Varenne - 2010 - In Alexandre Muzy, David R. C. Hill & Bernard P. Zeigler (eds.), Activity-Based Modeling and Simulation. Presses Universitaires Blaise-Pascal.
    The aim of this paper is to discuss the “Framework for M&S with Agents” (FMSA) proposed by Zeigler et al. [2000, 2009] in regard to the diverse epistemological aims of agent simulations in social sciences. We first show that there surely are great similitudes, hence that the aim to emulate a universal “automated modeler agent” opens new ways of interactions between these two domains of M&S with agents. E.g., it can be shown that the multi-level conception at the core (...)
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  7. Bayesian Models and Simulations in Cognitive Science.Giuseppe Boccignone & Roberto Cordeschi - 2007 - Workshop Models and Simulations 2, Tillburg, NL.
    Bayesian models can be related to cognitive processes in a variety of ways that can be usefully understood in terms of Marr's distinction among three levels of explanation: computational, algorithmic and implementation. In this note, we discuss how an integrated probabilistic account of the different levels of explanation in cognitive science is resulting, at least for the current research practice, in a sort of unpredicted epistemological shift with respect to Marr's original proposal.
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  8. Tools or Toys? On Specific Challenges for Modeling and the Epistemology of Models and Computer Simulations in the Social Sciences.Eckhart Arnold - manuscript
    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 (...)
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  9. The Structure and Logic of Interdisciplinary Research in Agent-Based Social Simulation.Nuno David, Maria Marietto, Jaime Sichman & Helder Coelho - 2004 - Journal of Artificial Societies and Social Simulation 7 (3).
    This article reports an exploratory survey of the structure of interdisciplinary research in Agent-Based Social Simulation. One hundred and ninety six researchers participated in the survey completing an on-line questionnaire. The questionnaire had three distinct sections, a classification of research domains, a classification of models, and an inquiry into software requirements for designing simulation platforms. The survey results allowed us to disambiguate the variety of scientific goals and modus operandi of researchers with a reasonable level of detail, (...)
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  10. Layers of Models in Computer Simulations.Thomas Boyer-Kassem - 2014 - 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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  11. Simulation Typology and Termination Risks.Alexey Turchin & Roman Yampolskiy - manuscript
    The goal of the article is to explore what is the most probable type of simulation in which humanity lives (if any) and how this affects simulation termination risks. We firstly explore the question of what kind of simulation in which humanity is most likely located based on pure theoretical reasoning. We suggest a new patch to the classical simulation argument, showing that we are likely simulated not by our own descendants, but by alien civilizations. Based on this, we provide (...)
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  12. Direct Perception and Simulation: Stein’s Account of Empathy.Monika Dullstein - 2013 - Review of Philosophy and Psychology 4 (2):333-350.
    The notion of empathy has been explicated in different ways in the current debate on how to understand others. Whereas defenders of simulation-based approaches claim that empathy involves some kind of isomorphism between the empathizer’s and the target’s mental state, defenders of the phenomenological account vehemently deny this and claim that empathy allows us to directly perceive someone else’s mental states. Although these views are typically presented as being opposed, I argue that at least one version of a simulation-based approach—the (...)
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  13. Classification of Anomalies in Gastrointestinal Tract Using Deep Learning.Ibtesam M. Dheir & Samy S. Abu-Naser - 2022 - International Journal of Academic Engineering Research (IJAER) 6 (3):15-28.
    Automatic detection of diseases and anatomical landmarks in medical images by the use of computers is important and considered a challenging process that could help medical diagnosis and reduce the cost and time of investigational procedures and refine health care systems all over the world. Recently, gastrointestinal (GI) tract disease diagnosis through endoscopic image classification is an active research area in the biomedical field. Several GI tract disease classification methods based on image processing and machine learning techniques have (...)
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  14. Classification of A Few Fruits Using Deep Learning.Mohammed Alkahlout, Samy S. Abu-Naser, Azmi H. Alsaqqa & Tanseem N. Abu-Jamie - 2022 - International Journal of Academic Engineering Research (IJAER) 5 (12):56-63.
    Abstract: Fruits are a rich source of energy, minerals and vitamins. They also contain fiber. There are many fruits types such as: Apple and pears, Citrus, Stone fruit, Tropical and exotic, Berries, Melons, Tomatoes and avocado. Classification of fruits can be used in many applications, whether industrial or in agriculture or services, for example, it can help the cashier in the hyper mall to determine the price and type of fruit and also may help some people to determining whether (...)
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  15. Classification of Alzheimer's Disease Using Convolutional Neural Networks.Lamis F. Samhan, Amjad H. Alfarra & Samy S. Abu-Naser - 2022 - International Journal of Academic Information Systems Research (IJAISR) 6 (3):18-23.
    Brain-related diseases are among the most difficult diseases due to their sensitivity, the difficulty of performing operations, and their high costs. In contrast, the operation is not necessary to succeed, as the results of the operation may be unsuccessful. One of the most common diseases that affect the brain is Alzheimer’s disease, which affects adults, a disease that leads to memory loss and forgetting information in varying degrees. According to the condition of each patient. For these reasons, it is important (...)
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  16. Computer Models and the Evidence of Anthropogenic Climate Change: An Epistemology of Variety-of-Evidence Inferences and Robustness Analysis.Martin Vezer - 2016 - Computer Models and the Evidence of Anthropogenic Climate Change: An Epistemology of Variety-of-Evidence Inferences and Robustness Analysis MA Vezér Studies in History and Philosophy of Science 56:95-102.
    To study climate change, scientists employ computer models, which approximate target systems with various levels of skill. Given the imperfection of climate models, how do scientists use simulations to generate knowledge about the causes of observed climate change? Addressing a similar question in the context of biological modelling, Levins (1966) proposed an account grounded in robustness analysis. Recent philosophical discussions dispute the confirmatory power of robustness, raising the question of how the results of computer modelling studies contribute (...)
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  17. Varieties of Support and Confirmation of Climate Models.Elisabeth A. Lloyd - 2009 - Aristotelian Society Supplementary Volume 83 (1):213-232.
    Today's climate models are supported in a couple of ways that receive little attention from philosophers or climate scientists. In addition to standard 'model fit', wherein a model's simulation is compared to observational data, there is an additional type of confirmation available through the variety of instances of model fit. When a model performs well at fitting first one variable and then another, the probability of the model under some standard confirmation function, say, likelihood, goes up more than under (...)
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  18.  59
    Models, Mathematics, and Measurement: A Review of Reconstructing Reality by Margaret Morrison - Margaret Morrison, Reconstructing Reality: Models, Mathematics, and Simulations. Oxford: Oxford University Press (2015), Viii+334 Pp., $65.00 (Cloth). [REVIEW]Paul Humphreys - 2016 - Philosophy of Science 83 (4):627-633.
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  19.  58
    Classifications of Pineapple Using Deep Learning.Amjad H. Alfarra, Lamis F. Samhan, Yasmin E. Aslem, Marah M. Almasawabe & Samy S. Abu-Naser - 2021 - International Journal of Academic Information Systems Research (IJAISR) 5 (12):37-41.
    A pineapple is a tropical plant with eatable leafy foods most monetarily critical plant in the family Bromeliaceous. The pineapple is native to South America, where it has been developed for a long time. The acquaintance of the pineapple with Europe in the seventeenth century made it a critical social symbol of extravagance. Since the 1820s, pineapple has been industrially filled in nurseries and numerous tropical manors. Further, it is the third most significant tropical natural product in world creation. In (...)
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  20. Design and Simulation of Voltage Amplidyne System Using Robust Control Technique.Mustefa Jibril, Messay Tadese & Eliyas Alemayehu -
    In this paper, modelling designing and simulation of a simple voltage amplidyne system is done using robust control theory. In order to increase the performance of the voltage amplidyne system with H infinity optimal control synthesis and H infinity optimal control synthesis via gamma-iteration controllers are used. The open loop response of the voltage amplidyne system shows that the system can amplify the input 7 times. Comparison of the voltage amplidyne system with H infinity optimal control synthesis and H infinity (...)
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  21. On Triplet Classification of Concepts.Vladimir Kuznetsov - 1997 - Knowledge Organization 24 (3):163-175.
    The scheme for classifications of concepts is introduced. It has founded on the triplet model of concepts. In this model a concept is depicted by means of three kinds of knowledge: a concept base, a concept representing part and the linkage between them. The idea of triplet classifications of concepts is connected with a usage of various specifications of these knowledge kinds as classification criteria.
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  22. 10cubes and 3N3: Using Interactive Diagrams to Investigate Charles Peirces Classifications of Signs.Priscila Farias & João Queiroz - 2004 - Semiotica 2004 (151):41-63.
    This article presents some results of a research on computational strategies for the visualization of sign classification structures and sign processes. The focus of this research is the various classifications of signs described by Peirce. Two models are presented. One of them concerns specifically the 10-fold classification as described in the 1903 Syllabus (MS 540, EP 2: 289–299), while the other deals with the deep structure of Peirce’s various trichotomic classifications. The first is 10cubes, an interactive 3-D (...)
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  23.  58
    Design and Simulation of Voltage Amplidyne System Using Robust Control Technique.Mustefa Jibril, Messay Tadese & Eliyas Alemayehu - 2020 - Researcher Journal 12 (8):13-17.
    In this paper, modelling designing and simulation of a simple voltage amplidyne system is done using robust control theory. In order to increase the performance of the voltage amplidyne system with H optimal control synthesis and H optimal control synthesis via-iteration controllers are used. The open loop response of the voltage amplidyne system shows that the system can amplify the input 7 times. Comparison of the voltage amplidyne system with H optimal control synthesis and H optimal control synthesis via-iteration controllers (...)
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  24.  53
    Modeling and Simulation of Vehicle Windshield Wiper System Using H  Loop Shaping and Robust Pole Placement Controllers.Mustefa Jibril, Messay Tadese & Eliyas Alemayehu - 2020 - Report and Opinion Journal 12 (9):14-18.
    Vehicle windshield wiper system increases the driving safety by contributing a clear shot viewing to the driver. In this paper, modelling, designing and simulation of a vehicle windshield wiper system with robust control theory is done successfully. H  loop shaping and robust pole placement controllers are used to improve the wiping speed by tracking a reference speed signals. The reference speed signals used in this paper are step and sine wave signals. Comparison of the H  loop shaping and (...)
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  25.  41
    Modeling and Simulation of Vehicle Windshield Wiper System Using H Infinity Loop Shaping and Robust Pole Placement Controllers.Mustefa Jibril, Messay Tadese & Eliyas Alemayehu - 2020 - Report and Opinion Journal 12 (9):14-18.
    Vehicle windshield wiper system increases the driving safety by contributing a clear shot viewing to the driver. In this paper, modelling, designing and simulation of a vehicle windshield wiper system with robust control theory is done successfully. H infinity loop shaping and robust pole placement controllers are used to improve the wiping speed by tracking a reference speed signals. The reference speed signals used in this paper are step and sine wave signals. Comparison of the H infinity loop shaping and (...)
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  26. Design and Simulation of a Steam Turbine Generator Using Observer Based and LQR Controllers.Mustefa Jibril, Messay Tadese & Eliyas Alemayehu -
    Steam turbine generator is an electromechanical system which converts heat energy to electrical energy. In this paper, the modelling, design and analysis of a simple steam turbine generator have done using Matlab/Simulink Toolbox. The open loop system have been analyzed to have an efficiency of 76.92 %. Observer based & linear quadratic regulator (LQR) controllers have been designed to improve the generating voltage. Comparison of this two proposed controllers have been done for increasing the performance improvement to generate a 220 (...)
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  27. The Dark Side of the Force. When Computer Simulations Lead Us Astray and Model Think Narrows Our Imagination.Eckhart Arnold - manuscript
    This paper is intended as a critical examination of the question of when and under what conditions 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 (...)
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  28. Simulation Models of the Evolution of Cooperation as Proofs of Logical Possibilities. How Useful Are They?Eckhart Arnold - 2013 - Ethics and Politics 2 (XV):101-138.
    This paper discusses critically what simulation models of the evolution of cooperation can possibly prove by examining Axelrod’s “Evolution of Cooperation” (1984) and the modeling tradition it has inspired. Hardly any of the many simulation models in this tradition have been applicable empirically. Axelrod’s role model suggested a research design that seemingly allowed to draw general conclusions from simulation models even if the mechanisms that drive the simulation could not be identified empirically. But this research design was (...)
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  29. What Kind of Science is Simulation?Robb Eason, Robert Rosenberger, Trina Kokalis, Evan Selinger & Patrick Grim - 2007 - Journal for Experimental and Theoretical Artificial Intelligence 19:19-28.
    Is simulation some new kind of science? We argue that instead simulation fits smoothly into existing scientific practice, but does so in several importantly different ways. Simulations in general, and computer simulations in particular, ought to be understood as techniques which, like many scientific techniques, can be employed in the service of various and diverse epistemic goals. We focus our attentions on the way in which simulations can function as (i) explanatory and (ii) predictive tools. We argue (...)
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  30. Simulation Models of the Evolution of Cooperation as Proofs of Logical Possibilities. How Useful Are They?Eckhart Arnold - 2013 - Etica E Politica 15 (2):101-138.
    This paper discusses critically what simulation models of the evolution ofcooperation can possibly prove by examining Axelrod’s “Evolution of Cooperation” and the modeling tradition it has inspired. Hardly any of the many simulation models of the evolution of cooperation in this tradition have been applicable empirically. Axelrod’s role model suggested a research design that seemingly allowed to draw general conclusions from simulation models even if the mechanisms that drive the simulation could not be identified empirically. But this (...)
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  31.  90
    Computing, Modelling, and Scientific Practice: Foundational Analyses and Limitations.Philippos Papayannopoulos - 2018 - Dissertation,
    This dissertation examines aspects of the interplay between computing and scientific practice. The appropriate foundational framework for such an endeavour is rather real computability than the classical computability theory. This is so because physical sciences, engineering, and applied mathematics mostly employ functions defined in continuous domains. But, contrary to the case of computation over natural numbers, there is no universally accepted framework for real computation; rather, there are two incompatible approaches --computable analysis and BSS model--, both claiming to formalise algorithmic (...)
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  32. Classification of Approaches to Technological Resurrection.Alexey Turchin & Chernyakov Maxim - manuscript
    Abstract. Death seems to be a permanent event, but there is no actual proof of its irreversibility. Here we list all known ways to resurrect the dead that do not contradict our current scientific understanding of the world. While no method is currently possible, many of those listed here may become feasible with future technological development, and it may even be possible to act now to increase their probability. The most well-known such approach to technological resurrection is cryonics. Another method (...)
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  33.  39
    How Models Fail. A Critical Look at the History of Computer Simulations of the Evolution of Cooperation.Eckhart Arnold - 2015 - In Catrin Misselhorn (ed.), Collective Agency and Cooperation in Natural and Artificial Systems. Explanation, Implementation and Simulation, Philosophical Studies Series. Springer. pp. 261-279.
    Simulation models of the Reiterated Prisoner's Dilemma have been popular for studying the evolution of cooperation since more than 30 years now. However, there have been practically no successful instances of empirical application of any of these models. At the same time this lack of empirical testing and confirmation has almost entirely been ignored by the modelers community. In this paper, I examine some of the typical narratives and standard arguments with which these models are justified by (...)
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  34. Computer Simulation and the Features of Novel Empirical Data.Greg Lusk - 2016 - Studies in History and Philosophy of Science Part A 56:145-152.
    In an attempt to determine the epistemic status of computer simulation results, philosophers of science have recently explored the similarities and differences between computer simulations and experiments. One question that arises is whether and, if so, when, simulation results constitute novel empirical data. It is often supposed that computer simulation results could never be empirical or novel because simulations never interact with their targets, and cannot go beyond their programming. This paper argues against this position by examining whether, (...)
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  35. Diagnosis of Blood Cells Using Deep Learning.Ahmed J. Khalil & Samy S. Abu-Naser - 2022 - International Journal of Academic Engineering Research (IJAER) 6 (2):69-84.
    In computer science, Artificial Intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Deep Learning is a new field of research. One of the branches of Artificial Intelligence Science deals with the creation of theories and algorithms that (...)
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  36. The Interplay Between Models and Observations.Claudio Masolo, Alessander Botti Benevides & Daniele Porello - 2018 - Applied ontology 13 (1):41-71.
    We propose a formal framework to examine the relationship between models and observations. To make our analysis precise,models are reduced to first-order theories that represent both terminological knowledge – e.g., the laws that are supposed to regulate the domain under analysis and that allow for explanations, predictions, and simulations – and assertional knowledge – e.g., information about specific entities in the domain of interest. Observations are introduced into the domain of quantification of a distinct first-order theory that (...)
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  37.  9
    Classification of Age and Gender Using ResNet - Deep Learning.Aysha I. Mansour & Samy S. Abu-Naser - 2022 - International Journal of Academic Engineering Research (IJAER) 6 (8):20-29.
    Age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Even Nevertheless, contrast to the large performance improvements recently reported for the closely related task of audio. In this research, we show that performance on these tasks can be significantly improved by learning representations using deep convolutional neural networks (CNN). where we get in the ResNet the training accuracy was 98% ,validation accuracy 95%, testing accuracy 96% (...)
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  38. Taming the Tyranny of Scales: Models and Scale in the Geosciences.Alisa Bokulich - 2021 - Synthese 199 (5-6):14167-14199.
    While the predominant focus of the philosophical literature on scientific modeling has been on single-scale models, most systems in nature exhibit complex multiscale behavior, requiring new modeling methods. This challenge of modeling phenomena across a vast range of spatial and temporal scales has been called the tyranny of scales problem. Drawing on research in the geosciences, I synthesize and analyze a number of strategies for taming this tyranny in the context of conceptual, physical, and mathematical modeling. This includes several (...)
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  39. Refounding of the Activity Concept? Towards a Federative Paradigm for Modeling and Simulation.Alexandre Muzy, Franck Varenne, Bernard P. Zeigler, Jonathan Caux, Patrick Coquillard, Luc Touraille, Dominique Prunetti, Philippe Caillou, Olivier Michel & David R. C. Hill - 2013 - Simulation - Transactions of the Society for Modeling and Simulation International 89 (2):156-177.
    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 new simulation activity definition (...)
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  40. How Models Fail. A Critical Look at the History of Computer Simulations of the Evolution of Cooperation.Catrin Misselhorn (ed.) - 2015 - Springer.
    Simulation models of the Reiterated Prisoner's Dilemma have been popular for studying the evolution of cooperation since more than 30 years now. However, there have been practically no successful instances of empirical application of any of these models. At the same time this lack of empirical testing and confirmation has almost entirely been ignored by the modelers community. In this paper, I examine some of the typical narratives and standard arguments with which these models are justified by (...)
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  41.  89
    How Modeling Can Go Wrong: Some Cautions and Caveats on the Use of Models.Patrick Grim & Nicholas Rescher - 2013 - Philosophy and Technology 26 (1):75-80.
    Modeling and simulation clearly have an upside. My discussion here will deal with the inevitable downside of modeling — the sort of things that can go wrong. It will set out a taxonomy for the pathology of models — a catalogue of the various ways in which model contrivance can go awry. In the course of that discussion, I also call on some of my past experience with models and their vulnerabilities.
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  42. [deleted]Classification of Mushroom Using Artificial Neural Network.Alkronz Sameh, Meimeh Moghayer, Gazaz Mohanad & AlKahlout Mohammad - 2020 - International Journal of Academic and Applied Research (IJAAR) 3 (2):1-5.
    Predication is an application of Artificial Neural Network (ANN). It is a supervised learning due to predefined input and output attributes. Multi-Layer ANN model is used for training, validating, and testing of the data. In this paper, Multi-Layer ANN model was used to train and test the mushroom dataset to predict whether it is edible or poisonous. The Mushrooms dataset was prepared for training, 8124 instances were used for the training. JustNN software was used to training and validating the data. (...)
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  43.  23
    Opinion Dynamics and Bounded Confidence: Models, Analysis and Simulation.Hegselmann Rainer & Ulrich Krause - 2002 - Journal of Artificial Societies and Social Simulation 5 (3).
    When does opinion formation within an interacting group lead to consensus, polarization or fragmentation? The article investigates various models for the dynamics of continuous opinions by analytical methods as well as by computer simulations. Section 2 develops within a unified framework the classical model of consensus formation, the variant of this model due to Friedkin and Johnsen, a time-dependent version and a nonlinear version with bounded confidence of the agents. Section 3 presents for all these models major (...)
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  44. Programming Relativity and Gravity Via a Discrete Pixel Space in Planck Level Simulation Hypothesis Models.Malcolm J. Macleod - manuscript
    Outlined here is a simulation hypothesis approach that uses an expanding (the simulation clock-rate measured in units of Planck time) 4-axis hyper-sphere and mathematical particles that oscillate between an electric wave-state and a mass (unit of Planck mass per unit of Planck time) point-state. Particles are assigned a spin axis which determines the direction in which they are pulled by this (hyper-sphere pilot wave) expansion, thus all particles travel at, and only at, the velocity of expansion (the origin of $c$), (...)
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  45. A FORMAL CONCEPT OF CULTURE IN THE CLASSIFICATION OF ALFRED L. KROEBER AND CLYDE KLUCKHOHN.Boroch Robert - 2016 - Analecta 25 (2):61-101.
    The objective of this article is to analyse definitions of culture gathered by Alfred L. Kroeber and Clyde Kluckhohn and published in Culture. A Critical Review of Concepts and Definitions in 1952. This article emphasizes a possibility of re-analysing the material collected by these researchers (Kroeber–Kluckhohn Culture Classification, hereinafter referred to as KKCC). The article shows that the KKCC material constitutes a coherent conceptual and theoretical paradigm. This paradigm was subject to contextual, frequential and conceptual (Formal Conceptual Analysis, hereinafter (...)
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  46. Universal Etiology, Multifactorial Diseases and the Constitutive Model of Disease Classification.Jonathan Fuller - 2018 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 67:8-15.
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  47. The Logic of the Method of Agent-Based Simulation in the Social Sciences: Empirical and Intentional Adequacy of Computer Programs.Nuno David, Jaime Sichman & Helder Coleho - 2005 - Journal of Artificial Societies and Social Simulation 8 (4).
    The classical theory of computation does not represent an adequate model of reality for simulation in the social sciences. The aim of this paper is to construct a methodological perspective that is able to conciliate the formal and empirical logic of program verification in computer science, with the interpretative and multiparadigmatic logic of the social sciences. We attempt to evaluate whether social simulation implies an additional perspective about the way one can understand the concepts of program and computation. We demonstrate (...)
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  48. Confirmation and Robustness of Climate Models.Elisabeth A. Lloyd - 2010 - Philosophy of Science 77 (5):971–984.
    Recent philosophical attention to climate models has highlighted their weaknesses and uncertainties. Here I address the ways that models gain support through observational data. I review examples of model fit, variety of evidence, and independent support for aspects of the models, contrasting my analysis with that of other philosophers. I also investigate model robustness, which often emerges when comparing climate models simulating the same time period or set of conditions. Starting from Michael Weisberg’s analysis of robustness, (...)
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  49. Why Simpler Computer Simulation Models Can Be Epistemically Better for Informing Decisions.Casey Helgeson, Vivek Srikrishnan, Klaus Keller & Nancy Tuana - 2021 - Philosophy of Science 88 (2):213-233.
    For computer simulation models to usefully inform climate risk management, uncertainties in model projections must be explored and characterized. Because doing so requires running the model many ti...
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  50. THE IMAGINATIVE REHEARSAL MODEL – DEWEY, EMBODIED SIMULATION, AND THE NARRATIVE HYPOTHESIS.Italo Testa - 2017 - Pragmatism Today 8 (1):105-112.
    In this contribution I outline some ideas on what the pragmatist model of habit ontology could offer us as regards the appreciation of the constitutive role that imagery plays for social action and cognition. Accordingly, a Deweyan understanding of habit would allow for an understanding of imagery in terms of embodied cognition rather than in representational terms. I first underline the motor character of imagery, and the role its embodiment in habit plays for the anticipation of action. Secondly, I reconstruct (...)
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