Results for 'Computer models'

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  1.  71
    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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  2. 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 (...)
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  3. Computational Models (of Narrative) for Literary Studies.Antonio Lieto - 2015 - Semicerchio, Rivista di Poesia Comparata 2 (LIII):38-44.
    In the last decades a growing body of literature in Artificial Intelligence (AI) and Cognitive Science (CS) has approached the problem of narrative understanding by means of computational systems. Narrative, in fact, is an ubiquitous element in our everyday activity and the ability to generate and understand stories, and their structures, is a crucial cue of our intelligence. However, despite the fact that - from an historical standpoint - narrative (and narrative structures) have been an important topic of investigation in (...)
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  4. Computer Models of Constitutive Social Practices.Richard Evans - 2016 - In Vincent Müller (ed.), Fundamental Issues of Artificial Intelligence. Springer. pp. 389-409.
    Research in multi-agent systems typically assumes a regulative model of social practice. This model starts with agents who are already capable of acting autonomously to further their individual ends. A social practice, according to this view, is a way of achieving coordination between multiple agents by restricting the set of actions available. For example, in a world containing cars but no driving regulations, agents are free to drive on either side of the road. To prevent collisions, we introduce driving regulations, (...)
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  5. Psychological and Computational Models of Language Comprehension: In Defense of the Psychological Reality of Syntax.David Pereplyotchik - 2011 - Croatian Journal of Philosophy 11 (1):31-72.
    In this paper, I argue for a modified version of what Devitt calls the Representational Thesis. According to RT, syntactic rules or principles are psychologically real, in the sense that they are represented in the mind/brain of every linguistically competent speaker/hearer. I present a range of behavioral and neurophysiological evidence for the claim that the human sentence processing mechanism constructs mental representations of the syntactic properties of linguistic stimuli. I then survey a range of psychologically plausible computational models of (...)
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  6. Classical Computational Models.Richard Samuels - 2018 - In Mark Sprevak & Matteo Colombo (ed.), The Routledge Handbook of the Computational Mind. Oxford, UK: pp. 103-119.
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  7. The Nature and Function of Content in Computational Models.Frances Egan - 2018 - In Mark Sprevak & Matteo Colombo (eds.), The Routledge Handbook of the Computational Mind. Routledge.
    Much of computational cognitive science construes human cognitive capacities as representational capacities, or as involving representation in some way. Computational theories of vision, for example, typically posit structures that represent edges in the distal scene. Neurons are often said to represent elements of their receptive fields. Despite the ubiquity of representational talk in computational theorizing there is surprisingly little consensus about how such claims are to be understood. The point of this chapter is to sketch an account of the nature (...)
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  8.  25
    A Computational Model of Affects.Mika Turkia - 2009 - In D. Dietrich, G. Fodor, G. Zucker & D. Bruckner (eds.), Simulating the mind: A technical neuropsychoanalytical approach. pp. 277-289.
    Emotions and feelings (i.e. affects) are a central feature of human behavior. Due to complexity and interdisciplinarity of affective phenomena, attempts to define them have often been unsatisfactory. This article provides a simple logical structure, in which affective concepts can be defined. The set of affects defined is similar to the set of emotions covered in the OCC model, but the model presented in this article is fully computationally defined, whereas the OCC model depends on undefined concepts. Following Matthis, affects (...)
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  9.  71
    Tacit Knowledg and the Problem of Computer Modelling Cognitive Processes in Science.Stephen Turner - 1989 - In Steve Fuller (ed.), The Cognitive Turn: Sociological and Psychological Perspectives on Science. Kluwer Academic Publishers.
    In what follows I propose to bring out certain methodological properties of projects of modelling the tacit realm that bear on the kinds of modelling done in connection with scientific cognition by computer as well as by ethnomethodological sociologists, both of whom must make some claims about the tacit in the course of their efforts to model cognition. The same issues, I will suggest, bear on the project of a cognitive psychology of science as well.
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  10. From Silico to Vitro: Computational Models of Complex Biological Systems Reveal Real-World Emergent Phenomena.Orly Stettiner - 2014 - In Vincent C. Muller (ed.), Computing and Philosophy, Selected Papaers from IACAP 2014. Springer. pp. 133-147.
    Computer simulations constitute a significant scientific tool for promoting scientific understanding of natural phenomena and dynamic processes. Substantial leaps in computational force and software engineering methodologies now allow the design and development of large-scale biological models, which – when combined with advanced graphics tools – may produce realistic biological scenarios, that reveal new scientific explanations and knowledge about real life phenomena. A state-of-the-art simulation system termed Reactive Animation (RA) will serve as a study case to examine the contemporary (...)
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  11.  41
    A Shift From Cloud Computing Model to Fog Computing.C. Sailesh & S. Svermani - 2016 - Journal of Applied Computing 1 (1).
    Cloud computing has provided many opportunities to businesses and individuals. It enables global and on demand network access to a shared pool of resources with minimal management effort. However, this bliss has become a problem for latency-sensitive applications. To improve efficiency of cloud and to reduce the amount of data that needs to be transported to the cloud for data processing, analysis and storage, a new network architect technology 'Fog Computing' has been introduced. In fog computing, small applications and resources (...)
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  12. Hydrates Production Prediction With Computer Modelling Group (CMG) Stars. A Comprehensive Review.Daudi Matungwa Katabaro & Wang Jinjie - 2018 - International Journal of Academic Multidisciplinary Research (IJAMR) 2 (11):24-30.
    Abstract: Hydrates are an enormous energy resource with global circulation in the permafrost and in the oceans. Even if conventional estimates are deliberated and only a small fraction is recoverable, the pure size of the resource is so huge that it demands assessment as a potential energy source. In this research work, we discuss the hydrate production prediction with Computer Modeling Group STARS (CMG STARS). In this paper different literatures reviews have been visited concerning hydrate production prediction with CMG (...)
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  13.  13
    Models, Parameterization, and Software: Epistemic Opacity in Computational Chemistry.Frédéric Wieber & Alexandre Hocquet - 2020 - Perspectives on Science 28 (5):610-629.
    Computational chemistry grew in a new era of “desktop modeling,” which coincided with a growing demand for modeling software, especially from the pharmaceutical industry. Parameterization of models in computational chemistry is an arduous enterprise, and we argue that this activity leads, in this specific context, to tensions among scientists regarding the epistemic opacity transparency of parameterized methods and the software implementing them. We relate one flame war from the Computational Chemistry mailing List in order to assess in detail the (...)
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  14. Computational Mechanisms and Models of Computation.Marcin Miłkowski - 2014 - Philosophia Scientiae 18:215-228.
    In most accounts of realization of computational processes by physical mechanisms, it is presupposed that there is one-to-one correspondence between the causally active states of the physical process and the states of the computation. Yet such proposals either stipulate that only one model of computation is implemented, or they do not reflect upon the variety of models that could be implemented physically. -/- In this paper, I claim that mechanistic accounts of computation should allow for a broad variation of (...)
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  15. Computational Mechanisms and Models of Computation.Marcin Miłkowski - 2014 - Philosophia Scientae 18:215-228.
    In most accounts of realization of computational processes by physical mechanisms, it is presupposed that there is one-to-one correspondence between the causally active states of the physical process and the states of the computation. Yet such proposals either stipulate that only one model of computation is implemented, or they do not reflect upon the variety of models that could be implemented physically. In this paper, I claim that mechanistic accounts of computation should allow for a broad variation of (...) of computation. In particular, some non-standard models should not be excluded a priori. The relationship between mathematical models of computation and mechanistically adequate models is studied in more detail. (shrink)
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  16.  17
    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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  17. Quantum Computer: Quantum Model and Reality.Vasil Penchev - 2020 - Epistemology eJournal (Elsevier: SSRN) 13 (17):1-7.
    Any computer can create a model of reality. The hypothesis that quantum computer can generate such a model designated as quantum, which coincides with the modeled reality, is discussed. Its reasons are the theorems about the absence of “hidden variables” in quantum mechanics. The quantum modeling requires the axiom of choice. The following conclusions are deduced from the hypothesis. A quantum model unlike a classical model can coincide with reality. Reality can be interpreted as a quantum computer. (...)
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  18. HCI Model with Learning Mechanism for Cooperative Design in Pervasive Computing Environment.Hong Liu, Bin Hu & Philip Moore - 2015 - Journal of Internet Technology 16.
    This paper presents a human-computer interaction model with a three layers learning mechanism in a pervasive environment. We begin with a discussion around a number of important issues related to human-computer interaction followed by a description of the architecture for a multi-agent cooperative design system for pervasive computing environment. We present our proposed three- layer HCI model and introduce the group formation algorithm, which is predicated on a dynamic sharing niche technology. Finally, we explore the cooperative reinforcement learning (...)
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  19. 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 (...)
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  20. 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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  21. 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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  22. A Mathematical Model of Quantum Computer by Both Arithmetic and Set Theory.Vasil Penchev - 2020 - Information Theory and Research eJournal 1 (15):1-13.
    A practical viewpoint links reality, representation, and language to calculation by the concept of Turing (1936) machine being the mathematical model of our computers. After the Gödel incompleteness theorems (1931) or the insolvability of the so-called halting problem (Turing 1936; Church 1936) as to a classical machine of Turing, one of the simplest hypotheses is completeness to be suggested for two ones. That is consistent with the provability of completeness by means of two independent Peano arithmetics discussed in Section I. (...)
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  23. From Computer Metaphor to Computational Modeling: The Evolution of Computationalism.Marcin Miłkowski - 2018 - Minds and Machines 28 (3):515-541.
    In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working (...)
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  24.  61
    The Role of Mental Rotation in TetrisTM Gameplay: An ACT-R Computational Cognitive Model.Antonio Lieto - 2022 - Cognitive Systems Research 40 (1):1-38.
    The mental rotation ability is an essential spatial reasoning skill in human cognition and has proven to be an essential predictor of mathematical and STEM skills, critical and computational thinking. Despite its importance, little is known about when and how mental rotation processes are activated in games explicitly targeting spatial reasoning tasks. In particular, the relationship between spatial abilities and TetrisTM has been analysed several times in the literature. However, these analyses have shown contrasting results between the effectiveness of Tetris-based (...)
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  25.  44
    Use of Cloud Computing in University Libraries In View of the Technology Acceptance Model.Ahmewd L. Ferdi - 2017 - Iraqi Journal for Information 8 (12):98-131.
    Cloud computing is considered as a new type of technology, in fact, it is an extension of the information technology's developments which are based on the pooling of resources and infrastructure to provide services depend on using the cloud, in the sense that instead of these services and resources exist on local servers or personal devices, they are gathered in the cloud and be shared on the Internet. This technology has achieved an economic success no one can deny it and (...)
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  26. 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 (...)
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  27. 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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  28. 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 inaccuracies (...)
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  29. Manufacturing Morality A General Theory of Moral Agency Grounding Computational Implementations: The ACTWith Model.Jeffrey White - 2013 - In Floares (ed.), Computational Intelligence. Nova Publications. pp. 1-65.
    The ultimate goal of research into computational intelligence is the construction of a fully embodied and fully autonomous artificial agent. This ultimate artificial agent must not only be able to act, but it must be able to act morally. In order to realize this goal, a number of challenges must be met, and a number of questions must be answered, the upshot being that, in doing so, the form of agency to which we must aim in developing artificial agents comes (...)
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  30. Information, Computation, Cognition. Agency-Based Hierarchies of Levels.Gordana Dodig-Crnkovic - 2016 - In Vincent Müller (ed.), Fundamental Issues of Artificial Intelligence. Zurich: Springer. pp. 139-159.
    This paper connects information with computation and cognition via concept of agents that appear at variety of levels of organization of physical/chemical/cognitive systems – from elementary particles to atoms, molecules, life-like chemical systems, to cognitive systems starting with living cells, up to organisms and ecologies. In order to obtain this generalized framework, concepts of information, computation and cognition are generalized. In this framework, nature can be seen as informational structure with computational dynamics, where an (info-computational) agent is needed for the (...)
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  31. Models and Minds.Stuart C. Shapiro & William J. Rapaport - 1991 - In Robert E. Cummins & John L. Pollock (eds.), Philosophy and AI. Cambridge: MIT Press. pp. 215--259.
    Cognitive agents, whether human or computer, that engage in natural-language discourse and that have beliefs about the beliefs of other cognitive agents must be able to represent objects the way they believe them to be and the way they believe others believe them to be. They must be able to represent other cognitive agents both as objects of beliefs and as agents of beliefs. They must be able to represent their own beliefs, and they must be able to represent (...)
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  32. Morphological Computation: Nothing but Physical Computation.Marcin Miłkowski - 2018 - Entropy 10 (20):942.
    The purpose of this paper is to argue against the claim that morphological computation is substantially different from other kinds of physical computation. I show that some (but not all) purported cases of morphological computation do not count as specifically computational, and that those that do are solely physical computational systems. These latter cases are not, however, specific enough: all computational systems, not only morphological ones, may (and sometimes should) be studied in various ways, including their energy efficiency, cost, reliability, (...)
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  33.  42
    Neural Computation of Surface Border Ownership and Relative Surface Depth From Ambiguous Contrast Inputs.Birgitta Dresp-Langley & Stephen Grossberg - 2016 - Frontiers in Psychology 7.
    The segregation of image parts into foreground and background is an important aspect of the neural computation of 3D scene perception. To achieve such segregation, the brain needs information about border ownership; that is, the belongingness of a contour to a specific surface represented in the image. This article presents psychophysical data derived from 3D percepts of figure and ground that were generated by presenting 2D images composed of spatially disjoint shapes that pointed inward or outward relative to the continuous (...)
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  34. Computing and Philosophy: Selected Papers From IACAP 2014.Vincent C. Müller (ed.) - 2016 - Springer.
    This volume offers very selected papers from the 2014 conference of the “International Association for Computing and Philosophy” (IACAP) - a conference tradition of 28 years. - - - Table of Contents - 0 Vincent C. Müller: - Editorial - 1) Philosophy of computing - 1 Çem Bozsahin: - What is a computational constraint? - 2 Joe Dewhurst: - Computing Mechanisms and Autopoietic Systems - 3 Vincenzo Fano, Pierluigi Graziani, Roberto Macrelli and Gino Tarozzi: - Are Gandy Machines really local? (...)
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  35. 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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  36. Computers, Dynamical Systems, Phenomena, and the Mind.Marco Giunti - 1992 - Dissertation, Indiana University
    This work addresses a broad range of questions which belong to four fields: computation theory, general philosophy of science, philosophy of cognitive science, and philosophy of mind. Dynamical system theory provides the framework for a unified treatment of these questions. ;The main goal of this dissertation is to propose a new view of the aims and methods of cognitive science--the dynamical approach . According to this view, the object of cognitive science is a particular set of dynamical systems, which I (...)
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  37. The Nature of Computational Things.Franck Varenne - 2013 - In Frédéric Migayrou Brayer & Marie-Ange (eds.), Naturalizing Architecture. Orléans: HYX Editions. pp. 96-105.
    Architecture often relies on mathematical models, if only to anticipate the physical behavior of structures. Accordingly, mathematical modeling serves to find an optimal form given certain constraints, constraints themselves translated into a language which must be homogeneous to that of the model in order for resolution to be possible. Traditional modeling tied to design and architecture thus appears linked to a topdown vision of creation, of the modernist, voluntarist and uniformly normative type, because usually (mono)functionalist. One available instrument of (...)
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  38. Situatedness and Embodiment of Computational Systems.Marcin Miłkowski - 2017 - Entropy 19 (4):162.
    In this paper, the role of the environment and physical embodiment of computational systems for explanatory purposes will be analyzed. In particular, the focus will be on cognitive computational systems, understood in terms of mechanisms that manipulate semantic information. It will be argued that the role of the environment has long been appreciated, in particular in the work of Herbert A. Simon, which has inspired the mechanistic view on explanation. From Simon’s perspective, the embodied view on cognition seems natural but (...)
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  39.  47
    Model Templates Within and Between Disciplines: From Magnets to Gases – and Socio-Economic Systems.Tarja Knuuttila & Andrea Loettgers - 2016 - European Journal for Philosophy of Science 6 (3):377-400.
    One striking feature of the contemporary modelling practice is its interdisciplinary nature. The same equation forms, and mathematical and computational methods, are used across different disciplines, as well as within the same discipline. Are there, then, differences between intra- and interdisciplinary transfer, and can the comparison between the two provide more insight on the challenges of interdisciplinary theoretical work? We will study the development and various uses of the Ising model within physics, contrasting them to its applications to socio-economic systems. (...)
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  40. The Computational Modeling of Inferential and Referential Competence.Fabrizio Calzavarini & Antonio Lieto - 2018 - In AISC 2018 Proceedings.
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  41. Agent-Based Computational Economics: A Constructive Approach to Economic Theory.Leigh Tesfatsion - 2006 - In Leigh Tesfatsion & Kenneth L. Judd (eds.), Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics. Elsevier.
    Economies are complicated systems encompassing micro behaviors, interaction patterns, and global regularities. Whether partial or general in scope, studies of economic systems must consider how to handle difficult real-world aspects such as asymmetric information, imperfect competition, strategic interaction, collective learning, and the possibility of multiple equilibria. Recent advances in analytical and computational tools are permitting new approaches to the quantitative study of these aspects. One such approach is Agent-based Computational Economics (ACE), the computational study of economic processes modeled as dynamic (...)
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  42. The Computable Universe: From Prespace Metaphysics to Discrete Quantum Mechanics.Martin Leckey - 1997 - Dissertation, Monash University
    The central motivating idea behind the development of this work is the concept of prespace, a hypothetical structure that is postulated by some physicists to underlie the fabric of space or space-time. I consider how such a structure could relate to space and space-time, and the rest of reality as we know it, and the implications of the existence of this structure for quantum theory. Understanding how this structure could relate to space and to the rest of reality requires, I (...)
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  43.  86
    Computer Modeling and the Fate of Folk Psychology.John A. Barker - 2002 - Metaphilosophy 33 (1-2):30-48.
    Although Paul Churchland and Jerry Fodor both subscribe to the so-called theory-theory– the theory that folk psychology (FP) is an empirical theory of behavior – they disagree strongly about FP’s fate. Churchland contends that FP is a fundamentally flawed view analogous to folk biology, and he argues that recent advances in computational neuroscience and connectionist AI point toward development of a scientifically respectable replacement theory that will give rise to a new common-sense psychology. Fodor, however, wagers that FP will be (...)
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  44. Cognitive and Computer Systems for Understanding Narrative Text.William J. Rapaport, Erwin M. Segal, Stuart C. Shapiro, David A. Zubin, Gail A. Bruder, Judith Felson Duchan & David M. Mark - manuscript
    This project continues our interdisciplinary research into computational and cognitive aspects of narrative comprehension. Our ultimate goal is the development of a computational theory of how humans understand narrative texts. The theory will be informed by joint research from the viewpoints of linguistics, cognitive psychology, the study of language acquisition, literary theory, geography, philosophy, and artificial intelligence. The linguists, literary theorists, and geographers in our group are developing theories of narrative language and spatial understanding that are being tested by the (...)
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  45. Replicability or Reproducibility? On the Replication Crisis in Computational Neuroscience and Sharing Only Relevant Detail.Marcin Miłkowski, Witold M. Hensel & Mateusz Hohol - 2018 - Journal of Computational Neuroscience 3 (45):163-172.
    Replicability and reproducibility of computational models has been somewhat understudied by “the replication movement.” In this paper, we draw on methodological studies into the replicability of psychological experiments and on the mechanistic account of explanation to analyze the functions of model replications and model reproductions in computational neuroscience. We contend that model replicability, or independent researchers' ability to obtain the same output using original code and data, and model reproducibility, or independent researchers' ability to recreate a model without original (...)
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  46. A Cognitive Computation Fallacy? Cognition, Computations and Panpsychism.John Mark Bishop - 2009 - Cognitive Computation 1 (3):221-233.
    The journal of Cognitive Computation is defined in part by the notion that biologically inspired computational accounts are at the heart of cognitive processes in both natural and artificial systems. Many studies of various important aspects of cognition (memory, observational learning, decision making, reward prediction learning, attention control, etc.) have been made by modelling the various experimental results using ever-more sophisticated computer programs. In this manner progressive inroads have been made into gaining a better understanding of the many components (...)
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  47. Models of Moral Cognition.Jeffrey White - 2013 - In Lorenzo Magnani (ed.), Model-Based Reasoning in Science and Technology, 1. springer. pp. last 20.
    3 Abstract This paper is about modeling morality, with a proposal as to the best 4 way to do it. There is the small problem, however, in continuing disagreements 5 over what morality actually is, and so what is worth modeling. This paper resolves 6 this problem around an understanding of the purpose of a moral model, and from 7 this purpose approaches the best way to model morality.
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  48. ANN Model for Predicting Protein Localization Sites in Cells.Mohammed Nafez Abu Samra, Bilal Ezz El-Din Abed, Hossam Abdel Nasser Zaqout & Samy S. Abu-Naser - 2020 - International Journal of Academic and Applied Research (IJAAR) 4 (9):43-50.
    To automate examination of massive amounts of sequence data for biological function, it is important to computerize interpretation based on empirical knowledge of sequence-function relationships. For this purpose, we have been constructing an Artificial Neural Network (ANN) by organizing various experimental and computational observations as a collection ANN models. Here we propose an ANN model which utilizes the Dataset for UCI Machine Learning Repository, for predicting localization sites of proteins. We collected data for 336 proteins with known localization sites (...)
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  49.  59
    Alife Models as Epistemic Artefacts.Xabier Barandiaran & Alvaro Moreno - 2006 - In Luis Rocha, Larry Yaeger & Mark Bedau (eds.), Artificial Life X : Proceedings of the Tenth International Conference on the Simulation and Synthesis of Living Systems. MIT Press. pp. 513-519.
    Both the irreducible complexity of biological phenomena and the aim of a universalized biology (life-as-it-could-be) have lead to a deep methodological shift in the study of life; represented by the appearance of ALife, with its claim that computational modelling is the main tool for studying the general principles of biological phenomenology. However this methodological shift implies important questions concerning the aesthetic, engineering and specially the epistemological status of computational models in scientific research: halfway between the well established categories of (...)
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  50. Counterpossibles in Science: The Case of Relative Computability.Matthias Jenny - 2018 - Noûs 52 (3):530-560.
    I develop a theory of counterfactuals about relative computability, i.e. counterfactuals such as 'If the validity problem were algorithmically decidable, then the halting problem would also be algorithmically decidable,' which is true, and 'If the validity problem were algorithmically decidable, then arithmetical truth would also be algorithmically decidable,' which is false. These counterfactuals are counterpossibles, i.e. they have metaphysically impossible antecedents. They thus pose a challenge to the orthodoxy about counterfactuals, which would treat them as uniformly true. What’s more, I (...)
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