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  1. An Evolutionary Argument for a Self-Explanatory, Benevolent Metaphysics.Ward Blondé - 2015 - Symposion: Theoretical and Applied Inquiries in Philosophy and Social Sciences 2 (2):143-166.
    In this paper, a metaphysics is proposed that includes everything that can be represented by a well-founded multiset. It is shown that this metaphysics, apart from being self-explanatory, is also benevolent. Paradoxically, it turns out that the probability that we were born in another life than our own is zero. More insights are gained by inducing properties from a metaphysics that is not self-explanatory. In particular, digital metaphysics is analyzed, which claims that only computable things exist. First of all, it (...)
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  2. 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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  3. Psychological and Other Aspects of the Sign Arbitrariness.Miroslav Brada - 2017 - le Cours de Linguistique Générale 1916-2016.
    I confront arbitrariness of the sign to a criterion assessing the quality of language, logical system, psychometrics and art.
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  4. Inexplicit Information.Robert C. Cummins - 1986 - In Myles Brand & Robert M. Harnish (eds.), The Representation of Knowledge and Belief. University of Arizona Press.
    A discussion of a number of ways that information can be present in a computer program without being explicitly represented.
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  5. Validation and Verification in Social Simulation: Patterns and Clarification of Terminology.Nuno David - 2009 - Epistemological Aspects of Computer Simulation in the Social Sciences, EPOS 2006, Revised Selected and Invited Papers, Lecture Notes in Artificial Intelligence, Squazzoni, Flaminio (Ed.) 5466:117-129.
    The terms ‘verification’ and ‘validation’ are widely used in science, both in the natural and the social sciences. They are extensively used in simulation, often associated with the need to evaluate models in different stages of the simulation development process. Frequently, terminological ambiguities arise when researchers conflate, along the simulation development process, the technical meanings of both terms with other meanings found in the philosophy of science and the social sciences. This article considers the problem of verification and validation in (...)
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  6. 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, and to identify (...)
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  7. Simulation as Formal and Generative Social Science: The Very Idea.Nuno David, Jaime Sichman & Helder Coelho - 2007 - In Carlos Gershenson, Diederik Aerts & Bruce Edmonds (eds.), Worldviews, Science, and Us: Philosophy and Complexity. World Scientific. pp. 266--275.
    The formal and empirical-generative perspectives of computation are demonstrated to be inadequate to secure the goals of simulation in the social sciences. Simulation does not resemble formal demonstrations or generative mechanisms that deductively explain how certain models are sufficient to generate emergent macrostructures of interest. The description of scientific practice implies additional epistemic conceptions of scientific knowledge. Three kinds of knowledge that account for a comprehensive description of the discipline were identified: formal, empirical and intentional knowledge. The use of formal (...)
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  8. 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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  9. A Contradiction and P=NP Problem.Farzad Didehvar - manuscript
    Here, by introducing a version of “Unexpected hanging paradox” first we try to open a new way and a new explanation for paradoxes, similar to liar paradox. Also, we will show that we have a semantic situation which no syntactical logical system could support it. Finally, we propose a claim in Theory of Computation about the consistency of this Theory. One of the major claim is:Theory of Computation and Classical Logic leads us to a contradiction.
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  10. Gaming the Attention Economy.Daniel Estrada & Jon Lawhead - 2013 - In Pietro Michelucci (ed.), The Handbook of Human Computation. Springer. pp. 961-978.
    The future of human computation benefits from examining tasks that agents already perform and designing environments to give those tasks computational significance. We call this natural human computation. We consider the possible future of NHC through the lens of Swarm!, an application under development for Google Glass. Swarm! motivates users to compute the solutions to a class of economic optimization problems by engaging the attention dynamics of crowds. We argue that anticipating and managing economies of attention provides one of the (...)
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  11. Opinions and Outlooks on Morphological Computation.Helmut Hauser, Rudolf M. Füchslin & Rolf Pfeifer (eds.) - 2014 - E-Book.
    Morphological Computation is based on the observation that biological systems seem to carry out relevant computations with their morphology (physical body) in order to successfully interact with their environments. This can be observed in a whole range of systems and at many different scales. It has been studied in animals – e.g., while running, the functionality of coping with impact and slight unevenness in the ground is "delivered" by the shape of the legs and the damped elasticity of the muscle-tendon (...)
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  12. Is There Any Real Substance to the Claims for a 'New Computationalism'?Alberto Hernandez-Espinosa, Hernandez-Quiroz Francisco & Zenil Hector - forthcoming - In CiE Computability in Europe 2017. Springer Verlag.
    'Computationalism' is a relatively vague term used to describe attempts to apply Turing's model of computation to phenomena outside its original purview: in modelling the human mind, in physics, mathematics, etc. Early versions of computationalism faced strong objections from many (and varied) quarters, from philosophers to practitioners of the aforementioned disciplines. Here we will not address the fundamental question of whether computational models are appropriate for describing some or all of the wide range of processes that they have been applied (...)
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  13. Simple or Complex Bodies? Trade-Offs in Exploiting Body Morphology for Control.Matej Hoffmann & Vincent C. Müller - 2017 - In Gordana Dodig-Crnkovic & Raffaela Giovagnoli (eds.), Representation of Reality: Humans, Other Living Organisms and Intelligent Machines. Berlin: Springer. pp. 335-345.
    Engineers fine-tune the design of robot bodies for control purposes, however, a methodology or set of tools is largely absent, and optimization of morphology (shape, material properties of robot bodies, etc.) is lagging behind the development of controllers. This has become even more prominent with the advent of compliant, deformable or ”soft” bodies. These carry substantial potential regarding their exploitation for control—sometimes referred to as ”morphological computation”. In this article, we briefly review different notions of computation by physical systems and (...)
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  14. Socio-Technical Computation.Markus Luczak-Roesch, Ramine Tinati, Kieron O'Hara & Nigel Shadbolt - 2015 - In Proceedings of the 18th ACM Conference Companion on Computer Supported Cooperative Work & Social Computing.
    Motivated by the significant amount of successful collaborative problem solving activity on the Web, we ask: Can the accumulated information propagation behavior on the Web be conceived as a giant machine, and reasoned about accordingly? In this paper we elaborate a thesis about the computational capability embodied in information sharing activities that happen on the Web, which we term socio-technical computation, reflecting not only explicitly conditional activities but also the organic potential residing in information on the Web.
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  15. Suicidal Utopian Delusions in the 21st Century-Philosophy, Human Nature and the Collapse of Civilization 2nd Ed. (2018).Starks Michael (ed.) - 2018 - Las Vegas ,NV, USA: Reality Press.
    This collection of articles was written over the last 10 years and edited to bring them up to date (2018). All the articles are about human behavior (as are all articles by anyone about anything), and so about the limitations of having a recent monkey ancestry (8 million years or much less depending on viewpoint) and manifest words and deeds within the framework of our innate psychology as presented in the table of intentionality. As famous evolutionist Richard Leakey says, it (...)
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  16. Three Concepts of Decidability for General Subsets of Uncountable Spaces.Matthew W. Parker - 2003 - Theoretical Computer Science 351 (1):2-13.
    There is no uniquely standard concept of an effectively decidable set of real numbers or real n-tuples. Here we consider three notions: decidability up to measure zero [M.W. Parker, Undecidability in Rn: Riddled basins, the KAM tori, and the stability of the solar system, Phil. Sci. 70(2) (2003) 359–382], which we abbreviate d.m.z.; recursive approximability [or r.a.; K.-I. Ko, Complexity Theory of Real Functions, Birkhäuser, Boston, 1991]; and decidability ignoring boundaries [d.i.b.; W.C. Myrvold, The decision problem for entanglement, in: R.S. (...)
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  17. The Significance and Use of Absence.Varanasi Ramabrahmam - manuscript
    The significance and use of absence of a thing is highlighted taking examples from mathematics, physics, semi-conductor electronics, computer science and cognitive science. The profundity of absence is discussed.
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  18. Philosophy of Computer Science : An Introductory Course Philosophy of Computer Science : An Introductory Course.William J. Rapaport - 2005 - Teaching Philosophy 28 (4):319-341.
    There are many branches of philosophy called "the philosophy of X," where X = disciplines ranging from history to physics. The philosophy of artificial intelligence has a long history, and there are many courses and texts with that title. Surprisingly, the philosophy of computer science is not nearly as well-developed. This article proposes topics that might constitute the philosophy of computer science and describes a course covering those topics, along with suggested readings and assignments.
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  19. Philosophy of Computer Science: An Introductory Course.William J. Rapaport - 2005 - Teaching Philosophy 28 (4):319-341.
    There are many branches of philosophy called “the philosophy of X,” where X = disciplines ranging from history to physics. The philosophy of artificial intelligence has a long history, and there are many courses and texts with that title. Surprisingly, the philosophy of computer science is not nearly as well-developed. This article proposes topics that might constitute the philosophy of computer science and describes a course covering those topics, along with suggested readings and assignments.
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  20. Implementation Is Semantic Interpretation.William J. Rapaport - 1999 - The Monist 82 (1):109-130.
    What is the computational notion of "implementation"? It is not individuation, instantiation, reduction, or supervenience. It is, I suggest, semantic interpretation. The online version differs from the published version in being a bit longer and going into a bit more detail.
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  21. Intractability and the Use of Heuristics in Psychological Explanations.Iris Rooij, Cory Wright & Todd Wareham - 2012 - Synthese 187 (2):471-487.
    Many cognitive scientists, having discovered that some computational-level characterization f of a cognitive capacity φ is intractable, invoke heuristics as algorithmic-level explanations of how cognizers compute f. We argue that such explanations are actually dysfunctional, and rebut five possible objections. We then propose computational-level theory revision as a principled and workable alternative.
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  22. Single-Tape and Multi-Tape Turing Machines Through the Lens of the Grossone Methodology.Yaroslav Sergeyev & Alfredo Garro - 2013 - Journal of Supercomputing 65 (2):645-663.
    The paper investigates how the mathematical languages used to describe and to observe automatic computations influence the accuracy of the obtained results. In particular, we focus our attention on Single and Multi-tape Turing machines which are described and observed through the lens of a new mathematical language which is strongly based on three methodological ideas borrowed from Physics and applied to Mathematics, namely: the distinction between the object (we speak here about a mathematical object) of an observation and the instrument (...)
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  23. Formal Ontology for Natural Language Processing and the Integration of Biomedical Databases.Jonathan Simon, James M. Fielding, Mariana C. Dos Santos & Barry Smith - 2005 - International Journal of Medical Informatics 75 (3-4):224-231.
    The central hypothesis of the collaboration between Language and Computing (L&C) and the Institute for Formal Ontology and Medical Information Science (IFOMIS) is that the methodology and conceptual rigor of a philosophically inspired formal ontology greatly benefits application ontologies. To this end r®, L&C’s ontology, which is designed to integrate and reason across various external databases simultaneously, has been submitted to the conceptual demands of IFOMIS’s Basic Formal Ontology (BFO). With this project we aim to move beyond the level of (...)
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  24. Well-Structured Biology: Numerical Taxonomy's Epistemic Vision for Systematics.Beckett Sterner - 2014 - In Andrew Hamilton (ed.), The Evolution of Phylogenetic Systematics. University of California Press. pp. 213-244.
    What does it look like when a group of scientists set out to re-envision an entire field of biology in symbolic and formal terms? I analyze the founding and articulation of Numerical Taxonomy between 1950 and 1970, the period when it set out a radical new approach to classification and founded a tradition of mathematics in systematic biology. I argue that introducing mathematics in a comprehensive way also requires re-organizing the daily work of scientists in the field. Numerical taxonomists sought (...)
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  25. Object Spaces: An Organizing Strategy for Biological Theorizing.Beckett Sterner - 2009 - Biological Theory 4 (3):280-286.
    A classic analytic approach to biological phenomena seeks to refine definitions until classes are sufficiently homogenous to support prediction and explanation, but this approach founders on cases where a single process produces objects with similar forms but heterogeneous behaviors. I introduce object spaces as a tool to tackle this challenging diversity of biological objects in terms of causal processes with well-defined formal properties. Object spaces have three primary components: (1) a combinatorial biological process such as protein synthesis that generates objects (...)
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