Results for 'modeling'

368 found
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  1.  45
    Causal Modeling and the Efficacy of Action.Holly Andersen - forthcoming - In Michael Brent (ed.), Mental Action and the Conscious Mind. Routledge.
    This paper brings together Thompson's naive action explanation with interventionist modeling of causal structure to show how they work together to produce causal models that go beyond current modeling capabilities, when applied to specifically selected systems. By deploying well-justified assumptions about rationalization, we can strengthen existing causal modeling techniques' inferential power in cases where we take ourselves to be modeling causal systems that also involve actions. The internal connection between means and end exhibited in naive action (...)
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  2.  46
    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 Then I argue that early formulations of these reasons are outdated.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 memory (...)
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  3.  24
    Information: From Philosophic to Physics Concepts for Informational Modeling of Consciousness.Florin Gaiseanu - 2018 - Philosophy Study 8 (8).
    Information was a frequently used concept in many fields of investigation. However, this concept is still not really understood, when it is referred for instance to consciousness and its informational structure. In this paper it is followed the concept of information from philosophical to physics perspective, showing especially how this concept could be extended to matter in general and to the living in particular, as a result of the intimate interaction between matter and information, the human body appearing as a (...)
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  4. Experimental Modeling in Biology: In Vivo Representation and Stand-Ins As Modeling Strategies.Marcel Weber - 2014 - Philosophy of Science 81 (5):756-769.
    Experimental modeling in biology involves the use of living organisms (not necessarily so-called "model organisms") in order to model or simulate biological processes. I argue here that experimental modeling is a bona fide form of scientific modeling that plays an epistemic role that is distinct from that of ordinary biological experiments. What distinguishes them from ordinary experiments is that they use what I call "in vivo representations" where one kind of causal process is used to stand in (...)
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  5. Agent-Based Modeling: The Right Mathematics for the Social Sciences?Paul L. Borrill & Leigh Tesfatsion - 2011 - In J. B. Davis & D. W. Hands (eds.), Elgar Companion to Recent Economic Methodology. Edward Elgar Publishers. pp. 228.
    This study provides a basic introduction to agent-based modeling (ABM) as a powerful blend of classical and constructive mathematics, with a primary focus on its applicability for social science research. The typical goals of ABM social science researchers are discussed along with the culture-dish nature of their computer experiments. The applicability of ABM for science more generally is also considered, with special attention to physics. Finally, two distinct types of ABM applications are summarized in order to illustrate concretely the (...)
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  6.  42
    Modelling with Words: Narrative and Natural Selection.Dominic K. Dimech - 2017 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 62:20-24.
    I argue that verbal models should be included in a philosophical account of the scientific practice of modelling. Weisberg (2013) has directly opposed this thesis on the grounds that verbal structures, if they are used in science, only merely describe models. I look at examples from Darwin's On the Origin of Species (1859) of verbally constructed narratives that I claim model the general phenomenon of evolution by natural selection. In each of the cases I look at, a particular scenario is (...)
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  7. Modeling and Experimenting.Isabelle Peschard - 2009 - In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. Routledge.
    Experimental activity is traditionally identified with testing the empirical implications or numerical simulations of models against data. In critical reaction to the ‘tribunal view’ on experiments, this essay will show the constructive contribution of experimental activity to the processes of modeling and simulating. Based on the analysis of a case in fluid mechanics, it will focus specifically on two aspects. The first is the controversial specification of the conditions in which the data are to be obtained. The second is (...)
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  8. Modeling as a Case for the Empirical Philosophy of Science.Ekaterina Svetlova - 2015 - In Hanne Andersen, Nancy J. Nersessian & Susann Wagenknecht (eds.), Empirical Philosophy of Science. Springer Verlag. pp. 65-82.
    In recent years, the emergence of a new trend in contemporary philosophy has been observed in the increasing usage of empirical research methods to conduct philosophical inquiries. Although philosophers primarily use secondary data from other disciplines or apply quantitative methods (experiments, surveys, etc.), the rise of qualitative methods (e.g., in-depth interviews, participant observations and qualitative text analysis) can also be observed. In this paper, I focus on how qualitative research methods can be applied within philosophy of science, namely within the (...)
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  9. The Sum of the Parts: Large-Scale Modeling in Systems Biology.Fridolin Gross & Sara Green - 2017 - Philosophy, Theory, and Practice in Biology 9 (10).
    Systems biologists often distance themselves from reductionist approaches and formulate their aim as understanding living systems “as a whole.” Yet, it is often unclear what kind of reductionism they have in mind, and in what sense their methodologies would offer a superior approach. To address these questions, we distinguish between two types of reductionism which we call “modular reductionism” and “bottom-up reductionism.” Much knowledge in molecular biology has been gained by decomposing living systems into functional modules or through detailed studies (...)
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  10. Modeling Truth.Paul Teller - manuscript
    Many in philosophy understand truth in terms of precise semantic values, true propositions. Following Braun and Sider, I say that in this sense almost nothing we say is, literally, true. I take the stand that this account of truth nonetheless constitutes a vitally useful idealization in understanding many features of the structure of language. The Fregean problem discussed by Braun and Sider concerns issues about application of language to the world. In understanding these issues I propose an alternative modeling (...)
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  11.  73
    Is Scientific Modeling an Indirect Methodology?Karlis Podnieks - 2009 - The Reasoner 3 (1):4-5.
    If we consider modeling not as a heap of contingent structures, but (where possible) as evolving coordinated systems of models, then we can reasonably explain as "direct representations" even some very complicated model-based cognitive situations. Scientific modeling is not as indirect as it may seem. "Direct theorizing" comes later, as the result of a successful model evolution.
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  12. Variables of Scientific Concept Modeling and Their Formalization.Vladimir Kuznetsov - 2009 - In В.И Маркин (ed.), Philosophy of mathematics: current problems. Proceedings of the second international conference (Философия математики: актуальные проблемы. Тезисы второй международной конференции). pp. 268-270.
    There are no universally adopted answers to the natural questions about scientific concepts: What are they? What is their structure? What are their functions? How many kinds of them are there? Do they change? Ironically, most if not all scientific monographs or articles mention concepts, but the scientific studies of scientific concepts are rare in occurrence. It is well known that the necessary stage of any scientific study is constructing the model of objects in question. Many years logical modeling (...)
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  13. Beyond Explanation: Understanding as Dependency Modeling.Finnur Dellsén - 2018 - British Journal for the Philosophy of Science.
    This paper presents and argues for an account of objectual understanding that aims to do justice to the full range of cases of scientific understanding, including cases in which one does not have an explanation of the understood phenomenon. According to the proposed account, one understands a phenomenon just in case one grasps a sufficiently accurate and comprehensive model of the ways in which it or its features are situated within a network of dependence relations; one’s degree of understanding is (...)
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  14. What Does a Computer Simulation Prove? The Case of Plant Modeling at CIRAD.Franck Varenne - 2001 - In N. Giambiasi & C. Frydman (eds.), Simulation in industry - ESS 2001, Proc. of the 13th European Simulation Symposium. Society for Computer Simulation (SCS).
    The credibility of digital computer simulations has always been a problem. Today, through the debate on verification and validation, it has become a key issue. I will review the existing theses on that question. I will show that, due to the role of epistemological beliefs in science, no general agreement can be found on this matter. Hence, the complexity of the construction of sciences must be acknowledged. I illustrate these claims with a recent historical example. Finally I temperate this diversity (...)
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  15. Toward Modeling and Automating Ethical Decision Making: Design, Implementation, Limitations, and Responsibilities.Gregory S. Reed & Nicholaos Jones - 2013 - Topoi 32 (2):237-250.
    One recent priority of the U.S. government is developing autonomous robotic systems. The U.S. Army has funded research to design a metric of evil to support military commanders with ethical decision-making and, in the future, allow robotic military systems to make autonomous ethical judgments. We use this particular project as a case study for efforts that seek to frame morality in quantitative terms. We report preliminary results from this research, describing the assumptions and limitations of a program that assesses the (...)
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  16. Mathematical Modelling and Contrastive Explanation.Adam Morton - 1990 - Canadian Journal of Philosophy 20 (Supplement):251-270.
    Mathematical models provide explanations of limited power of specific aspects of phenomena. One way of articulating their limits here, without denying their essential powers, is in terms of contrastive explanation.
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  17.  32
    Blockchain Design and Modelling.Nicolae Sfetcu - manuscript
    Ontology engineering, along with semantic Web technologies, allow the semantic development and modeling of the operational flow required for blockchain design. The semantic Web, in accordance with W3C, "provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries" and can be seen as an integrator for various content, applications and information systems. The most widely used blockchain modelling system, by abstract representation, description and definition of structure, processes, information and resources, is (...)
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  18.  67
    Using Blinking Fractals for Mathematical Modelling of Processes of Growth in Biological Systems.Yaroslav Sergeyev - 2011 - Informatica 22 (4):559–576.
    Many biological processes and objects can be described by fractals. The paper uses a new type of objects – blinking fractals – that are not covered by traditional theories considering dynamics of self-similarity processes. It is shown that both traditional and blinking fractals can be successfully studied by a recent approach allowing one to work numerically with infinite and infinitesimal numbers. It is shown that blinking fractals can be applied for modeling complex processes of growth of biological systems including (...)
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  19.  75
    Philosophy of Modeling: Neglected Pages of History.Karlis Podnieks - 2018 - Baltic Journal of Modern Computing 6 (3):279–303.
    The work done in the philosophy of modeling by Vaihinger (1876), Craik (1943), Rosenblueth and Wiener (1945), Apostel (1960), Minsky (1965), Klaus (1966) and Stachowiak (1973) is still almost completely neglected in the mainstream literature. However, this work seems to contain original ideas worth to be discussed. For example, the idea that diverse functions of models can be better structured as follows: in fact, models perform only a single function – they are replacing their target systems, but for different (...)
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  20. SP2MN: A Software Process Meta-Modeling Language.Hisham Khdair - 2015 - International Review on Computers and Software 10 (7):726-734.
    In the last two decades, software process modeling has been an area of interest within both academia and industry. Software process modeling aims at defining and representing software processes in the form of models. A software process model represents the medium that allows better understanding, management and control of the software process. Software process metamodeling rather, provides standard metamodels which enable the defining of customized software process models for a specific project in hand by instantiation. Several software process (...)
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  21.  81
    Plato’s Philosophy of Cognition by Mathematical Modelling.Roman S. Kljujkov & Sergey Kljujkov - 2014 - Dialogue and Universalism 24 (3):110-115.
    By the end of his life Plato had rearranged the theory of ideas into his teaching about ideal numbers, but no written records have been left. The Ideal mathematics of Plato is present in all his dialogues. It can be clearly grasped in relation to the effective use of mathematical modelling. Many problems of mathematical modelling were laid in the foundation of the method by cutting the three-level idealism of Plato to the single-level “ideism” of Aristotle. For a long time, (...)
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  22. Bridging Emotion Theory and Neurobiology Through Dynamic Systems Modeling.Marc D. Lewis - 2005 - Behavioral and Brain Sciences 28 (2):169-194.
    Efforts to bridge emotion theory with neurobiology can be facilitated by dynamic systems (DS) modeling. DS principles stipulate higher-order wholes emerging from lower-order constituents through bidirectional causal processes cognition relations. I then present a psychological model based on this reconceptualization, identifying trigger, self-amplification, and self-stabilization phases of emotion-appraisal states, leading to consolidating traits. The article goes on to describe neural structures and functions involved in appraisal and emotion, as well as DS mechanisms of integration by which they interact. These (...)
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  23. Real Kinds in Real Time: On Responsible Social Modeling.Theodore Bach - 2019 - The Monist 102 (2):236-258.
    There is broad agreement among social researchers and social ontologists that the project of dividing humans into social kinds should be guided by at least two methodological commitments. First, a commitment to what best serves moral and political interests, and second, a commitment to describing accurately the causal structures of social reality. However, researchers have not sufficiently analyzed how these two commitments interact and constrain one another. In the absence of that analysis, several confusions have set in, threatening to undermine (...)
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  24. The Ontology of Theoretical Modelling: Models as Make-Believe.Adam Toon - 2010 - Synthese 172 (2):301-315.
    The descriptions and theoretical laws scientists write down when they model a system are often false of any real system. And yet we commonly talk as if there were objects that satisfy the scientists’ assumptions and as if we may learn about their properties. Many attempt to make sense of this by taking the scientists’ descriptions and theoretical laws to define abstract or fictional entities. In this paper, I propose an alternative account of theoretical modelling that draws upon Kendall Walton’s (...)
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  25. Optimality Modeling and Explanatory Generality.Angela Potochnik - 2007 - Philosophy of Science 74 (5):680-691.
    The optimality approach to modeling natural selection has been criticized by many biologists and philosophers of biology. For instance, Lewontin (1979) argues that the optimality approach is a shortcut that will be replaced by models incorporating genetic information, if and when such models become available. In contrast, I think that optimality models have a permanent role in evolutionary study. I base my argument for this claim on what I think it takes to best explain an event. In certain contexts, (...)
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  26. Modeling Economic Systems as Locally-Constructive Sequential Games.Leigh Tesfatsion - 2017 - Journal of Economic Methodology 24 (4):1-26.
    Real-world economies are open-ended dynamic systems consisting of heterogeneous interacting participants. Human participants are decision-makers who strategically take into account the past actions and potential future actions of other participants. All participants are forced to be locally constructive, meaning their actions at any given time must be based on their local states; and participant actions at any given time affect future local states. Taken together, these essential properties imply real-world economies are locally-constructive sequential games. This paper discusses a modeling (...)
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  27. Optimality Modeling in a Suboptimal World.Angela Potochnik - 2009 - Biology and Philosophy 24 (2):183-197.
    The fate of optimality modeling is typically linked to that of adaptationism: the two are thought to stand or fall together (Gould and Lewontin, Proc Relig Soc Lond 205:581–598, 1979; Orzack and Sober, Am Nat 143(3):361–380, 1994). I argue here that this is mistaken. The debate over adaptationism has tended to focus on one particular use of optimality models, which I refer to here as their strong use. The strong use of an optimality model involves the claim that selection (...)
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  28. Modeling Measurement: Error and Uncertainty.Alessandro Giordani & Luca Mari - 2014 - In Marcel Boumans, Giora Hon & Arthur Petersen (eds.), Error and Uncertainty in Scientific Practice. Pickering & Chatto. pp. 79-96.
    In the last few decades the role played by models and modeling activities has become a central topic in the scientific enterprise. In particular, it has been highlighted both that the development of models constitutes a crucial step for understanding the world and that the developed models operate as mediators between theories and the world. Such perspective is exploited here to cope with the issue as to whether error-based and uncertainty-based modeling of measurement are incompatible, and thus alternative (...)
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  29. Imagination in Scientific Modeling.Adam Toon - 2016 - In Amy Kind (ed.), The Routledge Handbook of Philosophy of Imagination. Routledge. pp. 451-462.
    Modeling is central to scientific inquiry. It also depends heavily upon the imagination. In modeling, scientists seem to turn their attention away from the complexity of the real world to imagine a realm of perfect spheres, frictionless planes and perfect rational agents. Modeling poses many questions. What are models? How do they relate to the real world? Recently, a number of philosophers have addressed these questions by focusing on the role of the imagination in modeling. Some (...)
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  30. Making Sense of Modeling: Beyond Representation. [REVIEW]Isabelle Peschard - 2011 - European Journal for Philosophy of Science 1 (3):335-352.
    Making sense of modeling: beyond representation Content Type Journal Article Category Original paper in Philosophy of Science Pages 335-352 DOI 10.1007/s13194-011-0032-8 Authors Isabelle Peschard, Philosophy Department, San Francisco State University, 1600 Holloway Ave, San Francisco, CA 94132, USA Journal European Journal for Philosophy of Science Online ISSN 1879-4920 Print ISSN 1879-4912 Journal Volume Volume 1 Journal Issue Volume 1, Number 3.
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  31. Modeling Practical Thinking.Matthew Mosdell - forthcoming - Mind and Language.
    Intellectualists about knowledge how argue that knowing how to do something is knowing the content of a proposition (i.e, a fact). An important component of this view is the idea that propositional knowledge is translated into behavior when it is presented to the mind in a peculiarly practical way. Until recently, however, intellectualists have not said much about what it means for propositional knowledge to be entertained under thought's practical guise. Carlotta Pavese fills this gap in the intellectualist view by (...)
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  32. A MODERN SCIENTIFIC INSIGHT OF SPHOTA VADA: IMPLICATIONS TO THE DEVELOPMENT OF SOFTWARE FOR MODELING NATURAL LANGUAGE COMPREHENSION.Varanasi Ramabrahmam - manuscript
    Sabdabrahma Siddhanta, popularized by Patanjali and Bhartruhari will be scientifically analyzed. Sphota Vada, proposed and nurtured by the Sanskrit grammarians will be interpreted from modern physics and communication engineering points of view. Insight about the theory of language and modes of language acquisition and communication available in the Brahma Kanda of Vakyapadeeyam will be translated into modern computational terms. A flowchart of language processing in humans will be given. A gross model of human language acquisition, comprehension and communication process forming (...)
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  33.  86
    Logics for Modelling Collective Attitudes.Daniele Porello - 2018 - Fundamenta Infromaticae 158 (1-3):239-27.
    We introduce a number of logics to reason about collective propositional attitudes that are defined by means of the majority rule. It is well known that majoritarian aggregation is subject to irrationality, as the results in social choice theory and judgment aggregation show. The proposed logics for modelling collective attitudes are based on a substructural propositional logic that allows for circumventing inconsistent outcomes. Individual and collective propositional attitudes, such as beliefs, desires, obligations, are then modelled by means of minimal modalities (...)
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  34.  96
    The Truth of False Idealizations in Modeling.Uskali Mäki - 2011 - In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. Routledge.
    Modeling involves the use of false idealizations, yet there is typically a belief or hope that modeling somehow manages to deliver true information about the world. The paper discusses one possible way of reconciling truth and falsehood in modeling. The key trick is to relocate truth claims by reinterpreting an apparently false idealizing assumption in order to make clear what possibly true assertion is intended when using it. These include interpretations in terms of negligibility, applicability, tractability, early-step, (...)
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  35. Mathematical Modeling in Biology: Philosophy and Pragmatics.Rasmus Grønfeldt Winther - 2012 - Frontiers in Plant Evolution and Development 2012:1-3.
    Philosophy can shed light on mathematical modeling and the juxtaposition of modeling and empirical data. This paper explores three philosophical traditions of the structure of scientific theory—Syntactic, Semantic, and Pragmatic—to show that each illuminates mathematical modeling. The Pragmatic View identifies four critical functions of mathematical modeling: (1) unification of both models and data, (2) model fitting to data, (3) mechanism identification accounting for observation, and (4) prediction of future observations. Such facets are explored using a recent (...)
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  36. Kuznetsov V. From studying theoretical physics to philosophical modeling scientific theories: Under influence of Pavel Kopnin and his school.Volodymyr Kuznetsov - 2017 - ФІЛОСОФСЬКІ ДІАЛОГИ’2016 ІСТОРІЯ ТА СУЧАСНІСТЬ У НАУКОВИХ РОЗМИСЛАХ ІНСТИТУТУ ФІЛОСОФІЇ 11:62-92.
    The paper explicates the stages of the author’s philosophical evolution in the light of Kopnin’s ideas and heritage. Starting from Kopnin’s understanding of dialectical materialism, the author has stated that category transformations of physics has opened from conceptualization of immutability to mutability and then to interaction, evolvement and emergence. He has connected the problem of physical cognition universals with an elaboration of the specific system of tools and methods of identifying, individuating and distinguishing objects from a scientific theory domain. The (...)
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  37. On the Limits of Causal Modeling: Spatially-Structurally Complex Biological Phenomena.Marie I. Kaiser - 2016 - Philosophy of Science 83 (5):921-933.
    This paper examines the adequacy of causal graph theory as a tool for modeling biological phenomena and formalizing biological explanations. I point out that the causal graph approach reaches it limits when it comes to modeling biological phenomena that involve complex spatial and structural relations. Using a case study from molecular biology, DNA-binding and -recognition of proteins, I argue that causal graph models fail to adequately represent and explain causal phenomena in this field. The inadequacy of these models (...)
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  38. Agent-Based Modeling and the Fallacies of Individualism.Brian Epstein - 2011 - In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. Routledge. pp. 115444.
    Agent-​​based modeling is showing great promise in the social sciences. However, two misconceptions about the relation between social macroproperties and microproperties afflict agent-based models. These lead current models to systematically ignore factors relevant to the properties they intend to model, and to overlook a wide range of model designs. Correcting for these brings painful trade-​​offs, but has the potential to transform the utility of such models.
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  39. Modelling the Truth of Scientific Beliefs with Cultural Evolutionary Theory.Krist Vaesen & Wybo Houkes - 2014 - Synthese 191 (1).
    Evolutionary anthropologists and archaeologists have been considerably successful in modelling the cumulative evolution of culture, of technological skills and knowledge in particular. Recently, one of these models has been introduced in the philosophy of science by De Cruz and De Smedt (Philos Stud 157:411–429, 2012), in an attempt to demonstrate that scientists may collectively come to hold more truth-approximating beliefs, despite the cognitive biases which they individually are known to be subject to. Here we identify a major shortcoming in that (...)
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  40.  32
    Modeling the Invention of a New Inference Rule: The Case of ‘Randomized Clinical Trial’ as an Argument Scheme for Medical Science.Jodi Schneider & Sally Jackson - 2018 - Argument and Computation 9 (2):77-89.
    A background assumption of this paper is that the repertoire of inference schemes available to humanity is not fixed, but subject to change as new schemes are invented or refined and as old ones are obsolesced or abandoned. This is particularly visible in areas like health and environmental sciences, where enormous societal investment has been made in finding ways to reach more dependable conclusions. Computational modeling of argumentation, at least for the discourse in expert fields, will require the possibility (...)
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  41. Reply to Rosanna Keefe’s ‘Modelling Higher-Order Vagueness: Columns, Borderlines and Boundaries’.Susanne Bobzien - 2016
    This paper is an expanded written version of my reply to Rosanna Keefe’s paper ‘Modelling higher-order vagueness: columns, borderlines and boundaries’ (Keefe 2015), which in turn is a reply to my paper ‘Columnar higher-order vagueness, or Vagueness is higher-order vagueness’ (Bobzien 2015). Both papers were presented at the Joint Session of the the Aristotelian Society and the Mind Association in July, 2015. At the Joint Session meeting, there was insufficient time to present all of my points in response to Keefe’s (...)
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  42.  29
    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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  43.  74
    The Altruism Paradox: A Consequence of Mistaken Genetic Modeling.Yussif Yakubu - 2013 - Biological Theory 8 (1):103-113.
    The theoretical heuristic of assuming distinct alleles (or genotypes) for alternative phenotypes is the foundation of the paradigm of evolutionary explanation we call the Modern Synthesis. In modeling the evolution of sociality, the heuristic has been to set altruism and selfishness as alternative phenotypes under distinct genotypes, which has been dubbed the “phenotypic gambit.” The prevalence of the altruistic genotype that is of lower evolutionary fitness relative to the alternative genotype for non-altruistic behavior in populations is the basis of (...)
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  44. 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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  45. 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 face in the social sciences. (...)
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  46. Modelling Deep Indeterminacy.George Darby & Martin Pickup - 2019 - Synthese:1-26.
    This paper constructs a model of metaphysical indeterminacy that can accommodate a kind of ‘deep’ worldly indeterminacy that arguably arises in quantum mechanics via the Kochen-Specker theorem, and that is incompatible with prominent theories of metaphysical indeterminacy such as that in Barnes and Williams (2011). We construct a variant of Barnes and Williams's theory that avoids this problem. Our version builds on situation semantics and uses incomplete, local situations rather than possible worlds to build a model. We evaluate the resulting (...)
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  47. The Normativity Problem as a Serious Obstacle to Modelling Gender.Weston Richey - 2018 - Aporia 18 (2):1-11.
    In this paper, I explore Sally Haslanger’s (2000) proposed approach to modelling gender which she intends to overcome several problems for such a project. I specifically focus on what Haslanger calls the normativity problem, in which definitions meant to overcome oppression only reinforce oppressive norms. I argue that the normativity problem is a serious one for defining gender and that Haslanger does not successfully overcome it with her definitions of man and woman. In §§1 and 2, I offer background for (...)
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  48. Some Resonances Between Eastern Thought and Integral Biomathics in the Framework of the WLIMES Formalism for Modelling Living Systems.Plamen L. Simeonov & Andree C. Ehresmann - forthcoming - Progress in Biophysics and Molecular Biology 131 (Special).
    Forty-two years ago, Capra published “The Tao of Physics” (Capra, 1975). In this book (page 17) he writes: “The exploration of the atomic and subatomic world in the twentieth century has …. necessitated a radical revision of many of our basic concepts” and that, unlike ‘classical’ physics, the sub-atomic and quantum “modern physics” shows resonances with Eastern thoughts and “leads us to a view of the world which is very similar to the views held by mystics of all ages and (...)
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  49. A Case for Machine Ethics in Modeling Human-Level Intelligent Agents.Robert James M. Boyles - 2018 - Kritike 12 (1):182–200.
    This paper focuses on the research field of machine ethics and how it relates to a technological singularity—a hypothesized, futuristic event where artificial machines will have greater-than-human-level intelligence. One problem related to the singularity centers on the issue of whether human values and norms would survive such an event. To somehow ensure this, a number of artificial intelligence researchers have opted to focus on the development of artificial moral agents, which refers to machines capable of moral reasoning, judgment, and decision-making. (...)
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  50.  55
    The Computational Modeling of Inferential and Referential Competence.Fabrizio Calzavarini & Antonio Lieto - 2018 - In AISC 2018 Proceedings.
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