Search results for 'Modelling' (try it on Scholar)

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  1. Tarja Knuuttila (2011). Modelling and Representing: An Artefactual Approach to Model-Based Representation. Studies in History and Philosophy of Science 42 (2):262-271.
    The recent discussion on scientific representation has focused on models and their relationship to the real world. It has been assumed that models give us knowledge because they represent their supposed real target systems. However, here agreement among philosophers of science has tended to end as they have presented widely different views on how representation should be understood. I will argue that the traditional representational approach is too limiting as regards the epistemic value of modelling given the focus on (...)
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  2.  53
    Diederik Aerts, Liane Gabora & Sandro Sozzo (2013). Concepts and Their Dynamics: A Quantum‐Theoretic Modeling of Human Thought. Topics in Cognitive Science 5 (4):737-772.
    We analyze different aspects of our quantum modeling approach of human concepts and, more specifically, focus on the quantum effects of contextuality, interference, entanglement, and emergence, illustrating how each of them makes its appearance in specific situations of the dynamics of human concepts and their combinations. We point out the relation of our approach, which is based on an ontology of a concept as an entity in a state changing under influence of a context, with the main traditional concept theories, (...)
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  3. Nicolas Fillion & Robert M. Corless (2014). On the Epistemological Analysis of Modeling and Computational Error in the Mathematical Sciences. Synthese 191 (7):1451-1467.
    Interest in the computational aspects of modeling has been steadily growing in philosophy of science. This paper aims to advance the discussion by articulating the way in which modeling and computational errors are related and by explaining the significance of error management strategies for the rational reconstruction of scientific practice. To this end, we first characterize the role and nature of modeling error in relation to a recipe for model construction known as Euler’s recipe. We then describe a general model (...)
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  4.  8
    Kenneth D. Forbus, Ronald W. Ferguson, Andrew Lovett & Dedre Gentner (2016). Extending SME to Handle Large‐Scale Cognitive Modeling. Cognitive Science 40 (4):n/a-n/a.
    Analogy and similarity are central phenomena in human cognition, involved in processes ranging from visual perception to conceptual change. To capture this centrality requires that a model of comparison must be able to integrate with other processes and handle the size and complexity of the representations required by the tasks being modeled. This paper describes extensions to Structure-Mapping Engine since its inception in 1986 that have increased its scope of operation. We first review the basic SME algorithm, describe psychological evidence (...)
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  5.  18
    Tjerk Gauderis (2013). Modelling Abduction in Science by Means of a Modal Adaptive Logic. Foundations of Science 18 (4):611-624.
    Scientists confronted with multiple explanatory hypotheses as a result of their abductive inferences, generally want to reason further on the different hypotheses one by one. This paper presents a modal adaptive logic MLA s that enables us to model abduction in such a way that the different explanatory hypotheses can be derived individually. This modelling is illustrated with a case study on the different hypotheses on the origin of the Moon.
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  6. Paul L. Borrill & Leigh Tesfatsion (2011). Agent-Based Modeling: The Right Mathematics for the Social Sciences? In J. B. Davis & D. W. Hands (eds.), Elgar Companion to Recent Economic Methodology. Edward Elgar Publishers 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 duality (...)
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  7.  42
    Caspar Addyman & Robert M. French (2012). Computational Modeling in Cognitive Science: A Manifesto for Change. Topics in Cognitive Science 4 (3):332-341.
    Computational modeling has long been one of the traditional pillars of cognitive science. Unfortunately, the computer models of cognition being developed today have not kept up with the enormous changes that have taken place in computer technology and, especially, in human-computer interfaces. For all intents and purposes, modeling is still done today as it was 25, or even 35, years ago. Everyone still programs in his or her own favorite programming language, source code is rarely made available, accessibility of models (...)
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  8. Marcel Weber (2014). Experimental Modeling in Biology: In Vivo Representation and Stand-Ins As Modeling Strategies. 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 for a physically (...)
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  9.  11
    Pablo Ruiz-Palomino & Ricardo Martinez-Cañas (2011). Supervisor Role Modeling, Ethics-Related Organizational Policies, and Employee Ethical Intention: The Moderating Impact of Moral Ideology. Journal of Business Ethics 102 (4):653-668.
    The moral ideology of banking and insurance employees in Spain was examined along with supervisor role modeling and ethics-related policies and procedures for their association with ethical behavioral intent. In addition to main effects, we found evidence supporting that the person–situation interactionist perspective in supervisor role modeling had a stronger positive relationship with ethical intention among employees with relativist moral ideology. Also as hypothesized, formal ethical polices and procedures were positively related to ethical intention among those with universal beliefs, but (...)
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  10.  25
    Alistair M. C. Isaac (2013). Modeling Without Representation. Synthese 190 (16):3611-3623.
    How can mathematical models which represent the causal structure of the world incompletely or incorrectly have any scientific value? I argue that this apparent puzzle is an artifact of a realist emphasis on representation in the philosophy of modeling. I offer an alternative, pragmatic methodology of modeling, inspired by classic papers by modelers themselves. The crux of the view is that models developed for purposes other than explanation may be justified without reference to their representational properties.
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  11.  41
    Arnon Levy (2015). Modeling Without Models. Philosophical Studies 172 (3):781-798.
    Modeling is an important scientific practice, yet it raises significant philosophical puzzles. Models are typically idealized, and they are often explored via imaginative engagement and at a certain “distance” from empirical reality. These features raise questions such as what models are and how they relate to the world. Recent years have seen a growing discussion of these issues, including a number of views that treat modeling in terms of indirect representation and analysis. Indirect views treat the model as a bona (...)
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  12.  28
    Mike Page (2000). Connectionist Modelling in Psychology: A Localist Manifesto. Behavioral and Brain Sciences 23 (4):443-467.
    Over the last decade, fully distributed models have become dominant in connectionist psychological modelling, whereas the virtues of localist models have been underestimated. This target article illustrates some of the benefits of localist modelling. Localist models are characterized by the presence of localist representations rather than the absence of distributed representations. A generalized localist model is proposed that exhibits many of the properties of fully distributed models. It can be applied to a number of problems that are difficult (...)
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  13.  8
    D. Kimbrough Oller, Ulrike Griebel & Anne S. Warlaumont (2016). Vocal Development as a Guide to Modeling the Evolution of Language. Topics in Cognitive Science 8 (2):382-392.
    Modeling of evolution and development of language has principally utilized mature units of spoken language, phonemes and words, as both targets and inputs. This approach cannot address the earliest phases of development because young infants are unable to produce such language features. We argue that units of early vocal development—protophones and their primitive illocutionary/perlocutionary forces—should be targeted in evolutionary modeling because they suggest likely units of hominin vocalization/communication shortly after the split from the chimpanzee/bonobo lineage, and because early development of (...)
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  14.  36
    Pompeu Casanovas, Núria Casellas, Christoph Tempich, Denny Vrandečić & Richard Benjamins (2007). OPJK and DILIGENT: Ontology Modeling in a Distributed Environment. [REVIEW] Artificial Intelligence and Law 15 (2):171-186.
    In the legal domain, ontologies enjoy quite some reputation as a way to model normative knowledge about laws and jurisprudence. This paper describes the methodology followed when developing the ontology used by the second version of the prototype Iuriservice, a web-based intelligent FAQ for judicial use. This modeling methodology has had two important requirements: on the one hand, the ontology needed to be extracted from a repository of professional judicial knowledge (containing nearly 800 questions regarding daily practice). Thus, the construction (...)
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  15.  27
    Wolfgang Pietsch (2016). The Causal Nature of Modeling with Big Data. Philosophy and Technology 29 (2):137-171.
    I argue for the causal character of modeling in data-intensive science, contrary to widespread claims that big data is only concerned with the search for correlations. After discussing the concept of data-intensive science and introducing two examples as illustration, several algorithms are examined. It is shown how they are able to identify causal relevance on the basis of eliminative induction and a related difference-making account of causation. I then situate data-intensive modeling within a broader framework of an epistemology of scientific (...)
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  16. Isabelle Peschard (2011). Modeling and Experimenting. 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 conceptual (...)
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  17.  37
    Brendan Clarke, Bert Leuridan & Jon Williamson (2013). Modelling Mechanisms with Causal Cycles. Synthese 191 (8):1-31.
    Mechanistic philosophy of science views a large part of scientific activity as engaged in modelling mechanisms. While science textbooks tend to offer qualitative models of mechanisms, there is increasing demand for models from which one can draw quantitative predictions and explanations. Casini et al. (Theoria 26(1):5–33, 2011) put forward the Recursive Bayesian Networks (RBN) formalism as well suited to this end. The RBN formalism is an extension of the standard Bayesian net formalism, an extension that allows for modelling (...)
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  18.  11
    Russell Richie, Charles Yang & Marie Coppola (2014). Modeling the Emergence of Lexicons in Homesign Systems. Topics in Cognitive Science 6 (1):183-195.
    It is largely acknowledged that natural languages emerge not just from human brains but also from rich communities of interacting human brains (Senghas, ). Yet the precise role of such communities and such interaction in the emergence of core properties of language has largely gone uninvestigated in naturally emerging systems, leaving the few existing computational investigations of this issue at an artificial setting. Here, we take a step toward investigating the precise role of community structure in the emergence of linguistic (...)
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  19.  3
    Paul Teller, Modeling Truth.
    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 tool (...)
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  20.  61
    Paul A. Dion (2008). Interpreting Structural Equation Modeling Results: A Reply to Martin and Cullen. [REVIEW] Journal of Business Ethics 83 (3):365 - 368.
    This article briefly review the fundamentals of structural equation modeling for readers unfamiliar with the technique then goes on to offer a review of the Martin and Cullen paper. In summary, a number of fit indices reported by the authors reveal that the data do not fit their theoretical model and thus the conclusion of the authors that the model was “promising” are unwarranted.
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  21.  9
    Niels A. Taatgen, Marieke K. Vugt, Jelmer P. Borst & Katja Mehlhorn (2016). Cognitive Modeling at ICCM: State of the Art and Future Directions. Topics in Cognitive Science 8 (1):259-263.
    The goal of cognitive modeling is to build faithful simulations of human cognition. One of the challenges is that multiple models can often explain the same phenomena. Another challenge is that models are often very hard to understand, explore, and reuse by others. We discuss some of the solutions that were discussed during the 2015 International Conference on Cognitive Modeling.
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  22.  3
    Daniel Freudenthal, Julian M. Pine & Fernand Gobet (2006). Modeling the Development of Children's Use of Optional Infinitives in Dutch and English Using MOSAIC. Cognitive Science 30 (2):277-310.
    In this study we use a computational model of language learning called model of syntax acquisition in children (MOSAIC) to investigate the extent to which the optional infinitive (OI) phenomenon in Dutch and English can be explained in terms of a resource-limited distributional analysis of Dutch and English child-directed speech. The results show that the same version of MOSAIC is able to simulate changes in the pattern of finiteness marking in 2 children learning Dutch and 2 children learning English as (...)
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  23.  80
    Ron Sun, Andrew Coward & Michael J. Zenzen (2005). On Levels of Cognitive Modeling. Philosophical Psychology 18 (5):613-637.
    The article first addresses the importance of cognitive modeling, in terms of its value to cognitive science (as well as other social and behavioral sciences). In particular, it emphasizes the use of cognitive architectures in this undertaking. Based on this approach, the article addresses, in detail, the idea of a multi-level approach that ranges from social to neural levels. In physical sciences, a rigorous set of theories is a hierarchy of descriptions/explanations, in which causal relationships among entities at a high (...)
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  24.  67
    Vladimir Kuznetsov (2009). Variables of Scientific Concept Modeling and Their Formalization. In В.И Маркин (ed.), Philosophy of mathematics: current problems. Proceedings of the second international conference (Философия математики: актуальные проблемы. Тезисы второй международной конференции). Макс Пресс 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 was (...)
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  25.  6
    Takashi Miura (2013). Modeling Lung Branching Morphogenesis. Biological Theory 8 (3):265-273.
    Biological forms are very complex, and mechanisms of pattern formation are not well understood. Although developmental biology deals with the mechanistic explanation of patterns, currently we do not know how to understand the mechanisms of pattern formation from huge amounts of molecular information. In this article, I present one useful tool, mathematical modeling, to obtain a mechanistic understanding of biological pattern formation, and show an actual example in lung branching morphogenesis. In this example, mathematical modeling plays an indispensable role in (...)
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  26.  34
    James Justus (2006). Loop Analysis and Qualitative Modeling: Limitations and Merits. [REVIEW] Biology and Philosophy 21 (5):647-666.
    Richard Levins has advocated the scientific merits of qualitative modeling throughout his career. He believed an excessive and uncritical focus on emulating the models used by physicists and maximizing quantitative precision was hindering biological theorizing in particular. Greater emphasis on qualitative properties of modeled systems would help counteract this tendency, and Levins subsequently developed one method of qualitative modeling, loop analysis, to study a wide variety of biological phenomena. Qualitative modeling has been criticized for being conceptually and methodologically problematic. As (...)
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  27.  12
    Sheldene K. Simola (2010). Use of a "Coping-Modeling, Problem-Solving" Program in Business Ethics Education. Journal of Business Ethics 96 (3):383 - 401.
    During the last decade, scholars have identified a number of factors that pose significant challenges to effective business ethics education. This article offers a "coping-modeling, problem-solving" (CMPS) approach (Cunningham, 2006) as one option for addressing these concerns. A rationale supporting the use of the CMPS framework for courses on ethical decisionmaking in business is provided, following which the implementation processes for this program are described. Evaluative data collected from N = 101 undergraduate business students enrolled in a third year required (...)
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  28.  20
    Roger Stanev (2012). Modelling and Simulating Early Stopping of RCTs: A Case Study of Early Stop Due to Harm. Journal of Experimental and Theoretical Artificial Intelligence 24 (4):513-526.
    Despite efforts from regulatory agencies (e.g. NIH, FDA), recent systematic reviews of randomised controlled trials (RCTs) show that top medical journals continue to publish trials without requiring authors to report details for readers to evaluate early stopping decisions carefully. This article presents a systematic way of modelling and simulating interim monitoring decisions of RCTs. By taking an approach that is both general and rigorous, the proposed framework models and evaluates early stopping decisions of RCTs based on a clear and (...)
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  29.  17
    Martin Thomson-Jones (2012). Modeling Without Mathematics. Philosophy of Science 79 (5):761-772.
    Inquiries into the nature of scientific modeling have tended to focus their attention on mathematical models and, relatedly, to think of nonconcrete models as mathematical structures. The arguments of this article are arguments for rethinking both tendencies. Nonmathematical models play an important role in the sciences, and our account of scientific modeling must accommodate that fact. One key to making such accommodations, moreover, is to recognize that one kind of thing we use the term ‘model’ to refer to is a (...)
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  30.  12
    Michael S. C. Thomas, Neil A. Forrester & Angelica Ronald (2016). Multiscale Modeling of Gene–Behavior Associations in an Artificial Neural Network Model of Cognitive Development. Cognitive Science 40 (1):51-99.
    In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction of the whole organism with a variable environment. Given (...)
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  31.  13
    Miles MacLeod (2016). Heuristic Approaches to Models and Modeling in Systems Biology. Biology and Philosophy 31 (3):353-372.
    Prediction and control sufficient for reliable medical and other interventions are prominent aims of modeling in systems biology. The short-term attainment of these goals has played a strong role in projecting the importance and value of the field. In this paper I identify the standard models must meet to achieve these objectives as predictive robustness—predictive reliability over large domains. Drawing on the results of an ethnographic investigation and various studies in the systems biology literature, I explore four current obstacles to (...)
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  32.  28
    John Stewart & Olivier Gapenne (2004). Reciprocal Modelling of Active Perception of 2-D Forms in a Simple Tactile-Vision Substitution System. Minds and Machines 14 (3):309-330.
    The strategies of action employed by a human subject in order to perceive simple 2-D forms on the basis of tactile sensory feedback have been modelled by an explicit computer algorithm. The modelling process has been constrained and informed by the capacity of human subjects both to consciously describe their own strategies, and to apply explicit strategies; thus, the strategies effectively employed by the human subject have been influenced by the modelling process itself. On this basis, good qualitative (...)
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  33.  30
    Robert A. Miller (2002). The Frankenstein Syndrome: The Creation of Mega-Media Conglomerates and Ethical Modeling in Journalism. [REVIEW] Journal of Business Ethics 36 (1-2):105 - 110.
    Aristotle saw ethics as a habit that is modeled and developed though practice. Shelly's Victor Frankenstein, though well intentioned in his goals, failed to model ethical behavior for his creation, abandoning it to its own recourse. Today we live in an era of unfettered mergers and acquisitions where once separate and independent media increasingly are concentrated under the control and leadership of the fictitious but legal personhood of a few conglomerated corporations. This paper will explore the impact of mega-media mergers (...)
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  34.  69
    Rosanna Keefe (2012). Modelling Vagueness: What Can We Ignore? Philosophical Studies 161 (3):453-470.
    A theory of vagueness gives a model of vague language and of reasoning within the language. Among the models that have been offered are Degree Theorists’ numerical models that assign values between 0 and 1 to sentences, rather than simply modelling sentences as true or false. In this paper, I ask whether we can benefit from employing a rich, well-understood numerical framework, while ignoring those aspects of it that impute a level of mathematical precision that is not present in (...)
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  35.  7
    Janet H. Hsiao & Kit Cheung (2016). Visual Similarity of Words Alone Can Modulate Hemispheric Lateralization in Visual Word Recognition: Evidence From Modeling Chinese Character Recognition. Cognitive Science 40 (2):351-372.
    In Chinese orthography, the most common character structure consists of a semantic radical on the left and a phonetic radical on the right ; the minority, opposite arrangement also exists. Recent studies showed that SP character processing is more left hemisphere lateralized than PS character processing. Nevertheless, it remains unclear whether this is due to phonetic radical position or character type frequency. Through computational modeling with artificial lexicons, in which we implement a theory of hemispheric asymmetry in perception but do (...)
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  36.  36
    Raphael Scholl & Tim Räz (2013). Modeling Causal Structures. European Journal for Philosophy of Science 3 (1):115-132.
    The Lotka–Volterra predator-prey-model is a widely known example of model-based science. Here we reexamine Vito Volterra’s and Umberto D’Ancona’s original publications on the model, and in particular their methodological reflections. On this basis we develop several ideas pertaining to the philosophical debate on the scientific practice of modeling. First, we show that Volterra and D’Ancona chose modeling because the problem in hand could not be approached by more direct methods such as causal inference. This suggests a philosophically insightful motivation for (...)
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  37.  19
    Jean Pierre Ponssard & Olivier Saulpic (2005). Economic Modeling Triggers More Efficient Planning: An Experimental Justification. [REVIEW] Theory and Decision 58 (3):239-282.
    Consider a firm as an organization that needs to efficiently coordinate several specialized departments in an uncertain environment. Decision making involves collective planning sessions and decentralized operational processes. In this setting this paper explores the role of economic modeling through an experimental game. Results support the idea that economic modeling favors higher performance. Economic modeling facilitates the emergence of common knowledge and the decomposition of a group decision problem into individual decision problems that are meaningfully interrelated.
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  38.  4
    William M. Goodwin (2015). Global Climate Modeling as Applied Science. Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 46 (2):339-350.
    In this paper I argue that the appropriate analogy for “understanding what makes simulation results reliable” in global climate modeling is not with scientific experimentation or measurement, but—at least in the case of the use of global climate models for policy development—with the applications of science in applied design problems. The prospects for using this analogy to argue for the quantitative reliability of GCMs are assessed and compared with other potential strategies.
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  39.  34
    Markus F. Peschl & Chris Stary (1998). The Role of Cognitive Modeling for User Interface Design Representations: An Epistemological Analysis of Knowledge Engineering in the Context of Human-Computer Interaction. [REVIEW] Minds and Machines 8 (2):203-236.
    In this paper we review some problems with traditional approaches for acquiring and representing knowledge in the context of developing user interfaces. Methodological implications for knowledge engineering and for human-computer interaction are studied. It turns out that in order to achieve the goal of developing human-oriented (in contrast to technology-oriented) human-computer interfaces developers have to develop sound knowledge of the structure and the representational dynamics of the cognitive system which is interacting with the computer.We show that in a first step (...)
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  40.  31
    Patrick Grim & Nicholas Rescher (2013). How Modeling Can Go Wrong. Philosophy and Technology 26 (1):75-80.
    Modeling and simulation clearly have an upside. My discussion here will deal with the inevitable downside of modeling — the sort of things that can go wrong. It will set out a taxonomy for the pathology of models — a catalogue of the various ways in which model contrivance can go awry. In the course of that discussion, I also call on some of my past experience with models and their vulnerabilities.
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  41.  18
    John Kingston, Burkhard Schafer & Wim Vandenberghe (2004). Towards a Financial Fraud Ontology: A Legal Modelling Approach. [REVIEW] Artificial Intelligence and Law 12 (4):419-446.
    This document discusses the status of research on detection and prevention of financial fraud undertaken as part of the IST European Commission funded FF POIROT (Financial Fraud Prevention Oriented Information Resources Using Ontology Technology) project. A first task has been the specification of the user requirements that define the functionality of the financial fraud ontology to be designed by the FF POIROT partners. It is claimed here that modeling fraudulent activity involves a mixture of law and facts as well as (...)
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  42.  14
    Han-Liang Chang (2009). Semioticians Make Strange Bedfellows! Or, Once Again: “Is Language a Primary Modelling System?”. [REVIEW] Biosemiotics 2 (2):169-179.
    Like other sciences, biosemiotics also has its time-honoured archive, consisting of writings by those who have been invented and revered as ancestors of the discipline. One such example is Jakob von Uexküll. As to the people who ‘invented’ him, they are either, to paraphrase a French cliché, ‘agents du cosmopolitisme sémiotique’ like Thomas Sebeok, or de jure and de facto progenitor like Thure von Uexküll. In the archive is the special issue of Semiotica 42. 1 (1982) edited by the late (...)
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  43.  7
    Zenonas Norkus (2012). Modeling in Historical Research Practice and Methodology: Contributions From Poland. History and Theory 51 (2):292-304.
    This selection of texts should interest those who study analytical philosophy of history, methodology of history, and historical sociology. It contains contributions by Polish historians and philosophers since 1931, with pride of place given to the work of the Poznań school in the philosophy of science and humanities. With Jerzy Kmita, Leszek Nowak, and Jerzy Topolski as its leaders, it emerged in late 1960s as a synthesis of Marxism and the Polish brand of logical positivism known as the Lwow-Warsaw school. (...)
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  44.  46
    William M. Goodwin (2015). Global Climate Modeling as Applied Science. Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 46 (2):339-350.
    In this paper I argue that the appropriate analogy for “understanding what makes simulation results reliable” in global climate modeling is not with scientific experimentation or measurement, but—at least in the case of the use of global climate models for policy development—with the applications of science in applied design problems. The prospects for using this analogy to argue for the quantitative reliability of GCMs are assessed and compared with other potential strategies.
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  45.  47
    Paulo Abrantes (1999). Analogical Reasoning and Modeling in the Sciences. Foundations of Science 4 (3):237-270.
    This paper aims at integrating the work onanalogical reasoning in Cognitive Science into thelong trend of philosophical interest, in this century,in analogical reasoning as a basis for scientificmodeling. In the first part of the paper, threesimulations of analogical reasoning, proposed incognitive science, are presented: Gentner''s StructureMatching Engine, Mitchel''s and Hofstadter''s COPYCATand the Analogical Constraint Mapping Engine, proposedby Holyoak and Thagard. The differences andcontroversial points in these simulations arehighlighted in order to make explicit theirpresuppositions concerning the nature of analogicalreasoning. In the (...)
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  46.  18
    Timothy R. Colburn (1998). Information Modeling Aspects of Software Development. Minds and Machines 8 (3):375-393.
    The distinction between the modeling of information and the modeling of data in the creation of automated systems has historically been important because the development tools available to programmers have been wedded to machine oriented data types and processes. However, advances in software engineering, particularly the move toward data abstraction in software design, allow activities reasonably described as information modeling to be performed in the software creation process. An examination of the evolution of programming languages and development of general programming (...)
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  47.  6
    Roberto Poli (2016). Belief Systems and the Modeling Relation. Foundations of Science 21 (1):195-206.
    The paper presents the most general aspects of scientific modeling and shows that social systems naturally include different belief systems. Belief systems differ in a variety of respects, most notably in the selection of suitable qualities to encode and the internal structure of the observables. The following results emerge from the analysis: conflict is explained by showing that different models encode different qualities, which implies that they model different realities; explicitly connecting models to the realities that they encode makes it (...)
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  48.  41
    Bruce Edmonds (2000). Complexity and Scientific Modelling. Foundations of Science 5 (3):379-390.
    It is argued that complexity is not attributable directly to systems or processes but rather to the descriptions of their `best' models, to reflect their difficulty. Thus it is relative to the modelling language and type of difficulty. This approach to complexity is situated in a model of modelling. Such an approach makes sense of a number of aspects of scientific modelling: complexity is not situated between order and disorder; noise can be explicated by approaches to excess (...)
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    Colleen Murphy, Paolo Gardoni & Charles Harris (2011). Classification and Moral Evaluation of Uncertainties in Engineering Modeling. Science and Engineering Ethics 17 (3):553-570.
    Engineers must deal with risks and uncertainties as a part of their professional work and, in particular, uncertainties are inherent to engineering models. Models play a central role in engineering. Models often represent an abstract and idealized version of the mathematical properties of a target. Using models, engineers can investigate and acquire understanding of how an object or phenomenon will perform under specified conditions. This paper defines the different stages of the modeling process in engineering, classifies the various sources of (...)
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    Christine W. Chan (2003). Cognitive Modeling and Representation of Knowledge in Ontological Engineering. Brain and Mind 4 (2):269-282.
    This paper describes the processes of cognitive modeling and representation of human expertise for developing an ontology and knowledge base of an expert system. An ontology is an organization and classification of knowledge. Ontological engineering in artificial intelligence (AI) has the practical goal of constructing frameworks for knowledge that allow computational systems to tackle knowledge-intensive problems and supports knowledge sharing and reuse. Ontological engineering is also a process that facilitates construction of the knowledge base of an intelligent system, which can (...)
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