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.score: 18.0
    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. Rosanna Keefe (2012). Modelling Vagueness: What Can We Ignore? Philosophical Studies 161 (3):453-470.score: 18.0
    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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  3. Benedikt Löwe & Thomas Müller (2011). Data and Phenomena in Conceptual Modelling. Synthese 182 (1):131-148.score: 18.0
    The distinction between data and phenomena introduced by Bogen and Woodward (Philosophical Review 97(3):303–352, 1988) was meant to help accounting for scientific practice, especially in relation with scientific theory testing. Their article and the subsequent discussion is primarily viewed as internal to philosophy of science. We shall argue that the data/phenomena distinction can be used much more broadly in modelling processes in philosophy.
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  4. Bruce Edmonds (2000). Complexity and Scientific Modelling. Foundations of Science 5 (3):379-390.score: 18.0
    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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  5. Mike Page (2000). Connectionist Modelling in Psychology: A Localist Manifesto. Behavioral and Brain Sciences 23 (4):443-467.score: 18.0
    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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  6. Tjerk Gauderis (2013). Modelling Abduction in Science by Means of a Modal Adaptive Logic. Foundations of Science 18 (4):611-624.score: 18.0
    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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  7. Brendan Clarke, Bert Leuridan & Jon Williamson (2013). Modelling Mechanisms with Causal Cycles. Synthese:1-31.score: 18.0
    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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  8. Jarmo J. Ahonen (1994). On Qualitative Modelling. AI and Society 8 (1):17-28.score: 18.0
    Fundamental assumptions behind qualitative modelling are critically considered and some inherent problems in that modelling approach are outlined. The problems outlined are due to the assumption that a sufficient set of symbols representing the fundamental features of the physical world exists. That assumption causes serious problems when modelling continuous systems. An alternative for intelligent system building for cases not suitable for qualitative modelling is proposed. The proposed alternative combines neural networks and quantitative modelling.
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  9. 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.score: 18.0
    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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  10. Guillaume Wunsch, Michel Mouchart & Federica Russo (2014). Functions and Mechanisms in Structural-Modelling Explanations. Journal for General Philosophy of Science 45 (1):187-208.score: 18.0
    One way social scientists explain phenomena is by building structural models. These models are explanatory insofar as they manage to perform a recursive decomposition on an initial multivariate probability distribution, which can be interpreted as a mechanism. Explanations in social sciences share important aspects that have been highlighted in the mechanisms literature. Notably, spelling out the functioning the mechanism gives it explanatory power. Thus social scientists should choose the variables to include in the model on the basis of their function (...)
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  11. Han-Liang Chang (2009). Semioticians Make Strange Bedfellows! Or, Once Again: “Is Language a Primary Modelling System?”. [REVIEW] Biosemiotics 2 (2):169-179.score: 18.0
    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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  12. Zhengxin Chen (1993). From Participatory Design to Participating Problem Solving: Enhancing System Adaptability Through User Modelling. [REVIEW] AI and Society 7 (3):238-247.score: 18.0
    The issue on the role of users in knowledge-based systems can be investigated from two aspects: the design aspect and the functionality aspect. Participatory design is an important approach for the first aspect while system adaptability supported by user modelling is crucial to the second aspect. In the article, we discuss the second aspect. We view a knowledge-based computer system as the partner of users' problem-solving process, and we argue that the system functionality can be enhanced by adapting the (...)
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  13. 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.score: 16.0
    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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  14. Dave Billinge & Tom Addis (2008). Seeking Allies: Modelling How Listeners Choose Their Musical Friends. [REVIEW] Foundations of Science 13 (1):53-66.score: 16.0
    In this paper we describe in some detail a formal computer model of inferential discourse based on a belief system. The key issue is that a logical model in a computer, based on rational sets, can usefully model a human situation based on irrational sets. The background of this work is explained elsewhere, as is the issue of rational and irrational sets (Billinge and Addis, in: Magnani and Dossena (eds.), Computing, philosophy and cognition, 2004; Stepney et al., Journey: Non-classical philosophy—socially (...)
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  15. Jacinto González-Pachón & Sixto Ríos-Insua (1999). Mixture of Maximal Quasi Orders: A New Approach to Preference Modelling. Theory and Decision 47 (1):73-88.score: 16.0
    Normative theories suggest that inconsistencies be pointed out to the Decision Maker who is thus given the chance to modify his/her judgments. In this paper, we suggest that the inconsistencies problem be transferred from the Decision Maker to the Analyst. With the Mixture of Maximal Quasi Orders, rather than pointing out incoherences for the Decision Maker to change, these inconsistencies may be used as new source of information to model his/her preferences.
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  16. Guido Boella, Dov M. Gabbay, Leendert van der Torre & Serena Villata (2009). Meta-Argumentation Modelling I: Methodology and Techniques. [REVIEW] Studia Logica 93 (2-3):297-355.score: 16.0
    In this paper, we introduce the methodology and techniques of meta-argumentation to model argumentation. The methodology of meta-argumentation instantiates Dung’s abstract argumentation theory with an extended argumentation theory, and is thus based on a combination of the methodology of instantiating abstract arguments, and the methodology of extending Dung’s basic argumentation frameworks with other relations among abstract arguments. The technique of meta-argumentation applies Dung’s theory of abstract argumentation to itself, by instantiating Dung’s abstract arguments with meta-arguments using a technique called flattening. (...)
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  17. Michael E. Brown & Linda K. Treviño (2013). Do Role Models Matter? An Investigation of Role Modeling as an Antecedent of Perceived Ethical Leadership. Journal of Business Ethics:1-12.score: 16.0
    Thus far, we know much more about the significant outcomes of perceived ethical leadership than we do about its antecedents. In this study, we focus on multiple types of ethical role models as antecedents of perceived ethical leadership. According to social learning theory, role models facilitate the acquisition of moral and other types of behavior. Yet, we do not know whether having had ethical role models influences follower perceptions of one’s ethical leadership and, if so, what kinds of role models (...)
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  18. Yves Maury, Morgane Gauthier, Marc Peschanski & Cécile Martinat (2012). Human Pluripotent Stem Cells for Disease Modelling and Drug Screening. Bioessays 34 (1):61-71.score: 15.0
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  19. Irmeli Luukkonen and Juha Mykkänen (2012). Analyzing Process Modelling as Work Activity. Iris 35.score: 15.0
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  20. Halfdan Petursson, Linn Getz, Johann A. Sigurdsson & Irene Hetlevik (2009). Can Individuals with a Significant Risk for Cardiovascular Disease Be Adequately Identified by Combination of Several Risk Factors? Modelling Study Based on the Norwegian HUNT 2 Population. Journal of Evaluation in Clinical Practice 15 (1):103-109.score: 15.0
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  21. Anna Conte & Peter G. Moffatt (2014). The Econometric Modelling of Social Preferences. Theory and Decision 76 (1):119-145.score: 15.0
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  22. H. J. Nock & S. J. Young (2002). Modelling Asynchrony in Automatic Speech Recognition Using Loosely Coupled Hidden Markov Models. Cognitive Science 26 (3):283-301.score: 15.0
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  23. Oz Pomp & Alan Colman (2013). Disease Modelling Using Induced Pluripotent Stem Cells: Status and Prospects. Bioessays 35 (3):271-280.score: 15.0
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  24. Adam Toon (2010). The Ontology of Theoretical Modelling: Models as Make-Believe. Synthese 172 (2):301-315.score: 14.0
    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 (...)
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  25. Nick Braisby (1998). Compositionality and the Modelling of Complex Concepts. Minds and Machines 8 (4):479-508.score: 14.0
    The nature of complex concepts has important implications for the computational modelling of the mind, as well as for the cognitive science of concepts. This paper outlines the way in which RVC – a Relational View of Concepts – accommodates a range of complex concepts, cases which have been argued to be non-compositional. RVC attempts to integrate a number of psychological, linguistic and psycholinguistic considerations with the situation-theoretic view that information-carrying relations hold only relative to background situations. The central (...)
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  26. Francesco Amigoni & Viola Schiaffonati (2008). A Multiagent Approach to Modelling Complex Phenomena. Foundations of Science 13 (2):113-125.score: 14.0
    Designing models of complex phenomena is a difficult task in engineering that can be tackled by composing a number of partial models to produce a global model of the phenomena. We propose to embed the partial models in software agents and to implement their composition as a cooperative negotiation between the agents. The resulting multiagent system provides a global model of a phenomenon. We applied this approach in modelling two complex physiological processes: the heart rate regulation and the glucose-insulin (...)
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  27. Samuel Ruhmkorff (2007). The Descriptive Criterion and Models of God-Modeling: Response to Hustwit's “Can Models of God Compete?”. Philosophia 35 (3-4):441-444.score: 14.0
    In “Can Models of God Compete?”, J. R. Hustwit engages with fundamental questions regarding the epistemological foundations of modeling God. He argues that the approach of fallibilism best captures the criteria he employs to choose among different “models of God-modeling,” including one criterion that I call the Descriptive Criterion. I argue that Hustwit’s case for fallibilism should include both a stronger defense for the Descriptive Criterion and an explanation of the reasons that fallibilism does not run awry of this criterion (...)
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  28. D. M. Bailer-Jones (1999). Creative Strategies Employed in Modelling: A Case Study. [REVIEW] Foundations of Science 4 (4):375-388.score: 14.0
    This paper examines creative strategies employed inscientific modelling. It is argued that being creativepresents not a discrete event, but rather an ongoingeffort consisting of many individual `creative acts''.These take place over extended periods of time andcan be carried out by different people, working ondifferent aspects of the same project. The example ofextended extragalactic radio sources shows that, inorder to model a complicated phenomenon in itsentirety, the modelling task is split up into smallerproblems that result in several sub-models. This (...)
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  29. Margaret Morrison (2009). Models, Measurement and Computer Simulation: The Changing Face of Experimentation. Philosophical Studies 143 (1):33 - 57.score: 12.0
    The paper presents an argument for treating certain types of computer simulation as having the same epistemic status as experimental measurement. While this may seem a rather counterintuitive view it becomes less so when one looks carefully at the role that models play in experimental activity, particularly measurement. I begin by discussing how models function as “measuring instruments” and go on to examine the ways in which simulation can be said to constitute an experimental activity. By focussing on the connections (...)
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  30. Darrell P. Rowbottom (2009). Models in Biology and Physics: What's the Difference? Foundations of Science 14 (4):281-294.score: 12.0
    In Making Sense of Life , Keller emphasizes several differences between biology and physics. Her analysis focuses on significant ways in which modelling practices in some areas of biology, especially developmental biology, differ from those of the physical sciences. She suggests that natural models and modelling by homology play a central role in the former but not the latter. In this paper, I focus instead on those practices that are importantly similar, from the point of view of epistemology (...)
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  31. Isabelle Peschard (2011). Modeling and Experimenting. In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. Routledge.score: 12.0
    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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  32. Rasmus Grønfeldt Winther (2006). On the Dangers of Making Scientific Models Ontologically Independent: Taking Richard Levins' Warnings Seriously. Biology and Philosophy 21 (5):703-724.score: 12.0
    Levins and Lewontin have contributed significantly to our philosophical understanding of the structures, processes, and purposes of biological mathematical theorizing and modeling. Here I explore their separate and joint pleas to avoid making abstract and ideal scientific models ontologically independent by confusing or conflating our scientific models and the world. I differentiate two views of theorizing and modeling, orthodox and dialectical, in order to examine Levins and Lewontin’s, among others, advocacy of the latter view. I compare the positions of these (...)
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  33. Paul A. Dion (2008). Interpreting Structural Equation Modeling Results: A Reply to Martin and Cullen. [REVIEW] Journal of Business Ethics 83 (3):365 - 368.score: 12.0
    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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  34. Ron Sun, Andrew Coward & Michael J. Zenzen (2005). On Levels of Cognitive Modeling. Philosophical Psychology 18 (5):613-637.score: 12.0
    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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  35. Christian Hennig (2010). Mathematical Models and Reality: A Constructivist Perspective. [REVIEW] Foundations of Science 15 (1):29-48.score: 12.0
    To explore the relation between mathematical models and reality, four different domains of reality are distinguished: observer-independent reality (to which there is no direct access), personal reality, social reality and mathematical/formal reality. The concepts of personal and social reality are strongly inspired by constructivist ideas. Mathematical reality is social as well, but constructed as an autonomous system in order to make absolute agreement possible. The essential problem of mathematical modelling is that within mathematics there is agreement about ‘truth’, but (...)
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  36. Marcel Weber (forthcoming). Experimental Modeling in Biology: In Vivo Representation and Stand-Ins As Modeling Strategies. Philosophy of Science.score: 12.0
    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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  37. Jordi Cat (2005). Modeling Cracks and Cracking Models: Structures, Mechanisms, Boundary Conditions, Constraints, Inconsistencies and the Proper Domains of Natural Laws. Synthese 146 (3):447 - 487.score: 12.0
    The emphasis on models hasn’t completely eliminated laws from scientific discourse and philosophical discussion. Instead, I want to argue that much of physics lies beyond the strict domain of laws. I shall argue that in important cases the physics, or physical understanding, does not lie either in laws or in their properties, such as universality, consistency and symmetry. I shall argue that the domain of application commonly attributed to laws is too narrow. That is, laws can still play an important, (...)
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  38. Rosária S. Justi & John K. Gilbert (2002). Philosophy of Chemistry in University Chemical Education: The Case of Models and Modelling. [REVIEW] Foundations of Chemistry 4 (3):213-240.score: 12.0
    If chemistry is to be taught successfully, teachers must have a good subject matter knowledge (SK) of the ideas with which they are dealing, the nature of this falling within the orbit of philosophy of chemistry. They must also have a good pedagogic content knowledge (PCK), the ability to communicate SK to students, the nature of this falling within the philosophy and psychology of chemical education. Taking the case of models and modelling, important themes in the philosophy of chemistry, (...)
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  39. 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.score: 12.0
    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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  40. 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.score: 12.0
    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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  41. Paulo Abrantes (1999). Analogical Reasoning and Modeling in the Sciences. Foundations of Science 4 (3):237-270.score: 12.0
    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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  42. James R. Griesemer (1990). Modeling in the Museum: On the Role of Remnant Models in the Work of Joseph Grinnell. [REVIEW] Biology and Philosophy 5 (1):3-36.score: 12.0
    Accounts of the relation between theories and models in biology concentrate on mathematical models. In this paper I consider the dual role of models as representations of natural systems and as a material basis for theorizing. In order to explicate the dual role, I develop the concept of a remnant model, a material entity made from parts of the natural system(s) under study. I present a case study of an important but neglected naturalist, Joseph Grinnell, to illustrate the extent to (...)
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  43. 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.score: 12.0
    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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  44. Colleen Murphy, Paolo Gardoni & Charles Harris (2011). Classification and Moral Evaluation of Uncertainties in Engineering Modeling. Science and Engineering Ethics 17 (3):553-570.score: 12.0
    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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  45. Alistair M. C. Isaac (2013). Modeling Without Representation. Synthese 190 (16):3611-3623.score: 12.0
    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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  46. Robert L. Ashenhurst (1996). Ontological Aspects of Information Modeling. Minds and Machines 6 (3):287-394.score: 12.0
    Information modeling (also known as conceptual modeling or semantic data modeling) may be characterized as the formulation of a model in which information aspects of objective and subjective reality are presented (the application), independent of datasets and processes by which they may be realized (the system).A methodology for information modeling should incorporate a number of concepts which have appeared in the literature, but should also be formulated in terms of constructs which are understandable to and expressible by the system user (...)
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  47. Nicos A. Scordis (2011). The Morality of Risk Modeling. Journal of Business Ethics 103 (S1):7-16.score: 12.0
    This article applies the concept of prudence to develop the characteristics of responsible risk-modeling practices in the insurance industry. A critical evaluation of the risk-modeling process suggests that ethical judgments are emergent rather than static, vague rather than clear, particular rather than universal, and still defensible according to the discipline’s established theory, which will support a range of judgments. Thus, positive moral guides for responsible behavior are of limited practical value. Instead, by being prudent, modelers can improve their ability to (...)
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  48. D. C. Gooding & T. R. Addis (2008). Modelling Experiments as Mediating Models. Foundations of Science 13 (1):17-35.score: 12.0
    Syntactic and structural models specify relationships between their constituents but cannot show what outcomes their interaction would produce over time in the world. Simulation consists in iterating the states of a model, so as to produce behaviour over a period of simulated time. Iteration enables us to trace the implications and outcomes of inference rules and other assumptions implemented in the models that make up a theory. We apply this method to experiments which we treat as models of the particular (...)
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  49. Patrick Grim & Nicholas Rescher (2013). How Modeling Can Go Wrong. Philosophy and Technology 26 (1):75-80.score: 12.0
    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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  50. Timothy R. Colburn (1998). Information Modeling Aspects of Software Development. Minds and Machines 8 (3):375-393.score: 12.0
    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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