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

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  1. Isabelle Peschard (2011). Modeling and Experimenting. In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. Routledge.score: 18.0
    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 (...)
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  2. 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: 18.0
    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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  3. Ron Sun, Andrew Coward & Michael J. Zenzen (2005). On Levels of Cognitive Modeling. Philosophical Psychology 18 (5):613-637.score: 18.0
    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 (...)
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  4. Marcel Weber (forthcoming). Experimental Modeling in Biology: In Vivo Representation and Stand-Ins As Modeling Strategies. Philosophy of Science.score: 18.0
    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 (...)
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  5. 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: 18.0
    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 (...)
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  6. Paulo Abrantes (1999). Analogical Reasoning and Modeling in the Sciences. Foundations of Science 4 (3):237-270.score: 18.0
    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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  7. 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: 18.0
    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 (...)
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  8. 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: 18.0
    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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  9. Alistair M. C. Isaac (2013). Modeling Without Representation. Synthese 190 (16):3611-3623.score: 18.0
    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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  10. Robert L. Ashenhurst (1996). Ontological Aspects of Information Modeling. Minds and Machines 6 (3):287-394.score: 18.0
    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 (...)
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  11. James Justus (2006). Loop Analysis and Qualitative Modeling: Limitations and Merits. [REVIEW] Biology and Philosophy 21 (5):647-666.score: 18.0
    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 (...)
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  12. Nicos A. Scordis (2011). The Morality of Risk Modeling. Journal of Business Ethics 103 (S1):7-16.score: 18.0
    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 (...)
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  13. Patrick Grim & Nicholas Rescher (2013). How Modeling Can Go Wrong. Philosophy and Technology 26 (1):75-80.score: 18.0
    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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  14. Timothy R. Colburn (1998). Information Modeling Aspects of Software Development. Minds and Machines 8 (3):375-393.score: 18.0
    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 (...)
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  15. 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.score: 18.0
    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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  16. Christine W. Chan (2003). Cognitive Modeling and Representation of Knowledge in Ontological Engineering. Brain and Mind 4 (2):269-282.score: 18.0
    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 (...)
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  17. Caspar Addyman & Robert M. French (2012). Computational Modeling in Cognitive Science: A Manifesto for Change. Topics in Cognitive Science 4 (3):332-341.score: 18.0
    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 (...)
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  18. Raphael Scholl & Tim Räz (2013). Modeling Causal Structures. European Journal for Philosophy of Science 3 (1):115-132.score: 18.0
    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 (...)
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  19. 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.score: 18.0
    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 (...)
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  20. Martin Thomson-Jones (2012). Modeling Without Mathematics. Philosophy of Science 79 (5):761-772.score: 18.0
    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 (...)
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  21. Arnon Levy (forthcoming). Modeling Without Models. Philosophical Studies:1-18.score: 18.0
    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 (...)
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  22. Sheldene K. Simola (2010). Use of a "Coping-Modeling, Problem-Solving" Program in Business Ethics Education. Journal of Business Ethics 96 (3):383 - 401.score: 18.0
    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 (...)
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  23. Nicolas Fillion & Robert M. Corless (2014). On the Epistemological Analysis of Modeling and Computational Error in the Mathematical Sciences. Synthese 191 (7):1451-1467.score: 18.0
    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 (...)
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  24. Robert G. Isaac, Irene M. Herremans & Theresa J. Kline (2010). Intellectual Capital Management Enablers: A Structural Equation Modeling Analysis. [REVIEW] Journal of Business Ethics 93 (3):373 - 391.score: 18.0
    Appropriate enablers are essential for management of intellectual capital. Through the use of structural equation modeling, we investigate whether organic renewal environments, interactive behaviors, and trust are conducive to intellectual capital management processes, as they each depend upon the establishment of a climate emphasizing mutual respect. Owing to a lack of clarity in the literature, we tested the ordering of the variables and found statistical significance for two ordering alternatives. However, the sequence presented in this article provides the best (...)
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  25. Alfred Kobsa (1990). User Modeling in Dialog Systems: Potentials and Hazards. [REVIEW] AI and Society 4 (3):214-231.score: 18.0
    In order to be capable of exhibiting a wide range of cooperative behavior, a computer-based dialog system must have available assumptions about the current user's goals, plans, background knowledge and (false) beliefs, i.e., maintain a so-called “user model”. Apart from cooperativity aspects, such a model is also necessary for intelligent coherent dialog behavior in general. This article surveys recent research on the problem of how such a model can be constructed, represented and used by a system during its interaction with (...)
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  26. Takashi Miura (2013). Modeling Lung Branching Morphogenesis. Biological Theory 8 (3):265-273.score: 18.0
    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 (...)
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  27. Jean Pierre Ponssard & Olivier Saulpic (2005). Economic Modeling Triggers More Efficient Planning: An Experimental Justification. [REVIEW] Theory and Decision 58 (3):239-282.score: 18.0
    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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  28. Thomas Goschke Stefan Scherbaum, Maja Dshemuchadse (2012). Building a Bridge Into the Future: Dynamic Connectionist Modeling as an Integrative Tool for Research on Intertemporal Choice. Frontiers in Psychology 3.score: 18.0
    Temporal discounting denotes the fact that individuals prefer smaller rewards delivered sooner over larger rewards delivered later, often to a higher extent than suggested by normative economical theories. In this article, we identify three lines of research studying this phenomenon which aim (i) to describe temporal discounting mathematically, (ii) to explain observed choice behavior psychologically, and (iii) to predict the influence of specific factors on intertemporal decisions. We then opt for an approach integrating postulated mechanisms and empirical findings from these (...)
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  29. 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.score: 18.0
    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 (...)
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  30. 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.score: 18.0
    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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  31. Ivilin P. Stoianov Marco Zorzi, Alberto Testolin (2013). Modeling Language and Cognition with Deep Unsupervised Learning: A Tutorial Overview. Frontiers in Psychology 4.score: 18.0
    Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cognitive processing. The classic letter and word perception (...)
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  32. Russell Richie, Charles Yang & Marie Coppola (2014). Modeling the Emergence of Lexicons in Homesign Systems. Topics in Cognitive Science 6 (1):183-195.score: 18.0
    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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  33. Marco Steinhauser, Heike Eichele, Hilde T. Juvodden, Rene J. Huster, Markus Md Phd Ullsperger & Tom Eichele (2012). Error-Preceding Brain Activity Reflects (Mal-)Adaptive Adjustments of Cognitive Control: A Modeling Study. Frontiers in Human Neuroscience 6.score: 18.0
    Errors in choice tasks are preceded by gradual changes in brain activity presumably related to fluctuations in cognitive control that promote the occurrence of errors. In the present paper, we use connectionist modeling to explore the hypothesis that these fluctuations reflect (mal-)adaptive adjustments of cognitive control. We considered ERP data from a study in which the probability of conflict in an Eriksen flanker task was manipulated in sub-blocks of trials. Errors in these data were preceded by a gradual decline (...)
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  34. Jaap van Brakel (2000). Modeling in Chemical Engineering. Hyle 6 (2):101 - 116.score: 18.0
    Models underlying the use of similarity considerations, dimensionless numbers, and dimensional analysis in chemical engineering are discussed. Special attention is given to the many levels at which models and ceteris paribus conditions play a role and to the modeling of initial and boundary conditions. It is shown that both the laws or dimensionless number correlations and the systems to which they apply are models. More generally, no matter which model or description one picks out, what is being modeled is (...)
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  35. 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: 16.0
    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 (...)
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  36. Giovanni Pezzulo, Lawrence W. Barsalou, Angelo Cangelosi, Martin H. Fischer, Michael Spivey & Ken McRae (2011). The Mechanics of Embodiment: A Dialog on Embodiment and Computational Modeling. Frontiers in Psychology 2.score: 16.0
    Embodied theories are increasingly challenging traditional views of cognition by arguing that conceptual representations that constitute our knowledge are grounded in sensory and motor experiences, and processed at this sensorimotor level, rather than being represented and processed abstractly in an amodal conceptual system. Given the established empirical foundation, and the relatively underspecified theories to date, many researchers are extremely interested in embodied cognition but are clamouring for more mechanistic implementations. What is needed at this stage is a push toward explicit (...)
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  37. Gordana Dodig-Crnkovic (2008). Empirical Modeling and Information Semantics. Mind & Society 7 (2):157.score: 15.0
    This paper investigates the relationship between reality and model, information and truth. It will argue that meaningful data need not be true in order to constitute information. Information to which truth-value cannot be ascribed, partially true information or even false information can lead to an interesting outcome such as technological innovation or scientific breakthrough. In the research process, during the transition between two theoretical frameworks, there is a dynamic mixture of old and new concepts in which truth is not well (...)
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  38. Franck Varenne (2001). What Does a Computer Simulation Prove? The Case of Plant Modeling at CIRAD. In N. Giambiasi & C. Frydman (eds.), Simulation in industry - ESS 2001, Proc. of the 13th European Simulation Symposium. Society for Computer Simulation (SCS).score: 15.0
    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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  39. Paul Teller (2012). Modeling, Truth, and Philosophy. Metaphilosophy 43 (3):257-274.score: 15.0
    Knowledge requires truth, and truth, we suppose, involves unflawed representation. Science does not provide knowledge in this sense but rather provides models, representations that are limited in their accuracy, precision, or, most often, both. Truth as we usually think of it is an idealization, one that serves wonderfully in most ordinary applications, but one that can terribly mislead for certain issues in philosophy. This article sketches how this happens for five important issues, thereby showing how philosophical method must take into (...)
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  40. 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: 15.0
    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 (...)
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  41. Ehud Lamm (2013). Theoreticians as Professional Outsiders: The Modeling Strategies of John von Neumann and Norbert Wiener. In Oren Harman & Michael Dietrich (eds.), Outsider Scientists: Routes to Innovation in Biology. Chicago University Press.score: 15.0
    Both von Neumann and Wiener were outsiders to biology. Both were inspired by biology and both proposed models and generalizations that proved inspirational for biologists. Around the same time in the 1940s von Neumann developed the notion of self reproducing automata and Wiener suggested an explication of teleology using the notion of negative feedback. These efforts were similar in spirit. Both von Neumann and Wiener used mathematical ideas to attack foundational issues in biology, and the concepts they articulated had lasting (...)
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  42. Anya Plutynski (2001). Modeling Evolution in Theory and Practice. Proceedings of the Philosophy of Science Association 2001 (3):S225-.score: 15.0
    This paper uses a number of examples of diverse types and functions of models in evolutionary biology to argue that the demarcation between theory and practice, or "theory model" and "data model," is often difficult to make. It is shown how both mathematical and laboratory models function as plausibility arguments, existence proofs, and refutations in the investigation of questions about the pattern and process of evolutionary history. I consider the consequences of this for the semantic approach to theories and theory (...)
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  43. Lauren N. Harkrider, Alexandra E. MacDougall, Zhanna Bagdasarov, James F. Johnson, Michael D. Mumford, Shane Connelly & Lynn D. Devenport (2014). Retracted Article: Improving Case-Based Ethics Training: How Modeling Behaviors and Forecasting Influence Effectiveness. Science and Engineering Ethics 20 (1):299-299.score: 15.0
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  44. Amit Dubey, Frank Keller & Patrick Sturt (2013). Probabilistic Modeling of Discourse‐Aware Sentence Processing. Topics in Cognitive Science 5 (3):425-451.score: 15.0
    Probabilistic models of sentence comprehension are increasingly relevant to questions concerning human language processing. However, such models are often limited to syntactic factors. This restriction is unrealistic in light of experimental results suggesting interactions between syntax and other forms of linguistic information in human sentence processing. To address this limitation, this article introduces two sentence processing models that augment a syntactic component with information about discourse co-reference. The novel combination of probabilistic syntactic components with co-reference classifiers permits them to more (...)
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  45. Franz-Peter Griesmaier (2006). Causality, Explanatoriness, and Understanding as Modeling. Journal for General Philosophy of Science 37 (1):41 - 59.score: 15.0
    The paper investigates the question as to which features of hypotheses make them explanatory. Given the intuitive appeal of causal explanations, one might suspect that explanatoriness is deeply connected with causation. I argue in detail that this is wrong by showing that none of the dominant analyses of causation are suited for general accounts of explanatoriness. In the second part, I provide the outlines of an account of explanatoriness that connects it with scientific understanding, which in turn is argued to (...)
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  46. Stefan Huber, Korbinian Moeller, Hans-Christoph Nuerk & Klaus Willmes (2013). A Computational Modeling Approach on Three‐Digit Number Processing. Topics in Cognitive Science 5 (2):317-334.score: 15.0
    Recent findings indicate that the constituting digits of multi-digit numbers are processed, decomposed into units, tens, and so on, rather than integrated into one entity. This is suggested by interfering effects of unit digit processing on two-digit number comparison. In the present study, we extended the computational model for two-digit number magnitude comparison of Moeller, Huber, Nuerk, and Willmes (2011a) to the case of three-digit number comparison (e.g., 371_826). In a second step, we evaluated how hundred-decade and hundred-unit compatibility effects (...)
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  47. Felix Engelmann, Shravan Vasishth, Ralf Engbert & Reinhold Kliegl (2013). A Framework for Modeling the Interaction of Syntactic Processing and Eye Movement Control. Topics in Cognitive Science 5 (3):452-474.score: 15.0
    We explore the interaction between oculomotor control and language comprehension on the sentence level using two well-tested computational accounts of parsing difficulty. Previous work (Boston, Hale, Vasishth, & Kliegl, 2011) has shown that surprisal (Hale, 2001; Levy, 2008) and cue-based memory retrieval (Lewis & Vasishth, 2005) are significant and complementary predictors of reading time in an eyetracking corpus. It remains an open question how the sentence processor interacts with oculomotor control. Using a simple linking hypothesis proposed in Reichle, Warren, and (...)
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  48. Patrick Grim (2002). Philosophy for Computers: Some Explorations in Philosophical Modeling. In James Moor & Terrell Ward Bynum (eds.), Cyberphilosophy: The Intersection of Philosophy and Computing. Blackwell Pub.. 181-209.score: 15.0
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  49. James L. McClelland (2009). The Place of Modeling in Cognitive Science. Topics in Cognitive Science 1 (1):11-38.score: 15.0
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  50. Michael J. North, Charles M. Macal, James St Aubin, Prakash Thimmapuram, Mark Bragen, June Hahn, James Karr, Nancy Brigham, Mark E. Lacy & Delaine Hampton (2010). Multiscale Agent‐Based Consumer Market Modeling. Complexity 15 (5):37-47.score: 15.0
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