Search results for '*Models' (try it on Scholar)

1000+ found
Sort by:
  1. Axel Gelfert (2011). Scientific Models, Simulation, and the Experimenter's Regress. In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. Routledge.score: 18.0
    According to the "experimenter's regress", disputes about the validity of experimental results cannot be closed by objective facts because no conclusive criteria other than the outcome of the experiment itself exist for deciding whether the experimental apparatus was functioning properly or not. Given the frequent characterization of simulations as "computer experiments", one might worry that an analogous regress arises for computer simulations. The present paper analyzes the most likely scenarios where one might expect such a "simulationist's regress" to surface, and, (...)
    Direct download  
     
    My bibliography  
     
    Export citation  
  2. Alisa Bokulich (2011). How Scientific Models Can Explain. Synthese 180 (1):33 - 45.score: 16.0
    Scientific models invariably involve some degree of idealization, abstraction, or nationalization of their target system. Nonetheless, I argue that there are circumstances under which such false models can offer genuine scientific explanations. After reviewing three different proposals in the literature for how models can explain, I shall introduce a more general account of what I call model explanations, which specify the conditions under which models can be counted as explanatory. I shall illustrate this new framework by applying it to the (...)
    Direct download (6 more)  
     
    My bibliography  
     
    Export citation  
  3. Gabriele Contessa (2010). Scientific Models and Fictional Objects. Synthese 172 (2):215 - 229.score: 16.0
    In this paper, I distinguish scientific models in three kinds on the basis of their ontological status—material models, mathematical models and fictional models, and develop and defend an account of fictional models as fictional objects—i.e. abstract objects that stand for possible concrete objects.
    Direct download (6 more)  
     
    My bibliography  
     
    Export citation  
  4. Adam Toon (2010). Models as Make-Believe. In Roman Frigg & Matthew Hunter (eds.), Beyond Mimesis and Convention: Representation in Art and Science. Boston Studies in Philosophy of Science.score: 16.0
    In this paper I propose an account of representation for scientific models based on Kendall Walton’s ‘make-believe’ theory of representation in art. I first set out the problem of scientific representation and respond to a recent argument due to Craig Callender and Jonathan Cohen, which aims to show that the problem may be easily dismissed. I then introduce my account of models as props in games of make-believe and show how it offers a solution to the problem. Finally, I demonstrate (...)
    Direct download (2 more)  
     
    My bibliography  
     
    Export citation  
  5. 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: 16.0
    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 (...)
    Direct download (7 more)  
     
    My bibliography  
     
    Export citation  
  6. Stathis Psillos (2011). Living with the Abstract: Realism and Models. Synthese 180 (1):3 - 17.score: 16.0
    A natural way to think of models is as abstract entities. If theories employ models to represent the world, theories traffic in abstract entities much more widely than is often assumed. This kind of thought seems to create a problem for a scientific realist approach to theories. Scientific realists claim theories should be understood literally. Do they then imply (and are they committed to) the reality of abstract entities? Or are theories simply—and incurably—false (if there are no abstract entities)? Or (...)
    Direct download (6 more)  
     
    My bibliography  
     
    Export citation  
  7. Daniel A. Weiskopf (2011). Models and Mechanisms in Psychological Explanation. Synthese 183 (3):313-338.score: 16.0
    Mechanistic explanation has an impressive track record of advancing our understanding of complex, hierarchically organized physical systems, particularly biological and neural systems. But not every complex system can be understood mechanistically. Psychological capacities are often understood by providing cognitive models of the systems that underlie them. I argue that these models, while superficially similar to mechanistic models, in fact have a substantially more complex relation to the real underlying system. They are typically constructed using a range of techniques for abstracting (...)
    Direct download (7 more)  
     
    My bibliography  
     
    Export citation  
  8. Ronald Giere (2010). An Agent-Based Conception of Models and Scientific Representation. Synthese 172 (2):269–281.score: 16.0
    I argue for an intentional conception of representation in science that requires bringing scientific agents and their intentions into the picture. So the formula is: Agents (1) intend; (2) to use model, M; (3) to represent a part of the world, W; (4) for some purpose, P. This conception legitimates using similarity as the basic relationship between models and the world. Moreover, since just about anything can be used to represent anything else, there can be no unified ontology of models. (...)
    Direct download (8 more)  
     
    My bibliography  
     
    Export citation  
  9. Sylvia Wenmackers & Danny E. P. Vanpoucke (2012). Models and Simulations in Material Science: Two Cases Without Error Bars. Statistica Neerlandica 66 (3):339–355.score: 16.0
    We discuss two research projects in material science in which the results cannot be stated with an estimation of the error: a spectroscopic ellipsometry study aimed at determining the orientation of DNA molecules on diamond and a scanning tunneling microscopy study of platinum-induced nanowires on germanium. To investigate the reliability of the results, we apply ideas from the philosophy of models in science. Even if the studies had reported an error value, the trustworthiness of the result would not depend on (...)
    Direct download (2 more)  
     
    My bibliography  
     
    Export citation  
  10. Thomas Mormann, McKinsey Algebras and Topological Models of S4.1.score: 16.0
    The aim of this paper is to show that every topological space gives rise to a wealth of topological models of the modal logic S4.1. The construction of these models is based on the fact that every space defines a Boolean closure algebra (to be called a McKinsey algebra) that neatly reflects the structure of the modal system S4.1. It is shown that the class of topological models based on McKinsey algebras contains a canonical model that can be used to (...)
    Translate to English
    | Direct download  
     
    My bibliography  
     
    Export citation  
  11. David Michael Kaplan & Carl F. Craver (2011). The Explanatory Force of Dynamical and Mathematical Models in Neuroscience: A Mechanistic Perspective. Philosophy of Science 78 (4):601-627.score: 16.0
    We argue that dynamical and mathematical models in systems and cognitive neuro- science explain (rather than redescribe) a phenomenon only if there is a plausible mapping between elements in the model and elements in the mechanism for the phe- nomenon. We demonstrate how this model-to-mechanism-mapping constraint, when satisfied, endows a model with explanatory force with respect to the phenomenon to be explained. Several paradigmatic models including the Haken-Kelso-Bunz model of bimanual coordination and the difference-of-Gaussians model of visual receptive fields are (...)
    Direct download (3 more)  
     
    My bibliography  
     
    Export citation  
  12. Patricia H. Werhane (2008). Mental Models, Moral Imagination and System Thinking in the Age of Globalization. Journal of Business Ethics 78 (3):463 - 474.score: 16.0
    After experiments with various economic systems, we appear to have conceded, to misquote Winston Churchill that "free enterprise is the worst economic system, except all the others that have been tried." Affirming that conclusion, I shall argue that in today's expanding global economy, we need to revisit our mind-sets about corporate governance and leadership to fit what will be new kinds of free enterprise. The aim is to develop a values-based model for corporate governance in this age of globalization that (...)
    Direct download (5 more)  
     
    My bibliography  
     
    Export citation  
  13. Isabelle Peschard (2007). The Value(s) of a Story: Theories, Models and Cognitive Values. Principia 11 (2):151-169.score: 16.0
    This paper aims 1) to introduce the notion of theoretical story as a resource and source of constraint for the construction and assessment of models of phenomena; 2) to show the relevance of this notion for a better understanding of the role and nature of values in scientific activity. The reflection on the role of values and value judgments in scientific activity should be attentive, I will argue, to the distinction between models and the theoretical story that guides and constrains (...)
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  14. Roman Frigg (2010). Models and Fiction. Synthese 172 (2):251 - 268.score: 16.0
    Most scientific models are not physical objects, and this raises important questions. What sort of entity are models, what is truth in a model, and how do we learn about models? In this paper I argue that models share important aspects in common with literary fiction, and that therefore theories of fiction can be brought to bear on these questions. In particular, I argue that the pretence theory as developed by Walton (1990, Mimesis as make-believe: on the foundations of (...)
    Direct download (10 more)  
     
    My bibliography  
     
    Export citation  
  15. Peter Godfrey-Smith (2009). Models and Fictions in Science. Philosophical Studies 143 (1):101 - 116.score: 16.0
    Non-actual model systems discussed in scientific theories are compared to fictions in literature. This comparison may help with the understanding of similarity relations between models and real-world target systems. The ontological problems surrounding fictions in science may be particularly difficult, however. A comparison is also made to ontological problems that arise in the philosophy of mathematics.
    Direct download (6 more)  
     
    My bibliography  
     
    Export citation  
  16. David M. Kaplan & William Bechtel (2011). Dynamical Models: An Alternative or Complement to Mechanistic Explanations? Topics in Cognitive Science 3 (2):438-444.score: 16.0
    Abstract While agreeing that dynamical models play a major role in cognitive science, we reject Stepp, Chemero, and Turvey's contention that they constitute an alternative to mechanistic explanations. We review several problems dynamical models face as putative explanations when they are not grounded in mechanisms. Further, we argue that the opposition of dynamical models and mechanisms is a false one and that those dynamical models that characterize the operations of mechanisms overcome these problems. By briefly considering examples involving the generation (...)
    Direct download (11 more)  
     
    My bibliography  
     
    Export citation  
  17. Margaret Morrison (2011). One Phenomenon, Many Models: Inconsistency and Complementarity. Studies in History and Philosophy of Science 42 (2):342-351.score: 16.0
    The paper examines philosophical issues that arise in contexts where one has many different models for treating the same system. I show why in some cases this appears relatively unproblematic (models of turbulence) while others represent genuine difficulties when attempting to interpret the information that models provide (nuclear models). What the examples show is that while complementary models needn’t be a hindrance to knowledge acquisition, the kind of inconsistency present in nuclear cases is, since it is indicative of a lack (...)
    Direct download (3 more)  
     
    My bibliography  
     
    Export citation  
  18. Toby Handfield, Charles R. Twardy, Kevin B. Korb & Graham Oppy (2008). The Metaphysics of Causal Models: Where's the Biff? Erkenntnis 68 (2):149-68.score: 16.0
    This paper presents an attempt to integrate theories of causal processes—of the kind developed by Wesley Salmon and Phil Dowe—into a theory of causal models using Bayesian networks. We suggest that arcs in causal models must correspond to possible causal processes. Moreover, we suggest that when processes are rendered physically impossible by what occurs on distinct paths, the original model must be restricted by removing the relevant arc. These two techniques suffice to explain cases of late preëmption and other cases (...)
    Direct download (9 more)  
     
    My bibliography  
     
    Export citation  
  19. H. G. Callaway (2014). Abduction, Competing Models and the Virtues of Hypotheses. In Lorenzo Magnani (ed.), (2014) Model-Based Reasoning in Science and Technology. Springer. 263-280.score: 16.0
    This paper focuses on abduction as explicit or readily formulatable inference to possible explanatory hypotheses--as contrasted with inference to conceptual innovations or abductive logic as a cycle of hypotheses, deduction of consequences and inductive testing. Inference to an explanation is often a matter of projection or extrapolation of elements of accepted theory for the solution of outstanding problems in particular domains of inquiry. I say "projections or extrapolation" of accepted theory, but I mean to point to something broader and suggest (...)
    Direct download  
     
    My bibliography  
     
    Export citation  
  20. Mauro Dorato & Laura Felline (2010). Structural Explanations in Minkowski Spacetime: Which Account of Models? In V. Petkov (ed.), Space, Time, and Spacetime. Springer. 193-207.score: 16.0
    In this paper we argue that structural explanations are an effective way of explaining well known relativistic phenomena like length contraction and time dilation, and then try to understand how this can be possible by looking at the literature on scientific models. In particular, we ask whether and how a model like that provided by Minkowski spacetime can be said to represent the physical world, in such a way that it can successfully explain physical phenomena structurally. We conclude by claiming (...)
    Translate to English
    | Direct download  
     
    My bibliography  
     
    Export citation  
  21. Bruno G. Bara & Monica Bucciarelli (2000). Deduction and Induction: Reasoning Through Mental Models. [REVIEW] Mind and Society 1 (1):95-107.score: 16.0
    In this paper we deal with two types of reasoning: induction, and deduction First, we present a unified computational model of deductive reasoning through models, where deduction occurs in five phases: Construction, Integration, Conclusion, Falsification, and Response. Second, we make an attempt, to analyze induction through the same phases. Our aim is an explorative evaluation of the mental processes possibly shared by deductive and inductive reasoning.
    Direct download (5 more)  
     
    My bibliography  
     
    Export citation  
  22. Jeffrey Koperski, Models. Internet Encyclopedia of Philosophy.score: 16.0
    The word “model” is highly ambiguous, and there is no uniform terminology used by either scientists or philosophers. Here, a model is considered to be a representation of some object, behavior, or system that one wants to understand. This article presents the most common type of models found in science as well as the different relations—traditionally called “analogies”—between models and between a given model and its subject. Although once considered merely heuristic devices, they are now seen as indispensable to modern (...)
    Direct download  
     
    My bibliography  
     
    Export citation  
  23. Sebastian Lutz (2014). What's Right with a Syntactic Approach to Theories and Models? Erkenntnis:1-18.score: 16.0
    Syntactic approaches in the philosophy of science, which are based on formalizations in predicate logic, are often considered in principle inferior to semantic approaches, which are based on formalizations with the help of structures. To compare the two kinds of approach, I identify some ambiguities in common semantic accounts and explicate the concept of a structure in a way that avoids hidden references to a specific vocabulary. From there, I argue that contrary to common opinion (i) unintended models do not (...)
    Direct download (3 more)  
     
    My bibliography  
     
    Export citation  
  24. Markku Roinila (2008). Leibniz's Models of Rational Decision. In Marcelo Dascal (ed.), Leibniz: What Kind of Rationalist? Springer. 357-370.score: 16.0
    Leibniz frequently argued that reasons are to be weighed against each other as in a pair of scales, as Professor Marcelo Dascal has shown in his article "The Balance of Reason." In this kind of weighing it is not necessary to reach demonstrative certainty – one need only judge whether the reasons weigh more on behalf of one or the other option However, a different kind of account about rational decision-making can be found in some of Leibniz's writings. In his (...)
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  25. Erich Schienke, Seth Baum, Nancy Tuana, Kenneth Davis & Klaus Keller (2011). Intrinsic Ethics Regarding Integrated Assessment Models for Climate Management. Science and Engineering Ethics 17 (3):503-523.score: 16.0
    In this essay we develop and argue for the adoption of a more comprehensive model of research ethics than is included within current conceptions of responsible conduct of research (RCR). We argue that our model, which we label the ethical dimensions of scientific research (EDSR), is a more comprehensive approach to encouraging ethically responsible scientific research compared to the currently typically adopted approach in RCR training. This essay focuses on developing a pedagogical approach that enables scientists to better understand and (...)
    Direct download (7 more)  
     
    My bibliography  
     
    Export citation  
  26. Ted Peters (2007). Models of God. Philosophia 35 (3-4):273-288.score: 16.0
    This essay compares and contrasts nine different conceptual models of God: atheism, agnosticism, deism, theism, pantheism, polytheism, henotheism, panentheism, and eschatological panentheism. This essay justifies employment of the model method in theology based on commitments within philosophical hermeneutics, philosophy of science, and the theological understanding of divine transcendence. The result is an array of conceptual models of the divine which have reference, but which make indirect rather than literal claims. Of the analyzed models, this essay defends “eschatological panentheism” as the (...)
    Direct download (6 more)  
     
    My bibliography  
     
    Export citation  
  27. Demetris Portides (2011). Seeking Representations of Phenomena: Phenomenological Models. Studies in History and Philosophy of Science 42 (2):334-341.score: 16.0
    A distinction is made between theory-driven and phenomenological models. It is argued that phenomenological models are significant means by which theory is applied to phenomena. They act both as sources of knowledge of their target systems and are explanatory of the behaviors of the latter. A version of the shell-model of nuclear structure is analyzed and it is explained why such a model cannot be understood as being subsumed under the theory structure of Quantum Mechanics. Thus its representational capacity does (...)
    Direct download (3 more)  
     
    My bibliography  
     
    Export citation  
  28. John Symons (2008). Computational Models of Emergent Properties. Minds and Machines 18 (4):475-491.score: 16.0
    Computational modeling plays an increasingly important explanatory role in cases where we investigate systems or problems that exceed our native epistemic capacities. One clear case where technological enhancement is indispensable involves the study of complex systems.1 However, even in contexts where the number of parameters and interactions that define a problem is small, simple systems sometimes exhibit non-linear features which computational models can illustrate and track. In recent decades, computational models have been proposed as a way to assist us in (...)
    Direct download (13 more)  
     
    My bibliography  
     
    Export citation  
  29. Hilary Putnam (2000). Nonstandard Models and Kripke's Proof of the Gödel Theorem. Notre Dame Journal of Formal Logic 41 (1):53-58.score: 16.0
    This lecture, given at Beijing University in 1984, presents a remarkable (previously unpublished) proof of the Gödel Incompleteness Theorem due to Kripke. Today we know purely algebraic techniques that can be used to give direct proofs of the existence of nonstandard models in a style with which ordinary mathematicians feel perfectly comfortable--techniques that do not even require knowledge of the Completeness Theorem or even require that logic itself be axiomatized. Kripke used these techniques to establish incompleteness by means that could, (...)
    Direct download (3 more)  
     
    My bibliography  
     
    Export citation  
  30. Brian Epstein (2008). When Local Models Fail. Philosophy of the Social Sciences 38 (1):3-24.score: 16.0
    Models treating the simple properties of social groups have a common shortcoming. Typically, they focus on the local properties of group members and the features of the world with which group members interact. I consider economic models of bureaucratic corruption, to show that (a) simple properties of groups are often constituted by the properties of the wider population, and (b) even sophisticated models are commonly inadequate to account for many simple social properties. Adequate models and social policies must treat certain (...)
    Direct download (7 more)  
     
    My bibliography  
     
    Export citation  
  31. Jeff Mitchell & Mirella Lapata (2010). Composition in Distributional Models of Semantics. Cognitive Science 34 (8):1388-1429.score: 16.0
    Vector-based models of word meaning have become increasingly popular in cognitive science. The appeal of these models lies in their ability to represent meaning simply by using distributional information under the assumption that words occurring within similar contexts are semantically similar. Despite their widespread use, vector-based models are typically directed at representing words in isolation, and methods for constructing representations for phrases or sentences have received little attention in the literature. This is in marked contrast to experimental evidence (e.g., in (...)
    Direct download (5 more)  
     
    My bibliography  
     
    Export citation  
  32. Denis Phan & Franck Varenne (2010). Agent-Based Models and Simulations in Economics and Social Sciences: From Conceptual Exploration to Distinct Ways of Experimenting. Journal of Artificial Societies and Social Simulation 13 (1).score: 16.0
    Now that complex Agent-Based Models and computer simulations spread over economics and social sciences - as in most sciences of complex systems -, epistemological puzzles (re)emerge. We introduce new epistemological concepts so as to show to what extent authors are right when they focus on some empirical, instrumental or conceptual significance of their model or simulation. By distinguishing between models and simulations, between types of models, between types of computer simulations and between types of empiricity obtained through a simulation, section (...)
    Direct download  
     
    My bibliography  
     
    Export citation  
  33. Bjørn Hofmann (2005). Simplified Models of the Relationship Between Health and Disease. Theoretical Medicine and Bioethics 26 (5):355-377.score: 16.0
    The concepts of health and disease are crucial in defining the aim and the limits of modern medicine. Accordingly it is important to understand them and their relationship. However, there appears to be a discrepancy between scholars in philosophy of medicine and health care professionals with regard to these concepts. This article investigates health care professionals’ concepts of health and disease and the relationship between them. In order to do so, four different models are described and analyzed: the ideal model, (...)
    Direct download (5 more)  
     
    My bibliography  
     
    Export citation  
  34. Alessandro Giordani & Luca Mari (2012). Measurement, Models, and Uncertainty. IEEE Transactions on Instrumentation and Measurement 61 (8):2144 - 2152.score: 16.0
    Against the tradition, which has considered measurement able to produce pure data on physical systems, the unavoidable role played by the modeling activity in measurement is increasingly acknowledged, particularly with respect to the evaluation of measurement uncertainty. This paper characterizes measurement as a knowledge-based process and proposes a framework to understand the function of models in measurement and to systematically analyze their influence in the production of measurement results and their interpretation. To this aim, a general model of measurement is (...)
    Direct download (2 more)  
     
    My bibliography  
     
    Export citation  
  35. Sandra L. Christensen (2008). The Role of Law in Models of Ethical Behavior. Journal of Business Ethics 77 (4):451 - 461.score: 16.0
    In attempting to improve ethical decision-making in business organizations, researchers have developed models of ethical decision-making processes. Most of these models do not include a role for law in ethical decision-making, or if law is mentioned, it is set as a boundary constraint, exogenous to the decision process. However, many decision models in business ethics are based on cognitive moral development theory, in which the law is thought to be the external referent of individuals at the level of cognitive development (...)
    Direct download (5 more)  
     
    My bibliography  
     
    Export citation  
  36. S. Ducheyne (2008). Towards an Ontology of Scientific Models. Metaphysica 9 (1):119-127.score: 16.0
    Scientific models occupy centre stage in scientific practice. Correspondingly, in recent literature in the philosophy of science, scientific models have been a focus of research. However, little attention has been paid so far to the ontology of scientific models. In this essay, I attempt to clarify the issues involved in formulating an informatively rich ontology of scientific models. Although no full-blown theory—containing all ontological issues involved—is provided, I make several distinctions and point to several characteristic properties exhibited by scientific models (...)
    Direct download (6 more)  
     
    My bibliography  
     
    Export citation  
  37. Sven Diekmann & Martin Peterson (2013). The Role of Non-Epistemic Values in Engineering Models. Science and Engineering Ethics 19 (1):207-218.score: 16.0
    We argue that non-epistemic values, including moral ones, play an important role in the construction and choice of models in science and engineering. Our main claim is that non-epistemic values are not only “secondary values” that become important just in case epistemic values leave some issues open. Our point is, on the contrary, that non-epistemic values are as important as epistemic ones when engineers seek to develop the best model of a process or problem. The upshot is that models are (...)
    Direct download (10 more)  
     
    My bibliography  
     
    Export citation  
  38. Gordana Dodig-Crnkovic (2011). Significance of Models of Computation, From Turing Model to Natural Computation. Minds and Machines 21 (2):301-322.score: 16.0
    The increased interactivity and connectivity of computational devices along with the spreading of computational tools and computational thinking across the fields, has changed our understanding of the nature of computing. In the course of this development computing models have been extended from the initial abstract symbol manipulating mechanisms of stand-alone, discrete sequential machines, to the models of natural computing in the physical world, generally concurrent asynchronous processes capable of modelling living systems, their informational structures and dynamics on both symbolic and (...)
    Direct download (16 more)  
     
    My bibliography  
     
    Export citation  
  39. James R. Griesemer & Michael J. Wade (1988). Laboratory Models, Causal Explanation and Group Selection. Biology and Philosophy 3 (1):67-96.score: 16.0
    We develop an account of laboratory models, which have been central to the group selection controversy. We compare arguments for group selection in nature with Darwin's arguments for natural selection to argue that laboratory models provide important grounds for causal claims about selection. Biologists get information about causes and cause-effect relationships in the laboratory because of the special role their own causal agency plays there. They can also get information about patterns of effects and antecedent conditions in nature. But to (...)
    Direct download (5 more)  
     
    My bibliography  
     
    Export citation  
  40. Fred C. Boogerd, Frank J. Bruggeman & Robert C. Richardson (2013). Mechanistic Explanations and Models in Molecular Systems Biology. Foundations of Science 18 (4):725-744.score: 16.0
    Mechanistic models in molecular systems biology are generally mathematical models of the action of networks of biochemical reactions, involving metabolism, signal transduction, and/or gene expression. They can be either simulated numerically or analyzed analytically. Systems biology integrates quantitative molecular data acquisition with mathematical models to design new experiments, discriminate between alternative mechanisms and explain the molecular basis of cellular properties. At the heart of this approach are mechanistic models of molecular networks. We focus on the articulation and development of mechanistic (...)
    Direct download (5 more)  
     
    My bibliography  
     
    Export citation  
  41. Jeremy R. Hustwit (2007). Can Models of God Compete? Philosophia 35 (3-4):433-439.score: 16.0
    Though the very task of modeling God implies that the reality of God is to some degree unknowable, there are a variety of positions one may take concerning the degree to which one has epistemic access to God. If our models of God are too influenced by subjectivity, it makes no sense to test them against each other in rational competition. In this essay, I define four possible positions that may underlie the task of God-modeling: mysteriosophy, theopoetics, critical realism, and (...)
    No categories
    Direct download (6 more)  
     
    My bibliography  
     
    Export citation  
  42. James E. Taylor (2007). Response to Ted Peters' “Models of God”. Philosophia 35 (3-4):289-292.score: 16.0
    In Models of God, Ted Peters discusses a methodology for formulating and evaluating models of God, surveys nine models, and proposes one that he entitles Eschatological Panentheism. This paper provides critical comments on Peters’ methodological claims, taxonomy of models of God, and specific proposal. This paper has been delivered during APA Pacific 2007 Mini-Conference on Models of God.Both Peters’ Models of God and these comments were presented at the Models of God mini-conference at the Pacific Division Meetings of the American (...)
    No categories
    Direct download (6 more)  
     
    My bibliography  
     
    Export citation  
  43. 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: 16.0
    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 (...)
    Direct download (6 more)  
     
    My bibliography  
     
    Export citation  
  44. Erik D. Reichle, Keith Rayner & Alexander Pollatsek (2003). The E-Z Reader Model of Eye-Movement Control in Reading: Comparisons to Other Models. Behavioral and Brain Sciences 26 (4):445-476.score: 16.0
    The E-Z Reader model (Reichle et al. 1998; 1999) provides a theoretical framework for understanding how word identification, visual processing, attention, and oculomotor control jointly determine when and where the eyes move during reading. In this article, we first review what is known about eye movements during reading. Then we provide an updated version of the model (E-Z Reader 7) and describe how it accounts for basic findings about eye movement control in reading. We then review several alternative models of (...)
    Direct download (5 more)  
     
    My bibliography  
     
    Export citation  
  45. Susan C. Johnson, Carol S. Dweck, Frances S. Chen, Hilarie L. Stern, Su-Jeong Ok & Maria Barth (2010). At the Intersection of Social and Cognitive Development: Internal Working Models of Attachment in Infancy. Cognitive Science 34 (5):807-825.score: 16.0
    Three visual habituation studies using abstract animations tested the claim that infants’ attachment behavior in the Strange Situation procedure corresponds to their expectations about caregiver–infant interactions. Three unique patterns of expectations were revealed. Securely attached infants expected infants to seek comfort from caregivers and expected caregivers to provide comfort. Insecure-resistant infants not only expected infants to seek comfort from caregivers but also expected caregivers to withhold comfort. Insecure-avoidant infants expected infants to avoid seeking comfort from caregivers and expected caregivers to (...)
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  46. Décio Krause, Jonas Arenhart & Fernando Moraes (2011). Axiomatization and Models of Scientific Theories. Foundations of Science 16 (4):363-382.score: 16.0
    In this paper we discuss two approaches to the axiomatization of scientific theories in the context of the so called semantic approach, according to which (roughly) a theory can be seen as a class of models. The two approaches are associated respectively to Suppes’ and to da Costa and Chuaqui’s works. We argue that theories can be developed both in a way more akin to the usual mathematical practice (Suppes), in an informal set theoretical environment, writing the set theoretical predicate (...)
    Direct download (7 more)  
     
    My bibliography  
     
    Export citation  
  47. Raoul Gervais & Erik Weber (2013). Plausibility Versus Richness in Mechanistic Models. Philosophical Psychology 26 (1):139-152.score: 16.0
    In this paper we argue that in recent literature on mechanistic explanations, authors tend to conflate two distinct features that mechanistic models can have or fail to have: plausibility and richness. By plausibility, we mean the probability that a model is correct in the assertions it makes regarding the parts and operations of the mechanism, i.e., that the model is correct as a description of the actual mechanism. By richness, we mean the amount of detail the model gives about the (...)
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  48. Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum (2010). Learning to Learn Causal Models. Cognitive Science 34 (7):1185-1243.score: 16.0
    Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The schema organizes the (...)
    Direct download (9 more)  
     
    My bibliography  
     
    Export citation  
  49. Piotr Kulicki (2013). On Minimal Models for Pure Calculi of Names. Logic and Logical Philosophy 22 (4):429–443.score: 16.0
    By pure calculus of names we mean a quantifier-free theory, based on the classical propositional calculus, which defines predicates known from Aristotle’s syllogistic and Leśniewski’s Ontology. For a large fragment of the theory decision procedures, defined by a combination of simple syntactic operations and models in two-membered domains, can be used. We compare the system which employs `ε’ as the only specific term with the system enriched with functors of Syllogistic. In the former, we do not need an empty name (...)
    Direct download (10 more)  
     
    My bibliography  
     
    Export citation  
  50. Chuang Liu, Fictional Models in Science.score: 16.0
    In this paper, I begin with a discussion of Giere’s recent work arguing against taking models as works of fiction. I then move on to explore a spectrum of scientific models that goes from the obviously fictional to the not so obviously fictional. And then I discuss the modeling of the unobservable and make a case for the idea that despite difficulties of defining them, unobservable systems are modeled in a fundamentally different way than the observable systems. While idealization and (...)
    Direct download  
     
    My bibliography  
     
    Export citation  
1 — 50 / 1000