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

1000+ found
Order:
  1. 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.
    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 (5 more)  
     
    Export citation  
     
    My bibliography   45 citations  
  2. Alisa Bokulich (2011). How Scientific Models Can Explain. Synthese 180 (1):33 - 45.
    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 (5 more)  
     
    Export citation  
     
    My bibliography   25 citations  
  3.  56
    Axel Gelfert (2011). Scientific Models, Simulation, and the Experimenter's Regress. In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. Routledge
    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  
     
    Export citation  
     
    My bibliography  
  4. Roman Frigg (2010). Models and Fiction. Synthese 172 (2):251-268.
    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 has the resources to answer these questions. I introduce (...)
    Direct download (12 more)  
     
    Export citation  
     
    My bibliography   29 citations  
  5.  22
    Adam Toon (2012). Models as Make-Believe: Imagination, Fiction, and Scientific Representation. Palgrave Macmillan.
    Models as Make-Believe offers a new approach to scientific modelling by looking to an unlikely source of inspiration: the dolls and toy trucks of children's games of make-believe.
    Direct download (2 more)  
     
    Export citation  
     
    My bibliography   10 citations  
  6. David M. Kaplan & William Bechtel (2011). Dynamical Models: An Alternative or Complement to Mechanistic Explanations? Topics in Cognitive Science 3 (2):438-444.
    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 (9 more)  
     
    Export citation  
     
    My bibliography   16 citations  
  7. Elisabeth A. Lloyd (2010). Confirmation and Robustness of Climate Models. Philosophy of Science 77 (5):971–984.
    Recent philosophical attention to climate models has highlighted their weaknesses and uncertainties. Here I address the ways that models gain support through observational data. I review examples of model fit, variety of evidence, and independent support for aspects of the models, contrasting my analysis with that of other philosophers. I also investigate model robustness, which often emerges when comparing climate models simulating the same time period or set of conditions. Starting from Michael Weisberg’s analysis of robustness, I conclude that his (...)
    Direct download (6 more)  
     
    Export citation  
     
    My bibliography   20 citations  
  8. Eric J. Hall (2002). A Characterization of Permutation Models in Terms of Forcing. Notre Dame Journal of Formal Logic 43 (3):157-168.
    We show that if N and M are transitive models of ZFA such that N M, N and M have the same kernel and same set of atoms, and M AC, then N is a Fraenkel-Mostowski-Specker (FMS) submodel of M if and only if M is a generic extension of N by some almost homogeneous notion of forcing. We also develop a slightly modified notion of FMS submodels to characterize the case where M is a generic extension of N not (...)
    Direct download (4 more)  
     
    Export citation  
     
    My bibliography  
  9. Daniel A. Weiskopf (2011). Models and Mechanisms in Psychological Explanation. Synthese 183 (3):313-338.
    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)  
     
    Export citation  
     
    My bibliography   15 citations  
  10. Arnon Levy & Adrian Currie (2015). Model Organisms Are Not Models. British Journal for the Philosophy of Science 66 (2):327-348.
    Many biological investigations are organized around a small group of species, often referred to as ‘model organisms’, such as the fruit fly Drosophila melanogaster. The terms ‘model’ and ‘modelling’ also occur in biology in association with mathematical and mechanistic theorizing, as in the Lotka–Volterra model of predator-prey dynamics. What is the relation between theoretical models and model organisms? Are these models in the same sense? We offer an account on which the two practices are shown to have different epistemic characters. (...)
    Direct download (5 more)  
     
    Export citation  
     
    My bibliography   6 citations  
  11. Ronald Giere (2010). An Agent-Based Conception of Models and Scientific Representation. Synthese 172 (2):269–281.
    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 (9 more)  
     
    Export citation  
     
    My bibliography   13 citations  
  12.  48
    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.
    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)  
     
    Export citation  
     
    My bibliography   31 citations  
  13.  34
    Francoise Monnoyeur (2015). What is the Value of Geometric Models to Understand Matter? Epekeina 6 (2):1-13.
    This article analyzes the value of geometric models to understand matter with the examples of the Platonic model for the primary four elements (fire, air, water, and earth) and the models of carbon atomic structures in the new science of crystallography. How the geometry of these models is built in order to discover the properties of matter is explained: movement and stability for the primary elements, and hardness, softness and elasticity for the carbon atoms. These geometric models appear to have (...)
    Direct download (3 more)  
     
    Export citation  
     
    My bibliography  
  14. Peter Godfrey-Smith (2009). Models and Fictions in Science. Philosophical Studies 143 (1):101 - 116.
    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 (5 more)  
     
    Export citation  
     
    My bibliography   15 citations  
  15. Patricia H. Werhane (2008). Mental Models, Moral Imagination and System Thinking in the Age of Globalization. Journal of Business Ethics 78 (3):463 - 474.
    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 (4 more)  
     
    Export citation  
     
    My bibliography   16 citations  
  16. Piotr Kulicki (2013). On Minimal Models for Pure Calculi of Names. Logic and Logical Philosophy 22 (4):429–443.
    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)  
     
    Export citation  
     
    My bibliography  
  17.  25
    Meagan E. Brock, Andrew Vert, Vykinta Kligyte, Ethan P. Waples, Sydney T. Sevier & Michael D. Mumford (2008). Mental Models: An Alternative Evaluation of a Sensemaking Approach to Ethics Instruction. Science and Engineering Ethics 14 (3):449-472.
    In spite of the wide variety of approaches to ethics training it is still debatable which approach has the highest potential to enhance professionals’ integrity. The current effort assesses a novel curriculum that focuses on metacognitive reasoning strategies researchers use when making sense of day-to-day professional practices that have ethical implications. The evaluated trainings effectiveness was assessed by examining five key sensemaking processes, such as framing, emotion regulation, forecasting, self-reflection, and information integration that experts and novices apply in ethical decision-making. (...)
    No categories
    Direct download (5 more)  
     
    Export citation  
     
    My bibliography   16 citations  
  18.  17
    Petri Ylikoski & N. Emrah Aydinonat (2014). Understanding with Theoretical Models. Journal of Economic Methodology 21 (1):19-36.
    This paper discusses the epistemic import of highly abstract and simplified theoretical models using Thomas Schelling’s checkerboard model as an example. We argue that the epistemic contribution of theoretical models can be better understood in the context of a cluster of models relevant to the explanatory task at hand. The central claim of the paper is that theoretical models make better sense in the context of a menu of possible explanations. In order to justify this claim, we introduce a distinction (...)
    Direct download (3 more)  
     
    Export citation  
     
    My bibliography   5 citations  
  19.  53
    H. G. Callaway (2016). Fundamental Physics, Partial Models and Time’s Arrow. In L. Magnani & C. Casadio (eds.), Model-based Reasoning in Science and Technology: Logical, Epistemological and Cognitive Issues (2016). Springer 601-618.
    This paper explores the scientific viability of the concept of causality—by questioning a central element of the distinction between “fundamental” and non-fundamental physics. It will be argued that the prevalent emphasis on fundamental physics involves formalistic and idealized partial models of physical regularities abstracting from and idealizing the causal evolution of physical systems. The accepted roles of partial models and of the special sciences in the growth of knowledge help demonstrate proper limitations of the concept of fundamental physics. We expect (...)
    Direct download (2 more)  
     
    Export citation  
     
    My bibliography  
  20.  25
    Raoul Gervais & Erik Weber (2013). Plausibility Versus Richness in Mechanistic Models. Philosophical Psychology 26 (1):139-152.
    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)  
     
    Export citation  
     
    My bibliography   5 citations  
  21.  2
    Uskali Mäki (2009). MISSing the World: Models as Isolations, Representations, and Credible Worlds. Erkenntnis 70 (1):29-43.
    This article shows how the MISS account of models—as isolations and surrogate systems—accommodates and elaborates Sugden’s account of models as credible worlds and Hausman’s account of models as explorations. Theoretical models typically isolate by means of idealization, and they are representatives of some target system, which prompts issues of resemblance between the two to arise. Models as representations are constrained both ontologically (by their targets) and pragmatically (by the purposes and audiences of the modeller), and these relations are coordinated by (...)
    Direct download (2 more)  
     
    Export citation  
     
    My bibliography   11 citations  
  22.  45
    James R. Griesemer & Michael J. Wade (1988). Laboratory Models, Causal Explanation and Group Selection. Biology and Philosophy 3 (1):67-96.
    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)  
     
    Export citation  
     
    My bibliography   42 citations  
  23. 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
    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 (5 more)  
     
    Export citation  
     
    My bibliography   6 citations  
  24.  62
    Eric Hochstein (2016). One Mechanism, Many Models: A Distributed Theory of Mechanistic Explanation. Synthese 193 (5):1387-1407.
    There have been recent disagreements in the philosophy of neuroscience regarding which sorts of scientific models provide mechanistic explanations, and which do not. These disagreements often hinge on two commonly adopted, but conflicting, ways of understanding mechanistic explanations: what I call the “representation-as” account, and the “representation-of” account. In this paper, I argue that neither account does justice to neuroscientific practice. In their place, I offer a new alternative that can defuse some of these disagreements. I argue that individual models (...)
    No categories
    Direct download (5 more)  
     
    Export citation  
     
    My bibliography   1 citation  
  25.  19
    Alexander Reutlinger, Dominik Hangleiter & Stephan Hartmann (forthcoming). Understanding (With) Toy Models. British Journal for the Philosophy of Science.
    Toy models are highly idealized and extremely simple models. Although they are omnipresent across scientific disciplines, toy models are a surprisingly under-appreciated subject in the philosophy of science. The main philosophical puzzle regarding toy models is that it is an unsettled question what the epistemic goal of toy modeling is. One promising proposal for answering this question is the claim that the epistemic goal of toy models is to provide individual scientists with understanding. The aim of this paper is to (...)
    Direct download  
     
    Export citation  
     
    My bibliography  
  26. Stathis Psillos (2011). Living with the Abstract: Realism and Models. Synthese 180 (1):3-17.
    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 the reality of abstract entities? Or are theories simply—and incurably—false? Or has the very idea of literal understanding to be abandoned? Is (...)
    Direct download (5 more)  
     
    Export citation  
     
    My bibliography   5 citations  
  27.  37
    Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum (2010). Learning to Learn Causal Models. Cognitive Science 34 (7):1185-1243.
    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)  
     
    Export citation  
     
    My bibliography   8 citations  
  28.  61
    Brian Riordan & Michael N. Jones (2011). Redundancy in Perceptual and Linguistic Experience: Comparing Feature-Based and Distributional Models of Semantic Representation. Topics in Cognitive Science 3 (2):303-345.
    Abstract Since their inception, distributional models of semantics have been criticized as inadequate cognitive theories of human semantic learning and representation. A principal challenge is that the representations derived by distributional models are purely symbolic and are not grounded in perception and action; this challenge has led many to favor feature-based models of semantic representation. We argue that the amount of perceptual and other semantic information that can be learned from purely distributional statistics has been underappreciated. We compare the representations (...)
    Direct download (6 more)  
     
    Export citation  
     
    My bibliography   6 citations  
  29. 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.
    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)  
     
    Export citation  
     
    My bibliography   8 citations  
  30.  69
    Nancy Cartwright, Towfic Shomar & Mauricio Suárez (1995). The Tool Box of Science: Tools for the Building of Models with a Superconductivity Example. Poznan Studies in the Philosophy of the Sciences and the Humanities 44:137-149.
    We call for a new philosophical conception of models in physics. Some standard conceptions take models to be useful approximations to theorems, that are the chief means to test theories. Hence the heuristics of model building is dictated by the requirements and practice of theory-testing. In this paper we argue that a theory-driven view of models can not account for common procedures used by scientists to model phenomena. We illustrate this thesis with a case study: the construction of one of (...)
    Direct download  
     
    Export citation  
     
    My bibliography   19 citations  
  31.  29
    Marion Vorms (2011). Representing with Imaginary Models: Formats Matter. Studies in History and Philosophy of Science 42 (2):287-295.
    Models such as the simple pendulum, isolated populations, and perfectly rational agents, play a central role in theorising. It is now widely acknowledged that a study of scientific representation should focus on the role of such imaginary entities in scientists’ reasoning. However, the question is most of the time cast as follows: How can fictional or abstract entities represent the phenomena? In this paper, I show that this question is not well posed. First, I clarify the notion of representation, and (...)
    Direct download (3 more)  
     
    Export citation  
     
    My bibliography   6 citations  
  32.  2
    Uskali Mäki (2009). Realistic Realism About Unrealistic Models. In Harold Kincaid & Don Ross (eds.), The Oxford handbook of philosophy of economics. Oxford University Press
    My philosophical intuitions are those of a scientific realist. In addition to being realist in its philosophical outlook, my philosophy of economics also aspires to be realistic in the sense of being descriptively adequate, or at least normatively non-utopian, about economics as a scientific discipline. The special challenge my philosophy of economics must meet is to provide a scientific realist account that is realistic of a discipline that deals with a complex subject matter and operates with highly unrealistic models. Unrealisticness (...)
    Direct download  
     
    Export citation  
     
    My bibliography   8 citations  
  33. Gabriele Contessa (2010). Scientific Models and Fictional Objects. Synthese 172 (2):215 - 229.
    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 (5 more)  
     
    Export citation  
     
    My bibliography   4 citations  
  34. Susan G. Sterrett, Experimentation on Analogue Models.
    Summary Analogue models are actual physical setups used to model something else. They are especially useful when what we wish to investigate is difficult to observe or experiment upon due to size or distance in space or time: for example, if the thing we wish to investigate is too large, too far away, takes place on a time scale that is too long, does not yet exist or has ceased to exist. The range and variety of analogue models is too (...)
    Direct download (4 more)  
     
    Export citation  
     
    My bibliography  
  35.  99
    Axel Gelfert (2009). Rigorous Results, Cross-Model Justification, and the Transfer of Empirical Warrant: The Case of Many-Body Models in Physics. Synthese 169 (3):497 - 519.
    This paper argues that a successful philosophical analysis of models and simulations must accommodate an account of mathematically rigorous results. Such rigorous results may be thought of as genuinely model-specific contributions, which can neither be deduced from fundamental theory nor inferred from empirical data. Rigorous results provide new indirect ways of assessing the success of models and simulations and are crucial to understanding the connections between different models. This is most obvious in cases where rigorous results map different models on (...)
    Direct download (9 more)  
     
    Export citation  
     
    My bibliography   7 citations  
  36.  74
    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.
    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)  
     
    Export citation  
     
    My bibliography   5 citations  
  37.  25
    Mathias Frisch (2013). Modeling Climate Policies: A Critical Look at Integrated Assessment Models. Philosophy and Technology 26 (2):117-137.
    Climate change presents us with a problem of intergenerational justice. While any costs associated with climate change mitigation measures will have to be borne by the world’s present generation, the main beneficiaries of mitigation measures will be future generations. This raises the question to what extent present generations have a responsibility to shoulder these costs. One influential approach for addressing this question is to appeal to neo-classical economic cost–benefit analyses and so-called economy-climate “integrated assessment models” to determine what course of (...)
    Direct download (5 more)  
     
    Export citation  
     
    My bibliography   3 citations  
  38.  84
    Adam Morton & Mauricio Suarez (2001). Kinds of Models. In Model Validation: perspectives in hydrological science. 11-22.
    We separate metaphysical from epistemic questions in the evaluation of models, taking into account the distinctive functions of models as opposed to theories. The examples a\are very varied.
    Direct download  
     
    Export citation  
     
    My bibliography  
  39. 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.
    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 (8 more)  
     
    Export citation  
     
    My bibliography   5 citations  
  40. Isabelle Peschard (2007). The Value(s) of a Story: Theories, Models and Cognitive Values. Principia 11 (2):151-169.
    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 (3 more)  
     
    Export citation  
     
    My bibliography   3 citations  
  41.  44
    Sergio A. Gallegos (2016). The Explanatory Role of Abstraction Processes in Models: The Case of Aggregations. Studies in History and Philosophy of Science Part A 56:161-167.
    Though it is held that some models in science have explanatory value, there is no conclusive agreement on what provides them with this value. One common view is that models have explanatory value vis-à-vis some target systems because they are developed using an abstraction process. Though I think this is correct, I believe it is not the whole picture. In this paper, I argue that, in addition to the well-known process of abstraction understood as an omission of features or information, (...)
    Direct download (3 more)  
     
    Export citation  
     
    My bibliography  
  42. Margaret Morrison (2011). One Phenomenon, Many Models: Inconsistency and Complementarity. Studies in History and Philosophy of Science 42 (2):342-351.
    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)  
     
    Export citation  
     
    My bibliography   3 citations  
  43.  84
    Marcin Miłkowski (2015). Evaluating Artificial Models of Cognition. Studies in Logic, Grammar and Rhetoric 40 (1):43-62.
    Artificial models of cognition serve different purposes, and their use determines the way they should be evaluated. There are also models that do not represent any particular biological agents, and there is controversy as to how they should be assessed. At the same time, modelers do evaluate such models as better or worse. There is also a widespread tendency to call for publicly available standards of replicability and benchmarking for such models. In this paper, I argue that proper evaluation ofmodels (...)
    Direct download (3 more)  
     
    Export citation  
     
    My bibliography  
  44.  27
    Elisabeth A. Lloyd (2012). The Role of 'Complex' Empiricism in the Debates About Satellite Data and Climate Models. Studies in History and Philosophy of Science Part A 43 (2):390-401.
    climate scientists have been engaged in a decades-long debate over the standing of satellite measurements of the temperature trends of the atmosphere above the surface of the earth. This is especially significant because skeptics of global warming and the greenhouse effect have utilized this debate to spread doubt about global climate models used to predict future states of climate. I use this case from an under-studied science to illustrate two distinct philosophical approaches to the relation among data, scientists, measurement, models, (...)
    Direct download (2 more)  
     
    Export citation  
     
    My bibliography   3 citations  
  45.  20
    Mark Pexton (2014). Can Asymptotic Models Be Explanatory? European Journal for Philosophy of Science 4 (2):233-252.
    Asymptotic models in which singular limits are taken are very common in physics. They are often used to investigate the general behaviour of systems undergoing rapid, discontinuous, changes. The singularities in the mathematics of these systems have no physical counterparts; these models operate by containing non-physically interpretable fictional elements. As such there is an intuition that states that asymptotics only offer descriptions of systems not explanations of them. By contrast, in different areas of science other models containing fictional elements which (...)
    Direct download (2 more)  
     
    Export citation  
     
    My bibliography   2 citations  
  46.  37
    Jani Raerinne (2013). Robustness and Sensitivity of Biological Models. Philosophical Studies 166 (2):285-303.
    The aim of this paper is to develop ideas about robustness analyses. I introduce a form of robustness analysis that I call sufficient parameter robustness, which has been neglected in the literature. I claim that sufficient parameter robustness is different from derivational robustness, the focus of previous research. My purpose is not only to suggest a new taxonomy of robustness, but also to argue that previous authors have concentrated on a narrow sense of robustness analysis, which they have inadequately distinguished (...)
    Direct download (4 more)  
     
    Export citation  
     
    My bibliography   2 citations  
  47.  47
    Sven Diekmann & Martin Peterson (2013). The Role of Non-Epistemic Values in Engineering Models. Science and Engineering Ethics 19 (1):207-218.
    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)  
     
    Export citation  
     
    My bibliography   2 citations  
  48.  23
    Paul Humphreys (2013). Data Analysis: Models or Techniques? [REVIEW] Foundations of Science 18 (3):579-581.
    In this commentary to Napoletani et al. (Found Sci 16:1–20, 2011), we argue that the approach the authors adopt suggests that neural nets are mathematical techniques rather than models of cognitive processing, that the general approach dates as far back as Ptolemy, and that applied mathematics is more than simply applying results from pure mathematics.
    Direct download (5 more)  
     
    Export citation  
     
    My bibliography   2 citations  
  49.  19
    John Symons & Fabio Boschetti (2013). How Computational Models Predict the Behavior of Complex Systems. Foundations of Science 18 (4):809-821.
    In this paper, we argue for the centrality of prediction in the use of computational models in science. We focus on the consequences of the irreversibility of computational models and on the conditional or ceteris paribus, nature of the kinds of their predictions. By irreversibility, we mean the fact that computational models can generally arrive at the same state via many possible sequences of previous states. Thus, while in the natural world, it is generally assumed that physical states have a (...)
    Direct download (5 more)  
     
    Export citation  
     
    My bibliography   2 citations  
  50. Thomas Mormann, McKinsey Algebras and Topological Models of S4.1.
    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
      Direct download  
     
    Export citation  
     
    My bibliography  
1 — 50 / 1000