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Modeling Practices

Assistant editor: Guilherme Sanches De Oliveira (University of Cincinnati, Technische Universität Berlin)
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63 found
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1 — 50 / 63
  1. Model Anarchism.Walter Veit - 2020
    This paper constitutes a radical departure from the existing philosophical literature on models, modeling-practices, and model-based science. I argue that the various entities and practices called 'models' and 'modeling-practices' are too diverse, too context-sensitive, and serve too many scientific purposes and roles, as to allow for a general philosophical analysis. From this recognition an alternative view emerges that I shall dub model anarchism.
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  2. Polycratic Hierarchies and Networks: What Simulation-Modeling at the LHC Can Teach Us About the Epistemology of Simulation.Florian J. Boge & Christian Zeitnitz - forthcoming - Synthese:1-35.
    Large scale experiments at CERN’s Large Hadron Collider (LHC) rely heavily on computer simulations (CSs), a fact that has recently caught philosophers’ attention. CSs obviously require appropriate modeling, and it is a common assumption among philosophers that the relevant models can be ordered into hierarchical structures. Focusing on LHC’s ATLAS experiment, we will establish three central results here: (a) With some distinct modifications, individual components of ATLAS’ overall simulation infrastructure can be ordered into hierarchical structures. Hence, to a good degree (...)
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  3. Idealisation in Natural Language Semantics: Truth-Conditions for Radical Contextualists.Gabe Dupre - forthcoming - Inquiry: An Interdisciplinary Journal of Philosophy.
    In this paper, I shall provide a novel response to the argument from context-sensitivity against truth-conditional semantics. It is often argued that the contextual influences on truth-conditions outstrip the resources of standard truth-conditional accounts, and so truth-conditional semantics rests on a mistake. The argument assumes that truth-conditional semantics is legitimate if and only if natural language sentences have truth-conditions. I shall argue that this assumption is mistaken. Truth-conditional analyses should be viewed as idealised approximations of the complexities of natural language (...)
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  4. Unifying the Essential Concepts of Biological Networks: Biological Insights and Philosophical Foundations.Daniel Kostic, Claus Hilgetag & Marc Tittgemeyer - forthcoming - Philosophical Transactions of the Royal Society B: Biological Sciences.
    Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the definition, motivation (...)
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  5. On the Relationship Between Modelling Practices and Interpretive Stances in Quantum Mechanics.Quentin Ruyant - forthcoming - Foundations of Science:1-19.
    The purpose of this article is to establish a connection between modelling practices and interpretive approaches in quantum mechanics, taking as a starting point the literature on scientific representation. Different types of modalities play different roles in scientific representation. I postulate that the way theoretical structures are interpreted in this respect affects the way models are constructed. In quantum mechanics, this would be the case in particular of initial conditions and observables. I examine two formulations of quantum mechanics, the standard (...)
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  6. The Exploratory Role of Idealizations and Limiting Cases in Models.Elay Shech & Axel Gelfert - forthcoming - Studia Metodologiczne.
    In this article we argue that idealizations and limiting cases in models play an exploratory role in science. Four senses of exploration are presented: exploration of the structure and representational capacities of theory; proof-of-principle demonstrations; potential explanations; and exploring the suitability of target systems. We illustrate our claims through three case studies, including the Aharonov-Bohm effect, the emergence of anyons and fractional quantum statistics, and the Hubbard model of the Mott phase transitions. We end by reflecting on how our case (...)
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  7. Data models, representation and adequacy-for-purpose.Alisa Bokulich & Wendy Parker - 2021 - European Journal for Philosophy of Science 11 (1):1-26.
    We critically engage two traditional views of scientific data and outline a novel philosophical view that we call the pragmatic-representational view of data. On the PR view, data are representations that are the product of a process of inquiry, and they should be evaluated in terms of their adequacy or fitness for particular purposes. Some important implications of the PR view for data assessment, related to misrepresentation, context-sensitivity, and complementary use, are highlighted. The PR view provides insight into the common (...)
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  8. Multiple-Models Juxtaposition and Trade-Offs Among Modeling Desiderata.Yoshinari Yoshida - 2021 - Philosophy of Science 88 (1):103-123.
    This article offers a characterization of what I call multiple-models juxtaposition, a strategy for managing trade-offs among modeling desiderata. MMJ displays models of distinct phenomena to...
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  9. Philosophical Perspectives on Earth System Modeling: Truth, Adequacy and Understanding.G. Gramelsberger, J. Lenhard & Wendy Parker - 2020 - Journal of Advances in Modeling Earth Systems 12 (1):e2019MS001720.
    We explore three questions about Earth system modeling that are of both scientific and philosophical interest: What kind of understanding can be gained via complex Earth system models? How can the limits of understanding be bypassed or managed? How should the task of evaluating Earth system models be conceptualized?
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  10. Perspectives, Questions, and Epistemic Value.Kareem Khalifa & Jared A. Millson - 2020 - In Michela Massimi & Ana-Maria Cretu (eds.), Knowledge From a Human Point of View. Cham: Springer Verlag. pp. 87-106.
    Many epistemologists endorse true-belief monism, the thesis that only true beliefs are of fundamental epistemic value. However, this view faces formidable counterexamples. In response to these challenges, we alter the letter, but not the spirit, of true-belief monism. We dub the resulting view “inquisitive truth monism”, which holds that only true answers to relevant questions are of fundamental epistemic value. Which questions are relevant is a function of an inquirer’s perspective, which is characterized by his/her interests, social role, and background (...)
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  11. Designing as Playing Games of Make-Believe.Michael Poznic, Martin Stacey, Rafaela Hillerbrand & Claudia Eckert - 2020 - Design Science 6:e10.
    Designing complex products involves working with uncertainties as the product, the requirements and the environment in which it is used co-evolve, and designers and external stakeholders make decisions that affect the evolving design. Rather than being held back by uncertainty, designers work, cooperate and communicate with each other notwithstanding these uncertainties by making assumptions to carry out their own tasks. To explain this, the paper proposes an adaptation of Kendall Walton’s make-believe theory to conceptualise designing as playing games of make-believe (...)
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  12. Deep Learning: A Philosophical Introduction.Cameron Buckner - 2019 - Philosophy Compass 14 (10).
  13. Integración de analogías en la investigación científica (Integration of Analogies in Scientific Modeling).Natalia Carrillo-Escalera - 2019 - Revista Colombiana de Filosofía de la Ciencia 37 (18):318-335.
    Discussion of modeling within philosophy of science has focused in how models, understood as finished products, represent the world. This approach has some issues accounting for the value of modeling in situations where there are controversies as to which should be the object of representation. In this work I show that a historical analysis of modeling complements the aforementioned representational program, since it allows us to examine processes of integration of analogies that play a role in the generation of criteria (...)
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  14. Probing Possibilities: Toy Models, Minimal Models, and Exploratory Models.Axel Gelfert - 2019 - In Matthieu Fontaine, Cristina Barés-Gómez, Francisco Salguero-Lamillar, Lorenzo Magnani & Ángel Nepomuceno-Fernández (eds.), Model-Based Reasoning in Science and Technology. Springer Verlag.
    According to one influential view, model-building in science is primarily a matter of simplifying theoretical descriptions of real-world target systems using abstraction and idealization. This view, however, does not adequately capture all types of models. Many contemporary models in the natural and social sciences – from physics to biology to economics – stand in a more tenuous relationship with real-world target systems and have a decidedly stipulative element, in that they create, by fiat, ‘model worlds’ that operate according to some (...)
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  15. The Epistemic Virtue of Robustness in Climate Modeling (MA Dissertation).Parjanya Joshi - 2019 - Dissertation, Tata Institute of Social Sciences
    The aim of this dissertation is to comprehensively study various robustness arguments proposed in the literature from Levins to Lloyd as well as the opposition offered to them and pose enquiry into the degree of epistemic virtue that they provide to the model prediction results with respect to climate science and modeling. Another critical issue that this dissertation strives to examine is that of the actual epistemic notion that is operational when scientists and philosophers appeal to robustness. In attempting to (...)
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  16. Uncertainty Quantification Using Multiple Models - Prospects and Challenges.Reto Knutti, Christoph Baumberger & Gertrude Hirsch Hadorn - 2019 - In Claus Beisbart & Nicole J. Saam (eds.), Computer Simulation Validation: Fundamental Concepts, Methodological Frameworks, and Philosophical Perspectives. Cham, Switzerland: pp. 835-855.
    Model evaluation for long term climate predictions must be done on quantities other than the actual prediction, and a comprehensive uncertainty quantification is impossible. An ad hoc alternative is provided by coordinated model intercomparisons which typically use a “one model one vote” approach. The problem with such an approach is that it treats all models as independent and equally plausible. Reweighting all models of the ensemble for performance and dependence seems like an obvious way to improve on model democracy, yet (...)
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  17. Investigating Interdisciplinary Practice: Methodological Challenges.Miles MacLeod, Martina Merz, Uskali Mäki & Michiru Nagatsu - 2019 - Perspectives on Science 27 (4):545-552.
    Interdisciplinarity is one of the most prominent ideas driving science and research policy today.1 It is applied widely as a conception of what particularly creative and socially relevant research processes should consist of, whether in the natural sciences, the social sciences, the humanities, or elsewhere. Its advocates, many of whom are located in current science and research administration themselves, are using ideas of interdisciplinarity to reshape university organization and research funding. For the last 40 years, researchers studying interdisciplinarity have built (...)
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  18. Stochastic Stability and Disagreements Between Dynamics.Aydin Mohseni - 2019 - Philosophy of Science 86 (3):497-521.
    The replicator dynamics and Moran process are the main deterministic and stochastic models of evolutionary game theory. The models are connected by a mean-field relationship—the former describes the expected behavior of the latter. However, there are conditions under which their predictions diverge. I demonstrate that the divergence between their predictions is a function of standard techniques used in their analysis and of differences in the idealizations involved in each. My analysis reveals problems for stochastic stability analysis in a broad class (...)
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  19. Idealizations and Understanding: Much Ado About Nothing?Emily Sullivan & Kareem Khalifa - 2019 - Australasian Journal of Philosophy 97 (4):673-689.
    Because idealizations frequently advance scientific understanding, many claim that falsehoods play an epistemic role. In this paper, we argue that these positions greatly overstate idealiza...
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  20. Understanding Does Not Depend on (Causal) Explanation.Philippe Verreault-Julien - 2019 - European Journal for Philosophy of Science 9 (2):18.
    One can find in the literature two sets of views concerning the relationship between understanding and explanation: that one understands only if 1) one has knowledge of causes and 2) that knowledge is provided by an explanation. Taken together, these tenets characterize what I call the narrow knowledge account of understanding. While the first tenet has recently come under severe attack, the second has been more resistant to change. I argue that we have good reasons to reject it on the (...)
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  21. Synthetic Versus Analytic Approaches to Protein and DNA Structure Determination.Agnes Bolinska - 2018 - Biology and Philosophy 33 (3-4):26.
    The structures of protein and DNA were discovered primarily by means of synthesizing component-level information about bond types, lengths, and angles, rather than analyzing X-ray diffraction photographs of these molecules. In this paper, I consider the synthetic and analytic approaches to exemplify alternative heuristics for approaching mid-twentieth-century macromolecular structure determination. I argue that the former was, all else being equal, likeliest to generate the correct structure in the shortest period of time. I begin by characterizing problem solving in these cases (...)
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  22. La red teórica de la dinámica de poblaciones.Martín Díaz & Pablo Lorenzano - 2018 - Scientiae Studia 15 (2):307.
    The general aim of this article is to carry out a reconstruction of the theory of Population Dynamics (DP) in Ecology, according to Castle’s (2001) general stance with regard to the semantic view of theories, but doing it within the framework of metatheoretical structuralism. Thus, we will first identify Population Dynamics’ basic theory-element: its core K(DP) – with the class of potential models, the class of models (through the identification of its fundamental law) and the class of partial potential models (...)
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  23. Learning About Reality Through Models and Computer Simulations.Melissa Jacquart - 2018 - Science & Education 27 (7-8):805-810.
    Margaret Morrison, (2015) Reconstructing Reality: Models, Mathematics, and Simulations. Oxford University Press, New York. -/- Scientific models, mathematical equations, and computer simulations are indispensable to scientific practice. Through the use of models, scientists are able to effectively learn about how the world works, and to discover new information. However, there is a challenge in understanding how scientists can generate knowledge from their use, stemming from the fact that models and computer simulations are necessarily incomplete representations, and partial descriptions, of their (...)
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  24. Simplified Models: A Different Perspective on Models as Mediators.C. D. McCoy & Michela Massimi - 2018 - European Journal for Philosophy of Science 8 (1):99-123.
    We introduce a novel point of view on the “models as mediators” framework in order to emphasize certain important epistemological questions about models in science which have so far been little investigated. To illustrate how this perspective can help answer these kinds of questions, we explore the use of simplified models in high energy physics research beyond the Standard Model. We show in detail how the construction of simplified models is grounded in the need to mitigate pressing epistemic problems concerning (...)
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  25. Research Habits in Financial Modelling: The Case of Non-Normativity of Market Returns in the 1970s and the 1980s.Boudewijn De Bruin & Christian Walter - 2017 - In Emiliano Ippoliti & Ping Chen (eds.), Methods and Finance: A Unifying View on Finance, Mathematics, and Philosophy. Cham: Springer. pp. 73-93.
    In this chapter, one considers finance at its very foundations, namely, at the place where assumptions are being made about the ways to measure the two key ingredients of finance: risk and return. It is well known that returns for a large class of assets display a number of stylized facts that cannot be squared with the traditional views of 1960s financial economics (normality and continuity assumptions, i.e. Brownian representation of market dynamics). Despite the empirical counterevidence, normality and continuity assumptions (...)
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  26. Connectomes as Constitutively Epistemic Objects: Critical Perspectives on Modeling in Current Neuroanatomy.Philipp Haueis & Jan Slaby - 2017 - In Progress in Brain Research Vol 233: The Making and Use of Animal Models in Neuroscience and Psychiatry. Amsterdam: pp. 149–177.
    in a nervous system of a given species. This chapter provides a critical perspective on the role of connectomes in neuroscientific practice and asks how the connectomic approach fits into a larger context in which network thinking permeates technology, infrastructure, social life, and the economy. In the first part of this chapter, we argue that, seen from the perspective of ongoing research, the notion of connectomes as “complete descriptions” is misguided. Our argument combines Rachel Ankeny’s analysis of neuroanatomical wiring diagrams (...)
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  27. Sustaining Rules: A Model and Application.John Turri - 2017 - In Knowledge first: approaches in epistemology and mind.
    I introduce an account of when a rule normatively sustains a practice. My basic proposal is that a rule normatively sustains a practice when the value achieved by following the rule explains why agents continue following that rule, thus establishing and sustaining a pattern of activity. I apply this model to practices of belief management and identifies a substantive normative connection between knowledge and belief. More specifically, I proposes one special way that knowledge might set the normative standard for belief: (...)
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  28. Modeling Information.Patrick Grim - 2016 - In Luciano Floridi (ed.), Routledge Handbook of Philosophy of Information. Routledge. pp. 137-152.
    The topics of modeling and information come together in at least two ways. Computational modeling and simulation play an increasingly important role in science, across disciplines from mathematics through physics to economics and political science. The philosophical questions at issue are questions as to what modeling and simulation are adding, altering, or amplifying in terms of scientific information. What changes with regard to information acquisition, theoretical development, or empirical confirmation with contemporary tools of computational modeling? In this sense the title (...)
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  29. Similarity, Adequacy, and Purpose: Understanding the Success of Scientific Models.Melissa Jacquart - 2016 - Dissertation, University of Western Ontario
    A central component to scientific practice is the construction and use of scientific models. Scientists believe that the success of a model justifies making claims that go beyond the model itself. However, philosophical analysis of models suggests that drawing inferences about the world from successful models is more complex. In this dissertation I develop a framework that can help disentangle the related strands of evaluation of model success, model extendibility, and the ability to draw ampliative inferences about the world from (...)
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  30. Standing on the Shoulders of Giants—And Then Looking the Other Way? Epistemic Opacity, Immersion, and Modeling in Hydraulic Engineering.Matthijs Kouw - 2016 - Perspectives on Science 24 (2):206-227.
    Over the course of the twentieth century, hydraulic engineering has come to rely primarily on the use of computational models. Disco and van den Ende hint towards the reasons for widespread adoption of computational models by pointing out that such models fulfill a crucial role as management tools in Dutch water management, and meet a more general desire to quantify water-related phenomena. The successful application of computational models implies blackboxing : “[w]hen a machine runs efficiently … one need focus only (...)
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  31. Prisoner's Dilemma Doesn't Explain Much.Robert Northcott & Anna Alexandrova - 2015 - In Martin Peterson (ed.), The Prisoner’s Dilemma. Classic philosophical arguments. Cambridge: Cambridge University Press. pp. 64-84.
    We make the case that the Prisoner’s Dilemma, notwithstanding its fame and the quantity of intellectual resources devoted to it, has largely failed to explain any phenomena of social scientific or biological interest. In the heart of the paper we examine in detail a famous purported example of Prisoner’s Dilemma empirical success, namely Axelrod’s analysis of WWI trench warfare, and argue that this success is greatly overstated. Further, we explain why this negative verdict is likely true generally and not just (...)
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  32. La surprise comme mesure de l'empiricité des simulations computationnelles.Franck Varenne - 2015 - In Natalie Depraz & Claudia Serban (eds.), La surprise. A l'épreuve des langues. Paris: Hermann. pp. 199-217.
    This chapter elaborates and develops the thesis originally put forward by Mary Morgan (2005) that some mathematical models may surprise us, but that none of them can completely confound us, i.e. let us unable to produce an ex post theoretical understanding of the outcome of the model calculations. This chapter intends to object and demonstrate that what is certainly true of classical mathematical models is however not true of pluri-formalized simulations with multiple axiomatic bases. This chapter thus proposes to show (...)
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  33. Heuristics, Descriptions, and the Scope of Mechanistic Explanation.Carlos Zednik - 2015 - In P. Braillard & C. Malaterre (eds.), Explanation in Biology. An Enquiry into the Diversity of Explanatory Patterns in the Life Sciences. Dordrecht: Springer. pp. 295-318.
    The philosophical conception of mechanistic explanation is grounded on a limited number of canonical examples. These examples provide an overly narrow view of contemporary scientific practice, because they do not reflect the extent to which the heuristic strategies and descriptive practices that contribute to mechanistic explanation have evolved beyond the well-known methods of decomposition, localization, and pictorial representation. Recent examples from evolutionary robotics and network approaches to biology and neuroscience demonstrate the increasingly important role played by computer simulations and mathematical (...)
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  34. Error and Uncertainty in Scientific Practice.Marcel Boumans, Giora Hon & Arthur Petersen (eds.) - 2014 - Pickering & Chatto.
  35. Idealized and Perspectival Representations: Some Reasons for Making a Distinction.Alexander Rueger - 2014 - Synthese 191 (8):1831-1845.
    I argue that an adequate understanding of the practice of constructing models in physics requires a distinction between two strategies that are commonly both labeled ‘idealization’. The formal characteristic of both methods is to let a parameter in the equations for a target system go to zero. But the discussion of examples from various applications of perturbation theory shows that there is in general a difference with respect to the aims such limiting procedures are supposed to serve; and with different (...)
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  36. Models, Simulations, and the Reduction of Complexity.Ulrich Gähde, Stephan Hartmann & Jörn Henning Wolf (eds.) - 2013 - De Gruyter.
    Modern science is, to a large extent, a model-building activity. But how are models contructed? How are they related to theories and data? How do they explain complex scientific phenomena, and which role do computer simulations play here? These questions have kept philosophers of science busy for many years, and much work has been done to identify modeling as the central activity of theoretical science. At the same time, these questions have been addressed by methodologically-minded scientists, albeit from a different (...)
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  37. Scientific Innovation as Eco-Epistemic Warfare: The Creative Role of on-Line Manipulative Abduction.Lorenzo Magnani - 2013 - Mind and Society 12 (1):49-59.
    Humans continuously delegate and distribute cognitive functions to the environment to lessen their limits. They build models, representations, and other various mediating structures, that are thought to be good to think. The case of scientific innovation is particularly important: the main aim of this paper is to revise and criticize the concept of scientific innovation, reframing it in what I will call an eco-epistemic perspective, taking advantage of recent results coming from the area of distributed cognition and abductive cognition . (...)
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  38. Scientific Uncertainty: A User's Guide.Seamus Bradley - 2012 - Grantham Institute on Climate Change Discussion Paper.
    There are different kinds of uncertainty. I outline some of the various ways that uncertainty enters science, focusing on uncertainty in climate science and weather prediction. I then show how we cope with some of these sources of error through sophisticated modelling techniques. I show how we maintain confidence in the face of error.
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  39. Introducción a “Nuevas contribuciones iberoamericanas a la metateoría estructuralista”.José A. Díez, José L. Falguera & Pablo Lorenzano - 2012 - Agora 31 (2).
    This is the introduction to the special issue of the Spanish journal Ágora-Papeles de Filosofía (31/2, 2012) devoted to new Ibero-American contributions to metatheoretical structuralism.
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  40. Modeling Social and Evolutionary Games.Angela Potochnik - 2012 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 43 (1):202-208.
    When game theory was introduced to biology, the components of classic game theory models were replaced with elements more befitting evolutionary phenomena. The actions of intelligent agents are replaced by phenotypic traits; utility is replaced by fitness; rational deliberation is replaced by natural selection. In this paper, I argue that this classic conception of comprehensive reapplication is misleading, for it overemphasizes the discontinuity between human behavior and evolved traits. Explicitly considering the representational roles of evolutionary game theory brings to attention (...)
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  41. Quelques Aspects de L’Œuvre de Jean-Marie Legay.Franck Varenne - 2012 - Natures Sciences Sociétés 20 (4):461-463.
    Cet article revient sur la pratique scientifique et les thèses épistémologiques de Jean-Marie Legay concernant les modèles, les simulations et les systèmes complexes. Il montre qu'il y a une cohérence entre sa thèse anti-représentationnaliste concernant les modèles et les simulations et sa caractérisation même des systèmes complexes : une simulation informatique, seule, n'est pas une expérience au sens fort car, en l'isolant, on perd la dimension complexe de toute entreprise d'expérimentation scientifique dès lors qu'il y manque le modélisateur, le terrain (...)
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  42. Modéliser le social. Méthodes fondatrices et évolutions récentes.Franck Varenne - 2011 - Paris, France: Dunod.
    Cet ouvrage très pédagogique informe les étudiants sur les méthodes quantitatives les plus classiques comme les plus récentes en sciences sociales, et notamment sur les différentes pratiques de modélisation et de simulation informatique des systèmes sociaux (sciences sociales computationnelles ou modèles informatiques).
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  43. Conceiving Processes in Atmospheric Models—General Equations, Subscale Parameterizations, and 'Superparameterizations'.Gabriele Gramelsberger - 2010 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 41 (3):233-241.
  44. Connections Between Simulations and Observation in Climate Computer Modeling. Scientist’s Practices and “Bottom-Up Epistemology” Lessons.Hélène Guillemot - 2010 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 41 (3):242-252.
  45. The Explanatory Potential of Artificial Societies.Till Grüne-Yanoff - 2009 - Synthese 169 (3):539 - 555.
    It is often claimed that artificial society simulations contribute to the explanation of social phenomena. At the hand of a particular example, this paper argues that artificial societies often cannot provide full explanations, because their models are not or cannot be validated. Despite that, many feel that such simulations somehow contribute to our understanding. This paper tries to clarify this intuition by investigating whether artificial societies provide potential explanations. It is shown that these potential explanations, if they contribute to our (...)
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  46. Alife Models as Epistemic Artefacts.Xabier Barandiaran & Alvaro Moreno - 2006 - In Luis Rocha, Larry Yaeger & Mark Bedau (eds.), Artificial Life X : Proceedings of the Tenth International Conference on the Simulation and Synthesis of Living Systems. MIT Press. pp. 513-519.
    Both the irreducible complexity of biological phenomena and the aim of a universalized biology (life-as-it-could-be) have lead to a deep methodological shift in the study of life; represented by the appearance of ALife, with its claim that computational modelling is the main tool for studying the general principles of biological phenomenology. However this methodological shift implies important questions concerning the aesthetic, engineering and specially the epistemological status of computational models in scientific research: halfway between the well established categories of theory (...)
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  47. Idealization XII: Correcting the Model. Idealization and Abstraction in the Sciences.Martin R. Jones & Nancy Cartwright (eds.) - 2005 - Rodopi.
    ContentsPrefaceAnalytical Table of ContentsKevin D. HOOVER: Quantitative Evaluation of Idealized Models in the New Classical MacroeconomicsJohn PEMBERTON: Why Idealized Models in Economics Have Limited UseAmos FUNKENSTEIN: The Revival of Aristotle’s NatureJames R. GRIESEMER: The Informational Gene and the Substantial Body: On the Generalization of Evolutionary Theory byionNancy J. NERSESSIAN: Abstraction via Generic Modeling in Concept Formation in ScienceMargaret MORRISON: Approximating the Real: The Role of Idealizations in Physical TheoryMartin R. JONES: Idealization and Abstraction: A FrameworkDavid S. NIVISON: Standard TimeJames BOGEN (...)
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  48. A Theory of Scientific Model Construction: The Conceptual Process of Abstraction and Concretisation. [REVIEW]Demetris P. Portides - 2005 - Foundations of Science 10 (1):67-88.
    The process of abstraction and concretisation is a label used for an explicative theory of scientific model-construction. In scientific theorising this process enters at various levels. We could identify two principal levels of abstraction that are useful to our understanding of theory-application. The first level is that of selecting a small number of variables and parameters abstracted from the universe of discourse and used to characterise the general laws of a theory. In classical mechanics, for example, we select position and (...)
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  49. The Aerodynamics of Insects: The Role of Models and Matter in Scientific Experimentation.Michelle R. Silva - 2005 - Social Epistemology 19 (4):325 – 337.
    Historians and philosophers of science have examined the relationship between language and practice for a long time. Scholars have made important contributions to the field by attending to the social, cultural and economic contexts in which scientific paradigms are created and re-created. However, this article posits that while it is true that scientific practice and the artifacts they generate are both socially and discursively constructed and therefore, inextricable from the human contexts that produce them, these artifacts are not only texts (...)
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  50. Methodology in Practice: Statistical Misspecification Testing.Deborah G. Mayo & Aris Spanos - 2004 - Philosophy of Science 71 (5):1007-1025.
    The growing availability of computer power and statistical software has greatly increased the ease with which practitioners apply statistical methods, but this has not been accompanied by attention to checking the assumptions on which these methods are based. At the same time, disagreements about inferences based on statistical research frequently revolve around whether the assumptions are actually met in the studies available, e.g., in psychology, ecology, biology, risk assessment. Philosophical scrutiny can help disentangle 'practical' problems of model validation, and conversely, (...)
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