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  1. Rachel A. Ankeny (2001). Model Organisms as Models: Understanding the 'Lingua Franca' of the Human Genome Project. Proceedings of the Philosophy of Science Association 2001 (3):S251-.
    Through an examination of the actual research strategies and assumptions underlying the Human Genome Project (HGP), it is argued that the epistemic basis of the initial model organism programs is not best understood as reasoning via causal analog models (CAMs). In order to answer a series of questions about what is being modeled and what claims about the models are warranted, a descriptive epistemological method is employed that uses historical techniques to develop detailed accounts which, in turn, help to reveal (...)
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  2. Daniela M. Bailer-Jones (2003). When Scientific Models Represent. International Studies in the Philosophy of Science 17 (1):59 – 74.
    Scientific models represent aspects of the empirical world. I explore to what extent this representational relationship, given the specific properties of models, can be analysed in terms of propositions to which truth or falsity can be attributed. For example, models frequently entail false propositions despite the fact that they are intended to say something "truthful" about phenomena. I argue that the representational relationship is constituted by model users "agreeing" on the function of a model, on the fit with data and (...)
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  3. Krzysztof Brzechczyn (2009). Logical Empiricism and Logical Positivism. In Aviezer Tucker (ed.), A Companion to the Philosophy of History and Historiography. Wiley-Blackwell.
  4. Jonathan Cohen & Callender Craig (2006). There is No Special Problem About Scientific Representation. Theoria 55 (1):67-85.
    We propose that scientific representation is a special case of a more general notion of representation, and that the relatively well worked-out and plausible theories of the latter are directly applicable to the scien- tific special case. Construing scientific representation in this way makes the so-called “problem of scientific representation” look much less inter- esting than it has seemed to many, and suggests that some of the (hotly contested) debates in the literature are concerned with non-issues.
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  5. Stephen M. Downes (2009). Models, Pictures, and Unified Accounts of Representation: Lessons From Aesthetics for Philosophy of Science. Perspectives on Science 17 (4):417-428.
    Several prominent philosophers of science, most notably Ron Giere, propose that scientific theories are collections of models and that models represent the objects of scientific study. Some, including Giere, argue that models represent in the same way that pictures represent. Aestheticians have brought the picturing relation under intense scrutiny and presented important arguments against the tenability of particular accounts of picturing. Many of these arguments from aesthetics can be used against accounts of representation in philosophy of science. I rely on (...)
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  6. Roman Frigg, Models and Representation: Why Structures Are Not Enough.
    Models occupy a central role in the scientific endeavour. Among the many purposes they serve, representation is of great importance. Many models are representations of something else; they stand for, depict, or imitate a selected part of the external world (often referred to as target system, parent system, original, or prototype). Well-known examples include the model of the solar system, the billiard ball model of a gas, the Bohr model of the atom, the Gaussian-chain model of a polymer, the MIT (...)
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  7. Joseph S. Fulda (1988). Ratings and Confirmation. Quality and Quantity 22 (4):435-438.
    We present a linear formalism which makes explicit and precise the confirming effect of independent multiple observers and repeated trials on composite ratings, taking as parameters quantitative estimates of the subjective inputs discussed. -/- Note that the subjective probability used here is so used to study the past not predict the future and is rather limited to what has been called in artificial intelligence "certainty factors," which are arbitrary, or, more well-known, the arbitrary values ascribed to predicates in fuzzy "logic." (...)
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  8. Joseph S. Fulda (1987). The Logistic Equation and Double Jeopardy. Ecological Modelling 36 (3/4):315-316.
    A second demonstration (more powerful because more subtle) of how a prevalent scope error can render a model invalid, and thus how difficult modeling really is. The prevalence indicates the difficulty, as the error is often built-in and very subtle and thus easily escapes notice.
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  9. Joseph S. Fulda (1981). The Logistic Equation and Population Decline. Journal of Theoretical Biology 91 (2):255-259.
    A demonstration of two difficulties, both prevalent, in modeling. The first is scopal errors, which are often hard to detect because of their subtlety. The second is that two equations, though facially identical, are implicitly conjoined to /different/ inequalities, limiting the range of the variables or parameters in the equations, thereby changing the (here, ecological) interpretation of the equation, and thus its meaning, and therefore whether it is or is not an adequate model.
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  10. 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. (...)
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  11. Ronald N. Giere (2004). How Models Are Used to Represent Reality. Philosophy of Science 71 (5):742-752.
    Most recent philosophical thought about the scientific representation of the world has focused on dyadic relationships between language-like entities and the world, particularly the semantic relationships of reference and truth. Drawing inspiration from diverse sources, I argue that we should focus on the pragmatic activity of representing, so that the basic representational relationship has the form: Scientists use models to represent aspects of the world for specific purposes. Leaving aside the terms "law" and "theory," I distinguish principles, specific conditions, models, (...)
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  12. Ronald N. Giere, How Models Are Used to Represent Physical Reality.
    What are models that they may be used to represent reality? Here is a first pass. Models are objects that can be used to represent reality by exhibiting a designated similarity to physical objects. To be more specific, I need to indicate the kinds of objects models may be and how they may exhibit a designated similarity to real objects. My prototype for a model is a standard road map. This is a physical object (usually made of paper) that I (...)
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  13. Ronald N. Giere (1999). Using Models to Represent Reality. In. In L. Magnani, N. J. Nersessian & P. Thagard (eds.), Model-Based Reasoning in Scientific Discovery. Kluwer/Plenum. 41--57.
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  14. R. I. G. Hughes (1997). Models and Representation. Philosophy of Science 64 (4):336.
    A general account of modeling in physics is proposed. Modeling is shown to involve three components: denotation, demonstration, and interpretation. Elements of the physical world are denoted by elements of the model; the model possesses an internal dynamic that allows us to demonstrate theoretical conclusions; these in turn need to be interpreted if we are to make predictions. The DDI account can be readily extended in ways that correspond to different aspects of scientific practice.
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  15. Robert Kowalenko (2009). How (Not) to Think About Idealisation and Ceteris Paribus -Laws. Synthese 167 (1):183 - 201.
    Semantic dispositionalism is the theory that a speaker’s meaning something by a given linguistic symbol is determined by her dispositions to use the symbol in a certain way. According to an objection by Saul Kripke, further elaborated in Kusch (2005), semantic dispositionalism involves ceteris paribus-clauses and idealisations, such as unbounded memory, that deviate from standard scientific methodology. I argue that Kusch misrepresents both ceteris paribus-laws and idealisation, neither of which factually approximate the behaviour of agents or the course of events, (...)
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  16. Sabina Leonelli (2007). Growing Weed, Producing Knowledge An Epistemic History of Arabidopsis Thaliana. History and Philosophy of the Life Sciences 29 (2):193 - 223.
    Arabidopsis is currently the most popular and well-researched model organism in plant biology. This paper documents this plant's rise to scientific fame by focusing on two interrelated aspects of Arabidopsis research. One is the extent to which the material features of the plant have constrained research directions and enabled scientific achievements. The other is the crucial role played by the international community of Arabidopsis researchers in making it possible to grow, distribute and use plant specimen that embody these material features. (...)
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  17. A. Levy & A. Currie (forthcoming). Model Organisms Are Not (Theoretical) Models. British Journal for the Philosophy of Science:axt055.
  18. Arnon Levy (forthcoming). Modeling Without Models. Philosophical Studies:1-18.
    Modeling is an important scientific practice, yet it raises significant philosophical puzzles. Models are typically idealized, and they are often explored via imaginative engagement and at a certain “distance” from empirical reality. These features raise questions such as what models are and how they relate to the world. Recent years have seen a growing discussion of these issues, including a number of views that treat modeling in terms of indirect representation and analysis. Indirect views treat the model as a bona (...)
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  19. Arnon Levy (2012). Models, Fictions, and Realism: Two Packages. Philosophy of Science 79 (5):738-748.
    Some philosophers of science – the present author included – appeal to fiction as an interpretation of the practice of modeling. This raises the specter of an incompatibility with realism, since fiction-making is essentially non-truth-regulated. I argue that the prima facie conflict can be resolved in two ways, each involving a distinct notion of fiction and a corresponding formulation of realism. The main goal of the paper is to describe these two packages. Toward the end I comment on how to (...)
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  20. Arnon Levy & William Bechtel (2013). Abstraction and the Organization of Mechanisms. Philosophy of Science 80 (2):241-261.
  21. Chuang LIu, Re-Inflating the Conception of Scientific Representation.
    This paper argues for an anti-deflationist view of scientific representation. Our discussion begins with an analysis of the recent Callender-Cohen deflationary view on scientific representation. We then argue that there are at least two radically different ways in which a thing can be used to represent: one is purely symbolic and therefore conventional, and the other is epistemic. The failure to recognize that scientific models are epistemic vehicles rather than symbolic ones has led to the mistaken (deflationary) view that whatever (...)
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  22. Uskali Mäki (2011). The Truth of False Idealizations in Modeling. In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. Routledge.
    Modeling involves the use of false idealizations, yet there is typically a belief or hope that modeling somehow manages to deliver true information about the world. The paper discusses one possible way of reconciling truth and falsehood in modeling. The key trick is to relocate truth claims by reinterpreting an apparently false idealizing assumption in order to make clear what possibly true assertion is intended when using it. These include interpretations in terms of negligibility, applicability, tractability, early-step, and more. Elaborations (...)
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  23. Ryan Muldoon (2007). Robust Simulations. Philosophy of Science 74 (5):873-883.
    As scientists begin to study increasingly complex questions, many have turned to computer simulation to assist in their inquiry. This methodology has been challenged by both analytic modelers and experimentalists. A primary objection of analytic modelers is that simulations are simply too complicated to perform model verification. From the experimentalist perspective it is that there is no means to demonstrate the reality of simulation. The aim of this paper is to consider objections from both of these perspectives, and to argue (...)
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  24. Alexandre Muzy, Franck Varenne, Bernard P. Zeigler, Jonathan Caux, Patrick Coquillard, Luc Touraille, Dominique Prunetti, Philippe Caillou, Olivier Michel & David R. C. Hill (2013). Refounding of the Activity Concept? Towards a Federative Paradigm for Modeling and Simulation. Simulation - Transactions of the Society for Modeling and Simulation International 89 (2):156-177.
    Currently, the widely used notion of activity is increasingly present in computer science. However, because this notion is used in specific contexts, it becomes vague. Here, the notion of activity is scrutinized in various contexts and, accordingly, put in perspective. It is discussed through four scientific disciplines: computer science, biology, economics, and epistemology. The definition of activity usually used in simulation is extended to new qualitative and quantitative definitions. In computer science, biology and economics disciplines, the new simulation activity definition (...)
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  25. Maarten Van Dyck (2009). On the Epistemological Foundations of the Law of the Lever. Studies in History and Philosophy of Science Part A 40 (3):315-318.
    In this paper I challenge Paolo Palmieri’s reading of the Mach-Vailati debate on Archimedes’s proof of the law of the lever. I argue that the actual import of the debate concerns the possible epistemic (as opposed to merely pragmatic) role of mathematical arguments in empirical physics, and that construed in this light Vailati carries the upper hand. This claim is defended by showing that Archimedes’s proof of the law of the lever is not a way of appealing to a non-empirical (...)
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  26. Steven E. Wallis (2011). Avoiding Policy Failure. Emergent Publications.
    Why do policies fail? How can we objectively choose the best policy from two (or more) competing alternatives? How can we create better policies? To answer these critical questions this book presents an innovative yet workable approach. Avoiding Policy Failure uses emerging metapolicy methodologies in case studies that compare successful policies with ones that have failed. Those studies investigate the systemic nature of each policy text to gain new insights into why policies fail. -/- In addition to providing intriguing directions (...)
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