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Alexandrova, Anna, , . Making Models Count
2008, Philosophy of Science 75(3): 383-404.
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Added by: Nick Novelli, Contributed by:

Abstract: What sort of claims do scientific models make and how do these claims then underwrite empirical successes such as explanations and reliable policy interventions? In this paper I propose answers to these questions for the class of models used throughout the social and biological sciences, namely idealized deductive ones with a causal interpretation. I argue that the two main existing accounts misrepresent how these models are actually used, and propose a new account.

Comment: A good exploration of the role of models in scientific practice. Provides a good overview of the main theories about models, and some objections to them, before suggesting an alternative. Good use of concrete examples, presented very clearly. Suitable for undergraduate teaching. Would form a useful part of an examination of modelling in philosophy of science.

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Bokulich, Alisa, , . How scientific models can explain
2009, Synthese 180(1): 33-45.
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Added by: Laura Jimenez, Contributed by:

Abstract: Scientific models invariably involve some degree of idealization, abstraction, or fictionalization 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 case of Bohr’s model of the atom, and conclude by drawing some distinctions between phenomenological models, explanatory models, and fictional models.

Comment: Interesting paper about scientific modelling. It is easy to read and could serve as an introduction to the topic. The paper explores three approaches to Model Explanations: mechanist model explanations, covering-law model explanations, and causal model explanations. The explanatory function in models is illustrated with the example of Bohr’s atom. This article is recommended for undergraduate students.

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Chuang, Liu, , . Models, fiction and fictional models
2014, In Guichun Guo, Chuang Liu (eds.) Scientific Explanation and Methodology in Science, World Scientific Publishing Co.
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Added by: Laura Jimenez, Contributed by:

Summary: The use of models to scientifically represent and study reality is widely recognized with good reasons as indispensable for the practice of science. Because models, unlikely pure verbal representation, are justifiably regarded as vehicles of representation that are not truth-apt, philosophical questions are natural raised concerning the nature of such vehicles and how they represent. A sizeable literature generated in recent years explores the possibility that ”scientific models are works of fiction”. Idealization and other similar strategies are often taken to be the means by which models are made. Arguing against this last claim, the thesis of this article is that most models in science are not fictional. The author argues against the idea that idealization is the means by which models of typically unobservable systems or mechanisms are made.

Comment: Interesting paper about scientific modeling and scientific representation. Useful for undergraduates and postgraduates courses in philosophy of science.

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Hesse, Mary, , . Models and analogies in science
1966, University of Notre dame Press.
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Added by: Laura Jimenez, Contributed by:

Summary: In this book Hesse argues, contra Duhem, that models and analogies are integral to understanding scientific practice in general and scientific advancement in particular, especially how the domain of a scientific theory is extended and how theories generate genuinely novel predictions. Hesse thinks that, in order help us to understand a new system or phenomenon, we will often create an analogical model that compares this new system or phenomenon with a more familiar system or phenomenon. Hesse distinguishes different types of analogies according to the kinds of similarity relations in which two objects enter: Positive analogies, negative analogies, and neutral analogies. The crux of the argument is that the recognition of similarities of meaning between paired terms and the recognition of similar causal relations within two analogies plays an essential role in theoretical explanation and prediction in science.

Comment: This book is an accessible introduction to the topic of scientific modelling. Useful for teaching in undergraduate courses.

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Hesse, Mary, , . Models in Physics
1953, British Journal for the Philosophy of Science 4(15): 198-214.
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Added by: Laura Jimenez, Contributed by:

Summary: In this article Hesse defends the idea that scientific theories are hypothetico-deductive in form. She examines this hypothetico-deductive method by considering some examples from nineteenth-century mathematical physics. By means of these examples she brings out two points about scientific hypothesis. The first is that mathematical formalisms, when used as hypotheses in the description of physical phenomena, may function like the mechanical models of an earlier stage in physics, without having in themselves any mechanical or other physical interpret. The second point is that most physicists do not regard models as literal descriptions of nature, but as standing in a relation of analogy to nature.

Comment: A really good paper about models in science, mathematical formalism and hypothesis. Highly recomended for postgraduates studying philosophy of physics, although it could also be readable by undergraduates (last years) with previous knowledge of scientific modelling.

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Morrison, Margaret, , . Fictions, representations, and reality
2009, In Mauricio Suárez (ed.), Fictions in Science: Philosophical Essays on Modeling and Idealization. Routledge.
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Added by: Jamie Collin, Contributed by:

Summary: Uses Maxwell’s model of the ether as a case study in accounting for the role of fictions in science. Argues that we should understand idealisation and abstraction as being different from fiction. Fictional models for Morrison are those that are deliberately intended to be such that the relationship between their structure and the structure of the concrete systems they model is not (immediately) apparent. This is different from mere idealisation, where certain structural features are omitted to make calculations more tractable.

Comment: Very useful as a primary or secondary reading in an advanced undergraduate course on philosophy of science (or perhaps on philosophy of fiction). It is philosophically sophisticated, but also treats the science in enough detail to provide students with some clear ideas about the nature of scientific representational practices themselves. Would be appropriate in sections on scientific representation or modelling.

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Morrison, Margaret and, , Mary S. Morgan. Models as mediating instruments
1999, In M. S. Morgan and M. Morrison (eds.), Models as Mediators: Perspectives on Natural and Social Science. Cambridge University Press.
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Added by: Jamie Collin, Contributed by:

Summary: Morrison and Morgan argue for a view of models as ‘mediating instruments’ whose role in scientific theorising goes beyond applying theory. Models are partially independent of both theories and the world. This autonomy allows for a unified account of their role as instruments that allow for exploration of both theories and the world.

Comment: Useful as a primary or secondary reading in an advanced undergraduate course on philosophy of science, particularly within a section on scientific modeling. The paper is particularly useful in teaching because it is not unduly technical.

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Nersessian, Nancy, , . Creating Scientific Concepts
2008, MIT Press.
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Added by: Laura Jimenez, Contributed by:

Publisher’s Note: How do novel scientific concepts arise? In Creating Scientific Concepts, Nancy Nersessian seeks to answer this central but virtually unasked question in the problem of conceptual change. She argues that the popular image of novel concepts and profound insight bursting forth in a blinding flash of inspiration is mistaken. Instead, novel concepts are shown to arise out of the interplay of three factors: an attempt to solve specific problems; the use of conceptual, analytical, and material resources provided by the cognitive-social-cultural context of the problem; and dynamic processes of reasoning that extend ordinary cognition. Focusing on the third factor, Nersessian draws on cognitive science research and historical accounts of scientific practices to show how scientific and ordinary cognition lie on a continuum, and how problem-solving practices in one illuminate practices in the other.

Comment: Nersessian’s book has a two-fold foundation, first, the empirical analysis of two cases of scientific thinking (one from Maxwell and one from a verbal protocol of a scientist); second, philosophical and cognitive analysis of the overall picture of meaning change in science that is the result of her work. The book presents her argument via an introductory chapter, followed by five chapters that develop the argument. Chapter 4 is particularly interesting for the cognitive-scientist: in this chapter Nersessian develops her account of the basic cognitive processes that underlie model-based reasoning. The new approach to mental modeling and analogy, together with Nersessian’s cognitive-historical approach, make Creating Scientific Concepts equally valuable to cognitive science and philosophy of science. The book is accessible and well-written, and should be a relatively quick read for anyone with a previous background in the mentioned fields. It is mainly recommended for postgraduate courses.

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Parker, Wendy S., , . When Climate Models Agree: The Significance of Robust Model Predictions
2011, Philosophy of Science 78 (4):579-600.
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Added by: Clotilde Torregrossa, Contributed by: Simon Fokt

Abstract: This article identifies conditions under which robust predictive modeling results have special epistemic significance—related to truth, confidence, and security—and considers whether those conditions hold in the context of present-day climate modeling. The findings are disappointing. When today’s climate models agree that an interesting hypothesis about future climate change is true, it cannot be inferred—via the arguments considered here anyway—that the hypothesis is likely to be true or that scientists’ confidence in the hypothesis should be significantly increased or that a claim to have evidence for the hypothesis is now more secure

Comment: [This is a stub entry. Please add your comments to help us expand it]

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