Abstract: According to an adequacy-for-purpose view, models should be assessed with respect to their adequacy or fitness for particular purposes. Such a view has been advocated by scientists and philosophers alike. Important details, however, have yet to be spelled out. This article attempts to make progress by addressing three key questions: What does it mean for a model to be adequate-for-purpose? What makes a model adequate-for-purpose? How does assessing a model’s adequacy-for-purpose differ from assessing its representational accuracy? In addition, responses are given to some objections that might be raised against an adequacy-for-purpose view.
When Climate Models Agree: The Significance of Robust Model Predictions
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
Creating Scientific Concepts
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.
Models in Physics
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.
Models and analogies in science
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.
Models, fiction and fictional models
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.
How scientific models can explain
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.
Models as mediating instruments
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.
Fictions, representations, and reality
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.
Making Models Count
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.