In semiclassical mechanics one finds explanations of quantum phenomena that appeal to classical structures. These explanations are prima facie problematic insofar as the classical structures they appeal to do not exist. Here I defend the view that fictional structures can be genuinely explanatory by introducing a model-based account of scientific explanation. Applying this framework to the semiclassical phenomenon of wavefunction scarring, I argue that not only can the fictional classical trajectories explain certain aspects of this quantum phenomenon, but also that (...) an explanation that does not make reference to these classical structures is, in a certain sense, deficient. (shrink)

Given that scientific realism is based on the assumption that there is a connection between a model's predictive success and its truth, and given the success of multiple incompatible models in scientific practice, the realist has a problem. When the different models can be shown to arise as different approximations to a unified theory, however, one might think the realist to be able to accommodate such cases. I discuss a special class of models and argue that a realist interpretation has (...) to understand these models of a system as ‘ perspectival ’, in close analogy to different spatial perspectives onto the same object. For this sort of case, I also respond to Morrison's recent claim that in the process of unifying models into an overarching theory, explanatory and descriptive power are lost, leaving the unified theory with less of a claim to a realist interpretation than the models themselves. Introduction Perspectival models from singular perturbation problems Unification of perspectives without losses of explanatory power Perspectives as different levels of a system Perspectival models, idealizations and pluralism. (shrink)

The official model of explanation proposed by the logical empiricists, the covering law model, is subject to familiar objections. The goal of the present paper is to explore an unofficial view of explanation which logical empiricists have sometimes suggested, the view of explanation as unification. I try to show that this view can be developed so as to provide insight into major episodes in the history of science, and that it can overcome some of the most serious difficulties besetting the (...) covering law model. (shrink)

To explain the phenomena in the world of our experience, to answer the question “why?” rather than only the question “what?”, is one of the foremost objectives of all rational inquiry; and especially, scientific research in its various branches strives to go beyond a mere description of its subject matter by providing an explanation of the phenomena it investigates. While there is rather general agreement about this chief objective of science, there exists considerable difference of opinion as to the function (...) and the essential characteristics of scientific explanation. In the present essay, an attempt will be made to shed some light on these issues by means of an elementary survey of the basic pattern of scientific explanation and a subsequent more rigorous analysis of the concept of law and of the logical structure of explanatory arguments. (shrink)

The classic logical positivist account of historical explanation, putting forward what is variously called the "regularity interpretation" (#Gardiner, The Nature of Historical Explanation), the "covering law model" (#Dray, Laws and Explanation in History), or the "deductive model" (Michael #Scriven, "Truisms as Grounds for Historical Explanations"). See also #Danto, Narration and Knowledge, for further criticisms of the model. Hempel formalizes historical explanation as involving (a) statements of determining (initial and boundary) conditions for the event to be explained, and (b) statements of (...) general laws that imply that whenever events of the kind described in (a) occur, an event of the kind to be explained will take place. He admits that in practice historians rarely explain historical events in this way, but instead give only fragmentary "explanation sketches." Such sketches can, however, be "scientifically acceptable," providing it points to where "more specific statements" are to be found (351). An interesting aspect of the article is its "late" positivist, proto‑Kuhnian assertion that "the separation of 'pure description' and 'hypothetical generalization and theory construction' in empirical science is unwarranted" (356). But Hempel does not work out the wider implications of this important conclusion. It is also worth noting that he himself was not much interested in the philosophy of history; other authors took up his model and discussed it within that context. Usefully discussed by #Ricoeur, Time and Narrative, 1:112‑17. (shrink)

It is a traditional empiricist doctrine that natural laws are universal truths. In order to overcome the obvious difficulties with this equation most empiricists qualify it by proposing to equate laws with universal truths that play a certain role, or have a certain function, within the larger scientific enterprise. This view is examined in detail and rejected; it fails to account for a variety of features that laws are acknowledged to have. An alternative view is advanced in which laws are (...) expressed by singular statements of fact describing the relationship between universal properties and magnitudes. (shrink)

The geosciences include a wide spectrum of disciplines ranging from paleontology to climate science, and involve studies of a vast range of spatial and temporal scales, from the deep-time history of microbial life to the future of a system no less immense and complex than the entire Earth. Modeling is thus a central and indispensable tool across the geosciences. Here, we review both the history and current state of model-based inquiry in the geosciences. Research in these fields makes use of (...) a wide variety of models, such as conceptual, physical, and numerical models, and more specifically cellular automata, artificial neural networks, agent-based models, coupled models, and hierarchical models. We note the increasing demands to incorporate biological and social systems into geoscience modeling, challenging the traditional boundaries of these fields. Understanding and articulating the many different sources of scientific uncertainty – and finding tools and methods to address them – has been at the forefront of most research in geoscience modeling. We discuss not only structuralmodel uncertainties, parameter uncertainties, and solution uncertainties, but also the diverse sources of uncertainty arising from the complex nature of geoscience systems themselves. Without an examination of the geosciences, our philosophies of science and our understanding of the nature of model-based science are incomplete. (shrink)

I discuss how modern cosmology illustrates under-determination of theoretical hypotheses by data, in ways that are different from most philosophical discussions. I emphasise cosmology's concern with what data could in principle be collected by a single observer ; and I give a broadly sceptical discussion of cosmology's appeal to the cosmological principle as a way of breaking the under-determination.I confine most of the discussion to the history of the observable universe from about one second after the Big Bang, as described (...) by the mainstream cosmological model: in effect, what cosmologists in the early 1970s dubbed the ‘standard model’, as elaborated since then. But in the closing Section 4, I broach some questions about times earlier than one second. (shrink)

In this paper, I draw a parallel between the stability of physical systems and that of economic ones, such as the US financial system. I argue that the use of equilibrium assumptions is central to the analysis of dynamic behavior for both kinds of systems, and that we ought to interpret such idealizing strategies as footholds for causal exploration and explanation. Our considerations suggest multi-scale modeling as a natural home for such reasoning strategies, which can provide a backdrop for the (...) assessment and prevention of financial crises. Equilibrium assumptions are critical elements of the epistemic scaffolding that make up multi-scale models, which we should understand as a means of constructing explanations of dynamic behavior. (shrink)

A common reductionist assumption is that macro-scale behaviors can be described "bottom-up" if only sufficient details about lower-scale processes are available. The view that an "ideal" or "fundamental" physics would be sufficient to explain all macro-scale phenomena has been met with criticism from philosophers of biology. Specifically, scholars have pointed to the impossibility of deducing biological explanations from physical ones, and to the irreducible nature of distinctively biological processes such as gene regulation and evolution. This paper takes a step back (...) in asking whether bottom-up modeling is feasible even when modeling simple physical systems across scales. By comparing examples of multi-scale modeling in physics and biology, we argue that the “tyranny of scales” problem presents a challenge to reductive explanations in both physics and biology. The problem refers to the scale-dependency of physical and biological behaviors that forces researchers to combine different models relying on different scale-specific mathematical strategies and boundary conditions. Analyzing the ways in which different models are combined in multi-scale modeling also has implications for the relation between physics and biology. Contrary to the assumption that physical science approaches provide reductive explanations in biology, we exemplify how inputs from physics often reveal the importance of macro-scale models and explanations. We illustrate this through an examination of the role of biomechanics modeling in developmental biology. In such contexts, the relation between models at different scales and from different disciplines is neither reductive nor completely autonomous, but interdependent. (shrink)

Many important explanations in physics are based on ideas and assumptions about symmetries, but little has been said about the nature of such explanations. This chapter aims to fill this lacuna, arguing that various symmetry explanations can be naturally captured in the spirit of the counterfactual-dependence account of Woodward, liberalized from its causal trappings. From the perspective of this account symmetries explain by providing modal information about an explanatory dependence, by showing how the explanandum would have been different, had the (...) facts about an explanatory symmetry been different. Furthermore, the authors argue that such explanatory dependencies need not be causal. (shrink)

Although predictive power and explanatory insight are both desiderata of scientific models, these features are often in tension with each other and cannot be simultaneously maximized. In such situations, scientists may adopt what I term a ‘division of cognitive labor’ among models, using different models for the purposes of explanation and prediction, respectively, even for the exact same phenomenon being investigated. Adopting this strategy raises a number of issues, however, which have received inadequate philosophical attention. More specifically, while one implication (...) may be that it is inappropriate to judge explanatory models by the same standards of quantitative accuracy as predictive models, there still needs to be some way of either confirming or rejecting these model explanations. Here I argue that robustness analyses have a central role to play in testing highly idealized explanatory models. I illustrate these points with two examples of explanatory models from the field of geomorphology. (shrink)

The paper begins with a generic discussion of modelling, focusing on some of its practices and problems. I then move on to a philosophical discussion about emergence and multi-scale modelling; more specifically, the reasons why what looks like a promising strategy for dealing with emergence is sometimes incapable of delivering interesting results. This becomes especially evident when we look more closely at turbulence and what I take to be the main ontological feature of emergent behavior—universality. Finally, I conclude by showing (...) why, despite displaying multi-scale behaviour and some of the characteristics we identify with emergence, turbulence fails to fit neatly into the latter category and is not successfully captured using multi-scale modelling. The complex nature of turbulence illustrates the difficulties in characterizing emergence and why specific criteria are needed in order to prevent every complex behaviour we don’t understand being classified as emergent. (shrink)