Alisa Bokulich

Scientific models invariably involve some degree of idealization, abstraction, or nationalization 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

There is a growing recognition that fictions have a number of legitimate functions in science, even when it comes to scientific explanation. However, the question then arises, what distinguishes an explanatory fiction from a nonexplanatory one? Here I examine two cases—one in which there is a consensus in the scientific community that the fiction is explanatory and another in which the fiction is not explanatory. I shall show how my account of “model explanations” is able to explain this asymmetry, and argue that realism—of a more subtle form—does have a role in distinguishing explanatory from non- explanatory fictions.

This article addresses the question whether supertasks are possible within the context of non-relativistic quantum mechanics. The supertask under consideration consists of performing an infinite number of quantum mechanical measurements in a finite amount of time. Recent arguments in the physics literature claim to show that continuous measurements, understood as N discrete measurements in the limit where N goes to infinity, are impossible. I show that there are certain kinds of measurements in quantum mechanics for which these arguments break down. This suggests that there is a new context in which quantum mechanics, in principle, permits the performance of a supertask.

Simulations using idealized numerical models can often generate behaviors or patterns that are visually very similar to the natural phenomenon being investigated and to be explained. The question arises, when should these model simulations be taken to provide an explanation for why the natural phenomena exhibit the patterns that they do? An important distinction for answering this question is that between ‘how-possibly’ explanations and ‘how-actually’ explanations. Despite the importance of this distinction there has been surprisingly little agreement over how exactly this distinction should bedrawn. I shall argue that inadequate attention has been paid to the different contexts in which an explanation can be given and the different levels of abstraction at which the explanandum phenomenon can be framed. By tracing how scientists are using model simulations to explain a striking periodic banding of vegetation known as tiger bush, I will show how our understanding of the distinction between how-possibly and how-actually model explanations needs to be revised.

The 2006 decision by the International Astronomical Union to strip Pluto of its status as a planet generated considerable uproar not only in scientific circles, but among the lay public as well. After all, how can a vote by 424 scientists in a conference room in Prague undermine what every well-educated second grader knows is a scientific fact? The Pluto controversy provides a new and fertile ground in which to revisit the traditional philosophical problems of natural kinds and scientific change. Before engaging these philosophical problems, however, there are two misguided reactions to the Pluto controversy worth dispelling from the start. The first misguided reaction is that this sort of classificatory ..

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.

The aim of this paper is to examine in detail the similarities and dissimilarities between Werner Heisenberg’s account of closed theories and Thomas Kuhn’s model of scientific revolutions. My analysis draws on a little‐known discussion that took place between Heisenberg and Kuhn in 1963, in which Heisenberg, having just read Kuhn’s Structure of Scientific Revolutions, compares Kuhn’s views to his own account of closed theories. I conclude that while Heisenberg and Kuhn share a holist conception of theories, a revolutionary model of theory change, and even a notion of incommensurability, their views diverge fundamentally when it comes to the issue of scientific realism. I show that, contrary to popular opinion, Heisenberg is not an instrumentalist, but rather a pluralistic realist.

: An examination of two thought experiments in contemporary physics reveals that the same thought experiment can be reanalyzed from the perspective of different and incompatible theories. This fact undermines those accounts of thought experiments that claim their justificatory power comes from their ability to reveal the laws of nature. While thought experiments do play a genuine evaluative role in science, they do so by testing the nonempirical virtues of a theory, such as consistency and explanatory power. I conclude that, while their interpretation presupposes a whole set of background theories and putative laws, thought experiments nonetheless can evolve and be retooled for different theories and ends

The fact that the same equations or mathematical models reappear in the descriptions of what are otherwise disparate physical systems can be seen as yet another manifestation of Wigner's “unreasonable effectiveness of mathematics.” James Clerk Maxwell famously exploited such formal similarities in what he called the “method of physical analogy.” Both Maxwell and Hermann von Helmholtz appealed to the physical analogies between electromagnetism and hydrodynamics in their development of these theories. I argue that a closer historical examination of the different ways in which Maxwell and Helmholtz each deployed this analogy gives further insight into debates about the representational and explanatory power of mathematical models.