Archive for February, 2015

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“On Bohr’s Correspondence Principle”

February 24, 2015

Shahin Kaveh

Abstract: The Correspondence Principle (CP) of old quantum theory is commonly considered to be the requirement that quantum and classical theories converge in their empirical predictions in the appropriate asymptotic limit. That perception has persisted despite the fact that Bohr and other early proponents of CP clearly did not intend it as a mere requirement, and despite much recent historical work. In this paper, I build on this work by first giving an explicit formulation to the mentioned asymptotic requirement (which I shall call the Congruence Requirement (CR)) and then discussing various possible formulations of CP for emission on the basis of the primary literature as well as general physical and metaphysical considerations. I shall then show that, in all of the most probable interpretations of CP that consider quantum theory as a universal theory, any system incorporating both CR and CP for emission would in fact be inconsistent. Old quantum theory measurably contradicts classical physics in the classical regime.

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“Climate Modeling and Scale Dependence” (

February 18, 2015

Marina Baldissera Pacchetti

Abstract: In this project I analyze the extent to which the way physical systems are defined is scale dependent, where the ‘scale’ is a spatiotemporal metric that is aimed at characterizing stable or semi stable dynamic systems. To do this, I develop an account of three crucial steps in mathematical modeling of physical systems that illustrates this scale dependency. The first step involves the individuation of the scale of the system. This can be done in terms of empirical or theoretical considerations. Where the empirical observations will also depend on the size of the observational networks.
The second step involves identifying various kinds of dynamical components of the system, such as defining its boundaries, its direct variables, and its parameters. To develop this step, I will borrow Wilson’s term of ‘effacement’ and develop it. The third step describes pragmatic features of model building such as those discussed by Fillion (2014).
To illustrate my framework, I will use a case study from climate science, and in particular I will focus on atmospheric dynamics. I will first look at the historical component of model building of large scale circulation, and focus on epistemic problems of ‘bottom up’ or ‘top down’ modeling. The difficulty in this case is that it is not possible to implement these ‘simple’ strategies when modeling a dynamics in which many scales of motion are present: an understanding of the dynamics of systems, especially one that is non-linear and anisotropic like the atmosphere, requires a careful analysis of how to identify systems at different scales and their interaction. Then, I will look at how sociocultural factors have inflated what philosophers have called ‘structural uncertainty’ of climate models. I argue that ‘structural uncertainty’ might be exacerbated by neglecting the importance of steps 1 and 2 outlined above.

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“Mechanistic Explanation Doesn’t Explain Much” (2/6/2015)

February 4, 2015

Morgan Thompson

Abstract: Although some mechanists worry that limiting the scope of mechanistic explanation to only a subset of all explanations will “marginalize” it, I argue that only by limiting the scope of mechanistic explanation can accounts of mechanistic explanation describe scientific practices and norms. When alternative theories of explanation are proposed based on specific examples from the biological sciences, many mechanists reply in the following two ways: (i) the purported counter-example is not actually explanatory and so the alternative theory is not a theory of explanation or (ii) the purported counter-example is actually mechanistic and so the alternative theory of explanation is not an actual alternative to mechanistic explanation. These two responses are unhelpful not only in the dialectic of the debate, but also in terms of providing descriptively adequate and normatively satisfying theories of explanation. Mechanists have begun discussing examples of network models in graph theory—graphs consisting of nodes and the connections between nodes to describe the structure of a system and system-level properties—to illustrate networks in the brain (Sporns 2010) or protein networks (Alon 2007).
Craver (2014) provides the first response when he argues that network models are not a new kind of explanation, but rather a descriptive tool useful for scientists to describe organization and one that might contribute to mechanistic explanation. In an ethnographic study of two systems biology labs, MacLeod & Nersessian (2015) found that these labs often aim to model a system for interventions on a particular aspect of the system, usually at the expense of distorting other parts of the model through the process of parameter-fitting. I argue that these models do not fit into Craver’s phenomenal-mechanistic dichotomy and so his version of mechanistic explanation is not descriptively adequate.
Zednik (2014a, 2014b) responds in the second way by suggesting that networks models indeed provide mechanistic explanations. This response requires the mechanist to expand many aspects of the mechanistic explanation picture to the point of triviality and at the expense of respecting scientific practices. In particular, these mechanists often treat nodes in network models as straight-forward components in a mechanistic explanation, which ignores the fact that nodes are defined by the modelers often arbitrarily (e.g., random parcellation schemes). Further, node choice significantly affects the extent to which certain system-level properties (e.g., small-world) emerge in the model (Zalensky et al. 2010). I argue that limiting the scope of mechanistic explanation allows it to be a more descriptively adequate account of scientific activities (e.g., explanation, modeling) and also provides more consistent, contentful norms for philosophers and scientists interested in successful explanations. Reducing the scope of mechanistic explanation allows the theory to contribute—along with other theories of explanation—to a more descriptively adequate account of modeling and explanation in the biological sciences.