Erik Weber

In this paper we argue that in recent literature on mechanistic explanations, authors tend to conflate two distinct features that mechanistic models can have or fail to have: plausibility and richness. By plausibility, we mean the probability that a model is correct in the assertions it makes regarding the parts and operations of the mechanism, i.e., that the model is correct as a description of the actual mechanism. By richness, we mean the amount of detail the model gives about the actual mechanism. First, we argue that there is at least a conceptual reason to keep these two features distinct, since they can vary independently from each other: models can be highly plausible while providing almost no details, while they can also be highly detailed but plainly wrong. Next, focusing on Craver's continuum of ?how-possibly,? to ?how-plausibly,? to ?how-actually? models, we argue that the conflation of plausibility and richness is harmful to the discussion because it leads to the view that both are necessary for a model to have explanatory power, while in fact, richness is only so with respect to a mechanism's activities, not its entities. This point is illustrated with two examples of functional models

In the past 25 years, many philosophers have endorsed the view that the practical value of causal knowledge lies in the fact that manipulation of causes is a good way to bring about a desired change in the effect. This view is intuitively very plausible. For instance, we can predict a storm on the basis of a barometer reading, but we cannot avoid the storm by manipulating the state of the barometer (barometer status and storm are effects of a common cause, viz. atmospheric conditions). In §1 we present textual evidence which shows that this view is very popular. In §2 we show that this standard view is too restrictive: the practical value of causal knowledge is wider. In §3 we introduce the distinction between ‘manipulative policy’ and ‘selective policy’ as a theoretical framework to account for this wider practical value.

Although there is a consensus among philosophers of mathematics and mathematicians that mathematical explanations exist, only a few authors have proposed accounts of explanation in mathematics. These accounts fit into the unificationist or top-down approach to explanation. We argue that these models can be complemented by a bottom-up approach to explanation in mathematics. We introduce the mechanistic model of explanation in science and discuss the possibility of using this model in mathematics, arguing that using it does not presuppose a Platonist view of mathematics and allows one to gain insight into why a theorem is true by answering what-if-things-had-been-different questions

In a recent paper on realism and pragmatism published in this journal, Osmo Kivinen and Tero Piiroinen have been pleading for more methodological work in the philosophy of the social sciences—refining the conceptual tools of social scientists—and less philosophically ontological theories. Following this de-ontologizing approach, we scrutinize the debates on social explanation and contribute to the development of a pragmatic social science methodology. Analyzing four classic debates concerning explanation in the social sciences, we propose to shift the debate away from (a) the ontologizing defenses of forms of social explanation, and (b) a winner-takes-all-approach. Instead, we advocate (c) a pragmatic approach towards social explanation, elaborating a rigorous framework for explanatory pluralism detached from the debates on social ontology.