Alexander Reutlinger

Renormalization group explanations account for the astonishing phenomenon that microscopically very different physical systems display the same macro-behavior when undergoing phase-transitions. Among philosophers, this explanandum phenomenon is often described as the occurrence of a particular kind of multiply realized macro-behavior. In several recent publications, Robert Batterman denies that RG explanations account for this explanandum phenomenon by following the commonality strategy, i.e. by identifying properties that microscopically very different physical systems have in common. Arguing against Batterman’s claim, I defend the view that RG explanations are in accord with the commonality strategy.

Biased research occurs frequently in the sciences. In this paper, I will focus on one particular kind of biased research: research that is subject to sponsorship bias. I will address the following epistemological question: what precisely is epistemically wrong with biased research of this kind? I will defend the evidential account of epistemic wrongness: that is, research affected by sponsorship bias is epistemically wrong if and only if the researchers in question make false claims about the evidential support of some hypothesis H by data E. I will argue that the evidential account captures the epistemic wrongness of three paradigmatic types of sponsorship bias.

Solving the “new demarcation problem” requires a distinction between epistemically legitimate and illegitimate roles for non-epistemic values in science. This paper addresses one ‘half’ (i.e. a sub-problem) of the new demarcation problem articulated by the Gretchenfrage: What makes the role of a non-epistemic value in science epistemically illegitimate? I will argue for the Explaining Epistemic Errors (EEE) account, according to which the epistemically illegitimate role of a non-epistemic value is defined via an explanatory claim: the fact that an epistemic agent is motivated by a non-epistemic value explains why the epistemic agent commits a particular epistemic error. The EEE account is inspired by Douglas’ and Steel’s “functionalist” or “epistemic constraint” accounts of epistemic illegitimacy. I will suggest that the EEE account is able to meet two challenges that these two accounts face, while preserving the key intuition underlying both accounts. If my arguments succeed, then the EEE account provides a solution to one half of the new demarcation problem (by providing a definition of what makes the role of a non-epistemic value epistemically illegitimate) and it opens up new ways for addressing the other half (i.e. characterizing an epistemically legitimate role for non-epistemic values).

Several philosophers of biology have argued for the claim that the generalizations of biology are historical and contingent.1–5 This claim divides into the following sub-claims, each of which I will contest: first, biological generalizations are restricted to a particular space-time region. I argue that biological generalizations are universal with respect to space and time. Secondly, biological generalizations are restricted to specific kinds of entities, i.e. these generalizations do not quantify over an unrestricted domain. I will challenge this second claim by providing an interpretation of biological generalizations that do quantify over an unrestricted domain of objects. Thirdly, biological generalizations are contingent in the sense that their truth depends on special initial and background conditions. I will argue that the contingent character of biological generalizations does not diminish their explanatory power nor is it the case that this sort of contingency is exclusively characteristic of biological generalizations.

In her new book Reconstructing Reality (henceforth RR), Margaret Morrison’s main target is the kind of information about the world (or, more specifically, about physical and biological systems) one can extract from the ‘reconstructive methods and practices of science’ (p. 1). To address this, Morrison focuses on three central kinds of interrelated strategies for ‘recasting nature’ (p. 2) by using reconstructive methods and practices: (i) abstract mathematical explanations and understanding (Part 1 of the book), (ii) scientific models (Part 2), and (iii) computer simulations (Part 3). Hence, RR is the ambitious attempt to connect and analyse three major topics in current philosophy of science.