At the interface of developmental biology and evolutionary biology, the very criteria of scientific knowledge are up for grabs. A central issue is the status of evolutionary genetics models, which some argue cannot coherently be used with complex gene regulatory network (GRN) models to explain the same evolutionary phenomena. Despite those claims, many researchers use evolutionary genetics models jointly with GRN models to study evolutionary phenomena. This dissertation compares two recent research projects in which researchers jointly use the two kinds (...) of models. I use methods from the empirical social sciences, such as digital corpus analysis, content analysis, and structured case analysis. I infer three primary conclusions about projects of the kind studied. First, they employ an implicit concept of gene that enables the joint use of both kinds of models. Second, they pursue more epistemic aims besides mechanistic explanation of phenomena. Third, they don’t work to create and export broad synthesized theories. Rather, they focus on phenomena too complex to be understood by a common general theory, they distinguish parts of the phenomena, and they apply models from different theories to the different parts. For such projects, seemingly incompatible models are synthesized largely through mediated representations of complex phenomena. (shrink)

Amalgamating evidence from heterogeneous sources and across levels of inquiry is becoming increasingly important in many pure and applied sciences. This special issue provides a forum for researchers from diverse scientific and philosophical perspectives to discuss evidence amalgamation, its methodologies, its history, its pitfalls, and its potential. We situate the contributions therein within six themes from the broad literature on this subject: the variety-of-evidence thesis, the philosophy of meta-analysis, the role of robustness/sensitivity analysis for evidence amalgamation, its bearing on questions (...) of extrapolation and external validity of experiments, its connection with theory development, and its interface with causal inference, especially regarding causal theories of cancer. (shrink)

There is a recent interest within both philosophy of science as well as within epistemology to provide a defensible account of understanding. In the present article I build on insights from previous work in attempt to provide an account of two related forms of understanding in terms of the ability to form rational intentions when using specific types of mental representations. I propose first that “understanding that X” requires that one form a representation of X and, further, that one must (...) be capable of forming rational intentions using this representation across a range of conceivable conditions. I then propose that “understanding why X” requires that one possess a representation of a successful explanation for why X, and that one must be similarly capable of forming rational intentions using this representation across a range of conceivable conditions. I conclude the manuscript by reviewing objections and considering the way this account relates to other literature on explanation and understanding. (shrink)

This paper is a case study. After formulating three norms for critical assessment of argumentation (section 1), I give a brief overview of Galileo’s argumentative strategy in his Dialogue and present his argument for the cause of the tides, which appears as an argument by analogy (section 2). I then discuss possible reconstructions of this argumentation, with one particular suggestion in detail. These arguments seem to fall short, given the aforementioned set of norms (section 3). This leads to my own (...) proposal of Galileo’s argument. I defend this proposal and it’s general idea - that is, the argument’s pattern. It will be classified as ‘interventionist’ and useful regarding the goals of critical assessment (section 4). Finally, I suggest that the pattern of argument is applicable to other cases and useful for applied theory of science (section 5). (shrink)

Amanda works in a library, and a patron asks for her help in learning about duty-to- rescue laws in China. She throws herself into the task, spending hours on retrieving documents from governmental and non-governmental sources, getting electronic translations, looking for literature on Scandinavian duty-to-rescue laws that mention Chinese laws for comparison, and so on. Why? She likes to gain this sort of general knowledge of the world; perhaps the reason she works so hard is that she is learning fascinating (...) facts in doing so. She is also a proud librarian; perhaps the reason she works so hard is that a patron needs her assistance. She is a library employee; perhaps her reason for working so hard is that this sort of effort is noted by her employer and gives her career a boost, or perhaps it is just that doing this sort of work is the condition of her continued employment. Perhaps the reason is that the patron is charming. (shrink)

Many approaches to evidence amalgamation focus on relatively static information or evidence: the data to be amalgamated involve different variables, contexts, or experiments, but not measurements over extended periods of time. However, much of scientific inquiry focuses on dynamical systems; the system’s behavior over time is critical. Moreover, novel problems of evidence amalgamation arise in these contexts. First, data can be collected at different measurement timescales, where potentially none of them correspond to the underlying system’s causal timescale. Second, missing variables (...) have a significantly different impact on time series measurements than they do in the traditional static setting; in particular, they make causal and structural inference much more difficult. In this paper, we argue that amalgamation should proceed by integrating causal knowledge, rather than at the level of “raw” evidence. We defend this claim by first outlining both of these problems, and then showing that they can be solved only if we operate on causal structures. We therefore must use causal discovery methods that are reliable given these problems. Such methods do exist, but their successful application requires careful consideration of the problems that we highlight. (shrink)

Mechanistic information is used in the field of risk assessment in order to clarify two controversial methodological issues, the selection of inference guides and the definition of standards of evidence. In this paper we present an analysis of the concept of mechanistic information in risk assessment by recurring to previous philosophical analyses of mechanistic explanation. Our conclusion is that the conceptual analysis of mechanistic explanation facilitates a better characterization of the concept of mechanistic information. However, it also shows that the (...) use of this kind of information in risk assessment is heavily influenced by pragmatic factors, which have not been sufficiently taken into account in philosophical analysis. Mechanistic models are like hypothesis that have to be validated empirically. Due to their dependence on the standards of evidence, they are subject to the same pragmatic factors. Therefore, recurring to mechanistic information does not lead to closure of the methodological controversies in risk assessment. (shrink)

This paper examines a case of failed interdisciplinary collaboration, between experimental stem cell research and theoretical systems biology. Recently, two groups of theoretical biologists have proposed dynamical systems models as a basis for understanding stem cells and their distinctive capacities. Experimental stem cell biologists, whose work focuses on manipulation of concrete cells, tissues and organisms, have largely ignored these proposals. I argue that ‘failure to communicate’ in this case is rooted in divergent views of explanation: the theoretically-inclined modelers are committed (...) to a version of the covering-law view, while experimental stem cell biologists aim at mechanistic explanations. I propose a way to reconcile these two explanatory approaches to cell development, and discuss the significance of this result for interdisciplinary collaboration in systems biology and beyond. (shrink)

We often face a bewildering array of different explanations for the same social facts (e.g. biological, psychological, economic, and historical accounts). But we have few guidelines for whether and when we should think of different accounts as competing or compatible. In this paper, I offer some guidelines for understanding when custom or norm accounts do and don’t compete with other types of accounts. I describe two families of non-competing accounts: (1) explanations of different (but similarly described) facts, and (2) accounts (...) which seem to differ but are really different parts or versions of the same underlying explanation. I argue that, while many types of apparent competitors don’t really compete with customs, there are some that do. I also describe some of the central problems, which suggest that custom accounts will compete poorly with their rivals. (shrink)