Philosophers of science have in recent years become increasingly interested in the notion of interdisciplinarity. One important form interdisciplinarity can take is that of a dynamic exchange of problems and solutions between disciplines—what has recently been called problem-feeding. On this model problems arising within specific disciplines are sometimes solved more effectively by, or in collaboration with, other disciplines. In this paper we explore this model as a framework for thinking about, and actively structuring, interdisciplinary research. We point to the applicability (...) of the problem-feeding model, and to some of the prerequisites of problem-feeding interdisciplinarity, highlighting in particular the harmonisation of goals and the establishment of mutual trust between disciplines. (shrink)

I envision new directions in the methodology of experimental games in the field of developmental, environmental and resource economics. Although there have been extensive discussions on experimenta...

Research on interdisciplinary science has for the most part concentrated on the institutional obstacles that discourage or hamper interdisciplinary work, with the expectation that interdisciplinary interaction can be improved through institutional reform strategies such as through reform of peer review systems. However institutional obstacles are not the only ones that confront interdisciplinary work. The design of policy strategies would benefit from more detailed investigation into the particular cognitive constraints, including the methodological and conceptual barriers, which also confront attempts to work (...) across disciplinary boundaries. Lessons from cognitive science and anthropological studies of labs in sociology of science suggest that scientific practices may be very domain specific, where domain specificity is an essential aspect of science that enables researchers to solve complex problems in a cognitively manageable way. The limit or extent of domain specificity in scientific practice, and how it constrains interdisciplinary research, is not yet fully understood, which attests to an important role for philosophers of science in the study of interdisciplinary science. This paper draws upon two cases of interdisciplinary collaboration; those between ecologists and economists, and those between molecular biologists and systems biologists, to illustrate some of the cognitive barriers which have contributed to failures and difficulties of interactions between these fields. Each exemplify some aspect of domain specificity in scientific practice and show how such specificity may constrain interdisciplinary work. (shrink)

Knowledge transfer across different contexts has become an increasingly prevalent feature of current science. As such, it is a relevant topic also for history and philosophy of science. This special issue presents a set of papers that study knowledge transfer in various disciplines. The contributions approach the topic from either an integrated history and philosophy of science perspective, 2) a systematic philosophical perspective, or 3) a historical perspective. This overview article is organized in three sections. The introduction provides some background (...) and context of the topic. The second section specifies the three components of knowledge transfer, i.e., knowledge, context, and the transfer process. It motivates our take on this topic and then outlines the central ideas of each paper. The third section reflects on the kinds of methods used to study processes of knowledge transfer. The article concludes by highlighting some of the features of such processes, some of the main challenges and obstacles that those processes involve, and possible ways of dealing with them. (shrink)

1. A multidisciplinary group of scholars within the International Commission on Stratigraphy – known as the Anthropocene Working Group – recently recommended the Anthropocene as a new geological ep...

Models provide scientists with knowledge about target systems. An important group of models are those that are called general. However, what exactly is meant by generality in this context is somewhat unclear. The aim of this paper is to draw out a distinction between two notions of generality that has implications for scientific practice. Some models are general in the sense that they apply to many systems in the world and have many particular targets. Another sense is captured by models (...) that are aimed at understanding the fundamental or underlying dynamics of a phenomenon, as opposed to how it manifests in each particular case. They have non-specific, i.e. generic targets. While both notions of generality and genericness are legitimate and correspond to different aspects of scientific practice, they must be distinguished. Failing to do so obscures the danger of overgeneralisation faced by general models and facilitates the illegitimate use of generic models as general models. This can lead to a reduction of the explanatory and predictive power of both. (shrink)