The “practice turn” in philosophy of science has strengthened the connections between philosophy and scientific practice. Apart from reinvigorating philosophy of science, this also increases the ...relevance of philosophical research for science, society, and science education. In this paper, we reflect on our extensive experience with teaching mandatory philosophy of science courses to science students from a range of programs at University of Copenhagen. We highlight some of the lessons we have learned in making philosophy of science “fit for teaching” outside of philosophy circles by taking selected cases from the students’ own field as the starting point. We argue for adapting philosophy of science teaching to particular audiences of science students, and discuss the benefits of drawing on research within science education to inform curriculum and course design. This involves reconsidering teaching resources, assumptions about students, intended learning outcomes, and teaching formats. We also argue that to make philosophy of science relevant and engaging to science students, it is important to consider their potential career trajectories. By anticipating future contexts and situations in which methodological, conceptual, and ethical questions could be relevant, philosophy of science can demonstrate its value in the education of science students.
How is the information contained in our genes read, memorized and interpreted? What mechanisms control gene expression in an individual or across generations? The understanding of these mechanisms is ...crucial to knowledge on life forms. Epigenetics studies the influence of a genome’s cellular history on the way in which it is read. Since the sequencing of the complete human genome in the early twenty-first century, epigenetics has also given rise to the hope that we are “more” than the sequencing of our genes. This is probably why there is currently a huge upsurge of interest in this discipline.
What makes teaching philosophy of science to non-philosophy students different from teaching it to philosophy students, and how should lecturers in philosophy adapt to an audience of practitioners of ...a field of study that they are reflecting on? In this paper we address this question by analyzing the differences between these student groups, and based on this analysis we make suggestions as to how philosophy of science can be taught to non-philosophy students in an effective and attractive way. Starting-point is the observation that not only the background knowledge and interests of these students but also the aims of the respective courses will differ. We present a comparative analysis of the demands and conditions for teaching philosophy of science to the different types of students, focusing on learning objectives and didactic approaches. Next, we apply our analysis to a concrete example, the role of values in science, and discuss how this may be taught to either philosophy students or non-philosophy students. Finally, we discuss an alternative format for teaching philosophy to non-philosophy students.
A lot of philosophy taught to science students consists of scientific methodology. But many philosophy of science textbooks have a fraught relationship with methodology, presenting it either a system ...of universal principles or entirely permeated by contingent factors not subject to normative assessment. In this paper, I argue for an alternative,
heuristic
perspective for teaching methodology: as fallible, purpose- and context-dependent, subject to cost-effectiveness considerations and systematically biased, but nevertheless subject to normative assessment. My pedagogical conclusion from this perspective is that philosophers should aim to teach science students heuristic reasoning: strategies of normative method choice appraisal that are sensitive to purposes, contexts, biases and cost-effectiveness considerations; and that we should do so by teaching them exemplars of such reasoning. I illustrate this proposal at the hand of three such exemplars, showing how they help students to appreciate the heuristic nature of both methods and methodology, and to normatively assess method choice in such circumstances.
What is a complex system? Ladyman, James; Lambert, James; Wiesner, Karoline
European journal for philosophy of science,
2013/1, Volume:
3, Issue:
1
Journal Article
Complex systems research is becoming ever more important in both the natural and social sciences. It is commonly implied that there is such a thing as a complex system, different examples of which ...are studied across many disciplines. However, there is no concise definition of a complex system, let alone a definition on which all scientists agree. We review various attempts to characterize a complex system, and consider a core set of features that are widely associated with complex systems in the literature and by those in the field. We argue that some of these features are neither necessary nor sufficient for complexity, and that some of them are too vague or confused to be of any analytical use. In order to bring mathematical rigour to the issue we then review some standard measures of complexity from the scientific literature, and offer a taxonomy for them, before arguing that the one that best captures the qualitative notion of the order produced by complex systems is that of the Statistical Complexity. Finally, we offer our own list of necessary conditions as a characterization of complexity. These conditions are qualitative and may not be jointly sufficient for complexity. We close with some suggestions for future work.
Some naturalistic philosophers of mind subscribing to the predictive processing theory of mind have adopted a realist attitude towards the results of Bayesian cognitive science. In this article, we ...argue that this realist attitude is unwarranted. The Bayesian research programme in cognitive science does not possess special epistemic virtues over alternative approaches for explaining mental phenomena involving uncertainty. In particular, the Bayesian approach is not simpler, more unifying, or more rational than alternatives. It is also contentious that the Bayesian approach is overall better supported by the empirical evidence. So, to develop philosophical theories of mind on the basis of a realist interpretation of results from Bayesian cognitive science is unwarranted. Naturalistic philosophers of mind should instead adopt an anti-realist attitude towards these results and remain agnostic as to whether Bayesian models are true. For continuing on with an exclusive focus and praise of Bayes within debates about the predictive processing theory will impede progress in philosophical understanding of scientific practice in computational cognitive science as well as of the architecture of the mind.
Graded Causation and Defaults Halpern, Joseph Y.; Hitchcock, Christopher
British journal for the philosophy of science,
06/2015, Volume:
66, Issue:
2
Journal Article
Peer reviewed
Open access
Recent work in psychology and experimental philosophy has shown that judgments of actual causation are often influenced by consideration of defaults, typicality, and normality. A number of ...philosophers and computer scientists have also suggested that an appeal to such factors can help deal with problems facing existing accounts of actual causation. This article develops a flexible formal framework for incorporating defaults, typicality, and normality into an account of actual causation. The resulting account takes actual causation to be both graded and comparative. We then show how our account would handle a number of standard cases.
The notion of natural kinds has been widely criticized in philosophy of science but also appears indispensable for philosophical engagement with classificatory practices. Rather than addressing this ...tension through a new definition of “natural kind”, this article suggests materiality as a substitute for naturalness in philosophical debates about scientific classification. It is argued that a theory of material kinds provides an alternative and more inclusive entry point for analyzing classificatory practices, which is specified through an account of “restricted malleability” of materiality and further analyzed as (1) gradual, (2) multi-dimensional, (3) scalable, (4) interactive, and (5) purpose sensitive.
Here, for the first time in English, is Georges Cuvier’s extraordinary “History of the Natural Sciences from Its Origin to the Present Day.” Based on a series of public lectures presented by Cuvier ...from 1829 to 1832, this fourth of a five-volume series, translated from the original French and heavily annotated with commentary, is a detailed chronological survey of the natural sciences in the eighteenth century. It is truly astonishing in its detail and scope. Cuvier was fluent in many languages, English, German, Spanish, and certainly Latin, in addition to French. He was therefore well prepared to investigate and interpret firsthand the scientific literature of Europe as a whole. The work is an affirmation of Cuvier’s vast encyclopedic knowledge, his complete command of the scientific and historical literature, and his incomparable memory. This history is remarkable also for providing in one place a large set of useful references to a vast ancient literature that is not easily found anywhere else. This huge body of information provides us furthermore with unique insight into Cuvier’s concept of the natural sciences, and to the vast breadth and progress of this human endeavor. With this work, Cuvier fills an important gap in philosophical thought between the time of Carl Linnaeus and Charles Darwin.
Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by ...utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding misguided? In this article, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding.
PDF
