CamPoS (Cambridge Philosophy of Science) is a network of academics and students working in the philosophy of science in various parts of the University of Cambridge, including the Department of History and Philosophy of Science and the Faculty of Philosophy. The Wednesday afternoon seminar series features current research by CamPoS members as well as visitors to Cambridge and scholars based in nearby institutions. If you are interested in presenting in the series, please contact Matt Farr (mwef2). If you have any queries or suggestions for other activities that CamPoS could undertake, please contact Huw Price, Jeremy Butterfield or Anna Alexandrova.
Seminars are held on Wednesdays, 1.00–2.30pm.
Easter Term 2022
4 May on Zoom
Catherine Herfeld (University of Zurich)
What empirical network analysis could offer to Integrated HPS
In recent years, there has been an increasing tendency in history of science and philosophy of science to use formal and empirical methods. Experimental and ethnographic tools, formal modeling, simulation techniques, and computational methods have been applied to study the spread of (mis-)information, the division of cognitive labor, the historical emergence of research fields, and the role of human social interaction in knowledge production. While the exact contribution of those methods to both fields is a matter of debate, their use has certainly been fueled by an abundance of available data relevant to study the development, the social organization, and the procedures of science. In this paper, I propose that this tendency in both, philosophy and history of science, has promising methodological implications for Integrated History and Philosophy of Science (&HPS). Specifically, I discuss the usefulness of empirical network analysis, a quantitative-empirical approach that – so I argue – has much to offer to &HPS. Empirical network analysis is particularly useful for research in &HPS because it has the potential to mitigate a number of methodological challenges that arise from using the historical case study methodology. However, while empirical network analysis has more advantages for &HPS than prima facie visible, it should not replace more traditional philosophical methods but must rely on them to fully develop its potentials.
18 May in Arts School Lecture Theatre A
Naftali Weinberger (Munich Center for Mathematical Philosophy, LMU Munich)
When do statistics provide evidence for discrimination by police? A causal approach
Benchmark tests are widely employed in testing for racial discrimination by police. Neil and Winship (2019) correctly point out that the use of such tests is threatened by the phenomenon of Simpson's paradox. Nevertheless, their analysis of the paradox is inadequate, in ways that point to a more general problem with how they relate statistical quantities to discrimination hypotheses. Simpson's paradox reveals that the statistics employed in benchmark tests will not, in general, be invariant to updating on new information. I argue that as a result of this, benchmark statistics should not by themselves be taken to provide any evidence for or against discrimination, absent additional modeling assumptions. Although Neil and Winship highlight ways in which benchmark statistics appearing to provide evidence for discrimination no longer appear to do so given additional assumptions, they lack an account of which sets of assumptions would ensure invariance. Causal models provide such an account. This motivates the use of causal models when using statistical methods as evidence for discrimination.