Helen Meskhidze

There has been much interest in the recent philosophical literature on increasing the reliability and trustworthiness of computer simulations. One method used to investigate the reliability of computer simulations is code comparison. Gueguen, however, has offered a convincing critique of code comparisons, arguing that they face a critical tension between the diversity of codes required for an informative comparison and the similarity required for the codes to be comparable. Here, I reflect on her critique in light of a recent code comparison investigating self-interacting dark matter in two computer simulation codes. I argue that the informativeness of this particular code comparison was due to its targeted approach and narrow focus. Its targeted approach (i.e., only the dark matter modules) allowed for simulation outputs that were diverse enough for an informative comparison and yet still comparable. Understanding the comparison as an instance of eliminative reasoning narrowed the focus: we could investigate whether code-specific differences in implementation contributed significantly to the results of self-interacting dark matter simulations. Based on this case study, I argue that code comparisons can be conducted in such a way that they serve as a method for increasing our confidence in computer simulations being, as Parker defines, adequate-for-purpose.

The increasing precision of observations of the large-scale structure of the universe has created a problem for simulators: running the simulations necessary to interpret these observations has become impractical. Simulators have thus turned to machine learning (ML) algorithms instead. Though ML decreases computational expense, one might be worried about the use of ML for scientific investigations: How can algorithms that have repeatedly been described as black-boxes deliver scientific understanding? In this paper, I investigate how cosmologists employ ML, arguing that in this context, ML algorithms should not be considered black-boxes and can deliver genuine scientific understanding. Accordingly, understanding the methodological role of ML algorithms is crucial to understanding the types of questions they are capable of, and ought to be responsible for, answering.

This paper is part of a larger project designed to examine and ameliorate the underrepresentation of female-identified students in the philosophy department at Elon University. The larger project involved a variety of research methods, including statistical analysis of extant registration and grade distribution data from our department as well as the administration of multiple surveys. Here, we provide a description and analysis of one aspect of our research: focus groups. We ran three focus groups of female-identified undergraduate students: one group consisted of students who had taken more than one philosophy class, one consisted of students who had taken only one philosophy class, and one consisted of students who had taken no philosophy classes. After analyzing the results of the focus groups, we find evidence that: one philosophy class alone did not cultivate a growth mindset among female-identified students of philosophy, professors have the potential to ameliorate students’ perceptions of philosophy; and students who have not taken philosophy are likely to see their manner of thinking as being at odds with that required by philosophy. We conclude by articulating a series of questions worthy of further study.