Oscar Westerblad

The sciences improve by extending our sensory and cognitive abilities through extrapolation, conversion, and augmentation, as Paul Humphreys has argued. While the opacity of some epistemic enhancers may challenge certain kinds of scientific understanding, I argue that such enhancement is compatible with and extends pragmatic understanding. Drawing on and developing aspects of an epistemology for instruments, I suggest that instrumental functions provide the grounds for extending pragmatic understanding when an inquiry procedure can rely on the instrument’s function to achieve some aim. By continually improving and extending our abilities by relying on instruments, the sciences improve our understanding of the world by extending and enhancing our practical and instrumental facility with it.

We argue that Ian Hacking’s work on experimentation and styles of reasoning offers valuable insights for the current debate on the nature of scientific understanding, which so far has largely been focused on the role of theories and explanations. Hacking’s suggestion that reasoning not only involves thinking but also doing helps to recognise the role of experimentation and manipulation in scientific understanding. We apply this idea by relating Hacking’s views on explanation and theorising to the contextual theory of scientific understanding developed by one of us (De Regt 2017). We then take a constructive approach by developing an account of pragmatic understanding that accommodates styles of reasoning and the centrality of manipulation and intervention in Hacking’s philosophy of science. Our overall aim in the paper is to bring the neglected role of experimentation in producing scientific understanding to the fore. We conclude by suggesting that scientific understanding involves both representing and intervening.

We claim that scientists working with deep learning (DL) models exhibit a form of pragmatic understanding that is not reducible to or dependent on explanation. This pragmatic understanding comprises a set of learned methodological principles that underlie DL model design-choices and secure their reliability. We illustrate this action-oriented pragmatic understanding with a case study of AlphaFold2, highlighting the interplay between background knowledge of a problem and methodological choices involving techniques for constraining how a model learns from data. Building successful models requires pragmatic understanding to apply modelling strategies that encourage the model to learn data patterns that will facilitate reliable generalisation.

Scientists strive to understand the world. Traditionally, philosophers of science have thought that this is a matter of constructing explanations, based on theories and laws, thereby gaining understanding of phenomena by explaining them. This thesis takes a radically different approach, instead relating the notion of understanding to the activities that scientists perform. Scientific understanding is not just a matter of representing or explaining the world, but a matter of practical and intelligent doing. Philosophers of science have continued to sell short the project of making sense of scientific understanding, limiting their views to the articulated and well-formed products of inquiry, like theory and explanation. In taking a pragmatist approach to the epistemology of understanding, I reorientate our theorising about understanding from products to processes, and from explanations to methods and concepts. I thereby provide an ecumenical account of scientific understanding that can accommodate the dynamics and heterogeneity of scientific practice. In Chapter 1, I argue against the dominant focus on explanatory understanding in philosophy of science, thereby making room for my own account of operational understanding. Chapter 2 provides an account of how concepts are essential constituents of understanding. Concepts frame entities so that scientists can think and talk about and interact with them intelligently. Chapter 3 gives an account of understanding-how, through my notion of operational understanding, which relates to the methods that structure and guide successful performances of activities. In Chapter 4, I take up the task of describing the psychology of understanding, the kind of state that understanding is, which I argue is accounted for in terms of skill. In Chapter 5, I develop an account of how cognitive devices — tools for thinking, like models and thought-experiments — facilitate and produce understanding.