Henk W. de Regt

In recent years, scientific understanding has become a focus of attention in philosophy of science. Since understanding is typically associated with the pragmatic and psychological dimensions of explanation, shifting the focus from explanation to understanding may induce a shift from accounts that embody normative ideals to accounts that provide accurate descriptions of scientific practice. Not surprisingly, many ‘friends of understanding’ sympathize with a naturalistic approach to the philosophy of science. However, this raises the question of whether the proposed theories of understanding can still have normative power. In this paper I address this question by examining two theories of scientific understanding: Jan Faye’s pragmatic-rhetorical theory and my own contextual theory of scientific understanding. I argue that both theories leave room for normativity, despite their naturalistic tendencies. The normative power of my contextual theory is illustrated with a case study of the chemical revolution.

This paper examines how scientific understanding is enhanced by virtual entities, focusing on the case of the synthetic cell. Comparing it to other virtual entities and environments in science, we argue that the synthetic cell has a virtual dimension, in that it is functionally similar to living cells, though it does not mimic any particular naturally evolved cell (nor is it constructed to do so). In being cell-like at most, the synthetic cell is akin to many other virtual objects as it is selective and only partially implemented. However, there is one important difference: it is constructed by using the same materials and, to some extent, the same kind of processes as its natural counterparts. In contrast to virtual reality, especially to that of digital entities and environments, the details of its implementation is what matters for the scientific understanding generated by the synthetic cell. We conclude by arguing for the close connection between the virtual and the artifactual.

Understanding natural phenomena is an important aim of science. Since the turn of the millennium the notion of scientific understanding has been a hot topic of debate in the philosophy of science. A bone of contention in this debate is the role of truth and representational accuracy in scientific understanding. So-called factivists and non-factivists disagree about the extent to which the theories and models that are used to achieve understanding must be true or accurate. In this paper we address this issue by examining a case from the practice of synthetic chemistry. We investigate how understanding is obtained in this field by means of an in-depth analysis of the famous synthesis of periplanone B by W. Clark Still. It turns out that highly idealized models—that are representationally inaccurate and sometimes even inconsistent—and qualitative concepts are essential for understanding the synthetic pathway and accordingly for achieving the synthesis. We compare the results of our case study to various factivist and non-factivist accounts of how idealizations may contribute to scientific understanding and conclude that non-factivism offers a more plausible interpretation of the practice of synthetic chemistry. Moreover, our case study supports a central thesis of the non-factivist theory of scientific understanding developed by De Regt, namely that scientific understanding requires intelligibility rather than representational accuracy, and that idealization is one way to enhance intelligibility.

Scientific understanding is a fundamental goal of science. However, there is currently no good way to measure the scientific understanding of agents, whether these be humans or Artificial Intelligence systems. Without a clear benchmark, it is challenging to evaluate and compare different levels of scientific understanding. In this paper, we propose a framework to create a benchmark for scientific understanding, utilizing tools from philosophy of science. We adopt a behavioral conception of understanding, according to which genuine understanding should be recognized as an ability to perform certain tasks. We extend this notion of scientific understanding by considering a set of questions that gauge different levels of scientific understanding, covering information retrieval, the capability to arrange information to produce an explanation, and the ability to infer how things would be different under different circumstances. We suggest building a Scientific Understanding Benchmark (SUB), formed by a set of these tests, allowing for the evaluation and comparison of scientific understanding. Benchmarking plays a crucial role in establishing trust, ensuring quality control, and providing a basis for performance evaluation. By aligning machine and human scientific understanding we can improve their utility, ultimately advancing scientific understanding and helping to discover new insights within machines.

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.

Scientific understanding is a fundamental goal of science. However, there is currently no good way to measure the scientific understanding of agents, whether these be humans or Artificial Intelligence systems. Without a clear benchmark, it is challenging to evaluate and compare different levels of scientific understanding. In this paper, we propose a framework to create a benchmark for scientific understanding, utilizing tools from philosophy of science. We adopt a behavioral conception of understanding, according to which genuine understanding should be recognized as an ability to perform certain tasks. We extend this notion of scientific understanding by considering a set of questions that gauge different levels of scientific understanding, covering information retrieval, the capability to arrange information to produce an explanation, and the ability to infer how things would be different under different circumstances. We suggest building a Scientific Understanding Benchmark (SUB), formed by a set of these tests, allowing for the evaluation and comparison of scientific understanding. Benchmarking plays a crucial role in establishing trust, ensuring quality control, and providing a basis for performance evaluation. By aligning machine and human scientific understanding we can improve their utility, ultimately advancing scientific understanding and helping to discover new insights within machines.

What is the future of science and technology? Will academic research become a commodity like so much else? Will technology and science become ever more intertwined? Such questions concern anyone to whom science and technology matter. A philosophical approach can shed light on them, as Hans Radder has amply shown. This volume contains essays by colleagues and friends that highlight the wide variety of topics he has addressed in his work. Whether it is the interaction between science, technology and society, the nature of scientific experimentation, or the scientific realism question, Radder's approach is always acute and committed. 0With contributions by Henk van den Belt, James Robert Brown, Kai Eigner, Andrew Feenberg, Steve Fuller, Makoto Katsumori, Peter Kirschenmann, Christian Krijnen, Peter Kroes, Chunglin Kwa, Sabina Leonelli, Loet Leydesdorff, Alfred Nordmann, Pieter Pekelharing, Arthur Petersen, Henk de Regt, Angela Roothaan, László Ropolyi, Frits Schipper, and Astrid Schwarz.

Science has not only produced a vast amount of knowledge about a wide range of phenomena, it has also enhanced our understanding of these phenomena. Indeed, understanding can be regarded as one of the central aims of science. But what exactly is it to understand phenomena scientifically, and how can scientific understanding be achieved? What is the difference between scientific knowledge and scientific understanding? These questions are hotly debated in contemporary epistemology and philosophy of science. While philosophers have long regarded understanding as a merely subjective and psychological notion that is irrelevant from an epistemological perspective, nowadays many of them acknowledge that a philosophical account of science and its aims should include an analysis of the nature of understanding. This chapter reviews the current debate on scientific understanding. It presents the main philosophical accounts of scientific understanding and discusses topical issues such as the relation between understanding, truth and knowledge, the phenomenology of understanding, and the role of understanding in scientific progress.