Claus Beisbart

In the last few decades, nursing scholars have drawn on philosophy to establish the scientific status of nursing. However, well‐known philosophical accounts of science, such as those by Popper and Kuhn, are primarily targeted at the pure natural sciences. Accordingly, the application of such accounts to nursing has led to dubious results. In this paper, we propose a fresh start and apply Hoyningen‐Huene's recent account of science to nursing. According to Hoyningen‐Huene, knowledge about a given topic such as nursing is scientific if, and only if, it is more systematic than other knowledge about this topic. Here, systematicity manifests in various dimensions such as description, explanation, and defense of knowledge claims. In our application of Hoyningen‐Huene's account of science to nursing, we focus on current nursing practice and compare it with the state of nursing before the so‐called Consensus Statement on Emerging Nursing Knowledge. Upon examining the dimensions considered by Hoyningen‐Huene, we find that the knowledge currently used and gained by nurses is more systematic than it was before the Consensus Statement. For instance, in the dimension of prediction, knowledge has become more systematic through the use of classification systems. In the dimension of defending knowledge claims, systematicity has increased due to the use of measurement devices. According to our analysis, the scientific status of current nursing knowledge is partly due to the fact that nurses draw on knowledge from nursing science, a discipline that is organized like other scientific disciplines. Still, it is also the case that knowledge‐gaining practices in nursing are more systematic than they were before the Consensus Statement.

Since the publication of his seminal monograph "The scientific image", Bas van Fraassen is a key figure in philosophy of science. In this book, other philosophers with various outlooks critically discuss his work on theories, empiricism and philosophical stances. The book starts with a new article by van Fraassen on his preferred account of theories, the so-called semantic view. This account is now 50 years old, and van Fraassen takes this anniversary as an opportunity to review the account, its history and the philosophical discussion about it. In the main part of the book, Nancy Cartwright, Finnur Dellsén, Matthias Egg, Steven French, Michael Friedman, Milena Ivanova and Michela Massimi discuss van Fraassen's contributions to philosophy. Three chapters focus on his engagement with realism (French, Friedman, Ivanova). Others study his voluntarism (Cartwright) and his view on representation (Massimi). Finally, there are contributions about his elaboration of empiricism (Dellsén) and his proposal to consider philosophical positions as stances (Egg). The volume includes a laudatio written by Steven French and finishes with a reply by van Fraassen to his critics.

Despite their successes at prediction and classification, deep neural networks (DNNs) are often claimed to fail when it comes to providing any understanding of real-world phenomena. However, recently, some authors have argued that DNNs can provide such understanding. To resolve this controversy, I first examine under which conditions DNNs provide humans with explanatory understanding in a clearly defined sense that refers to a simple setting. I adopt a systematic approach that draws on theories of explanation and explanatory understanding, but avoid dependence on any specific account by developing broad conditions of explanatory understanding that leave space for filling in the details in several alternative ways. I argue that the conditions are difficult to satisfy however these details are filled in. The main problem is that, to provide explanatory understanding in the sense I have defined, a DNN has to contain an explanation, and scientists typically do not know whether it does. Accordingly, they cannot feel committed to the explanation or use it, which means that other conditions of explanatory understanding are not satisfied. Still, in some attenuated senses, the conditions can be fulfilled. To complete my conciliatory project, I further show that my results so far are compatible with using DNNs to infer explanatorily relevant information in a thorough investigation. This is what the more optimistic literature on DNNs has focused on. In sum, then, the significance of DNNs for understanding real-world systems depends on what it means to say that they provide understanding, and on how humans use them.

Reflective equilibrium ("RE", for short) is a method of justification which works roughly as follows: We start with our pre-theoretical judgements (about, e.g. moral issues) and try to explain them by a systematic theory. This leads to a process in which judgements and principles are mutually adjusted to each other until a state of equilibrium is reached. For more than half a century, RE has been very popular, as well as controversial, among philosophers of many persuasions. Given how frequently the method is mentioned, embraced and attacked, it is surprising that only very few attempts have been made at analysing and developing RE in detail. This topical collection brings together papers that develop RE on a conceptual level, analyse it using formal methods or investigate applications of RE.

The pseudosciences often bear a striking resemblance to the sciences. Using a mimicry account as a framework, this paper investigates how the appearance of social media posts influences people’s perception of the content of such posts as scientific. We present the results of two empiri- cal studies. The first, preparatory study identifies typical characteristics of “scientificness” in social media posts to inform feature manipulations for the main study. The main study then examines what happens if the features are systematically manipulated. The findings support the hypothesis that pseudoscientific digital content benefits from using features of scientificness. We discuss implications for understanding the appeal and persistence of pseudoscience.

In contemporary computer simulations, particles attract each other and form clusters, cells interact, and agents communicate with one another. This is at least how computer simulations are commonly described. But how can we make sense of such talk? One answer is that the particles, cells, and agents inside simulations are digital artifacts, and thus real objects. In this paper, I cast doubt on this realist position by raising the question: To what objects does a simulation give rise, if it does so at all? I tentatively suggest answers that try to determine what objects arise in computer simulations at both a fundamental and a higher level. However, closer analysis reveals that we run into underdetermination problems. Even for a single run of a simulation program, the acknowledged facts about the simulations often do not suffice to determine the objects and their properties uniquely. I suggest that, from an ontological perspective, it is more attractive to say that computer simulations involve computations, the results of which may then be interpreted in various ways. To make sense of talk about objects in simulations it is more attractive to develop a fictionalist account, or so I argue.

The goal of this paper is to re-assess reflective equilibrium (“RE”). We ask whether there is a conception of RE that can be defended against the various objections that have been raised against RE in the literature. To answer this question, we provide a systematic overview of the main objections, and for each objection, we investigate why it looks plausible, on what standard or expectation it is based, how it can be answered and which features RE must have to meet the objection. We find that there is a conception of RE that promises to withstand all objections. However, this conception has some features that may be unexpected: it aims at a justification that is tailored to understanding and it is neither tied to intuitions nor does it imply coherentism. We conclude by pointing out a cluster of questions we think RE theorists should pay more attention to.

Reflective equilibrium (RE) is often regarded as a powerful method in ethics, logic, and even philosophy in general. Despite this popularity, characterizations of the method have been fairly vague and unspecific so far. It thus may be doubted whether RE is more than a jumble of appealing but ultimately sketchy ideas that cannot be spelled out consistently. In this paper, we dispel such doubts by devising a formal model of RE. The model contains as components the agent’s commitments and a theory that tries to systematize the commitments. It yields a precise picture of how the commitments and the theory are adjusted to each other. The model differentiates between equilibrium as a target state and the dynamic equilibration process. First solutions to the model, obtained by computer simulation, show that the method allows for consistent specification and that the model’s implications are plausible in view of expectations on RE. In particular, the mutual adjustment of commitments and theory can improve one’s commitments, as proponents of RE have suggested. We argue that our model is fruitful not only because it points to issues that need to be dealt with for a better understanding of RE, but also because it provides the means to address these issues.

This paper discusses recent attempts to solve the problem of induction. Two broad strategies to escape Hume's fork can be distinguished. The first tries to localize the justification of specific inductions in uncontroversial empirical knowledge, e.g.mundane scientific knowledge (J. D. Norton) or perception (M. Lange). I argue that related attempts to (dis)solve the problem fail. The second strategy tries to put forward an argument in favor of induction. As a discussion of work by R. White shows, this argument can barely prove that induction is reliable or at least not unreliable. But D. Steel, F. Huber and G. Schurz could show that enumerative induction is necessary and sufficient for a certain epistemic goal or optimal in a certain sense. These proofs, however, only solve the problem of induction if the goal or a certain standard has priority over the avoidance of error. This suggests that the difficulties to justify induction do not so much derive from Hume's fork, but rather from a plurality of sensible epistemic goals that can conflict with each other.

Some machine learning models, in particular deep neural networks (DNNs), are not very well understood; nevertheless, they are frequently used in science. Does this lack of understanding pose a problem for using DNNs to understand empirical phenomena? Emily Sullivan has recently argued that understanding with DNNs is not limited by our lack of understanding of DNNs themselves. In the present paper, we will argue, _contra_ Sullivan, that our current lack of understanding of DNNs does limit our ability to understand with DNNs. Sullivan’s claim hinges on which notion of understanding is at play. If we employ a weak notion of understanding, then her claim is tenable, but rather weak. If, however, we employ a strong notion of understanding, particularly explanatory understanding, then her claim is not tenable.

This paper details the content and structure of the folk concept of life, and discusses its relevance for scientific research on life. In four empirical studies, we investigate which features of life are considered salient, universal, central, and necessary. Functionings, such as nutrition and reproduction, but not material composition, turn out to be salient features commonly associated with living beings (Study 1). By contrast, being made of cells is considered a universal feature of living species (Study 2), a central aspect of life (Study 3), and our best candidate for being necessary for life (Study 4). These results are best explained by the hypothesis that people take life to be a natural kind subject to scientific scrutiny.

In computer science, there are efforts to make machine learning more interpretable or explainable, and thus to better understand the underlying models, algorithms, and their behavior. But what exactly is interpretability, and how can it be achieved? Such questions lead into philosophical waters because their answers depend on what explanation and understanding are—and thus on issues that have been central to the philosophy of science. In this paper, we review the recent philosophical literature on interpretability. We propose a systematization in terms of four tasks for philosophers: (i) clarify the notion of interpretability, (ii) explain the value of interpretability, (iii) provide frameworks to think about interpretability, and (iv) explore important features of it to adjust our expectations about it.

In his Critique of Pure Reason, in the chapter on the antinomy of pure reason, Kant not only argues that aprioristic cosmology is doomed to failure; he also implies that empirical knowledge about the universe is impossible. Today, such a negative verdict about the possibility of cosmological knowledge seems implausible because physical cosmology has made substantial progress. In particular, the spatiotemporal extension of the universe now seems a matter of empirical investigation in which models figure centrally. But I think it is worth considering the possibility that Kant got something right and that he offers insights that can help us to better understand problems in present-day cosmology. In this article, I explore a striking coincidence: according to both Kant and current wisdom, cosmology faces a serious underdetermination problem regarding the spatiotemporal extension of the world. As a closer analysis reveals, however, Kant and modern cosmology differ on the reasons why underdetermination arises. In current cosmology, underdetermination follows from laws that are knowable a posteriori, and not only from the very idea of cosmological knowledge, as Kant would have it. This suggests that the current underdetermination problem is not fully a Kantian one.

Computer simulations are often claimed to be opaque and thus to lack transparency. But what exactly is the opacity of simulations? This paper aims to answer that question by proposing an explication of opacity. Such an explication is needed, I argue, because the pioneering definition of opacity by P. Humphreys and a recent elaboration by Durán and Formanek are too narrow. While it is true that simulations are opaque in that they include too many computations and thus cannot be checked by hand, this doesn’t exhaust what we might want to call the opacity of simulations. I thus make a fresh start with the natural idea that the opacity of a method is its disposition to resist knowledge and understanding. I draw on recent work on understanding and elaborate the idea by a systematic investigation into what type of knowledge and what type of understanding are required if opacity is to be avoided and why the required sort of understanding, in particular, is difficult to achieve. My proposal is that a method is opaque to the degree that it’s difficult for humans to know and to understand why its outcomes arise. This proposal allows for a comparison between different methods regarding opacity. It further refers to a kind of epistemic access that is important in scientific work with simulations.

Philosophie kann dazu beitragen, dass wir vernünftiger mit den Problemen umgehen, die unsere Gesellschaft und ihr Selbstverständnis herausfordern. Dazu muss die Philosophie sich aber öffentlich einmischen und verstärkt in die Bildung Einzug halten – diese Position vertritt vorliegender Band. Die Beiträge von Anne Burkard, Rainer Hegselmann, Romy Jaster und Markus Wild zeigen einerseits auf, welche Rolle die Philosophie in öffentlichen Debatten spielen kann und soll. Andererseits analysieren sie, welchen Beitrag Philosophie zur schulischen und universitären Bildung liefert.

Cosmological questions (e.g., how far the world extends and how it all began) have occupied humans for ages and given rise to numerous conjectures, both within and outside philosophy. To put to rest fruitless speculation, Kant argued that these questions move beyond the limits of human knowledge. This article begins with Kant’s doubts about cosmology and shows that his arguments presuppose unreasonably high standards on knowledge and unwarranted assumptions about space-time. As an analysis of the foundations of twentieth-century cosmology reveals, other worries about the discipline can be avoided too if the universe is modeled using Einstein’s general theory of relativity. There is now strong observational support for one particular model. However, due to underdetermination problems, the big cosmological questions cannot be fully answered using this model either. This opens the space for more speculative proposals again (e.g., that the universe is only part of a huge multiverse).

We construct a new measure of voting power that yields reasonable measurements even if the individual votes are not cast independently. Our measure hinges on probabilities of counterfactuals, such as the probability that the outcome of a collective decision would have been yes, had a voter voted yes rather than no as she did in the real world. The probabilities of such counterfactuals are calculated on the basis of causal information, following the approach by Balke and Pearl. Opinion leaders whose votes have causal influence on other voters' votes can have significantly more voting power under our measure. But the new measure of voting power is also sensitive to the voting rule. We show that our measure can be regarded as an average treatment effect, we provide examples in which it yields intuitively plausible results and we prove that it reduces to Banzhaf voting power in the limiting case of independent and equiprobable votes.