Finnur Dellsén

On many topics of common interest, there are multiple scientific experts who are more-or-less equally reliable as far as most laypeople can tell. In such situations, it would generally be a bad idea for those laypeople to form or update their opinions by deferring to the opinions of individual experts; rather, when possible, they should consider the more general distribution of opinions among all the experts. Building on this thought, this paper argues that scientific experts themselves should ideally not simply report their own opinions on topics within their expertise; rather, if possible, they should provide information about expert opinion distributions more generally, e.g. by communicating that a majority of their fellow experts take such-and-such position on a topic while the minority has a contrary view – even when they are themselves in the minority. I go on to trace some important implications of this argument, e.g. that scientific communities have an epistemic obligation to periodically gather data on expert opinion distributions regarding topics that are of interest to the general public.

Inference to the Best Explanation (IBE) is traditionally conceived of as a rule of inference, in which one infers to the hypothesis that provides the best explanation of one’s evidence. But what if some of that evidence is uncertain?How, if at all, can the traditional conception of IBE be extended to handle this common class of cases? This paper presents a new general model for investigating rules of inference from uncertain evidence, and then applies this approach to evaluate several different ways of extending IBE to handle uncertain evidence. The model employs computer simulations in which inference rules receive rewards (penalties) for making correct (incorrect) inferences in randomly generated scenarios in which the evidence is uncertain to various degrees. The model allows us to move beyond reliance on intuitive or historical examples in evaluating how well different inference rules handle cases in which the evidence is uncertain.

In recent years, notable figures within the medical community have expressed concerns about the rate of medical progress, suggesting that the rapid advances of medicine’s ‘golden age’ are now giving way to an ‘age of disappointment’. While these pessimistic pronouncements about medical progress must–implicitly if not explicitly–appeal to some criteria for what medical progress would be, the task of explicitly defining medical progress has been notably neglected. We take up this task, drawing on insights from the philosophy of science concerning both scientific progress and the aims of medicine. Among other things, we differentiate between medical scientific progress and progress in medical practice, and suggest that this distinction helps to evaluate the aforementioned concerns about the current rate of medical progress. While it is not our goal to draw conclusions relating to the state of medicine at the present time, we propose a unifying account of medical scientific progress according to which such progress leads, necessarily, to progress in medical practice, and show how this account both plausibly distinguishes between medical scientific progress and other (non-medical) instances of scientific progress, and does justice to the ‘dual character’ of medicine.

Building upon previous investigations of scientific realism “in the wild” – i.e., among practicing scientists – we report the results of an empirical study that examined the attitudes of scientists from physics, biology, psychology, and anthropology (N = 777) toward various issues in the scientific realism debate. Out of all the major issues that have fallen under the heading of scientific realism, we found that the mind-independence of scientific phenomena and the ideas that comprise the no-miracles argument were most central to scientists’ thinking about this cluster of issues and that ideas pertaining to instrumentalism and the pessimistic induction were least central. We also found that scientists’ judgments about the status of unobservable entities posited in their fields failed to cohere with their responses to other issues in the debate and that the fields where scientists most strongly endorsed realism on the whole were the ones that expressed the most skepticism about unobservables. Additionally, we found that scientists were significantly inclined toward realism, with natural scientists being more so than social scientists, and that this inclination was generally accompanied by a modestly antirealist endorsement of the pessimistic induction. Implications of these findings for the philosophical debate over scientific realism are considered.

The paper examines the nature of scientific progress through the lens of the history of modern cosmology (i.e. from Einstein’s, 1917 static universe to the present-day Standard (ΛCDM) model of cosmology). We distil three novel lessons, germane to the debate between the two main accounts of scientific progress (the noetic and the epistemic one, respectively). First, it’s difficult to sharply locate—to precisely pinpoint the locus of—the epistemic content of scientific knowledge. Cosmology displays stark epistemic holism: epistemic content and evidence are typically inextricably distributed over a wider “web of beliefs”. Secondly, cosmologists employ a variety of justificatory practices and modes of reasoning. More often than not, they fall short of the fastidious standards of traditional epistemology. Thirdly, cosmological claims typically defy easy and unambiguous characterisation in terms of truth. These three lessons are shown to pose grave challenges to the epistemic account of scientific progress (on which progress consists in the accumulation of knowledge). By contrast, the rivalling noetic account (which characterises progress in terms of improved understanding) can naturally accommodate those lessons.

How is progress in the academic discipline of ethics related to improvements in people's ethical thoughts and behaviours? We propose that there is disciplinary ethical progress on some phenomenon to the extent that the discipline puts people in a position to increase their understanding of that phenomenon. Even though progress, thus construed, does not guarantee improved thoughts and behaviours, progressive ethical theorising nonetheless enables these improvements. Our account thus has the resources with which to substantiate and shed light on concerns that academic work in ethics is too isolated, obtuse, or trivial.

Around 97% of climate scientists endorse anthropogenic global warming (AGW), the theory that human activities are partly responsible for recent increases in global average temperatures. Clearly, this widespread endorsement of AGW is a reason for non-experts to believe in AGW. But what is the epistemic significance of the fact that some climate scientists do not endorse AGW? This article contrasts expert unanimity, in which virtually no expert disagrees with some theory, with expert consensus, in which some non-negligible proportion either rejects or is uncertain about the theory. It is argued that from a layperson’s point of view, an expert consensus is often stronger evidence for a theory’s truth than unanimity. Several lessons are drawn from this conclusion, for example, concerning what laypeople should infer from expert pronouncements, how journalists should report on scientific theories, and how working scientists should communicate with the public.

How should a layperson respond to learning that the experts on a given topic disagree amongst themselves? This paper argues that, epistemically, the appropriate response to an expert disagreement depends greatly on what explains the disagreement, and that there are several quite different types of explanations for a given disagreement. Accordingly, expert disagreement calls for different epistemic responses in different circumstances. However, the paper also supplements this pluralist account of how to respond epistemically to expert disagreement with a unifying account of how laypeople should response ‘zetetically’ to expert disagreement – that is, how they should direct their inquiry concerning the contested claim. Indeed, it is precisely because of the plurality of appropriate epistemic responses to expert disagreements that laypeople often have strong reasons to inquire into what explains the presence of disagreement among experts.

What is the point of inquiry? Some say that the aim of inquiring into some question is to come to know its answer; others, that the aim is to attain justified belief, epistemic improvement, or some other coveted epistemic status. Still others eschew “aim” talk altogether, and instead formulate norms governing inquiry. However, virtually all extant work on inquiry has agreed on at least this much: the aims or norms of inquiry can be specified in terms of the epistemic states of the inquirer (i.e., the agent conducting the inquiry). This paper argues that this conception of inquiry struggles to account for some central features of what is arguably the most successful form of inquiry in the modern era: scientific inquiry. We show that scientific inquiry is governed by several distinctive norms that are difficult to explain if inquiry is all about achieving epistemic benefits for the inquirer. Instead, many inquiries aim to confer epistemic benefits on others. This “inclusive” conception of inquiry has important advantages and implications. It avoids some pressing counterexamples for standard conceptions of inquiry; it allows us to reconcile the epistemology of inquiry with relevant upshots of work in the philosophy and sociology of science; and it brings the “zetetic turn” within the fold of social epistemology.

It is widely acknowledged that understanding something is a particularly valuable cognitive achievement. Perhaps relatedly, understanding is often taken to be an important aim of science. But what, exactly, is understanding? And what philosophical work can a concept of understanding do for us, e.g., in accounting for scientific practice or in shedding light on epistemological conundrums? The 15 essays in Explaining Understanding: New Perspectives from Epistemology and Philosophy of Science address questions of this sort. The volume includes essays by several leading scholars from both epistemology and philosophy of science—the two fields in which such questions about understanding have recently become widely discussed. The stated aim of the book is to bring the two fields together by having philosophers from each side ‘join forces to address fundamental philosophical questions about the nature of understanding’ (xi).

We report new findings from an empirical study of scientists from seven disciplines and scholars working in history and philosophy of science (HPS) regarding their views about scientific realism. We found that researchers’ general disposition to endorse or reject realism was better predicted by their views regarding scientific progress than their views about the mindindependence of scientific phenomena or other common theses in the realism debate. Age and gender also significantly predicted endorsement of scientific realism. Implications of these findings for philosophical debates about scientific realism and scientific progress are considered.

Many instances of scientific progress feature the development of theories that are not fully true, but merely approximately true to various extents. Since only fully true propositions can be known, this seems to rule out the view that scientific progress consists in the accumulation of knowledge. According to Bird's Cumulative Knowledge Account of progress, however, what becomes known in such instances is a (fully true) proposition expressing that the theory in question is approximately true to some extent. We present a general challenge for this idea -- the Epistemic Mismatch Problem -- and consider various strategies by which proponents of the Cumulative Knowledge Account might respond to it. We suggest, however, that the only plausible such strategies involve giving up on aspects of the Cumulative Knowledge Account that are central to why it has seemed plausible to begin with.

In the philosophical debate about scientific progress, several authors appeal to a distinction between what constitutes scientific progress and what promotes it (e.g., Bird, 2008; Rowbottom, 2008; Dellsén, 2016). However, the extant literature is almost completely silent on what exactly it is for scientific progress to be promoted. Here I provide a precise account of progress promotion on which it consists, roughly, in increasing expected progress. This account may be combined with any of the major theories of what constitutes scientific progress, such as the truthlikeness, problem-solving, epistemic, and noetic accounts. However, I will also suggest that once we have this account of progress promotion up and running, some accounts of what constitutes progress become harder to motivate by the sorts of considerations often adduced in their favor, while others turn out to be easier to defend against common objections.

We take up the challenge of developing an international network with capacity to survey the world’s scientists on an ongoing basis, providing rich datasets regarding the opinions of scientists and scientific sub-communities, both at a time and also over time. The novel methodology employed sees local coordinators, at each institution in the network, sending survey invitation emails internally to scientists at their home institution. The emails link to a ‘10 second survey’, where the participant is presented with a single statement to consider, and a standard five-point Likert scale. In June 2023, a group of 30 philosophers and social scientists invited 20,085 scientists across 30 institutions in 12 countries to participate, gathering 6,807 responses to the statement Science has put it beyond reasonable doubt that COVID-19 is caused by a virus. The study demonstrates that it is possible to establish a global network to quickly ascertain scientific opinion on a large international scale, with high response rate, low opt-out rate, and in a way that allows for significant (perhaps indefinite) repeatability. Measuring scientific opinion in this new way would be a valuable complement to currently available approaches, potentially informing policy decisions and public understanding across diverse fields.

This article presents and argues for an account of objectual understanding that aims to do justice to the full range of cases of scientific understanding, including cases in which one does not have an explanation of the understood phenomenon. According to the proposed account, one understands a phenomenon just in case one grasps a sufficiently accurate and comprehensive model of the ways in which it or its features are situated within a network of dependence relations; one’s degree of understanding is proportional to the comprehensiveness and accuracy of such a model. I compare this account with accounts of scientific understanding that explicate understanding in terms of having an explanation of the understood phenomenon. I discuss three distinct types of cases in which scientific understanding does not amount to possessing an explanation of any kind and argue that the proposed model-based account can accommodate these cases while still retaining a strong link between understanding and explanation. 1Introduction 2Objectual Understanding 3Understanding as Dependency Modelling 4Explanatory Accounts 5Explanatory Bruteness 6Explanatory Targetedness 7Explanatory Disconnectedness 8Conclusion.

This paper presents a new problem for the inference rule commonly known as Inference to the Best Explanation (IBE). The problem is that uncertainty about parts of one’s evidence may undermine the inferrability of a hypothesis that would provide the best explanation of that evidence, especially in cases where there is an alternative hypothesis that would provide a better explanation of only the more certain pieces of evidence. A potential solution to the problem is sketched, in which IBE is generalized to handle uncertain evidence by invoking a notion of evidential robustness.

In abductive reasoning, scientific theories are evaluated on the basis of how well they would explain the available evidence. There are a number of subtly different accounts of this type of reasoning, most of which are inspired by the popular slogan 'Inference to the Best Explanation.' However, these accounts disagree about exactly how to spell out the slogan so as to avoid various problems for abductive reasoning. This Element aims, firstly, to give an opinionated overview both of the many accounts of abductive reasoning that have been proposed and the problems that have motivated them; and, secondly, to critically evaluate these accounts in a way that points toward a systematic view of the nature and purpose of abductive reasoning in science. This title is also available as Open Access on Cambridge Core.

We are often urged to figure things out for ourselves rather than to rely on other people’s say-so, and thus be ‘epistemically autonomous’ in one sense of the term. But why? For almost any important question, there will be someone around you who is at least as well placed to answer it correctly. So why bother making up your own mind at all? I consider, and then reject, two ‘egoistic’ answers to this question according to which thinking for oneself is beneficial for the autonomous agent herself. I go on to suggest that the reason we should (sometimes) think for ourselves is that doing so (sometimes) increases the collective reliability of the epistemic community to which we belong. In many cases, this will do nothing at all to increase our own chances of forming correct beliefs. So, at least in this respect, the rationale for being epistemically autonomous is entirely ‘altruistic’.

Stegenga (forthcoming) formulates and defends a novel account of scientific progress, according to which science makes progress just in case there is a change in scientific justification. Here we present several problems for Stegenga’s account, concerning respectively (i) obtaining misleading evidence, (ii) losses or destruction of evidence, (iii) oscillations in scientific justification, and (iv) the possibility of scientific regress. We conclude by sketching a substantially different justification-based account of scientific progress that avoids these problems.

Many prominent critiques of philosophical methods proceed by suggesting that some method is unreliable, especially in comparison to some alternative method. If one agrees with these critiques, it may seem natural to conclude that these (comparatively) unreliable methods should be abandoned. Drawing upon work on the division of cognitive labour in science, we argue that things are not so straightforward. Rather, whether an unreliable method should be abandoned depends heavily on the crucial question of how we should divide philosophers’ time and effort between different methods in order to maximize our prospects of achieving epistemic success. We show that, in a range of cases, even a (comparatively) unreliable method may deserve to be allocated some of these resources.

What is it for philosophy to make progress? While various putative forms of philosophical progress have been explored in some depth, this overarching question is rarely addressed explicitly, perhaps because it has been assumed to be intractable or unlikely to have a single, unified answer. In this paper, we aim to show that the question is tractable, that it does admit of a single, unified answer, and that one such answer is plausible. This answer is, roughly, that philosophical progress consists in putting people in a position to increase their understanding, where ‘increased understanding’ is a matter of better representing the network of dependence relations between phenomena. After identifying four desiderata for an account of philosophical progress, we argue that our account meets the desiderata in a particularly satisfying way. Among other things, the account explains how various other achievements, such as philosophical arguments, counterexamples, and distinctions, may contribute to progress. Finally, we consider the implications of our account for the pressing and contentious question of how much progress has been made in philosophy.

Manifestationalism holds that science aims only to give us theories that are correct about what has been observed thus far. Several philosophers, including Bas van Fraassen, have argued that manifestationalism cannot make sense of the scientific impetus to make new observations, since such observations only risk turning manifestationally adequate theories into inadequate ones. This paper argues that a strikingly similar objection applies to van Fraassen’s own constructive empiricism, the view that science aims only to find theories that are empirically adequate. Roughly, the objection is that constructive empiricism cannot make sense of the scientific impetus to expand the limits of what can be observed, since such expansions only risk turning empirically adequate theories into inadequate ones.

This paper aims to flesh out the celebrated notion of reflective equilibrium within a probabilistic framework for epistemic rationality. On the account developed here, an agent's attitudes are in reflective equilibrium when there is a certain sort of harmony between the agent's credences, on the one hand, and what the agent accepts, on the other hand. Somewhat more precisely, reflective equilibrium is taken to consist in the agent accepting, or being prepared to accept, all and only claims that follow from a maximally comprehensive theory that is more probable than any other such theory. Drawing on previous work, the paper shows that when an agent is in reflective equilibrium in this sense, the set of claims they accept or are prepared to accept is bound to be logically consistent and closed under logical implication. The paper also argues that this account can explain various features of philosophical argumentation in which the notion of reflective equilibrium features centrally, such as the emphasis on evaluating philosophical theories holistically rather than in a piecemeal fashion.

Consensus and disagreement play important roles in the practice, development, and dissemination of science. This raises a host of important philosophical questions. Some of these issues are conceptual: When, exactly, does a scientific agreement count as a consensus? And in what sense, if any, is disagreement the opposite of consensus? Other questions concern the role of consensus and disagreement in the development of science: For example, is consensus on central methodological issues and assumptions necessary for scientific work to proceed normally? Yet other questions are epistemological: From a layperson’s perspective, does the presence of a scientific consensus ever indicate that the relevant theory is probably correct? If so, what are the conditions under which it does so? Relatedly, should scientists themselves also defer to the consensus position among their peers whenever such a consensus exists? Or should they instead evaluate consensus theories for themselves, or even actively aim to dissent against such theories?

In recent epistemology, belief polarization is generally defined as a process by which a disagreement on a single proposition becomes more extreme over time. Outside of the philosophical literature, however, ‘polarization’ is often used for a different epistemic phenomenon, namely the process by which people’s beliefs on unrelated topics become increasingly correlated over time. This paper argues that the latter type of polarization, here labeled interthematic polarization, is often rational from each individual’s point of view. This suggests that belief polarization is not necessarily a failure of individual rationality, but instead a failure of the social structures within which we live our epistemic lives.

In the course of developing an account of scientific progress, C. D. McCoy (2022) appeals centrally to understanding as well as to problem-solving. On the face of it, McCoy’s account could thus be described as a kind of hybrid of the understanding-based account that I favor (Dellsén 2016, 2021) and the functional (a.k.a. problem-solving) account developed most prominently by Laudan (1977; see also Kuhn 1970; Shan 2019). In this commentary, I offer two possible interpretations of McCoy’s account and explain why I do not find it entirely compelling on either interpretation.