Science is our most reliable producer of knowledge. Nonetheless, a significant amount of evidence shows that pluralities of members of publics question a variety of accepted scientific claims as well as policies and recommendation informed by the scientific evidence. Scientific misinformation is considered to play a central role in this state of affairs. In this paper, I challenge the emphasis on misinformation as a primary culprit on two grounds. First, the phenomenon of misinformation is (...) far less clear than what much discussion about the topic would lead one to believe. The evidence regarding the amount of misinformation that exits as well as its role in people’s harmful behaviors is at best conflicting and at worst completely useless. Second, the prominence given to misinformation and its harms on people’s behaviors disregards the role of values in policymaking and treats scientific information as if it were the only information necessary to make policy decisions. At a minimum, these problems call for caution regarding the emphasis on this phenomenon. After all, if the problem is incorrectly diagnosed, the solutions that are being offered to address the problem of misinformation are bound to at best inadequate and at worst dangerous. (shrink)

Misinformation can have significant societal consequences. For example, misinformation about climate change has confused the public and stalled support for mitigation policies. When people lack the expertise and skill to evaluate the science behind a claim, they typically rely on heuristics such as substituting judgment about something complex (i.e. climate science) with judgment about something simple (i.e. the character of people who speak about climate science) and are therefore vulnerable to misleading information. Inoculation theory offers one approach to (...) effectively neutralize the influence of misinformation. Typically, inoculations convey resistance by providing people with information that counters misinformation. In contrast, we propose inoculating against misinformation by explaining the fallacious reasoning within misleading denialist claims. We offer a strategy based on critical thinking methods to analyse and detect poor reasoning within denialist claims. This strategy includes detailing argument structure, determining the truth of the premises, and checking for validity, hidden premises, or ambiguous language. Focusing on argument structure also facilitates the identification of reasoning fallacies by locating them in the reasoning process. Because this reason-based form of inoculation is based on general critical thinking methods, it offers the distinct advantage of being accessible to those who lack expertise in climate science. We applied this approach to 42 common denialist claims and find that they all demonstrate fallacious reasoning and fail to refute the scientific consensus regarding anthropogenic global warming. This comprehensive deconstruction and refutation of the most common denialist claims about climate change is designed to act as a resource for communicators and educators who teach climate science and/or critical thinking. (shrink)

Climate change prevention necessitates the communication of transparent and reliable scientific evidence to improve public awareness and support. Felt responsibility is an essential factor influencing human environment-related psychology and behavior. However, the knowledge about the relationship between the felt responsibility and perceived uncertainty of scientific evidence regarding climate change has remained limited. The current study examines factors associated with the perceived uncertainty of scientific evidence (including felt responsibility to act on climate change) among stakeholders of marine and (...) coastal ecosystems in 42 countries. Employing the Bayesian Mindsponge Framework (BMF) analytics on a dataset of 709 stakeholders generated by MaCoBioS—a research project funded by the European Commission Horizon 2020, we reveal several main insights. Stakeholders with lower educational levels, being males and not from high-income countries, are more likely to think scientific evidence regarding how to act on climate change is uncertain. Moreover, people with a higher felt responsibility to act on climate change are also more likely to perceive higher uncertainty of scientific evidence. Based on these findings, we discuss how scientific evidence should be communicated to build the eco-surplus culture and, subsequently, the felt responsibility of stakeholders while inoculating them from climate change misinformation and disinformation. (shrink)

In an ever-changing world, when we search for answers on our present challenges, it can be tricky to extrapolate past realities when concerning science-based issues. Climate change, public health or artificial intelligence embody issues on how scientific evidence is often challenged, as false beliefs could drive the design of public policies and legislation. Therefore , how can we foresee if science can tip the scales of political legislation? In this article, we outline how models of historical cases can be (...) used to predict and understand how scientific evidence can influence the emergence (or fall-back) of science based-legislation. We also present frameworks on how to use past episodes of History of Science and Alternative History insights to build epistemic models, based on previous successful approaches on the spread of scientific misinformation. These models will help the accuracy of the design of eventual alternative realities, that can come insightful on present decision making methodologies. (shrink)

The world has surpassed three million deaths from COVID-19, and faces potentially catastrophic tipping points in the global climate system. Despite the urgency, governments have struggled to address either problem. In this paper, we argue that COVID-19 and anthropogenic climate change (ACC) are critical examples of an emerging type of governance challenge: severe collective action problems that require significant individual behavior change under conditions of hyper- partisanship and scientific misinformation. Building on foundational political science work demonstrating the potential (...) for norms (or informal rules of behavior) to solve collective action problems, we analyze more recent work on norms from neighboring disciplines to offer novel recommendations for more difficult challenges like COVID-19 and ACC. Key insights include more attention to (1) norm-based messaging strategies that appeal to individuals across the ideological spectrum or that reframe collective action as consistent with resistant subgroups’ pre-existing values, (2) messages that emphasize both the prevalence and the social desirability of individual behaviors required to address these challenges, (3) careful use of public policies and incentives that make individual behavior change easier without threatening norm internalization, and (4) greater attention to epistemic norms governing trust in different information sources. We conclude by pointing out that COVID-19 and climate change are likely harbingers of other polarized collective action problems that governments will face in the future. By connecting work on norms and political governance with a broader, interdisciplinary literature on norm psychology, motivation, and behavior change, we aim to improve the ability of political scientists and policy makers to respond to these and future collective action challenges. (shrink)

The weaponizing of scientific expertise to oppose regulation has been extensively studied. However, the relevant studies, belonging to the emerging discipline of agnotology, remain focused on the analysis of empirical corruption: of misinformation, doubt mongering, and other practices that cynically deploy expertise to render audiences ignorant of empirical facts. This paper explores the wrongful deployment of expertise beyond empirical corruption. To do so, I develop a broader framework of morally subversive expertise, building on recent work in political philosophy (...) (Howard, 2016). Expertise is subversive if it sets up its audience to fail morally, either intentionally or negligently. I distinguish three modes of subversive expertise: empirical subversion (the focus of agnotology), normative subversion and motivational subversion. Drawing on these distinctions, I offer a revisionary account of the Trump Administration’s regulatory science as a case study. I show that the Trump Administration’s use of expertise to dismantle climate regulation, contra the standard charge, cannot be explained using the resources of agnotology alone: the Administration produced highly reliable climate assessments, detailing the risks of climate change, candidly admitting the harms of its proposed policies, and still successfully deployed these findings to justify massive climate deregulation. The lesson of the analysis is that dismissing the expertise that underpins climate deregulation as empirically corrupt ‘anti-science’ both obscures its actual role in the politics of climate change and understates its wrongfulness: it misses the breadth of the assault on moral agency that sustains climate injustice. (shrink)

Technology firms are now purchasing access to research papers from academic publishers to train their artificial intelligence (AI) models. Using scientific content to train AI can come with multiple benefits, which help improve AI's capability to generate trustworthy outcomes, understand and process issues across a wide range of topics, as well as analyze information, make logical deductions, and draw conclusions. Journal articles are generally considered reliable because of the rigorous peer review system. However, the evaluation process is constrained by (...) several limitations, so a number of unreliable and invalid studies have been published. Retraction is often viewed as a crucial self-correction mechanism within science, allowing the academic community to identify and flag seriously flawed research that has been published. If retracted articles are used to train AI, it could lead to the spread of misinformation. Given the power of AI, this misinformation could be disseminated on a large scale and in a very short time, which might misinform and significantly increase the entropy (uncertainty) within society. Retracted notices can serve as a signal for AI developers to exclude seriously flawed articles from the training process. However, if the scientific content used to train AI is later discovered to be seriously distorted and retracted, it cannot be removed from the model’s knowledge base after the training is complete. As AI is increasingly applied in critical areas such as education and decision-making, training AI on seriously flawed scientific findings could lead to significant systematic consequences. Therefore, collaboration between the scientific community and AI developers to address these issues is crucial. (shrink)

This paper presents the results of two corpus studies investigating the discourse surrounding conspiracy theories and genuine theories. The results of these studies show that conspiracy theories lack the epistemic and scientific standing characteristic of theories more generally. Instead, our findings indicate that conspiracy theories are spread in a manner that resembles the dissemination of rumors and falsehoods. Based on these empirical results, we argue that it is time for both re-engineering conspiracy theory and for relabeling "conspiracy theory". We (...) propose relabelling conspiracy theories as either "conspiracy stories" or "conspiracy narratives" to more accurately convey their true nature. (shrink)

The fact that Internet companies may record our personal data and track our online behavior for commercial or political purpose has emphasized aspects related to online privacy. This has also led to the development of search engines that promise no tracking and privacy. Search engines also have a major role in spreading low-quality health information such as that of anti-vaccine websites. This study investigates the relationship between search engines’ approach to privacy and the scientific quality of the information they (...) return. We analyzed the first 30 webpages returned searching “vaccines autism” in English, Spanish, Italian, and French. The results show that not only “alternative” search engines but also other commercial engines often return more anti-vaccine pages (10–53%) than Google (0%). Some localized versions of Google, however, returned more anti-vaccine webpages (up to 10%) than Google. Health information returned by search engines has an impact on public health and, specifically, in the acceptance of vaccines. The issue of information quality when seeking information for making health-related decisions also impact the ethical aspect represented by the right to an informed consent. Our study suggests that designing a search engine that is privacy savvy and avoids issues with filter bubbles that can result from user-tracking is necessary but insufficient; instead, mechanisms should be developed to test search engines from the perspective of information quality (particularly for health-related webpages) before they can be deemed trustworthy providers of public health information. (shrink)

On 11 March 2020, the World Health Organization declared a pandemic of the COVID-19 coronavirus disease that was first recognized in China in late 2019. Among the primary effects caused by the pandemic, there was the dissemination of health preventive measures such as physical distancing, travel restrictions, self-isolation, quarantines, and facility closures. This includes the global disruption of socio-economic systems including the postponement or cancellation of various public events (e.g., sporting, cultural, or religious), supply shortages and fears of the same, (...) schools and universities closure, evacuation of foreign citizens, a rise of unemployment, changes in the international aid schemes, misinformation, and incidents of discrimination toward people affected by or suspected of having the COVID-19 disease. The pandemic has brought to the fore unpreparedness in critical areas that require attention, amid prospects and challenges. Moreover, considerable reorganization efforts are required with implications for assets, resources, norms, and value systems. COVID-19 is challenging the concept of globalization and stimulating responses at the levels of local and regional socio-economic systems that lead to the mobilization of assets that have been unrecognized earlier on, such as various forms of economic capital, social capital, cultural capital, human capital, and creative capital. For example, through digital channels, local groups are forming to create schemes of support for physical and mental wellbeing. These emerging exchanges lead to various social and technological innovations by building on skills and assets that are less important in the free-market economy, such as empathy, skills for crafts, making and fixing; locally grown microgreens; and micromanufacturing. Isolation and local living are also making it much harder to ignore the civic responsibilities towards communities, meant as individuals, vulnerable groups, and local businesses. Whilst the pandemic is limiting physical participation, this challenging time is uncovering alternative ways of mutual support, which may create long-term benefits for socio-economic systems, including environmental and biodiversity protection, reduction of the air pollution, and climate action. The pandemic’s threat to public health will hopefully be overcome with implications for disruption for an extended period that we are unable to forecast at this stage. It is key to focus on studies recognizing the activities and interventions leading to the recovery of socio-economic systems after the pandemic. Reflecting and planning on how societies and economies will go back to “business as usual” requires new forms of communication and cooperation, imaginative design thinking, new styles of management, as well as new tools and forms of participation in various public policies. Many questions related to the care of the vulnerable, economic restart, and the risk of future pandemics, to mention but a few, are already occupying the academic, scientific, experts, and activist communities, who have started to imagine the “new normal.”. (shrink)

It is easier to talk frankly to a person whom one trusts. It is also easier to agree with a scientist whom one trusts. Even though in both cases the psychological state that underlies the behavior is called ‘trust’, it is controversial whether it is a token of the same psychological type. Trust can serve an affective, epistemic, or other social function, and comes to interact with other psychological states in a variety of ways. The way that the functional role (...) of trust changes across contexts and objects is further complicated when communities and individuals mediate it through technologies, and even more so when that mediation involves artificial intelligence (AI) and machine learning (ML). In this chapter I look at the ways in which trust in institutions, and specifically the medical profession, is affected by the use of AI and ML. There are two key elements of this analysis. The first is a disanalogy between institutional trust in medicine and institutional trust in science (Irzik and Kurtulmus 2021, 2019; Kitcher 2001). I note that as AI and ML become a more prominent part of medicine, trust in a medical institution becomes more like trust in a scientific institution. This is problematic for institutional trust in medicine and the practice of medicine, since institutional trust in science has been undermined by, among other things, the spread of misinformation online and the replication crisis (Romero 2019). There is also a strong analogy between the psychological state of the person who trusts a scientific report or testimony and the psychological state of a patient who trusts individual recommendations made by a medical professional in a clinical setting. In both cases, institutional trust makes it less likely that a mistake or malfeasance will result in reactive attitudes, such as blame or anger, directed at other individual members of that institution. However, it also renders people vulnerable enough to blame the institution itself. This, with time, can erode trust in the institution and naturally leads to policy recommendations that aim to preserve institutional trust. I survey two ways in which that can be done with institutional trust in medicine in the age of AI and ML. (shrink)

The issue of how best to combat the negative impacts of misinformation distributed via social media hangs on the following question: are there methods that most individuals can reasonably be expected to employ that would largely protect them from the negative impact that encountering misinformation on social media would otherwise have on their beliefs? If the answer is “yes,” then presumably individuals bear significant responsibility for those negative impacts; and, further, presumably there are feasible educational remedies for the (...) problem of misinformation. However, I argue that the answer is “no.” Accordingly, I maintain that individuals do not bear significant responsibility for the negative impacts at issue; and, further, I maintain that the only effective remedies for the problem of misinformation involve changing the information environment itself. (shrink)

If citizens are to make enlightened collective decisions, they need to rely on true factual beliefs, but misinformation impairs their ability to do so. Although some cases of misinformation are deliberate and amount to propaganda, cases of inadvertent misinformation are just as problematic in affecting the beliefs and behavior of democratic citizens. A review of empirical evidence suggests that this is a serious problem that cannot entirely be corrected by means of deliberation.

This chapter introduces a novel account of fake news and explains how it differs from other definitions on the market. The account locates the fakeness of an alleged news report in two main aspects related to its production, namely that its creators do not think to have sufficient evidence in favor of what they divulge and they fail to display the appropriate attitude towards the truth of the information they share. A key feature of our analysis is that it does (...) not require that fake news must be circulated with the intention to deceive one’s audience. In this way, our account overcomes a potential limitation of the current philosophical discussion about fake news, which appears to individuate the main problem with this phenomenon in the fact that fake news consumers are misled and misinformed. In contrast, the proposed analysis shows that an additional (and perhaps equally fundamental) problem uncovered by the spread of fake news is a widespread pathological relationship with information, one on which we consume information not to satisfy our interest in the truth but to strengthen our social identities and quench our hunger for social recognition. (shrink)

This chapter addresses the relationship between misinformation and disagreement. We begin by arguing that one traditional bogeyman in this domain, ideological polarization, does not account for the many problems that have been documented. Instead, affective polarization seems to be the root cause of most of these problems. We then discuss the relationships between moral outrage, misinformation, and affective polarization. We next turn to the political implications of affective polarization and conclude by discussing some potential solutions to the problems (...) that arise in this area. (shrink)

In this paper, we examine how the availability of massive quantities of data i.e., the “Big Data” phenomenon, contributes to the creation, spread, and harms of online misinformation. Specifically, we argue that a factor in the problem of online misinformation is the evolved human instinct to recognize patterns. While the pattern-recognition instinct is a crucial evolutionary adaptation, we argue that in the age of Big Data, these capacities have, unfortunately, rendered us vulnerable. Given the ways in which online (...) media outlets profit from the spread of misinformation by preying on this pattern-finding instinct, we conceptualize the problem that we identify as a morally objectionable form of “epistemic exploitation.” As we argue, the consumer of digital misinformation is often exploited by having her pattern-recognition instinct used against her. This exploitation is morally objectionable because it deprives her of an epistemic good to which she has a right. This epistemic good is the integrity of the pattern-recognition instinct itself which, we argue, is a capacity that allows us to participate in uniquely human goods. While our primary goal is to bring attention to this form of epistemic exploitation, we conclude by briefly evaluating some general solutions to the growing problem of online misinformation. (shrink)

Current methods of operationalizing concepts of misinformation in machine learning are often problematic given idiosyncrasies in their success conditions compared to other models employed in the natural and social sciences. The intrinsic value-ladenness of misinformation and the dynamic relationship between citizens' and social scientists' concepts of misinformation jointly suggest that both the construct legitimacy and the construct validity of these models needs to be assessed via more democratic criteria than has previously been recognized.

Scientific realism is the view that our best scientific theories can be regarded as (approximately) true. This is connected with the view that science, physics in particular, and metaphysics could (and should) inform one another: on the one hand, science tells us what the world is like, and on the other hand, metaphysical principles allow us to select between the various possible theories which are underdetermined by the data. Nonetheless, quantum mechanics has always been regarded as, at best, (...) puzzling, if not contradictory. As such, it has been considered for a long time at odds with scientific realism, and thus a naturalized quantum metaphysics was deemed impossible. Luckily, now we have many quantum theories compatible with a realist interpretation. However, scientific realists assumed that the wave-function, regarded as the principal ingredient of quantum theories, had to represent a physical entity, and because of this they struggled with quantum superpositions. In this paper I discuss a particular approach which makes quantum mechanics compatible with scientific realism without doing that. In this approach, the wave-function does not represent matter which is instead represented by some spatio-temporal entity dubbed the primitive ontology: point-particles, continuous matter fields, space-time events. I argue how within this framework one develops a distinctive theory-construction schema, which allows to perform a more informed theory evaluation by analyzing the various ingredients of the approach and their inter-relations. (shrink)

This paper argues that the well-established fact of political irrationality imposes substantial constraints on how governments may combat the threat of political misinformation. Though attempts at regulating misinformation are becoming increasingly popular, both among policymakers and theorists, I intend to show that, for a wide range of anti-misinformation interventions (collectively termed “debunking” and “source labeling”), these attempts ought to be abandoned. My argument relies primarily on the fact that most people process politically-relevant information in biased and motivated (...) ways. Since debunking or factual correction of politically relevant misinformation (as well as source labeling) themselves consist of providing politically-relevant information, they are also very likely to be processed in irrational ways. This makes it extremely difficult to effectively correct people’s political beliefs and political information processing. Since governments should not pursue policies likely to be futile, they should refrain from mandating such interventions. My conclusion is of relevance to considerable literature in digital ethics of misinformation. It shows that many celebrated works in the field ignore political irrationality and fail to consider its implications. (shrink)

This book argues that misinformation poses a multi-faceted threat to knowledge, while arguing that some forms of content moderation risk exacerbating these threats. It proposes alternative forms of content moderation that aim to address this complexity while enhancing human epistemic agency. The proliferation of fake news, false conspiracy theories, and other forms of misinformation on the internet and especially social media is widely recognized as a threat to individual knowledge and, consequently, to collective deliberation and democracy itself. This (...) book argues that misinformation presents a three-pronged threat to knowledge. While researchers often focus on the role of misinformation in causing false beliefs, this deceptive potential of misinformation exists alongside the potential to suppress trust and to distort the perception of evidence. Recognizing the multi-faceted nature of this threat is essential to the development of effective measures to mitigate the harms associated with misinformation. The book weaves together work in analytic epistemology with emerging empirical work in other disciplines to offer novel insights into the threats posed by misinformation. Additionally, it breaks new ground by systematically assessing different forms of content moderation from the perspective of epistemology. Misinformation, Content Moderation, and Epistemology will appeal to philosophers working in applied and social epistemology, as well as scholars and advanced students in disciplines such as communication studies, political science, and social psychology who are researching misinformation. (shrink)

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.

Herein below I will try to set out certain innate traits of scientific rationality, by means of making a comparison between leading subjective and objective accounts of it in aspects representative for their explanatory potential. Scientific rationality might well be taken in as a system of specific norms, originating from, and upheld by, a scientific community; norms offering a choice of best decisions in a set of rival alternatives. Hence, a study may be developed up to the (...) evolvement of a uniform conception of scientific rationality and its variants. (shrink)

Experts are sharply divided concerning the prevalence and influence of misinformation. Some have emphasized the severe epistemic and political threats posed by misinformation and have argued that some such threats have been realized in the real world. Others have argued that such concerns overstate the prevalence of misinformation and the gullibility of ordinary persons. Rather than taking a stand on this issue, I consider what would follow from the supposition that this latter perspective is correct. I argue (...) that, if the prevalence and influence of misinformation are indeed overstated, then many reports as to the prevalence and influence of misinformation constitute a kind of higher-order misinformation. I argue that higher-order misinformation presents its own challenges. In particular, higher-order misinformation, ironically, would lend credibility to the very misinformation whose influence it exaggerates. Additionally, higher-order misinformation would lead to underestimations of the reasons favoring opposing views. In short, higher-order misinformation constitutes misleading higher-order evidence concerning the quality of the evidence on which individuals form their beliefs. (shrink)

This is an introduction to the volume "Explanation Beyond Causation: Philosophical Perspectives on Non-Causal Explanations", edited by A. Reutlinger and J. Saatsi (OUP, forthcoming in 2017). -/- Explanations are very important to us in many contexts: in science, mathematics, philosophy, and also in everyday and juridical contexts. But what is an explanation? In the philosophical study of explanation, there is long-standing, influential tradition that links explanation intimately to causation: we often explain by providing accurate information about the causes of the (...) phenomenon to be explained. Such causal accounts have been the received view of the nature of explanation, particularly in philosophy of science, since the 1980s. However, philosophers have recently begun to break with this causal tradition by shifting their focus to kinds of explanation that do not turn on causal information. The increasing recognition of the importance of such non-causal explanations in the sciences and elsewhere raises pressing questions for philosophers of explanation. What is the nature of non-causal explanations - and which theory best captures it? How do non-causal explanations relate to causal ones? How are non-causal explanations in the sciences related to those in mathematics and metaphysics? This volume of new essays explores answers to these and other questions at the heart of contemporary philosophy of explanation. The essays address these questions from a variety of perspectives, including general accounts of non-causal and causal explanations, as well as a wide range of detailed case studies of non-causal explanations from the sciences, mathematics and metaphysics. (shrink)

The functional approach to scientific progress has been mainly developed by Kuhn, Lakatos, Popper, Laudan, and more recently by Shan. The basic idea is that science progresses if key functions of science are fulfilled in a better way. This chapter defends the function approach. It begins with an overview of the two old versions of the functional approach by examining the work of Kuhn, Laudan, Popper, and Lakatos. It then argues for Shan’s new functional approach, in which scientific (...) progress is defined as an increase of usefulness of exemplary practices. (shrink)

What is scientific progress? This paper advances an interpretation of this question, and an account that serves to answer it. Roughly, the question is here understood to concern what type of cognitive change with respect to a topic X constitutes a scientific improvement with respect to X. The answer explored in the paper is that the requisite type of cognitive change occurs when scientific results are made publicly available so as to make it possible for anyone to (...) increase their understanding of X. This account is briefly compared to two rival accounts of scientific progress, based respectively on increasing truthlikeness and accumulating knowledge, and is argued to be preferable to both. (shrink)

In Kant, Science, and Human Nature, Robert Hanna argues against a version of scientific realism founded on the Kripke/Putnam theory of reference, and defends a Kant-inspired manifest realism in its place. I reject Kriple/Putnam for different reasons than Hanna does, and argue that what should replace it is not manifest realism, but Kant‘s own scientific realism, which rests on a radically different theory of reference. Kant holds that we picture manifest objects by uniting manifolds of sensation using concepts-qua-inferential-rules. (...) When these rules are demonstrated to be invalid, we replace the picture of the macroscopic world with a picture of the microscopic entities of theoretical science that unites the very same manifolds using different rules of inference. Thus, we refer to "unobservable" theoretical entities in the same way that we do manifest ones: by specifying both their determinate location in space and time and the concepts by which they are understood. (shrink)

This is the Introduction and Chapter 1.1 of the book ‘Scientific Testimony. Its roles in science and society’ (OUP 2022). The introduction contains a brief survey of the book’s chapters and main conclusions, which I hope will be useful to the curious ones.

The debate on whether and why misinformation is bad primarily focuses on the spread of false beliefs as its main harm. From the assumption that misinformation primarily causes harm through the spread of false beliefs as a starting point, it has been contended that the problem of misinformation has been exaggerated. Its tendency to generate false beliefs appears to be limited. However, the near-exclusive focus on whether or not misinformation dupes people with false beliefs neglects other (...) epistemic harms associated with it. Specifically, I show that misinformation also causes trouble for the epistemic goods of truth attainment, intellectual autonomy and debate pluriformity. Moreover, for each of these goods, I argue that emphasizing error-avoidance exacerbates, rather than mitigates, the harms caused by misinformation. These oversights and dilemmas show that prioritizing error-avoidance in the fight against misinformation is not a neutral default policy or necessarily a net positive. A shift in focus away from the spread of false beliefs as the main harm of misinformation is needed to better understand and counter its negative effects. (shrink)

Scientific realism driven by inference to the best explanation (IBE) takes empirically confirmed objects to exist, independent, pace empiricism, of whether those objects are observable or not. This kind of realism, it has been claimed, does not need probabilistic reasoning to justify the claim that these objects exist. But I show that there are scientific contexts in which a non-probabilistic IBE-driven realism leads to a puzzle. Since IBE can be applied in scientific contexts in which empirical confirmation (...) has not yet been reached, realists will in these contexts be committed to the existence of empirically unconfirmed objects. As a consequence of such commitments, because they lack probabilistic features, the possible empirical confirmation of those objects is epistemically redundant with respect to realism. (shrink)

Scientific theories are used for a variety of purposes. For example, physical theories such as classical mechanics and electrodynamics have important applications in engineering and technology, and we trust that this results in useful machines, stable bridges, and the like. Similarly, theories such as quantum mechanics and relativity theory have many applications as well. Beyond that, these theories provide us with an understanding of the world and address fundamental questions about space, time, and matter. Here we trust that the (...) answers scientific theories give are reliable and that we have good reason to believe that the features of the world are similar to what the theories say about them. But why do we trust scientific theories, and what counts as evidence in favor of them? (shrink)

The problem of fake news and the spread of misinformation has garnered a lot of attention in recent years. The incentives and norms that give rise to the problem, however, are not unique to journalism. Insofar as academics and journalists are working towards the same goal, i.e., publication, they are both under pressures that pervert. This chapter has two aims. First, to integrate conversations in philosophy of science, epistemology, and metaphilosophy to draw out the publishing incentives that promote analogous (...) problems of fake news and misinformation in academic research communities more broadly. Second, to argue for a (partial) solution. This chapter argues that research communities benefit when authors believe what they argue for in print and it warns against attempts to loosen our publication norms to permit publishing without belief. (shrink)

Scientists often diverge widely when choosing between research programs. This can seem to be rooted in disagreements about which of several theories, competing to address shared questions or phenomena, is currently the most epistemically or explanatorily valuable—i.e. most successful. But many such cases are actually more directly rooted in differing judgments of pursuit-worthiness, concerning which theory will be best down the line, or which addresses the most significant data or questions. Using case studies from 16th-century astronomy and 20th-century geology and (...) biology, I argue that divergent theory choice is thus often driven by considerations of scientific process, even where direct epistemic or explanatory evaluation of its final products appears more relevant. Broadly following Kuhn’s analysis of theoretical virtues, I suggest that widely shared criteria for pursuit-worthiness function as imprecise, mutually-conflicting values. However, even Kuhn and others sensitive to pragmatic dimensions of theory ‘acceptance’, including the virtue of fruitfulness, still commonly understate the role of pursuit-worthiness—especially by exaggerating the impact of more present-oriented virtues, or failing to stress how ‘competing’ theories excel at addressing different questions or data. This framework clarifies the nature of the choice and competition involved in theory choice, and the role of alternative theoretical virtues. (shrink)

Scientific realism is the position that the aim of science is to advance on truth and increase knowledge about observable and unobservable aspects of the mind-independent world which we inhabit. This book articulates and defends that position. In presenting a clear formulation and addressing the major arguments for scientific realism Sankey appeals to philosophers beyond the community of, typically Anglo-American, analytic philosophers of science to appreciate and understand the doctrine. The book emphasizes the epistemological aspects of scientific (...) realism and contains an original solution to the problem of induction that rests on an appeal to the principle of uniformity of nature. (shrink)

A prominent type of scientific realism holds that some important parts of our best current scientific theories are at least approximately true. According to such realists, radically distinct alternatives to these theories or theory-parts are unlikely to be approximately true. Thus one might be tempted to argue, as the prominent anti-realist Kyle Stanford recently did, that realists of this kind have little or no reason to encourage scientists to attempt to identify and develop theoretical alternatives that are radically (...) distinct from currently accepted theories in the relevant respects. In other words, it may seem that realists should recommend that scientists be relatively conservative in their theoretical endeavors. This paper aims to show that this argument is mistaken. While realists should indeed be less optimistic of finding radically distinct alternatives to replace current theories, realists also have greater reasons to value the outcomes of such searches. Interestingly, this holds both for successful and failed attempts to identify and develop such alternatives. (shrink)

Broadly speaking, the contemporary scientific realist is concerned to justify belief in what we might call theoretical truth, which includes truth based on ampliative inference and truth about unobservables. Many, if not most, contemporary realists say scientific realism should be treated as ‘an overarching scientific hypothesis’ (Putnam 1978, p. 18). In its most basic form, the realist hypothesis states that theories enjoying general predictive success are true. This hypothesis becomes a hypothesis to be tested. To justify our (...) belief in the realist hypothesis, realists commonly put forward an argument known as the ‘no-miracles argument’. With respect to the basic hypothesis this argument can be stated as follows: it would be a miracle were our theories as successful as they are, were they not true; the only possible explanation for the general predictive success of our scientific theories is that they are true. (shrink)

"Understanding Scientific Progress constitutes a potentially enormous and revolutionary advancement in philosophy of science. It deserves to be read and studied by everyone with any interest in or connection with physics or the theory of science. Maxwell cites the work of Hume, Kant, J.S. Mill, Ludwig Bolzmann, Pierre Duhem, Einstein, Henri Poincaré, C.S. Peirce, Whitehead, Russell, Carnap, A.J. Ayer, Karl Popper, Thomas Kuhn, Imre Lakatos, Paul Feyerabend, Nelson Goodman, Bas van Fraassen, and numerous others. He lauds Popper for advancing (...) beyond verificationism and Hume’s problem of induction, but faults both Kuhn and Popper for being unable to show that and how their work could lead nearer to the truth." —Dr. LLOYD EBY teaches philosophy at The George Washington University and The Catholic University of America, in Washington, DC "Maxwell's aim-oriented empiricism is in my opinion a very significant contribution to the philosophy of science. I hope that it will be widely discussed and debated." – ALAN SOKAL, Professor of Physics, New York University "Maxwell takes up the philosophical challenge of how natural science makes progress and provides a superb treatment of the problem in terms of the contrast between traditional conceptions and his own scientifically-informed theory—aim-oriented empiricism. This clear and rigorously-argued work deserves the attention of scientists and philosophers alike, especially those who believe that it is the accumulation of knowledge and technology that answers the question."—LEEMON McHENRY, California State University, Northridge "Maxwell has distilled the finest essence of the scientific enterprise. Science is about making the world a better place. Sometimes science loses its way. The future depends on scientists doing the right things for the right reasons. Maxwell's Aim-Oriented Empiricism is a map to put science back on the right track."—TIMOTHY McGETTIGAN, Professor of Sociology, Colorado State University - Pueblo "Maxwell has a great deal to offer with these important ideas, and deserves to be much more widely recognised than he is. Readers with a background in philosophy of science will appreciate the rigour and thoroughness of his argument, while more general readers will find his aim-oriented rationality a promising way forward in terms of a future sustainable and wise social order."—David Lorimer, Paradigm Explorer, 2017/2 "This is a book about the very core problems of the philosophy of science. The idea of replacing Standard Empiricism with Aim-Oriented Empiricism is understood by Maxwell as the key to the solution of these central problems. Maxwell handles his main tool masterfully, producing a fascinating and important reading to his colleagues in the field. However, Nicholas Maxwell is much more than just a philosopher of science. In the closing part of the book he lets the reader know about his deep concern and possible solutions of the biggest problems humanity is facing."—Professor PEETER MŰŰREPP, Tallinn University of Technology, Estonia “For many years, Maxwell has been arguing that fundamental philosophical problems about scientific progress, especially the problem of induction, cannot be solved granted standard empiricism (SE), a doctrine which, he thinks, most scientists and philosophers of science take for granted. A key tenet of SE is that no permanent thesis about the world can be accepted as a part of scientific knowledge independent of evidence. For a number of reasons, Maxwell argues, we need to adopt a rather different conception of science which he calls aim-oriented empiricism (AOE). This holds that we need to construe physics as accepting, as a part of theoretical scientific knowledge, a hierarchy of metaphysical theses about the comprehensibility and knowability of the universe, which become increasingly insubstantial as we go up the hierarchy. In his book “Understanding Scientific Progress: Aim-Oriented Empiricism”, Maxwell gives a concise and excellent illustration of this view and the arguments supporting it… Maxwell’s book is a potentially important contribution to our understanding of scientific progress and philosophy of science more generally. Maybe it is the time for scientists and philosophers to acknowledge that science has to make metaphysical assumptions concerning the knowability and comprehensibility of the universe. Fundamental philosophical problems about scientific progress, which cannot be solved granted SE, may be solved granted AOE.” Professor SHAN GAO, Shanxi University, China . (shrink)

First, I argue that scientific progress is possible in the absence of increasing verisimilitude in science’s theories. Second, I argue that increasing theoretical verisimilitude is not the central, or primary, dimension of scientific progress. Third, I defend my previous argument that unjustified changes in scientific belief may be progressive. Fourth, I illustrate how false beliefs can promote scientific progress in ways that cannot be explicated by appeal to verisimilitude.

Ecological-enactive approaches to cognition aim to explain cognition in terms of the dynamic coupling between agent and environment. Accordingly, cognition of one’s immediate environment (which is sometimes labeled “basic” cognition) depends on enaction and the picking up of affordances. However, ecological-enactive views supposedly fail to account for what is sometimes called “higher” cognition, i.e., cognition about potentially absent targets, which therefore can only be explained by postulating representational content. This challenge levelled against ecological-enactive approaches highlights a putative explanatory gap between (...) basic and higher cognition. In this paper, we examine scientific cognition—a paradigmatic case of higher cognition—and argue that it shares fundamental features with basic cognition, for enaction and affordance selection are central to the scientific enterprise. Our argument focuses on modeling, and on how models promote scientific understanding. We base our argument on a non-representational account of scientific understanding and on the material engagement theory, for models are hereby conceived as material objects designed for scientific engagements. Having done so, we conclude that the explanatory gap is significantly less threatening to the ecological-enactive approach than it might appear. (shrink)

Imagination is necessary for scientific practice, yet there are no in vivo sociological studies on the ways that imagination is taught, thought of, or evaluated by scientists. This article begins to remedy this by presenting the results of a qualitative study performed on two systems biology laboratories. I found that the more advanced a participant was in their scientific career, the more they valued imagination. Further, positive attitudes toward imagination were primarily due to the perceived role of imagination (...) in problem-solving. But not all problem-solving episodes involved clear appeals to imagination, only maximally specific problems did. This pattern is explained by the presence of an implicit norm governing imagination use in the two labs: only use imagination on maximally specific problems, and only when all other available methods have failed. This norm was confirmed by the participants, and I argue that it has epistemological reasons in its favour. I also found that its strength varies inversely with career stage, such that more advanced scientists do (and should) occasionally bring their imaginations to bear on more general problems. A story about scientific pedagogy explains the trend away from (and back to) imagination over the course of a scientific career. Finally, some positive recommendations are given for a more imagination-friendly scientific pedagogy. (shrink)

We model scientific theories as Bayesian networks. Nodes carry credences and function as abstract representations of propositions within the structure. Directed links carry conditional probabilities and represent connections between those propositions. Updating is Bayesian across the network as a whole. The impact of evidence at one point within a scientific theory can have a very different impact on the network than does evidence of the same strength at a different point. A Bayesian model allows us to envisage and (...) analyze the differential impact of evidence and credence change at different points within a single network and across different theoretical structures. (shrink)

John Searle offers what he thinks to be a reasonable scientific approach to the understanding of consciousness. I argue that Searle is demanding nothing less than a Kuhnian-type revolution with respect to how scientists should study consciousness given his rejection of the subject-object distinction and affirmation of mental causation. As part of my analysis, I reveal that Searle embraces a version of emergentism that is in tension, not only with his own account, but also with some of the theoretical (...) tenets of science. I conclude that Searle has offered little to motivate scientists to adopt his proposal. (shrink)

We report the results of a study that investigated the views of researchers working in seven scientific disciplines and in history and philosophy of science in regard to four hypothesized dimensions of scientific realism. Among other things, we found that natural scientists tended to express more strongly realist views than social scientists, that history and philosophy of science scholars tended to express more antirealist views than natural scientists, that van Fraassen’s characterization of scientific realism failed to cluster (...) with more standard characterizations, and that those who endorsed the pessimistic induction were no more or less likely to endorse antirealism. (shrink)

Epistemic accounts of scientific collaboration usually assume that, one way or another, two heads really are more than twice better than one. We show that this hypothesis is unduly strong. We present a deliberately crude model with unfavorable hypotheses. We show that, even then, when the priority rule is applied, large differences in successfulness can emerge from small differences in efficiency, with sometimes increasing marginal returns. We emphasize that success is sensitive to the structure of competing communities. Our results (...) suggest that purely epistemic explanations of the efficiency of collaborations are less plausible but have much more powerful socioepistemic versions. (shrink)

James Nguyen & Roman Frigg (2022). Scientific Representation. Cambridge University Press, 90pp., €21.23 (Paperback), ISBN: 9781009009157.

What is scientific progress? On Alexander Bird’s epistemic account of scientific progress, an episode in science is progressive precisely when there is more scientific knowledge at the end of the episode than at the beginning. Using Bird’s epistemic account as a foil, this paper develops an alternative understanding-based account on which an episode in science is progressive precisely when scientists grasp how to correctly explain or predict more aspects of the world at the end of the episode (...) than at the beginning. This account is shown to be superior to the epistemic account by examining cases in which knowledge and understanding come apart. In these cases, it is argued that scientific progress matches increases in scientific understanding rather than accumulations of knowledge. In addition, considerations having to do with minimalist idealizations, pragmatic virtues, and epistemic value all favor this understanding-based account over its epistemic counterpart. (shrink)

Scientists are constantly making observations, carrying out experiments, and analyzing empirical data. Meanwhile, scientific theories are routinely being adopted, revised, discarded, and replaced. But when are such changes to the content of science improvements on what came before? This is the question of scientific progress. One answer is that progress occurs when scientific theories ‘get closer to the truth’, i.e. increase their degree of truthlikeness. A second answer is that progress consists in increasing theories’ effectiveness for solving (...) scientific problems. A third answer is that progress occurs when the stock of scientific knowledge accumulates. A fourth and final answer is that scientific progress consists in increasing scientific understanding, i.e. the capacity to correctly explain and reliably predict relevant phenomena. This paper compares and contrasts these four accounts of scientific progress, considers some of the most prominent arguments for and against each account, and briefly explores connections to different forms of scientific realism. (shrink)

If we consider modeling not as a heap of contingent structures, but (where possible) as evolving coordinated systems of models, then we can reasonably explain as "direct representations" even some very complicated model-based cognitive situations. Scientific modeling is not as indirect as it may seem. "Direct theorizing" comes later, as the result of a successful model evolution.

In this article I investigate online misinformation from a media philosophy perspective. I, thus move away from the debate focused on the semantic content, concerned with what is true or not about misinformation. I argue rather that online misinformation is the effect of an informational climate promoted by user micro-behaviours such as liking, sharing, and posting. Misinformation online is explained as the effect of an informational environment saturated with and shaped by techno-images in which most users (...) act automatically under the constant assault of stirred emotions, a state resembling what media philosopher Vilém Flusser has called techno-magical consciousness. I describe three ways in which images function on social media to induce this distinctive, uncritical mode of consciousness, and complement Flusser’s explanation with insights from the phenomenology of emotions. (shrink)

Many philosophers have drawn parallels between scientific models and fictions. In this paper I will be concerned with a recent version of the analogy, which compares models to the imagined characters of fictional literature. Though versions of the position differ, the shared idea is that modeling essentially involves imagining concrete systems analogously to the way that we imagine characters and events in response to works of fiction. Advocates of this view argue that imagining concrete systems plays an ineliminable role (...) in the practice of modeling that cannot be captured by other accounts. The approach thus leaves open what we should say about the ontological status of model-systems, and here advocates differ among themselves, defending a variety of realist or anti-realist positions. I argue that this debate over the ontological status of model-systems is misguided. If model-systems are the kinds of objects fictional realists posit, they can play no role in explaining the epistemology of modeling for an advocate of this approach. So they are at best superfluous. Defenders of the approach should focus on developing an account of the epistemological role of imagining model-systems. (shrink)