Elements in the Philosophy of Science – serie

The holistic thesis developed by Pierre Duhem challenges the idea that our evidence can conclusively falsify a theory. Given that no scientific theory is tested in isolation, a negative experiment can always be attributed to components other than the theory we test - to the auxiliary hypotheses and background assumptions. How do scientists decide whether the experimental result undermines the theory or points at an error in the underlying assumptions? Duhem argues that we cannot offer a rule that directs when the scientist should employ a radical or conservative strategy in light of a negative result, and ultimately they will appeal to their intuition. More recently philosophers have offered a number of strategies of how to locate error and justify the abandonment of a theory or an auxiliary hypothesis. This Element analyses Duhem's response to holism and subsequent accounts of how the problem can be resolved.

Scientists cannot devise theories, construct models, propose explanations, make predictions, or even carry out observations, without first classifying their subject matter. The goal of scientific taxonomy is to come up with classification schemes that conform to nature's own. Another way of putting this is that science aims to devise categories that correspond to 'natural kinds.' The interest in ascertaining the real kinds of things in nature is as old as philosophy itself, but it takes on a different guise when one adopts a naturalist stance in philosophy, that is when one looks closely at scientific practice and takes it as a guide for identifying natural kinds and investigating their general features. This Element surveys existing philosophical accounts of natural kinds, defends a naturalist alternative, and applies it to case studies in a diverse set of sciences. This title is also available as Open Access on Cambridge Core.

This Element presents a philosophical exploration of the notion of scientific representation. It does so by focussing on an important class of scientific representations, namely scientific models. Models are important in the scientific process because scientists can study a model to discover features of reality. But what does it mean for something to represent something else? This is the question discussed in this Element. The authors begin by disentangling different aspects of the problem of representation and then discuss the dominant accounts in the philosophical literature: the resemblance view and inferentialism. They find them both wanting and submit that their own preferred option, the so-called DEKI account, not only eschews the problems that beset these conceptions, but further provides a comprehensive answer to the question of how scientific representation works. This title is also available as Open Access on Cambridge Core.

This Element introduces the philosophical literature on models, with an emphasis on normative considerations relevant to models for decision-making. Chapter 1 gives an overview of core questions in the philosophy of modeling. Chapter 2 examines the concept of model adequacy for purpose, using three examples of models from the atmospheric sciences to describe how this sort of adequacy is determined in practice. Chapter 3 explores the significance of using models that are not adequate for purpose, including the purpose of informing public decisions. Chapter 4 provides a basic framework for values in modelling, using a case study to highlight the ethical challenges in building models for decision making. It concludes by establishing the need for strategies to manage value judgments in modelling, including the potential for public participation in the process.

Science is a product of society: in its funding, its participation, and its application. This Element explores the relationship between science and the public with resources from philosophy of science. Chapter 1 defines the questions about science's relationship to the public and outlines science's obligation to the public. Chapter 2 considers the Vienna Circle as a case study in how science, philosophy, and the public can relate very differently than they do at present. Chapter 3 examines how public understanding of science can have a variety of different goals and introduces philosophical discussions of scientific understanding as a resource. Chapter 4 addresses public trust in science, including responding to science denial. Chapter 5 considers how expanded participation in science can contribute to public trust of science. Finally, Chapter 6 casts light on how science might discharge its obligations to the public.

This Element introduces the philosophical literature on values in science by examining four questions: (1) How do values influence science? (2) Should we actively incorporate values in science? (3) How can we manage values in science responsibly? (4) What are some next steps for those who want to help promote responsible roles for values in science? It explores arguments for and against the "value-free ideal" for science (i.e., the notion that values should be excluded from scientific reasoning) and concludes that it should be rejected. Nonetheless, this does not mean that value influences are always acceptable. The Element explores a range of strategies for distinguishing between appropriate and inappropriate value influences. It concludes by proposing an approach for managing values in science that relies on justifying, prioritising, and implementing norms for scientific research practices and institutions.

Objectivity is a key concept both in how we talk about science in everyday life and in the philosophy of science. This Element explores various ways in which recent philosophers of science have thought about the nature, value and achievability of objectivity. The first section explains the general trend in recent philosophy of science away from a notion of objectivity as a 'view from nowhere' to a focus on the relationship between objectivity and trust. Section 2 discusses the relationship between objectivity and recent arguments attacking the viability or desirability of 'value free' science. Section 3 outlines Longino's influential 'social' account of objectivity, suggesting some worries about drawing too strong a link between epistemic and ethical virtues. Section 4 turns to the value of objectivity, exploring concerns that notions of objectivity are politically problematic, and cautiously advocating in response a view of objectivity in terms of invariance.

This Element advances a novel view - the Epistemic Labour View - about the role, limits, and potential of the theoretical virtues as the arbiters of various versions of underdetermination. A central focus is to go beyond the often abstract discussions in this area and to show how the theoretical virtues can illuminate and resolve issues surrounding actual cases of underdetermination found in scientific practice. This title is also available as Open Access on Cambridge Core.

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.

This Element will overview research using models to understand scientific practice. Models are useful for reasoning about groups and processes that are complicated and distributed across time and space, i.e., those that are difficult to study using empirical methods alone. Science fits this picture. For this reason, it is no surprise that researchers have turned to models over the last few decades to study various features of science. The different sections of the element are mostly organized around different modeling approaches. The models described in this element sometimes yield take-aways that are straightforward, and at other times more nuanced. The Element ultimately argues that while these models are epistemically useful, the best way to employ most of them to understand and improve science is in combination with empirical methods and other sorts of theorizing.

The Open Science [OS] movement aims to foster the wide dissemination, scrutiny and re-use of research components for the good of science and society. This Element examines the role played by OS principles and practices within contemporary research and how this relates to the epistemology of science. After reviewing some of the concerns that have prompted calls for more openness, it highlights how the interpretation of openness as the sharing of resources, so often encountered in OS initiatives and policies, may have the unwanted effect of constraining epistemic diversity and worsening epistemic injustice, resulting in unreliable and unethical scientific knowledge. By contrast, this Element proposes to frame openness as the effort to establish judicious connections among systems of practice, predicated on a process-oriented view of research as a tool for effective and responsible agency. This title is also available as Open Access on Cambridge Core.

This Element examines various aspects of the demarcation problem: finding a distinction between science and pseudoscience. Section 1 introduces issues surrounding pseudoscience in the recent literature. Popper's falsificationism is presented in Section 2, alongside some of its early critics, such as Thomas Kuhn and Imre Lakatos. It is followed in Section 3 by the notable criticism of the Popperian program by Larry Laudan that put the issue out of fashion for decades. Section 4 explores recent multi-criteria approaches that seek to define pseudoscience not only along a single criterion, but by considering the diversity and historical dimension of science. Section 5 introduces the problem of values (the 'new demarcation problem') and addresses how we can use values in the problem of pseudoscience. Finally, Section 6 concludes by emphasizing the need for an attitude-oriented approach over a rigid, method-based demarcation, recognizing scientific practice's evolving and multifaceted nature.

This Element will overview research using models to understand scientific practice. Models are useful for reasoning about groups and processes that are complicated and distributed across time and space, i.e., those that are difficult to study using empirical methods alone. Science fits this picture. For this reason, it is no surprise that researchers have turned to models over the last few decades to study various features of science. The different sections of the element are mostly organized around different modeling approaches. The models described in this element sometimes yield take-aways that are straightforward, and at other times more nuanced. The Element ultimately argues that while these models are epistemically useful, the best way to employ most of them to understand and improve science is in combination with empirical methods and other sorts of theorizing.

This Element introduces the philosophical literature on models, with an emphasis on normative considerations relevant to models for decision-making. Chapter 1 gives an overview of core questions in the philosophy of modeling. Chapter 2 examines the concept of model adequacy for purpose, using three examples of models from the atmospheric sciences to describe how this sort of adequacy is determined in practice. Chapter 3 explores the significance of using models that are not adequate for purpose, including the purpose of informing public decisions. Chapter 4 provides a basic framework for values in modelling, using a case study to highlight the ethical challenges in building models for decision making. It concludes by establishing the need for strategies to manage value judgments in modelling, including the potential for public participation in the process.

This Element offers a new account of the philosophical significance of logical empiricism that relies on the past forty years of literature reassessing the project. It argues that while logical empiricism was committed to empiricism and did become tied to the trajectory of analytic philosophy, neither empiricism nor logical analysis per se was the deepest philosophical commitment of logical empiricism. That commitment was, rather, securing the scientific status of philosophy, bringing philosophy into a scientific conception of the world.

This Element offers a new account of the philosophical significance of logical empiricism that relies on the past forty years of literature reassessing the project. It argues that while logical empiricism was committed to empiricism and did become tied to the trajectory of analytic philosophy, neither empiricism nor logical analysis per se was the deepest philosophical commitment of logical empiricism. That commitment was, rather, securing the scientific status of philosophy, bringing philosophy into a scientific conception of the world.

Science is a product of society: in its funding, its participation, and its application. This Element explores the relationship between science and the public with resources from philosophy of science. Chapter 1 defines the questions about science's relationship to the public and outlines science's obligation to the public. Chapter 2 considers the Vienna Circle as a case study in how science, philosophy, and the public can relate very differently than they do at present. Chapter 3 examines how public understanding of science can have a variety of different goals and introduces philosophical discussions of scientific understanding as a resource. Chapter 4 addresses public trust in science, including responding to science denial. Chapter 5 considers how expanded participation in science can contribute to public trust of science. Finally, Chapter 6 casts light on how science might discharge its obligations to the public.

The philosophy of linguistics reflects on multiple scientific disciplines aimed at the understanding of one of the most fundamental aspects of human existence, our ability to produce and understand natural language. Linguistics, viewed as a science, has a long history but it was the advent of the formal (and computational) revolution in cognitive science that established the field as both scientifically and philosophically appealing. In this Element, the topic will be approached as a means for understanding larger issues in the philosophy of science more generally.

The philosophy of linguistics reflects on multiple scientific disciplines aimed at the understanding of one of the most fundamental aspects of human existence, our ability to produce and understand natural language. Linguistics, viewed as a science, has a long history but it was the advent of the formal (and computational) revolution in cognitive science that established the field as both scientifically and philosophically appealing. In this Element, the topic will be approached as a means for understanding larger issues in the philosophy of science more generally.

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.

The scientific realism debate directly addresses the relation between human thought and the reality in which it finds itself. A core question: Can we justifiably believe that science accurately describes the reality that lies beneath the limits of human experience? Exploring this question, this Element begins at the most foundational level of scientific realism, the endeavor to justify belief in the existence of unobservables by way of abduction. Raising anti-realist challenges, some much discussed in the literature but also some generally overlooked, it works its way toward more refined variants of scientific realism. Because scientific realism is the default position of many scientific realists themselves often assuming it is the default position of scientists- the emphasis will be on the challenges. Those challenges will also motivate the variants of scientific realism traced. The Element concludes with a brief articulation of the author's own position, Socratic scientific realism.

This Element is about the social dimensions of scientific knowledge. The first section asks in what ways scientific knowledge is social. The second section develops a conception of scientific knowledge that accommodates the insights of the first section, and is consonant with mainstream thinking about knowledge in analytic epistemology. The third section asks under what conditions we can tell, in the real world, that a consensus in a scientific community amounts to shared scientific knowledge, as characterized in the second section, and how to deal with scientific dissent. The fourth section reviews the ways epistemic and social elements mutually interact to coproduce scientific knowledge. This Element engages with literature from philosophy of science and social epistemology, especially social epistemology of science, as well as Science, Technology, and Society (STS), and analytic epistemology. The Element focuses on themes and debates that date from the start of the second millennium.

Feminist scholars have identified pervasive gender discrimination in science as an institution, as well as gender bias in the very content of many scientific theories. An ameliorative project at heart, feminist philosophy of science has inquired into the social and epistemological roots and consequences of these problems and into their potential solutions. Most feminist philosophers agree on a need for diversity in scientific communities to counter the detrimental effects of gender bias. Diversity could thus serve as a unifying concept for a potential consensus of the field. Yet there are substantial differences in the kinds and roles of diversity envisaged. This element argues that we need diversity, both in terms of social locations and of values, to overcome former biases and blind spots. Diversity as such, however, is insufficient. To reap its epistemic benefits, diversity also needs to be institutionalised in a way that counters various forms of epistemic injustice.

When faced with a difficult problem or limited information about a novel domain, how do scientists advance their research? As historians of science have widely noted, one strategy common to the natural and the social sciences is to make use of analogy. Formulating hypotheses about an unknown system construed by analogy with what is observed in a more familiar system has repeatedly proven to be a source of discoveries. But what makes analogy such a useful tool for scientific inquiry? Although early reflections trace back to Aristotle, the question of the exact role of analogy in science remains an outstanding one in contemporary philosophy of science. This Element aims to clarify the main epistemological questions at stake and why seemingly obvious answers to them do not survive scrutiny. We provide an overview of the current debate and summarize insights from relevant case studies in the natural and social sciences.

When faced with a difficult problem or limited information about a novel domain, how do scientists advance their research? As historians of science have widely noted, one strategy common to the natural and the social sciences is to make use of analogy. Formulating hypotheses about an unknown system construed by analogy with what is observed in a more familiar system has repeatedly proven to be a source of discoveries. But what makes analogy such a useful tool for scientific inquiry? Although early reflections trace back to Aristotle, the question of the exact role of analogy in science remains an outstanding one in contemporary philosophy of science. This Element aims to clarify the main epistemological questions at stake and why seemingly obvious answers to them do not survive scrutiny. We provide an overview of the current debate and summarize insights from relevant case studies in the natural and social sciences.

This Element advances a novel view - the Epistemic Labour View - about the role, limits, and potential of the theoretical virtues as the arbiters of various versions of underdetermination. A central focus is to go beyond the often abstract discussions in this area and to show how the theoretical virtues can illuminate and resolve issues surrounding actual cases of underdetermination found in scientific practice. This title is also available as Open Access on Cambridge Core.

This Element examines various aspects of the demarcation problem: finding a distinction between science and pseudoscience. Section 1 introduces issues surrounding pseudoscience in the recent literature. Popper's falsificationism is presented in Section 2, alongside some of its early critics, such as Thomas Kuhn and Imre Lakatos. It is followed in Section 3 by the notable criticism of the Popperian program by Larry Laudan that put the issue out of fashion for decades. Section 4 explores recent multi-criteria approaches that seek to define pseudoscience not only along a single criterion, but by considering the diversity and historical dimension of science. Section 5 introduces the problem of values (the 'new demarcation problem') and addresses how we can use values in the problem of pseudoscience. Finally, Section 6 concludes by emphasizing the need for an attitude-oriented approach over a rigid, method-based demarcation, recognizing scientific practice's evolving and multifaceted nature.

This Element examines how climate scientists have arrived at answers to three key questions about climate change: How much is earth's climate warming? What is causing this warming? What will climate be like in the future? Resources from philosophy of science are employed to analyse the methods that climate scientists use to address these questions and the inferences that they make from the evidence collected. Along the way, the analysis contributes to broader philosophical discussions of data modelling and measurement, robustness analysis, explanation, and model evaluation.

This Element examines how climate scientists have arrived at answers to three key questions about climate change: How much is earth's climate warming? What is causing this warming? What will climate be like in the future? Resources from philosophy of science are employed to analyse the methods that climate scientists use to address these questions and the inferences that they make from the evidence collected. Along the way, the analysis contributes to broader philosophical discussions of data modelling and measurement, robustness analysis, explanation, and model evaluation.