Proponents of the value ladenness of science rely primarily on arguments from underdetermination or inductive risk, which share the premise that we should only consider values where the evidence runs out or leaves uncertainty; they adopt a criterion of lexical priority of evidence over values. The motivation behind lexical priority is to avoid reaching conclusions on the basis of wishful thinking rather than good evidence. This is a real concern, however, that giving lexical priority to evidential considerations over values is (...) a mistake and unnecessary for avoiding the wishful thinking. Values have a deeper role to play in science. (shrink)

This paper examines the state of the field of “science and values”—particularly regarding the implications of the thesis of transient underdetermination for the ideal of value-free science, or what I call the “ideal of epistemic purity.” I do this by discussing some of the main arguments in the literature, both for and against the ideal. I examine a preliminary argument from transient underdetermination against the ideal of epistemic purity, and I discuss two different formulations of an objection to this argument—an (...) objection that requires the strict separation of the epistemic from the practical. A secondary aim of the paper is to suggest some future directions for the field, one of which is to replace the vocabulary of values that is often employed in the literature with a more precise one. (shrink)

The ideal of value free science states that the justification of scientific findings should not be based on non-epistemic (e.g. moral or political) values. It has been criticized on the grounds that scientists have to employ moral judgements in managing inductive risks. The paper seeks to defuse this methodological critique. Allegedly value-laden decisions can be systematically avoided, it argues, by making uncertainties explicit and articulating findings carefully. Such careful uncertainty articulation, understood as a methodological strategy, is exemplified by the current (...) practice of the Intergovernmental Panel on Climate Change (IPCC). (shrink)

Understanding is a central aim of science and highly important in present-day society. But what precisely is scientific understanding and how can it be achieved? This book answers these questions, through philosophical analysis and historical case studies, and presents a philosophical theory of scientific understanding that highlights its contextual nature.

Helen Longino seeks to break the current deadlock in the ongoing wars between philosophers of science and sociologists of science--academic battles founded on disagreement about the role of social forces in constructing scientific knowledge. While many philosophers of science downplay social forces, claiming that scientific knowledge is best considered as a product of cognitive processes, sociologists tend to argue that numerous noncognitive factors influence what scientists learn, how they package it, and how readily it is accepted. Underlying this disagreement, however, (...) is a common assumption that social forces are a source of bias and irrationality. Longino challenges this assumption, arguing that social interaction actually assists us in securing firm, rationally based knowledge. This important insight allows her to develop a durable and novel account of scientific knowledge that integrates the social and cognitive. Longino begins with a detailed discussion of a wide range of contemporary thinkers who write on scientific knowledge, clarifying the philosophical points at issue. She then critically analyzes the dichotomous understanding of the rational and the social that characterizes both sides of the science studies stalemate and the social account that she sees as necessary for an epistemology of science that includes the full spectrum of cognitive processes. Throughout, her account is responsive both to the normative uses of the term knowledge and to the social conditions in which scientific knowledge is produced. Building on ideas first advanced in her influential book Science as Social Knowledge, Longino brings her account into dialogue with current work in social epistemology and science studies and shows how her critical social approach can help solve a variety of stubborn problems. While the book focuses on epistemological concerns related to the sociality of inquiry, Longino also takes up its implications for scientific pluralism. The social approach, she concludes, best allows us to retain a meaningful concept of knowledge in the face of theoretical plurality and uncertainty. (shrink)

The idea that science is or should be value-free, and that values are or should be formed independently of science, has been under fire by philosophers of science for decades. Science and Moral Imagination directly challenges the idea that science and values cannot and should not influence each other. Matthew J. Brown argues that science and values mutually influence and implicate one another, that the influence of values on science is pervasive and must be responsibly managed, and that science can (...) and should have an influence on our values. This interplay, he explains, must be guided by accounts of scientific inquiry and value judgment that are sensitive to the complexities of their interactions. Brown presents scientific inquiry and value judgment as types of problem-solving practices and provides a new framework for thinking about how we might ethically evaluate episodes and decisions in science, while offering guidance for scientific practitioners and institutions about how they can incorporate value judgments into their work. His framework, dubbed "the ideal of moral imagination," emphasizes the role of imagination in value judgment and the positive role that value judgment plays in science. (shrink)

Science is the study of our world, as it is in its messy reality. Nonetheless, science requires idealization to function—if we are to attempt to understand the world, we have to find ways to reduce its complexity. Idealization and the Aims of Science shows just how crucial idealization is to science and why it matters. Beginning with the acknowledgment of our status as limited human agents trying to make sense of an exceedingly complex world, Angela Potochnik moves on to explain (...) how science aims to depict and make use of causal patterns—a project that makes essential use of idealization. She offers case studies from a number of branches of science to demonstrate the ubiquity of idealization, shows how causal patterns are used to develop scientific explanations, and describes how the necessarily imperfect connection between science and truth leads to researchers’ values influencing their findings. The resulting book is a tour de force, a synthesis of the study of idealization that also offers countless new insights and avenues for future exploration. (shrink)

Philosophical work on values in science is held back by widespread ambiguity about how values bear on sci entific choices. Here, I disambiguate several ways in which a choice can be value-laden and show that this disambiguation has the potential to solve and dissolve philosophical problems about values in science. First, I characterize four ways in which values relate to choices: values can motivate, justify, cause, or be impacted by the choices we make. Next, I put my proposed taxonomy to (...) work, using it to clarify one version of the argument from inductive risk. The claim that non-epistemic values must play a role in scientific choices that run inductive risk makes most sense as a claim about values being needed to justify such choices. The argument from inductive risk is not unique: many philosophical arguments about values in science can be more clearly understood and assessed by paying close attention to how values and choices are related. (shrink)

Critics of the ideal of value‐free science often assume that they must reject the distinction between epistemic and nonepistemic values. I argue that this assumption is mistaken and that the distinction can be used to clarify and defend the argument from inductive risk, which challenges the value‐free ideal. I develop the idea that the characteristic feature of epistemic values is that they promote, either intrinsically or extrinsically, the attainment of truths. This proposal is shown to answer common objections to the (...) distinction and provide a principled basis for separating legitimate from illegitimate influences of nonepistemic values in scientific inference. *Received June 2009; revised September 2009. †To contact the author, please write to: 503 S. Kedzie Hall, Michigan State University, East Lansing, MI 48824‐1032; e‐mail: [email protected]. (shrink)

Kevin Elliott and Dan McKaughan argue that, in some cases, nonepistemic values provide legitimate reasons for scientists to accept an epistemically inferior option, a claim that they support with two case studies. This essay argues that Elliott and McKaughan have not shown that their case studies are indeed ones in which an epistemically inferior option was accepted. Specifically, their interpretation of these cases depends on problematic premises that it is epistemically better to wait for a slower-but-more-reliable method than to accept (...) the result of a quicker-but-less-reliable one and that a more detailed model is epistemically preferable to a simpler one. (shrink)

The question of the relationship of the making of value judgments in a typically ethical sense to the methods and procedures of science has been discussed in the literature at least to that point which e. e. cummings somewhere refers to as “The Mystical Moment of Dullness.” Nevertheless, albeit with some trepidation, I feel that something more may fruitfully be said on the subject.

In this paper, I first outline the view developed in my recent book on the role of idealization in scientific understanding. I discuss how this view leads to the recognition of a number of kinds of variability among scientific representations, including variability introduced by the many different aims of scientific projects. I then argue that the role of idealization in securing understanding distances understanding from truth, but that this understanding nonetheless gives rise to scientific knowledge. This discussion will clarify how (...) my view relates to three other recent books on understanding by Henk de Regt, Catherine Elgin, and Kareem Khalifa. (shrink)

According to an adequacy-for-purpose view, models should be assessed with respect to their adequacy or fitness for particular purposes. Such a view has been advocated by scientists and philosophers...

Lloyd (2009) contends that climate models are confirmed by various instances of fit between their output and observational data. The present paper argues that what these instances of fit might confirm are not climate models themselves, but rather hypotheses about the adequacy of climate models for particular purposes. This required shift in thinking—from confirming climate models to confirming their adequacy-for-purpose—may sound trivial, but it is shown to complicate the evaluation of climate models considerably, both in principle and in practice.

This paper argues that the appraisal of theory is in important respects closer in structure to value-judgement than it is to the rule-governed inference that the classical tradition in philosophy of science took for granted.

In this essay, which was his presidential address to the Philosophy of Science Association, Ernan McMullin argued that the watershed between “classic” philosophy of science and the “new” philosophy of science can best be understood by analyzing the change in our perception of the role played by values in science. He begins with some general remarks about the nature of value, goes on to explore some of the historical sources for the claim that judgement in science is value‐laden, and concludes (...) by reflecting on the implications of this claim for traditional views of the objectivity of scientific knowledge‐claims. (shrink)

Wendy S. Parker and I have advanced an inductive-risk approach to the provision of climate information that relies on the contextual values of information users. This approach aims to improve the e...

"--Richard Grandy, Rice University "This is the first compelling diagnosis of what has gone awry in the raging 'science wars.

Several argue that truth cannot be science’s sole epistemic goal, for it would fail to do justice to several scientific practices that advance understanding. I challenge these arguments, but only after making a small concession: science’s sole epistemic goal is not truth as such; rather, its goal is finding true answers to relevant questions. Using examples from the natural and social sciences, I then show that scientific understanding’s epistemically valuable features are either true answers to relevant questions or a means (...) thereof. (shrink)

While it is widely acknowledged that science is not “free” of non-epistemic values, there is disagreement about the roles that values can appropriately play. Several have argued that non-epistemic values can play important roles in modeling decisions, particularly in addressing uncertainties ; Risbey 2007; Biddle and Winsberg 2010; Winsberg : 111-137, 2012); van der Sluijs 359-389, 2012). On the other hand, such values can lead to bias ; Bray ; Oreskes and Conway 2010). Thus, it is important to identify when (...) it is legitimate to appeal to non-epistemic values in modeling decisions. An approach is defended here whereby such value judgments are legitimate when they promote democratically endorsed epistemological and social aims of research. This framework accounts for why it is legitimate to appeal to non-epistemic values in a range of modeling decisions, while addressing concerns that the presence of such values will lead to bias or give scientists disproportionate power in deciding what values ought to be endorsed. (shrink)

In recent years, the value-freeness of science has come under extensive critique. Early objectors to the notion of value-free science can be found in Rudner and Churchman, later objections occur in Leach and Gaa, and more recent critics are Kitcher, Douglas, and Elliott. The goal of this paper is to examine and critique two arguments opposed to the notion of a value-free science. The first argument, the uncertainty argument, cites the endemic uncertainty of science and concludes that values are needed (...) to give direction to scientific investigation. The second, or moral argument, cites the fact that scientists have moral obligations just like everyone else, and.. (shrink)

The controversy over the old ideal of “value-free science” has cooled significantly over the past decade. Many philosophers of science now agree that even ethical and political values may play a substantial role in all aspects of scientific inquiry. Consequently, in the last few years, work in science and values has become more specific: Which values may influence science, and in which ways? Or, how do we distinguish illegitimate from illegitimate kinds of influence? In this paper, I argue that this (...) problem requires philosophers of science to take a new direction. I present two case studies in the influence of values on scientific inquiry: feminist values in archaeology and commercial values in pharmaceutical research. I offer a preliminary assessment of these cases, that the influence of values was legitimate in the feminist case, but not in the pharmaceutical case. I then turn to three major approaches to distinguish legitimate from illegitimate influences of values, including the distinction between epistemic and non-epistemic values and Heather Douglas’ distinction between direct and indirect roles for values. I argue that none of these three approaches gives an adequate analysis of the two cases. In the concluding section, I briefly sketch my own approach, which draws more heavily on ethics than the others, and is more promising as a solution to the current problem. This is the new direction in which I think science and values should move. (shrink)

Recent efforts to argue that nonepistemic values have a legitimate role to play in assessing scientific models, theories, and hypotheses typically either reject the distinction between epistemic and nonepistemic values or incorporate nonepistemic values only as a secondary consideration for resolving epistemic uncertainty. Given that scientific representations can legitimately be evaluated not only based on their fit with the world but also with respect to their fit with the needs of their users, we show in two case studies that nonepistemic (...) values can play a legitimate role as factors that override epistemic considerations in assessing scientific representations for practical purposes. (shrink)

In recent papers and a book, Heather Douglas has expanded on the well-known argument from inductive risk, thereby launching an influential contemporary critique of the value-free ideal for science. This paper distills Douglas’s critique into four major claims. The first three claims provide a significant challenge to the value-free ideal for science. However, the fourth claim, which delineates her positive proposal to regulate values in science by distinguishing direct and indirect roles for values, is ambiguous between two interpretations, and both (...) have weaknesses. Fortunately, two elements of Douglas’s work that have previously received much less emphasis provide resources for developing a more promising approach for regulating values in science. (shrink)

Traditionally, cognitive values have been thought of as a collective pool of considerations in science that frequently trade against each other. I argue here that a finer-grained account of the value of cognitive values can help reduce such tensions. I separate the values into groups, minimal epistemic criteria, pragmatic considerations, and genuine epistemic assurance, based in part on the distinction between values that describe theories per se and values that describe theory-evidence relationships. This allows us to clarify why these values (...) are central to science and what role they should play, while reducing the tensions among them. (shrink)

Although epistemic values have become widely accepted as part of scientific reasoning, non-epistemic values have been largely relegated to the "external" parts of science (the selection of hypotheses, restrictions on methodologies, and the use of scientific technologies). I argue that because of inductive risk, or the risk of error, non-epistemic values are required in science wherever non-epistemic consequences of error should be considered. I use examples from dioxin studies to illustrate how non-epistemic consequences of error can and should be considered (...) in the internal stages of science: choice of methodology, characterization of data, and interpretation of results. (shrink)

This book brings together eleven case studies of inductive risk-the chance that scientific inference is incorrect-that range over a wide variety of scientific contexts and fields. The chapters are designed to illustrate the pervasiveness of inductive risk, assist scientists and policymakers in responding to it, and productively move theoretical discussions of the topic forward.

one takes to be the most salient, any pair could be judged more similar to each other than to the third. Goodman uses this second problem to showthat there can be no context-free similarity metric, either in the trivial case or in a scientifically ...

Douglas proposes a new ideal in which values serve an essential function throughout scientific inquiry, but where the role values play is constrained at key points, protecting the integrity and objectivity of science.

Models as Mediators discusses the ways in which models function in modern science, particularly in the fields of physics and economics. Models play a variety of roles in the sciences: they are used in the development, exploration and application of theories and in measurement methods. They also provide instruments for using scientific concepts and principles to intervene in the world. The editors provide a framework which covers the construction and function of scientific models, and explore the ways in which they (...) enable us to learn about both theories and the world. The contributors to the volume offer their own individual theoretical perspectives to cover a wide range of examples of modelling, from physics, economics and chemistry. These papers provide ideal case-study material for understanding both the concepts and typical elements of modelling, using analytical approaches from the philosophy and history of science. (shrink)

A feminist primer for philosophers of science -- The legacy of twentieth century philosophy of science -- What feminist science studies can offer -- Challenges from every direction -- The prospects of twenty-first century philosophy of science.