This essay compares two philosophical proposals concerning the relation between values and science, both of which reject the value-free ideal but nevertheless place restrictions on how values and science should interact. The first of these proposals relies on a distinction between the direct and indirect roles of values, while the second emphasizes instead a distinction between epistemic and nonepistemic values. We consider these two proposals in connection with a case study of disputed research on the topic of environmental justice and (...) argue that the second proposal has several advantages over the first. (shrink)

Over the last several years, there has been an explosion of interest and attention devoted to the problem of Uncertainty Quantification (UQ) in climate science—that is, to giving quantitative estimates of the degree of uncertainty associated with the predictions of global and regional climate models. The technical challenges associated with this project are formidable, and so the statistical community has understandably devoted itself primarily to overcoming them. But even as these technical challenges are being met, a number of persistent conceptual (...) difficulties remain. So why is UQ so important in climate science? UQ, I would like to argue, is first and foremost a tool for communicating knowledge from experts to policy makers in a way that is meant to be free from the influence of social and ethical values. But the standard ways of using probabilities to separate ethical and social values from scientific practice cannot be applied in a great deal of climate modeling, because the roles of values in creating the models cannot be discerned after the fact—the models are too complex and the result of too much distributed epistemic labor. I argue, therefore, that typical approaches for handling ethical/social values in science do not work well here. (shrink)

Philosophers of science increasingly recognize the importance of idealization: the intentional introduction of distortion into scientific theories. Yet this recognition has not yielded consensus about the nature of idealization. e literature of the past thirty years contains disparate characterizations and justifications, but little evidence of convergence towards a common position.

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.

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)

1. Deductive and Inductive Inference. Within the traditional treatments of scientific method, e.g., in and, it was customary to divide scientific inference into two parts: deductive and inductive. Deductive inference was taken to mean the activity of deducing theorems from postulates and definitions, whereas inductive inference represented the activity of constructing a general statement from a set of particular “facts.” Deductive inference was relegated to the mathematical sciences, and inductive inference to the empirical sciences. As a consequence, the whole of (...) science was split into two quite distinct parts: deductive science was conceived to start with precise axioms, postulates, and definitions, and its method consisted in drawing inferences from these “givens” in such a way that if the original assumptions are taken as true, then the theorems must be taken as true also. The obligation behind this “must” of the mathematical sciences was an obligation based on strict rules of deductive inference; in the earlier analyses, it was supposed that these rules were all aspects of the general theory of the syllogism, as it was described in an almost complete form in but in later work the rules have been formalized in more general terms. Inductive inference, on the other hand, was supposed to start with observed facts; these facts, or “data,” were presumably given by the senses, and described particular aspects of the external world. If the facts were given in a certain way, then the empirical scientist could “infer” a generalized description; this generalization had the characteristic that if it were granted as true, then all the facts could be deduced as consequences. The inductive process admittedly does not lead to unique generalizations, and the philosopher of science searched for other defining characteristics of good inference, such as “simplicity” and “convenience.” These empirical generalizations were taken to be “descriptions of the phenomenal world of the scientist”; a further inductive step might be taken by constructing a set of very general principles out of the more specialized generalizations within specific research problems. These “higher” inductive inferences were regarded as “theories,” which could be studied by themselves within the method of deductive inference. The scientist was then described as “checking” theory by determining whether or not the predicted consequences deduced from theory actually hold in the phenomenal world. (shrink)

I examine whether or not it is apt to attribute knowledge to groups of scientists. I argue that though research teams can be aptly described as having knowledge, communities of scientists identified with research fields, and the scientific community as a whole are not capable of knowing. Scientists involved in research teams are dependent on each other, and are organized in a manner to advance a goal. Such teams also adopt views that may not be identical to the views of (...) the individual members of the group. (shrink)

When interests and preferences of researchers or their sponsors cause bias in experimental design, data interpretation or dissemination of research results, we normally think of it as an epistemic shortcoming. But as a result of the debate on science and values, the idea that all extra-scientific influences on research could be singled out and separated from pure science is now widely believed to be an illusion. I argue that nonetheless, there are cases in which research is rightfully regarded as epistemologically (...) deficient due to the influence of preferences on its outcomes. I present examples from biomedical research and offer an analysis in terms of social epistemology. (shrink)

Underdetermination arguments support the conclusion that no amount of empirical data can uniquely determine theory choice. The full content of a theory outreaches those elements of it (the observational elements) that can be shown to be true (or in agreement with actual observations).2 A number of strategies have been developed to minimize the threat such arguments pose to our aspirations to scientific knowledge. I want to focus on one such strategy: the invocation of additional criteria drawn from a pool of (...) cognitive or theoretical values, such as simplicity or generality, to bolster judgements about the worth of models, theories, and hypotheses. What is the status of such criteria? Larry Laudan, in Science and Values, argued that cognitive values could not be treated as self-validating, beyond justification, but are embedded in a three-way reticulational system containing theories, methods, and aims or values, which are involved in mutually supportive relationships (Laudan, 1984). My interest in this paper is not the purportedly self-validating nature of cognitive values, but their cognitive nature. Although Laudan rejects the idea that what he calls cognitive values are exempt from rational criticism and disagreement, he does seem to think that the reticulational system he identifies is independent of non-cognitive considerations. It is this cognitive/non-cognitive distinction that I wish to query in this paper. Let me begin by summarizing those of my own views about inquiry in which this worry about the distinction arises. (shrink)

Richard Rudner famously argues that the communication of scientific advice to policy makers involves ethical value judgments. His argument has, however, been rightly criticized. This article revives Rudner’s conclusion, by strengthening both his lines of argument: we generalize his initial assumption regarding the form in which scientists must communicate their results and complete his ‘backup’ argument by appealing to the difference between private and public decisions. Our conclusion that science advisors must, for deep-seated pragmatic reasons, make value judgments is further (...) bolstered by reflections on how the scientific contribution to policy is far less straightforward than the Rudner-style model suggests. (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)

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)

Weighing complex sets of evidence (i.e., from multiple disciplines and often divergent in implications) is increasingly central to properly informed decision-making. Determining “where the weight of evidence lies” is essential both for making maximal use of available evidence and figuring out what to make of such evidence. Weighing evidence in this sense requires an approach that can handle a wide range of evidential sources (completeness), that can combine the evidence with rigor, and that can do so in a way other (...) experts can assess and critique (transparency). But the democratic context in need of weight-of-evidence analysis also places additional constraints on the process, including communicability of the process to the general public, the need for an approach that can be used across a broad range of contexts (scope), and timeliness of process (practicality). I will compare qualitative and quantitative approaches with respect to both traditional epistemic criteria and criteria that arise from the democratic context, and argue that a qualitative explanatory approach can best meet the criteria and elucidate how to utilize the other approaches. This should not be surprising, as the approach I argue for is the one that most closely tracks general scientific reasoning. (shrink)

The debate about the rational and the social in science has sometimes been developed in the context of a distinction between epistemic and non-epistemic values. Paying particular attention to two important discussion in the last decade, by Longino and by McMullin, I argue that a fuller understanding of values in science ultimately requires abandoning the distinction itself. This is argued directly in terms of an analysis of the lack of clarity concerning what epistemic values are. I also argue that the (...) philosophical import of much of the feminist work in philosophy of science is restricted by any kind of strict adherence to the distinction. (shrink)

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.

I contend that Janet Kourany's "A Philosophy of Science for the Twenty-First Century" contains three levels of projects: (1) a naturalistic project, (2) a critical project, and (3) a political project. The naturalistic project is already well established. The critical project is less valued and less established within the profession, but seems a worthy and achievable goal. The political project, I argue, takes one outside the professional pursuit of the philosophy of science. The critical project encompasses both the evaluation of (...) scientific research programs and of empirical conclusions. I contend that the former is widely acknowledged as legitimate while the latter is unacceptable. (shrink)

Although many philosophers have employed the distinction between “direct” and “indirect” roles for values in science, I argue that it merits further clarification. The distinction can be formulated in several ways: as a logical point, as a distinction between epistemic attitudes, or as a clarification of different consequences associated with accepting scientific claims. Moreover, it can serve either as part of a normative ideal or as a tool for policing how values influence science. While various formulations of the distinction may (...) contribute to a normative ideal, they have limited effectiveness for regulating how values influence science. (shrink)

We argue that the analysis of cognitive attitudes should play a central role in developing more sophisticated accounts of the proper roles for values in science. First, we show that the major recent efforts to delineate appropriate roles for values in science would be strengthened by making clearer distinctions among cognitive attitudes. Next, we turn to a specific example and argue that a more careful account of the distinction between the attitudes of belief and acceptance can contribute to a better (...) understanding of the proper roles for values in a case study from paleoanthropology. (shrink)

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)

When communicating scientific information, experts often face difficult choices about how to promote public understanding while also maintaining an appropriate level of objectivity. We argue that one way for scientists and others involved in communicating scientific information to alleviate these tensions is to pay closer attention to the major frames employed in the contexts in which they work. By doing so, they can ideally employ useful frames while also enabling the recipients of information to “backtrack” to relatively uncontroversial facts and (...) recognize how these frames relate to their own values and perspectives. Important strategies for promoting this sort of backtracking include identifying the weaknesses of particular frames, preventing misunderstanding of them, differentiating well-supported findings from more speculative claims, and acknowledging major alternative frames. (shrink)