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)

Climate policy decisions are decisions under uncertainty and are, therefore, based on a range of future climate scenarios, describing possible consequences of alternative policies. Accordingly, the methodology for setting up such a scenario range becomes pivotal in climate policy advice. The preferred methodology of the Intergovernmental Panel on Climate Change will be characterised as ,,modal verificationism"; it suffers from severe shortcomings which disqualify it for scientific policy advice. Modal falsificationism, as a more sound alternative, would radically alter the way the (...) climate scenario range is set up. Climate science's inability to find robust upper bounds for future temperature rise in line with modal falsificationism does not disprove that methodology, rather, this very fact prescribes even more drastic efforts to curb CO2 emissions than currently proposed. (shrink)

Frank Knight (1921) famously distinguished the epistemic modes of certainty, risk, and uncertainty in order to characterize situations where deterministic, probabilistic or possibilistic foreknowledge is available. Because our probabilistic knowledge is limited, i.e. because many systems, e.g. the global climate, cannot be described and predicted probabilistically in a reliable way, Knight's third category, possibilistic foreknowledge, is not simply swept by the probabilistic mode. This raises the question how to justify possibilistic predictionsincluding the identication of the worst case. The development of (...) such a modal methodology is particularly vital with respect to predictions of climate change. I show that a methodological dilemma emerges when possibilistic predictions are framed in traditional terms and argue that a more nuanced conceptual framework, distinguishing dierent types of possibility, should be used in order to convey our uncertain knowledge about the future. The new conceptual scheme, however, questions the applicability of standard rules of rational decision-making, thus generating new challenges. (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)

Climate models don’t give us probabilistic forecasts. To interpret their results, alternatively, as serious possibilities seems problematic inasmuch as climate models rely on contrary-to-fact assumptions: why should we consider their implications as possible if their assumptions are known to be false? The paper explores a way to address this possibilistic challenge. It introduces the concepts of a perfect and of an imperfect credible world, and discusses whether climate models can be interpreted as imperfect credible worlds. That would allow one to (...) use models for possibilistic prediction and salvage widespread scientific practice. (shrink)

Introduction -- Sanctioning models : theories and their scope -- Methodology for a virtual world -- A tale of two methods -- When theories shake hands -- Models of climate : values and uncertainties -- Reliability without truth -- Conclusion.

A scientific community cannot practice its trade without some set of received beliefs. These beliefs form the foundation of the "educational initiation that prepares and licenses the student for professional practice". The nature of the "rigorous and rigid" preparation helps ensure that the received beliefs are firmly fixed in the student's mind. Scientists take great pains to defend the assumption that scientists know what the world is like...To this end, "normal science" will often suppress novelties which undermine its foundations. Research (...) is therefore not about discovering the unknown, but rather "a strenuous and devoted attempt to force nature into the conceptual boxes supplied by professional education". (shrink)

Thomas S. Kuhn's classic book is now available with a new index.

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)

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)

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)

This essay examines the relationship between the precautionary principle and uncertainty factors used by toxicologists to estimate acceptable exposure levels for toxic chemicals from animal experiments. It shows that the adoption of uncertainty factors in the United States in the 1950s can be understood by reference to the precautionary principle, but not by cost-benefit analysis because of a lack of relevant quantitative data at that time. In addition, it argues that uncertainty factors continue to be relevant to efforts to implement (...) the precautionary principle and that the precautionary principle should not be restricted to cases involving unquantifiable hazards. (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)

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)

The argument from inductive risk attempts to show that practical and ethical costs of errors should influence standards of evidence for accepting scientific claims. A common objection charges that this argument presupposes a behavioral theory of acceptance that is inappropriate for science. I respond by showing that the argument from inductive risk is supported by a nonbehavioral theory of acceptance developed by Cohen, which defines acceptance in terms of premising. Moreover, I argue that theories designed to explain how acceptance can (...) be guided exclusively by epistemic values suffer from difficulties that do not afflict Cohen’s theory. (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.

Today’s most sophisticated simulation studies of future climate employ not just one climate model but a number of models. I explain why this “ensemble” approach has been adopted—namely, as a means of taking account of uncertainty—and why a comprehensive investigation of uncertainty remains elusive. I then defend a middle ground between two camps in an ongoing debate over the transformation of ensemble results into probabilistic predictions of climate change, highlighting requirements that I refer to as ownership, justification, and robustness.

Philosophers continue to debate both the actual and the ideal roles of values in science. Recently, Eric Winsberg has offered a novel, model-based challenge to those who argue that the internal workings of science can and should be kept free from the influence of social values. He contends that model-based assignments of probability to hypotheses about future climate change are unavoidably influenced by social values. I raise two objections to Winsberg’s argument, neither of which can wholly undermine its conclusion but (...) each of which suggests that his argument exaggerates the influence of social values on estimates of uncertainty in climate prediction. I then show how a more traditional challenge to the value-free ideal seems tailor-made for the climate context. (shrink)

Simulation-based weather and climate prediction now involves the use of methods that reflect a deep concern with uncertainty. These methods, known as ensemble prediction methods, produce multiple simulations for predictive periods of interest, using different initial conditions, parameter values and/or model structures. This paper provides a non-technical overview of current ensemble methods and considers how the results of studies employing these methods should be interpreted, paying special attention to probabilistic interpretations. A key conclusion is that, while complicated inductive arguments might (...) be given for the trustworthiness of probabilistic weather forecasts obtained from ensemble studies, analogous arguments are out of reach in the case of long-term climate prediction. In light of this, the paper considers how predictive uncertainty should be conveyed to decision makers. (shrink)

In this paper I argue for a distinction between subjective and value laden aspects of judgements showing why equating the former with the latter has the potential to confuse matters when the goal is uncovering the influence of political influences on scientific practice. I will focus on three separate but interrelated issues. The first concerns the issue of ‘verification’ in computational modelling. This is a practice that involves a number of formal techniques but as I show, even these allegedly objective (...) methods ultimately rely on subjective estimation and evaluation of different types of parameters. This has implications for my second point which relates to uncertainty quantification—an assessment of the degree of uncertainty present in a particular modelling scenario. I argue that while this practice also involves subjective elements, in no way does that detract from its status as an epistemic exercise. Finally I discuss the relation between accuracy and uncertainty and how each relates to judgements that embody social/ethical/political concerns, in particular those associated with high consequence systems. (shrink)

In part one, I identify the core logical structure of the precautionary principle and distinguish it from the various key concepts that appear in the many different formulations of the principle. I survey these concepts and suggest a program of further conceptual analysis. In part two, I examine a particular version of the precautionary principle dubbed “the catastrophe principle” and criticize it in light of its similarities to the principle at work in Pascal’s Wager. I conclude with some suggestions for (...) advocates of the precautionary principle who wish their formulation to avoid the pitfalls confronting the catastrophe principle. (shrink)

What is the relationship between fear, danger, and the law? Cass Sunstein attacks the increasingly influential Precautionary Principle - the idea that regulators should take steps to protect against potential harms, even if causal chains are uncertain and even if we do not know that harms are likely to come to fruition. Focusing on such problems as global warming, terrorism, DDT, and genetic engineering, Professor Sunstein argues that the Precautionary Principle is incoherent. Risks exist on all sides of social situations, (...) and precautionary steps create dangers of their own. Diverse cultures focus on very different risks, often because social influences and peer pressures accentuate some fears and reduce others. Instead of adopting the Precautionary Principle, Professor Sunstein argues for three steps: a narrow Anti-Catastrophe Principle, designed for the most serious risks; close attention to costs and benefits; and an approach called 'libertarian paternalism', designed to respect freedom of choice while also moving people in directions that will make their lives go better. He also shows how free societies can protect liberty amidst fears about terrorism and national security. Laws of Fear represents a major statement from one of the most influential political and legal theorists writing today. (shrink)

This major work challenges some widely held positions in epistemology - those of Peirce and Popper on the one hand and those of Quine and Kuhn on the other.

Striving to boldly redirect the philosophy of science, this book by renowned philosopher Philip Kitcher examines the heated debate surrounding the role of science in shaping our lives. Kitcher explores the sharp divide between those who believe that the pursuit of scientific knowledge is always valuable and necessary--the purists--and those who believe that it invariably serves the interests of people in positions of power. In a daring turn, he rejects both perspectives, working out a more realistic image of the sciences--one (...) that allows for the possibility of scientific truth, but nonetheless permits social consensus to determine which avenues to investigate. He then proposes a democratic and deliberative framework for responsible scientists to follow. Controversial, powerful, yet engaging, this volume will appeal to a wide range of readers. Kitcher's nuanced analysis and authorititative conclusion will interest countless scientists as well as all readers of science--scholars and laypersons alike. (shrink)

In a recent paper, Gregor Betz has defended the value-free ideal: “the justification of scientific findings should not be based on non-epistemic values”against the methodological critique, by reference to the work of the International Panel on Climate Change . This paper argues that Betz’s defence is unsuccessful. First, Betz’s argument is sketched, and it is shown that the IPCC does not avoid the need to “translate” claims. In Section 2, it is argued that Betz mischaracterises the force of the methodological (...) critique. Section 3 shows why the methodological critique still applies to the work of the IPCC even on a refined version of Betz’s argument. Section 4 then considers an alternative way of defending the work of the IPCC which is in-line with, but does not clearly vindicate, the value-free ideal. (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)

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)

The United Kingdom Climate Impacts Program’s UKCP09 project makes high-resolution forecasts of climate during the 21st century using state of the art global climate models. The aim of this paper is to introduce and analyze the methodology used and then urge some caution. Given the acknowledged systematic errors in all current climate models, treating model outputs as decision relevant probabilistic forecasts can be seriously misleading. This casts doubt on our ability, today, to make trustworthy, high-resolution predictions out to the end (...) of this century. (shrink)

The sensitive dependence on initial conditions (SDIC) associated with nonlinear models imposes limitations on the models’ predictive power. We draw attention to an additional limitation than has been underappreciated, namely, structural model error (SME). A model has SME if the model dynamics differ from the dynamics in the target system. If a nonlinear model has only the slightest SME, then its ability to generate decision-relevant predictions is compromised. Given a perfect model, we can take the effects of SDIC into account (...) by substituting probabilistic predictions for point predictions. This route is foreclosed in the case of SME, which puts us in a worse epistemic situation than SDIC. (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)

The moral terrain of science, the full range of ethical considerations that are part of the scientific endeavor, has not been mapped. Without such a map, we cannot examine the responsibilities of scientists to see if the institutions of science are adequately constructed. This paper attempts such a map by describing four dimensions of the terrain: (1) the bases to which scientists are responsible (scientific reasoning, the scientific community, and the broader society); (2) the nature of the responsibility (general or (...) role); (3) the level of responsibility (minimum demand or ideal); and (4) who bears the responsibility (the individual or the community). Such a map will be used to elucidate the recent debate over the publication of studies concerning H5N1 flu virus. (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)

Scholars in philosophy, law, economics and other fields have widely debated how science, environmental precaution, and economic interests should be balanced in urgent contemporary problems, such as climate change. One controversial focus of these discussions is the precautionary principle, according to which scientific uncertainty should not be a reason for delay in the face of serious threats to the environment or health. While the precautionary principle has been very influential, no generally accepted definition of it exists and critics charge that (...) it is incoherent or hopelessly vague. This book presents and defends an interpretation of the precautionary principle from the perspective of philosophy of science, looking particularly at how it connects to decisions, scientific procedures, and evidence. Through careful analysis of numerous case studies, it shows how this interpretation leads to important insights on scientific uncertainty, intergenerational justice, and the relationship between values and policy-relevant science. (shrink)

What Will Work makes a rigorous and compelling case that energy efficiencies and renewable energy-and not nuclear fission or "clean coal"-are the most effective, cheapest, and equitable solutions to the pressing problem of climate change.

Claims that science should be more democratic than it is frequently arouse opposition. In this essay, I distinguish my own views about the democratization of science from the more ambitious theses defended by Paul Feyerabend. I argue that it is unlikely that the complexity of some scientific debates will allow for resolution according to the methodological principles of any formal confirmation theory, suggesting instead that major revolutions rest on conflicts of values. Yet these conflicts should not be dismissed as irresoluble.

Exploring the role of values in scientific inquiry, Hugh Lacey examines the nature and meaning of values, and looks at challenges to the view, posed by postmodernists, feminists, radical ecologists, Third-World advocates and religious fundamentalists, that science is value free. He also focuses on discussions of 'development', especially in Third World countries. This paperback edition includes a new preface.

Claims that science should be more democratic than it is frequently arouse opposition. In this essay, I distinguish my own views about the democratization of science from the more ambitious theses defended by Paul Feyerabend. I argue that it is unlikely that the complexity of some scientific debates will allow for resolution according to the methodological principles of any formal confirmation theory, suggesting instead that major revolutions rest on conflicts of values. Yet these conflicts should not be dismissed as irresoluble.

Only ten to twelve percent of Americans would voluntarily live within a mile of a nuclear plant or hazardous waste facility. But industry spokespersons claim that such risk aversion represents ignorance and paranoia, and they lament that citizen protests have delayed valuable projects and increased their costs. Who is right? In _Risk and Rationality_, Kristin Shrader-Frechette argues that neither charges of irresponsible endangerment nor countercharges of scientific illiteracy frame the issues properly. She examines the debate over methodological norms for risk (...) evaluation and finds analysts arrayed in a spectrum. Points of view extend from cultural relativists who believe that any risk can be justified to naive positivists who believe that risk evaluation can be objective, neutral, and value free. Both camps, she argues, are wrong, because risk evaluation as a social process is rational and objective, even though all risk-evaluation rules are value-laden. Shrader-Frechette defends a middle position called "scientific proceduralism." She shows why extremist views are unreliable, reveals misconceptions underlying current risk-evaluation methods and strategies, and sketches the reforms needed to set hazard assessment and risk evaluation on a publicly defensible foundation. These reforms involve mathematical, economic, ethical, and legal procedures. They constitute a new paradigm for assessment when acceptance of public hazards is rational, recognizing that laypersons are often more rational in their evaluation of societal risks than either experts or governments have acknowledged. Such reforms would provide citizens with more influence in risk decisions and focus on mediating ethical conflicts, rather than seeking to impose the will of experts. Science, she argues, need not preclude democracy. (shrink)