Inferences like these are known as extrapolations.

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)

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)

_Current Controversies in Values and Science_ asks ten philosophers to debate five questions that are driving contemporary work in this important area of philosophy of science. The book is perfect for the advanced student, building up her knowledge of the foundations of the field while also engaging its most cutting-edge questions. Introductions and annotated bibliographies for each debate, preliminary descriptions of each chapter, study questions, and a supplemental guide to further controversies involving values in science help provide clearer and richer (...) snapshots of active controversies for all readers._ _. (shrink)

Supervised injectable opioid assisted treament prescribes injectable opioids to individuals for whom other forms of addiction treatment have been ineffective. In this article, we examine arguments that opioid-dependent people should be assumed incompetent to voluntarily consent to clinical research on siOAT unless proven otherwise. We agree that concerns about competence and voluntary consent deserve careful attention in this context. But we oppose framing the issue solely as a matter of the competence of opioid-dependent people and emphasize that it should be (...) considered in the context of inequities in access to siOAT as a medical treatment. Consequently, we suggest that bioethics literature on nonexploitation, which focuses on clinical research in low-income countries, is helpful due to locating ethical issues within systemic social conditions. Finally, we consider the implications of our argument for the ethics of clinical research on siOAT. (shrink)

We suggest that philosophical accounts of epistemic effects of diversity have given insufficient attention to the relationship between demographic diversity and information elaboration, the process whereby knowledge dispersed in a group is elicited and examined. We propose an analysis of IE that clarifies hypotheses proposed in the empirical literature and their relationship to philosophical accounts of diversity effects. Philosophical accounts have largely overlooked the possibility that demographic diversity may improve group performance by enhancing IE, and sometimes fail to explore the (...) relationship between diversity and IE altogether. We claim these omissions are significant from both a practical and theoretical perspective. Moreover, we explain how the overlooked explanations suggest that epistemic benefits of diversity can depend on epistemically unjust social dynamics. (shrink)

The causal Markov condition (CMC) plays an important role in much recent work on the problem of causal inference from statistical data. It is commonly thought that the CMC is a more problematic assumption for genuinely indeterministic systems than for deterministic ones. In this essay, I critically examine this proposition. I show how the usual motivation for the CMC—that it is true of any acyclic, deterministic causal system in which the exogenous variables are independent—can be extended to the indeterministic case. (...) In light of this result, I consider several arguments for supposing indeterminism a particularly hostile environment for the CMC, but conclude that none are persuasive. Introduction Functional models and directed graphs The causal Markov theorem The causal Markov theorem and genuine indeterminism Are the exogenous variables independent? EPR Conclusion. (shrink)

Several authors have claimed that mechanisms play a vital role in distinguishing between causation and mere correlation in the social sciences. Such claims are sometimes interpreted to mean that without mechanisms, causal inference in social science is impossible. The author agrees with critics of this proposition but explains how the account of how mechanisms aid causal inference can be interpreted in a way that does not depend on it. Nevertheless, he shows that this more charitable version of the account is (...) still unsuccessful as it stands. Consequently, he advances a proposal for shoring up the account, which is founded on the possibility of acquiring knowledge of social mechanisms by linking together norms or practices found in a society. Key Words: causality • social mechanisms • interpretation • anthropology. (shrink)

This article examines the relevance of survey data of scientists’ attitudes about science and values to case studies in philosophy of science. We describe two methodological challenges confronting such case studies: 1) small samples, and 2) potential for bias in selection, emphasis, and interpretation. Examples are given to illustrate that these challenges can arise for case studies in the science and values literature. We propose that these challenges can be mitigated through an approach in which case studies and survey methods (...) are viewed as complementary, and use data from the Toolbox Dialogue Initiative to illustrate this claim. (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)

Pluralism is often put forth as a counter-position to reductionism. In this essay, I argue that reductionism and pluralism are in fact consistent. I propose that there are several potential goals for reductions and that the proper form of a reduction should be considered in tandem with the goal that it aims to achieve. This insight provides a basis for clarifying what version of reductionism are currently defended, for explicating the notion of a fundamental level of explanation, and for showing (...) how one can be both a reductionist and a pluralist. (shrink)

The likelihood principle (LP) is a core issue in disagreements between Bayesian and frequentist statistical theories. Yet statements of the LP are often ambiguous, while arguments for why a Bayesian must accept it rely upon unexamined implicit premises. I distinguish two propositions associated with the LP, which I label LP1 and LP2. I maintain that there is a compelling Bayesian argument for LP1, based upon strict conditionalization, standard Bayesian decision theory, and a proposition I call the practical relevance principle. In (...) contrast, I argue that there is no similarly compelling argument for or against LP2. I suggest that these conclusions lead to a restrictedly pluralistic view of Bayesian confirmation measures. (shrink)

In order to make scientific results relevant to practical decision making, it is often necessary to transfer a result obtained in one set of circumstances—an animal model, a computer simulation, an economic experiment—to another that may differ in relevant respects—for example, to humans, the global climate, or an auction. Such inferences, which we can call extrapolations, are a type of argument by analogy. This essay sketches a new approach to analogical inference that utilizes chain graphs, which resemble directed acyclic graphs (...) (DAGs) except in allowing that nodes may be connected by lines as well as arrows. This chain graph approach generalizes the account of extrapolation I provided in my (2008) book and leads to new insights that integrate the contributions of the other participants of this symposium. More specifically, this approach explicates the role of “fingerprints,” or distinctive markers, as a strategy for avoiding an underdetermination problem having to do with spurious analogies. Moreover, it shows how the extrapolator’s circle, one of the central challenges for extrapolation highlighted in my book, is closely tied to distinctive markers and the Markov condition as it applies to chain graphs. Finally, the approach suggests additional ways in which investigations of a model can provide information about a target that are illustrated by examples concerning nanomaterials in sunscreens and Wendy Parker’s discussion of fingerprints in climate science. (shrink)

This article critically examines a recent philosophical debate on the role of values in climate change forecasts, such as those found in assessment reports of the Intergovernmental Panel on Climate Change. On one side, several philosophers insist that the argument from inductive risk, as developed by Rudner and Douglas among others, applies to this case. AIR aims to show that ethical value judgments should influence decisions about what is sufficient evidence for accepting scientific hypotheses that have implications for policy issues. (...) Advocates of extending AIR to climate science claim that values are deeply... (shrink)

The whole drug industry campaign for mood drugs in the 1950s was to broaden to absurd limits the definition of illness.... If the facts in these ads were not untruths, then their implications often were.1Sponsorship bias occurs when a funder of scientific research has a vested interest in what claims the research supports, which consequently shapes the research or the reporting of its results to align with that interest. This article examines the relationship between sponsorship bias and misleading claims, understood (...) as claims that are not necessarily false but which encourage those exposed to them to infer false conclusions. Such claims are important insofar as being common in examples of alleged sponsorship... (shrink)

A concept of diversity is an understanding of what makes a group diverse that may be applicable in a variety of contexts. We distinguish three diversity concepts, show that each can be found in discussions of diversity in science, and explain how they tend to be associated with distinct epistemic and ethical rationales. Yet philosophical literature on diversity among scientists has given little attention to distinct concepts of diversity. This is significant because the unappreciated existence of multiple diversity concepts can (...) generate unclarity about the meaning of “diversity,” lead to problematic inferences from empirical research, and obscure complex ethical-epistemic questions about how to define diversity in specific cases. We illustrate some ethical-epistemic implications of our proposal by reference to an example of deliberative mini-publics on human tissue biobanking. (shrink)

Woodward present an argument for the Causal Markov Condition (CMC) on the basis of a principle they dub ‘modularity’ ([1999, 2004]). I show that the conclusion of their argument is not in fact the CMC but a substantially weaker proposition. In addition, I show that their argument is invalid and trace this invalidity to two features of modularity, namely, that it is stated in terms of pairwise independence and ‘arrow-breaking’ interventions. Hausman & Woodward's argument can be rendered valid through a (...) reformulation of modularity, but it is doubtful that the argument so revised provides any substantially new insight regarding the basis of the CMC. Introduction The CMC versus Hausman & Woodward's conclusion Hausman & Woodward's argument Modularity and independent error terms Conclusion Appendix: D-separation. (shrink)

This article examines methodological individualism in terms of the theory that invariance under intervention is the signal feature of generalizations that serve as a basis for causal explanation. This theory supports the holist contention that macro-level generalizations can explain, but it also suggests a defense of methodological individualism on the grounds that greater range of invariance under intervention entails deeper explanation. Although this individualist position is not threatened by multiple-realizability, an argument for it based on rational choice theory is called (...) into question by experimental results concerning preference reversals. Key Words: methodological individualism • mechanisms • explanation • invariance • preference reversal. (shrink)

Previous simulation models have found positive effects of cognitive diversity on group performance, but have not explored effects of diversity in demographics (e.g., gender, ethnicity). In this paper, we present an agent-based model that captures two empirically supported hypotheses about how demographic diversity can improve group performance. The results of our simulations suggest that, even when social identities are not associated with distinctive task-related cognitive resources, demographic diversity can, in certain circumstances, benefit collective performance by counteracting two types of conformity (...) that can arise in homogeneous groups: those relating to group-based trust and those connected to normative expectations towards in-groups. (shrink)

Many philosophers have challenged the ideal of value-free science on the grounds that social or moral values are relevant to inferential thresholds. But given this view, how precisely and to what extent should scientists adjust their inferential thresholds in light of nonepistemic values? We suggest that signal detection theory provides a useful framework for addressing this question. Moreover, this approach opens up further avenues for philosophical inquiry and has important implications for philosophical debates concerning inductive risk. For example, the signal (...) detection theory framework entails that considerations of inductive risk and inferential-threshold placement cannot be conducted in isolation from base-rate information. (shrink)

This article examines the concept of wishful thinking in philosophical literature on science and values. It suggests that this term tends to be used in an overly broad manner that fails to distinguish between separate types of bias, mechanisms that generate biases, and general theories that might explain those mechanisms. I explain how confirmation bias is distinct from wishful thinking and why it is more useful for examining the relationship between cognitive bias and beliefs about the existence of injustices.

The dilemma objection charges that ?weak? versions of the precautionary principle (PP) are vacuous while ?strong? ones are incoherent. I respond that the ?weak? versus ?strong? distinction is misleading and should be replaced with a contrast between PP as a meta-rule and PP proper. Meta versions of PP require that the decision-making procedures used for environmental policy not be susceptible to paralysis by scientific uncertainty. Such claims are substantive because they often recommend against basing environmental policy decisions on cost?benefit analysis. (...) I argue that the second horn of the dilemma fails as a result of disregarding the role of proportionality in applications of PP. (shrink)

This article argues that a successful answer to Hume's problem of induction can be developed from a sub-genre of philosophy of science known as formal learning theory. One of the central concepts of formal learning theory is logical reliability: roughly, a method is logically reliable when it is assured of eventually settling on the truth for every sequence of data that is possible given what we know. I show that the principle of induction (PI) is necessary and sufficient for logical (...) reliability in what I call simple enumerative induction. This answer to Hume's problem rests on interpreting PI as a normative claim justified by a non-empirical epistemic means-ends argument. In such an argument, a rule of inference is shown by mathematical or logical proof to promote a specified epistemic end. Since the proof concerning PI and logical reliability is not based on inductive reasoning, this argument avoids the circularity that Hume argued was inherent in any attempt to justify PI. (shrink)

A small but growing body of philosophically informed survey work calls into question whether the value-free ideal is a dominant viewpoint among scientists. However, the survey instruments in used in these studies have important limitations. Previous work has also made little headway in developing hypotheses that might predict or explain differing views about the value-free ideal among scientists. In this article, we review previous survey work on this topic, describe an improved survey instrument, report results from an initial administration of (...) it that strengthen and refine previous results, and develop two hypotheses that may account for gender effects found in the data. (shrink)

Disputes between advocates of Bayesians and more orthodox approaches to statistical inference presuppose that Bayesians must regard must regard stopping rules, which play an important role in orthodox statistical methods, as evidentially irrelevant.In this essay, I show that this is not the case and that the stopping rule is evidentially relevant given some Bayesian confirmation measures that have been seriously proposed. However, I show that accepting a confirmation measure of this sort comes at the cost of rejecting two useful ancillaryBayesian (...) principles. (shrink)

The faithfulness condition (FC) is a useful principle for inferring causal structure from statistical data. The usual motivation for the FC appeals to theorems showing that exceptions to it have probability zero, provided that some apparently reasonable assumptions obtain. However, some have objected that, the theorems notwithstanding, exceptions to the FC are probable in commonly occurring circumstances. I argue that exceptions to the FC are probable in the circumstances specified by this objection only given the presence of a condition that (...) I label homogeneity, and furthermore that this condition typically does not obtain in the FC’s intended domain of application. (shrink)

Research integrating the perspectives of different disciplines, or interdisciplinary research, has become increasingly common in academia and is considered important for its ability to address complex questions and problems. This mode of research aims to leverage differences among disciplines in generating a more complex understanding of the research landscape. To interact successfully with other disciplines, researchers must appreciate their differences, and this requires recognizing how the research landscape looks from the perspective of other disciplines. One central aspect of these disciplinary (...) perspectives involves values, and more specifically, the roles that values do, may, and should play in research practice. It is reasonable to think that disciplines differ in part because of the different views that their practitioners have on these roles. This paper represents a step in the direction of evaluating this thought. Operating at the level of academic branches, which comprise relevantly similar disciplines (e.g. social and behavioral sciences), this paper uses quantitative techniques to investigate whether academic branches differ in terms of views on the impact of values on research. Somewhat surprisingly, we find very little relation between differences in these views and differences in academic branch. We discuss these findings from a philosophical perspective to conclude the paper. (shrink)

In a recent article, Elliot Sober responds to challenges to a counter-example that he posed some years earlier to the Principle of the Common Cause (PCC). I agree that Sober has indeed produced a genuine counter-example to the PCC, but argue against the methodological moral that Sober wishes to draw from it. Contrary to Sober, I argue that the possibility of exceptions to the PCC does not undermine its status as a central assumption for methods that endeavor to draw causal (...) conclusions from statistical data. 1 The PCC and the counter-example 2 Making non-stationary time series stand still 3 Sober's alternative. (shrink)

I develop a critique of Hume’s infamous problem of induction based upon the idea that the principle of induction (PI) is a normative rather than descriptive claim. I argue that Hume’s problem is a false dilemma, since the PI might be neither a “relation of ideas” nor a “matter of fact” but rather what I call a contingent normative statement. In this case, the PI could be justified by a means-ends argument in which the link between means and end is (...) established solely by deductive reasoning. The means-ends argument is an elementary result from formal learning theory that you must be willing to make inductive generalizations if you want to be logically reliable in the types of examples Hume described. This justification of the PI avoids both horns of Hume’s dilemma. Since no contradiction ensues from rejecting logical reliability as an aim, the PI is contingent. Yet since the proof concerning the PI and logical reliability is not based on inductive reasoning, there is no threat of circularity. (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)

Nelson Goodman’s new riddle of induction forcefully illustrates a challenge that must be confronted by any adequate theory of inductive inference: provide some basis for choosing among alternative hypotheses that fit past data but make divergent predictions. One response to this challenge is to distinguish among alternatives by means of some epistemically significant characteristic beyond fit with the data. Statistical learning theory takes this approach by showing how a concept similar to Popper’s notion of degrees of testability is linked to (...) minimizing expected predictive error. In contrast, formal learning theory appeals to Ockham’s razor, which it justifies by reference to the goal of enhancing efficient convergence to the truth. In this essay, I show that, despite their differences, statistical and formal learning theory yield precisely the same result for a class of inductive problems that I call strongly VC ordered , of which Goodman’s riddle is just one example. (shrink)

This reply to Erik Weber's commentary agrees that mechanisms are important for causal inference in social science, but argues that Weber makes the mistake that was the main focus of my original essay: inferring that since a problem cannot be solved without mechanisms, it can be solved with them. As it stands, this inference is invalid since the problem might be unsolvable with or without mechanisms. Any claim about the usefulness of mechanisms for some purpose requires an adequate account of (...) how mechanisms can actually fulfill that function, which Weber has not provided with regard to the issues he discusses. (shrink)

Critics of Bayesianism often assert that scientists are not Bayesians. The widespread use of Bayesian statistics in the field of radiocarbon calibration is discussed in relation to this charge. This case study illustrates the willingness of scientists to use Bayesian statistics when the approach offers some advantage, while continuing to use orthodox methods in other contexts. The case of radiocarbon calibration, therefore, suggests a picture of statistical practice in science as eclectic and pragmatic rather than rigidly adhering to any one (...) theoretical position. (shrink)

The naturalism versus interpretivism debate the in philosophy of social science is traditionally framed as the question of whether social science should attempt to emulate the methods of natural science. I show that this manner of formulating the issue is problematic insofar as it presupposes an implausibly strong unity of method among the natural sciences. I propose instead that what is at stake in this debate is the feasibility and desirability of what I call the Enlightenment ideal of social science. (...) I argue that this characterization of the issue is preferable, since it highlights the central disagreement between advocates of naturalism and interpretivism, makes connections with recent work on the topics of causal inference and social epistemology, while avoiding unfruitful comparisons between the social and natural sciences. (shrink)

In a recent essay (1995), Andrew Wayne charges that Bayesian attempts to account for the rule that, ceteris paribus, diverse evidence confirms better than narrow evidence are inadequate. I reply to these criticisms and argue that, on the contrary, one of the Bayesian approaches considered by Wayne does an excellent job of explaining why, and under what circumstances, diverse evidence is valuable.

While there is an extensive literature on how the precautionary principle should be interpreted and when precautions should be taken, relatively little discussion exists about the fair distribution of costs of taking precautions. We address this issue by proposing a general framework for deciding how costs of precautions should be shared, which consists of a series of default principles that are triggered according to desert, rights, and ability to pay. The framework is developed with close attention to the pragmatics of (...) how distributions will affect actual behaviours. It is intended to help decision-makers think more systematically about distributional consequences of taking precautionary measures, thereby to improve decision-making. Two case studies—one about a ban on turtle fishing in Costa Rica, and one about a deep-sea mining project in Papua New Guinea—are given to show how the framework can be applied. (shrink)

The naturalism versus interpretivism debate in social science is traditionally framed as the question of whether social science should attempt to emulate the methods of natural science. I argue that this manner of formulating the issue is problematic insofar as it presupposes an implausibly strong unity of method among the natural sciences. I propose instead that the core question of the debate is the extent to which reliable causal inference is possible in social science, a question that cannot be answered (...) by comparisons between social and natural science. I explore how some common arguments on both sides of the issue should be reexamined if the naturalism versus interpretivism debate is understood as I propose. (shrink)

(Chapter 5 of Across the Boundaries, forthcoming, from Oxford University Press) This chapter argues that previous accounts of extrapolation, either by reference to capacities or mechanisms, do not adequately address the challenges confronting extrapolation. It then begins the account of how the mechanisms-approach can be developed so as to do better. The central concept in this account is what I term comparative process tracing.