The most widely debated conception of democracy in recent years is deliberative democracy--the idea that citizens or their representatives owe each other mutually acceptable reasons for the laws they enact. Two prominent voices in the ongoing discussion are Amy Gutmann and Dennis Thompson. In Why Deliberative Democracy?, they move the debate forward beyond their influential book, Democracy and Disagreement.What exactly is deliberative democracy? Why is it more defensible than its rivals? By offering clear answers to these timely questions, Gutmann and (...) Thompson illuminate the theory and practice of justifying public policies in contemporary democracies. They not only develop their theory of deliberative democracy in new directions but also apply it to new practical problems. They discuss bioethics, health care, truth commissions, educational policy, and decisions to declare war. In "What Deliberative Democracy Means," which opens this collection of essays, they provide the most accessible exposition of deliberative democracy to date. They show how deliberative democracy should play an important role even in the debates about military intervention abroad.Why Deliberative Democracy? contributes to our understanding of how democratic citizens and their representatives can make justifiable decisions for their society in the face of the fundamental disagreements that are inevitable in diverse societies. Gutmann and Thompson provide a balanced and fair-minded approach that will benefit anyone intent on giving reason and reciprocity a more prominent place in politics than power and special interests. (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 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)

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)

Many philosophers of science have argued that social and ethical values have a significant role to play in core parts of the scientific process. This naturally suggests the following question: when such value choices need to be made, which or whose values should be used? A common answer to this question turns to democratic values—the values of the public or its representatives. I argue that this imposes a morally significant burden on certain scientists, effectively requiring them to advocate for policy (...) positions they strongly disagree with. I conclude by discussing under what conditions this burden might be justified. (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)