Increasingly, epistemologists are becoming interested in social structures and their effect on epistemic enterprises, but little attention has been paid to the proper distribution of experimental results among scientists. This paper will analyze a model first suggested by two economists, which nicely captures one type of learning situation faced by scientists. The results of a computer simulation study of this model provide two interesting conclusions. First, in some contexts, a community of scientists is, as a whole, more reliable when its (...) members are less aware of their colleagues' experimental results. Second, there is a robust tradeoff between the reliability of a community and the speed with which it reaches a correct conclusion. ‡The author would like to thank Brian Skyrms, Kyle Stanford, Jeffrey Barrett, Bruce Glymour, and the participants in the Social Dynamics Seminar at University of California–Irvine for their helpful comments. Generous financial support was provided by the School of Social Science and Institute for Mathematical Behavioral Sciences at UCI. †To contact the author, please write to: Department of Philosophy, Baker Hall 135, Carnegie Mellon University, Pittsburgh, PA 15213-3890; e-mail: [email protected]. (shrink)

My aim in this paper is to develop and defend a novel answer to a question that has recently generated a considerable amount of controversy. The question concerns the normative significance of peer disagreement. Suppose that you and I have been exposed to the same evidence and arguments that bear on some proposition: there is no relevant consideration which is available to you but not to me, or vice versa. For the sake of concreteness, we might picture.

Looking back on it, it seems almost incredible that so many equally educated, equally sincere compatriots and contemporaries, all drawing from the same limited stock of evidence, should have reached so many totally different conclusions---and always with complete certainty.

How should you take into account the opinions of an advisor? When you completely defer to the advisor's judgment, then you should treat the advisor as a guru. Roughly, that means you should believe what you expect she would believe, if supplied with your extra evidence. When the advisor is your own future self, the resulting principle amounts to a version of the Reflection Principle---a version amended to handle cases of information loss. When you count an advisor as an epistemic (...) peer, you should give her conclusions the same weight as your own. Denying that view---call it the ``equal weight view''---leads to absurdity: the absurdity that you could reasonably come to believe yourself to be an epistemic superior to an advisor simply by noting cases of disagreement with her, and taking it that she made most of the mistakes. Accepting the view seems to lead to another absurdity: that one should suspend judgment about everything that one's smart and well-informed friends disagree on, which means suspending judgment about almost everything interesting. But despite appearances, the equal weight view does not have this absurd consequence. Furthermore, the view can be generalized to handle cases involving not just epistemic peers, but also epistemic superiors and inferiors. (shrink)

How should one react when one has a belief, but knows that other people—who have roughly the same evidence as one has, and seem roughly as likely to react to it correctly—disagree? This paper argues that the disagreement of other competent inquirers often requires one to be much less confident in one’s opinions than one would otherwise be.

Diversity of opinion both presents problems and aff ords opportunities. Diff erences of opinion can stand in the way of reaching an agreement within a group on what decisions to take. But at the same time, the fact that the differences in question could derive from access to different information or from the exercise of diff erent judgemental skills means that they present individuals with the opportunity to improve their own opinions. This paper explores the implications for solutions to the (...) former (aggregation) problem of supposing that individuals exploit these opportunities. In particular, it argues that rational individual revision of opinion implies that aggregation problems are unstable in a certain sense and that solving them by exploiting the information embedded in individual opinion has profound implications for the conditions that we should impose on aggregation procedures. (shrink)

When one encounters disagreement about the truth of a factual claim from a trusted advisor who has access to all of one's evidence, should that move one in the direction of the advisor's view? Conciliatory views on disagreement say "yes, at least a little." Such views are extremely natural, but they can give incoherent advice when the issue under dispute is disagreement itself. So conciliatory views stand refuted. But despite first appearances, this makes no trouble for *partly* conciliatory views: views (...) that recommend giving ground in the face of disagreement about many matters, but not about disagreement itself. (shrink)

Looking back on it, it seems almost incredible that so many equally educated, equally sincere compatriots and contemporaries, all drawing from the same limited stock of evidence, should have reached so many totally different conclusions---and always with complete certainty.

This paper explores some aspects of the relation between different ways of achieving a consensus on the judgemental values of a group of indviduals; in particular, aggregation and deliberation. We argue firstly that the framing of an aggregation problem itself generates information that individuals are rationally obliged to take into account. And secondly that outputs of the deliberative process that this initiates is in tension with constraints on consensual values typically imposed by aggregation theory, at least when deliberation is modelled (...) as process of learning from others compatible with Bayesian updating principles. (shrink)

Group decisions raise a number of substantial philosophical and methodological issues. We focus on the goal of the group decision exercise itself. We ask: What should be counted as a good group decision-making result? The right decision might not be accessible to, or please, any of the group members. Conversely, a popular decision can fail to be the correct decision. In this paper we discuss what it means for a decision to be "right" and what components are required in a (...) decision process to produce happy decision-makers. Importantly, we discuss how "right" decisions can produce happy decision-makers, or rather, the conditions under which happy decision-makers and right decisions coincide. In a large range of contexts, we argue for the adoption of formal consensus models to assist in the group decision-making process. In particular, we advocate the formal consensus convergence model of Lehrer and Wagner (1981), because a strong case can be made as to why the underlying algorithm produces a result that should make each of the experts in a group happy. Arguably, this model facilitates true consensus, where the group choice is effectively each person's individual choice. We analyse Lehrer and Wagner's algorithm for reaching consensus on group probabilities/utilities in the context of complex decision-making for conservation biology. While many conservation decisions are driven by a search for objective utility/probability distributions (regarding extinction risks of species and the like), other components of conservation management primarily concern the interests of stakeholders. We conclude with cautionary notes on mandating consensus in decision scenarios for which no fact of the matter exists. For such decision settings alternative types of social choice methods are more appropriate. (shrink)