This paper reviews current debates in social epistemology about the relations ‎between ‎knowledge ‎and consensus. These relations are philosophically interesting on their ‎own, but ‎also have ‎practical consequences, as consensus takes an increasingly significant ‎role in ‎informing public ‎decision making. The paper addresses the following questions. ‎When is a ‎consensus attributable to an epistemic community? Under what conditions may ‎we ‎legitimately infer that a consensual view is knowledge-based or otherwise ‎epistemically ‎justified? Should consensus be the aim of scientific inquiry, and (...) if so, what ‎kind of ‎consensus? How should dissent be handled? It is argued that a legitimate inference ‎that a ‎theory is correct from the fact that there is a scientific consensus on it requires taking ‎into ‎consideration both cognitive properties of the theory as well as social properties of ‎the ‎consensus. The last section of the paper reviews computational models of ‎consensus ‎formation.‎. (shrink)

The now retired NIH Consensus Development Program has been used as a model for similar programs in other countries. However, the epistemic value of this kind of program has been disputed. This article provides an overview of the arguments levelled at Miriam Solomon by Laszlo Kosolosky and Jeroen Van Bouwel, who provide an opposing philosophical position on this issue. Here, I argue for a middle ground position that highlights the methodological interest of a retrospective analysis of the NIH consensus conferences (...) as a way to assess the impact of a public forum on guidelines ultimately relayed to healthcare professionals and citizens. (shrink)

Despite decades of concerted efforts to communicate to the public on important scientific issues pertaining to the environment and public health, gaps between public acceptance and the scientific consensus on these issues remain stubborn. One strategy for dealing with this shortcoming has been to focus on the existence of scientific consensus on the relevant matters. Recent science communication research has added support to this general idea, though the interpretation of these studies and their generalizability remains a matter of contention. In (...) this paper, we describe results of a qualitative interview study on different models of scientific consensus and the relationship between such models and trust of science, finding that familiarity with scientific consensus is rarer than might be expected. These results suggest that consensus messaging strategies may not be effective. (shrink)

The value-free ideal for science holds that values should not influence the core features of scientific reasoning. We defend the difference-to-inference model of value-permeation, which holds that value-permeation in science is problematic when values make a difference to the inferences made about a hypothesis. This view of value-permeation is superior to existing views, and it suggests a corresponding maxim—namely, that scientists should strive to eliminate differences to inference. This maxim is the basis of a novel value-free ideal for science. -/- (...) . (shrink)

A number of formal models, including a highly influential model from Hong and Page, purport to show that functionally diverse groups often beat groups of individually high-performing agents in solving problems. Thompson argues that in Hong and Page’s model, that the diverse groups are created by a random process explains their success, not the diversity. Here, I defend the diversity interpretation of the Hong and Page result. The failure of Thompson’s argument shows that to understand the value of functional diversity, (...) we should be clearer about how we conceive of and measure that diversity. (shrink)

Social inequality may obstruct the generation of knowledge, as the rich and powerful may bring about social acceptance of skewed views that suit their interests. Epistemic equality in the context of justification is a means of preventing such obstruction. Drawing on social epistemology and theories of equality and distributive justice, we provide an account of epistemic equality. We regard participation in, and influence over a knowledge-generating discourse in an epistemic community as a limited good that needs to be justly distributed (...) among putative members of the community. We argue that rather than trying to operationally formulate an exact criterion for distributing this good, epistemic equality may be realized by insisting on active participation of members of three groups in addition to credited experts: relevant disempowered groups, relevant uncredited experts, and relevant stakeholders. Meeting these conditions fulfills the political, moral, and epistemic aims of epistemic equality. (shrink)

The Hong and Page ‘diversity trumps ability’ result has been used to argue for the more general claim that a diverse set of agents is epistemically superior to a comparable group of experts. Here we extend Hong and Page’s model to landscapes of different degrees of randomness and demonstrate the sensitivity of the ‘diversity trumps ability’ result. This analysis offers a more nuanced picture of how diversity, ability, and expertise may relate. Although models of this sort can indeed be suggestive (...) for diversity policies, we advise against interpreting such results overly broadly. (shrink)

In a series of formal studies and less formal applications, Hong and Page offer a ‘diversity trumps ability’ result on the basis of a computational experiment accompanied by a mathematical theorem as explanatory background (Hong & Page 2004, 2009; Page 2007, 2011). “[W]e find that a random collection of agents drawn from a large set of limited-ability agents typically outperforms a collection of the very best agents from that same set” (2004, p. 16386). The result has been extremely influential as (...) an epistemic justification for diversity policy initiatives. Here we show that the ‘diversity trumps ability’ result is tied to the particular random landscape used in Hong and Page’s simulation. We argue against interpreting results on that random landscape in terms of ‘ability’ or ‘expertise.’ These concepts are better modeled on smother and more realistic landscapes, but keeping other parameters the same those are landscapes on which it is groups of the best performing that do better. Smoother landscapes seem to vindicate both the concept and the value of expertise. Change in other parameters, however, also vindicates diversity. With an increase in the pool of available heuristics, diverse groups again do better. Group dynamics makes a difference as well; simultaneous ‘tournament’ deliberation in a group in place of the ‘relay’ deliberation in Hong and Page’s original model further emphasizes an advantage for diversity. ‘Tournament’ 2 dynamics particularly shows the advantage of mixed groups that include both experts and non-experts. As a whole, our modeling results suggest that relative to problem characteristics and conceptual resources, the wisdom of crowds and the wisdom of the few each have a place. We regard ours as a step toward attempting to calibrate their relative virtues in different modelled contexts of intellectual exploration. (shrink)