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When is consensus knowledge based? Distinguishing shared knowledge from mere agreement

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Abstract

Scientific consensus is widely deferred to in public debates as a social indicator of the existence of knowledge. However, it is far from clear that such deference to consensus is always justified. The existence of agreement in a community of researchers is a contingent fact, and researchers may reach a consensus for all kinds of reasons, such as fighting a common foe or sharing a common bias. Scientific consensus, by itself, does not necessarily indicate the existence of shared knowledge among the members of the consensus community. I address the question of under what conditions it is likely that a consensus is in fact knowledge based. I argue that a consensus is likely to be knowledge based when knowledge is the best explanation of the consensus, and I identify three conditions—social calibration, apparent consilience of evidence, and social diversity, for knowledge being the best explanation of a consensus.

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Notes

  1. http://consensus.nih.gov/ABOUTCDP.htm.

  2. http://www.ipcc.ch/ipccreports/ar4-syr.htm.

  3. http://en.wikipedia.org/wiki/Wikipedia:Consensus.

  4. See Laudan (1996) at 156–157 and Solomon (2002) at 137–141 for two statements of normative naturalized social epistemology. For an influential naturalized social epistemology, see also Longino (2002).

  5. The chief dissenter is biologist Peter Duesberg and he argues for his views in his (1996). See Epstein (1996, Chaps. 3 & 4) for the history of the AIDS controversy.

  6. For example, Peirce (1877); Lehrer and Wagner (1981), and Habermas (1984).

  7. The distinction between epistemic misfortune and veritic epistemic luck is similar to the distinction that Statman (1991) draws between two forms of epistemic luck, respectively: (1) luck in the causes or circumstances that bring some subject \(S\) to believe \(p,\) or to be such a person who believes \(p;\) (2) luck with regard to \(p\) being true or false.

  8. Analytic epistemologists, interested in conceptual analysis of knowledge, have not directed much attention to epistemic misfortune, and have not tried to rule it out from their conceptions of knowledge. This is probably because when agents are epistemically misfortunate, their beliefs are false. Hence, any conception of knowledge as true belief will have already ruled out their beliefs as knowledge. When agents are vertically lucky, on the other hand, their beliefs are accidentally true, and need to be ruled out for being a fluke. As opposed to epistemologists, philosophers of science, who are interested in the different ways beliefs or theories may seem or be justified or rational and still be false, discuss cases involving epistemic misfortune, though have not explicitly invoked this term. Recall that the aim of the theory proposed in this paper is to identify when we can infer that \(p\) from there being a consensus that \(p,\) when we do not know whether

    \(p\) and cannot establish the truth or falsehood of \(p\) in an independent way. We therefore must go in a roundabout way and exclude veritic epistemic luck and epistemic misfortune as likely to be present.

  9. This is what distinguishes epistemic misfortune form Pritchard’s notion of reflective epistemic luck (2005, p. 175).

  10. For example, in The Decent of Man, Darwin (1871) repeatedly draws on the accepted belief of his time that savages are cognitively inferior to Europeans. He often portrays them as a brutal animal-like intermediate step in the evolution of man as evidence for human evolution. Today such claims are regarded as false and inadequate evidence for evolution. Yet, it is unclear whether a white upper-class man brought up in Victorian England could have seen these people in a different light. By contrast, Drawin’s (1871, p. 326) depiction of women as cognitively inferior to men is arguably not a case of epistemic misfortune, as Darwin knew women of comparable cognitive abilities to men, and his argument from evolutionary theory is designed to counter claims by his contemporaries that women are cognitively equal to men.

  11. I borrow the term “social calibration” from Goldberg (2007, p. 61), who argues that a great overlap in the meaning of lexical terms between the idiolects of members of a speech community is necessary for satisfying the conditions on successful transmission of knowledge through testimony.

  12. See Kusch (2002, pp. 152–157) for an account of evidential standards as shared communal exemplars.

  13. It is not required that all parties to the consensus actively use the same formalism, only that they all accept each others’ formalisms. For example, organic chemists use a specific formalism that is an extension of the more general formalism of analytic chemistry.

  14. Goldman’s reasoning echoes with Condorcet’s jury theorems, which state, roughly, that sufficiently large groups of individuals in which there is a sufficiently large subgroup of individuals who have a higher than 0.5 probability to form a correct belief on a given matter will reach the correct decision on that matter by the method of majority voting. The application of these mathematical theorems to concrete real-world cases, however, is far from trivial. It is not clear in which concrete cases we would expect the conditions of statistical independence and higher than 0.5 probability to obtain, or even how to judge whether they obtain ((Vermeule 2009, pp. 28–33)).

  15. This formulation may already not accurately reflect Solomon’s claim, since biases are usually not mutually exclusive. I will not delve into this point.

  16. My explanation of the endurance of the consensus over the excess acidity theory differs from other accounts. Thagard (1999, pp. 64–69; 2000, pp. 230–237) argues that the consensus over the excess acidity theory endured because until the mid 1980s, the rival bacterial theory did not exhibit explanatory coherence. Zollman (2010) argues that the scientific community failed to converge on the truth due to the mutual effect of two factors: the prevalence of extreme views within the community, and rapid information sharing. Both Thagard and Zollman, however, do not take into account the effect of social factors, such as commercial interests and differences in social status between actors, without supporting their exclusion of these factors with argument. For a useful timeline of the events surrounding the discovery see: http://en.wikipedia.org/wiki/Timeline_of_peptic_ulcer_disease_and_Helicobacter_pylori.

  17. It is \(0.95^{100}=0.0059.\)

  18. The probability that \(m\) or more people will believe that \(p\) is\(\sum _{i=0}^m {\left( {_{{}{}{} i}^{100} } \right)} {}0.05^{i}\cdot 0.95^{100-i}.\) For example, if \(m=90,\) the probability is 0.9885.

  19. I borrow the term “consilience” from Whewell, who talks about the principle of “consilience of inductions”, according to which hypotheses are more supported when they independently stem from different inductive inferences (1858, pp. 87–90).

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Acknowledgments

I thank Hagit Benbaji, Joseph Berkovitz, Jim Brown, Anjan Chakravartty, Steve Fuller, Yves Gingras, Sandy Goldberg, Arnon Keren, Kareem Khalifa, Laszlo Kosolosky, Yakir Levin, Isaac (Yanni) Nevo, Isaac Record, Jacob Stegenga, Eran Tal, Brad Wray, and two anonymous reviewers for useful comments and suggestions. I am grateful to the Azrieli Foundation for an award of an Azrieli Fellowship. Special thanks to Meital Pinto for her support and inspiration.

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Miller, B. When is consensus knowledge based? Distinguishing shared knowledge from mere agreement. Synthese 190, 1293–1316 (2013). https://doi.org/10.1007/s11229-012-0225-5

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