Norms of assertion and communication in social networks


Epistemologists can be divided into two camps: those who think that nothing short of certainty or (subjective) probability 1 can warrant assertion and those who disagree with this claim. This paper addressed this issue by inquiring into the problem of setting the probability threshold required for assertion in such a way that that the social epistemic good is maximized, where the latter is taken to be the veritistic value in the sense of Goldman (Knowledge in a social world, 1999). We provide a Bayesian model of a test case involving a community of inquirers in a social network engaged in group deliberation regarding the truth or falsity of a proposition \(p.\) Results obtained by means of computer simulation indicate that the certainty rule is optimal in the limit of inquiry and communication but that a lower threshold is preferable in less idealized cases.

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  1. 1.

    For some proponents, such as Williamson (2000), the knowledge rule does not link any specific psychological state, such as belief, to assertion.

  2. 2.

    We focus in this paper on identifying the isolated impact of specific rules of assertion. A problem in this regard concerns the background against which such an investigation takes place. In general, there may be other rules that are used by a given community and those other rules may affect the consequences of a new rule under consideration. These are broad issues that we cannot address extensively in this paper. Nevertheless, the stability tests that we perform in Sect. 4 serve to address some of these concerns.

  3. 3.

    This section is based on Goldman (1999, pp. 87–100) and on the exposition in Olsson (2011).

  4. 4.

    Goldman also mentions an alternative “trichotomous” model of V-value. Suppose \(S\) takes interest in the question whether \(p.\) The basic principles of this model are: if \(S\) believes the true proposition, the V-value is 1; if \(S\) rejects the true proposition, the V-value is 0; and if \(S\) withholds judgment, the V-value is .5. This alternative way of thinking about V-value will play no role in this article.

  5. 5.

    This section is based on the account in Angere (unpublished manuscript).

  6. 6.

    The issue of source independence is discussed at length in Olsson (2013).

  7. 7.

    See also Zollman (2007) for a defense of the less-is-more thesis regarding communication. Zollman also works in a Bayesian setting although at the level of details his model is quite different from ours.

  8. 8.

    Aron Vallinder contributed to this paper by performing and summarizing the results of the robustness tests described in Sect. 4. He also wrote parts of Sect. 5. We thank two anonymous referees for their suggestions.


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Correspondence to Erik J. Olsson.

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Olsson, E.J., Vallinder, A. Norms of assertion and communication in social networks. Synthese 190, 2557–2571 (2013).

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  • Norms of assertion
  • Probability
  • Veritistic value
  • Computer simulation
  • Social network
  • Bayesianism
  • Social epistemology