In this paper I analyze four so-called “principles of expertise”; that is, good epistemic practices that are normatively motivated by the epistemological literature on expert judgment. I highlight some of the problems that the four principles of expertise run into, when we try to implement them in concrete contexts of application (e.g. in science committees). I suggest some possible alternatives and adjustments to the principles, arguing in general that the epistemology of expertise should be informed both by case studies and by the literature on the use of experts in science practice.
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At least if we assume a minimal requirement for justification. Of course, there are many notions of epistemic justification, but what I am considering here is a minimal requirement for a belief to be justified.
To be fair, Goldman provides a number of important qualifications to the principle of large numbers (see Goldman 2001, Sect. 4); that discussion is left aside in this paper, because I wish to focus on different and less explored issues related to the principle discussed here.
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Self-interest biases can also be cognitive biases, when the interest-seeking is not conscious, and yet the bias still favors one’s interests. In this section, with ‘self-interest bias’ I have in mind the more specific case where an agent consciously chooses to serve one’s personal interest.
This is a version of the old problem of induction (Hume 1739–1740). But whereas Hume’s problem of induction, generalized to all events, normally does not hinder practical applications of science, in the cases analyzed in this section the lack of a frame of reference on which experts can be calibrated is a serious problem for the reliability of their predictions, and therefore the use of those predictions.
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Martini, C. Experts in science: a view from the trenches. Synthese 191, 3–15 (2014). https://doi.org/10.1007/s11229-013-0321-1
- Epistemology of expertise
- Principles of expertise
- Social epistemology
- Science practice