Abstract
I present a social scientific approach to epistemic questions about expertise such as: Which properties make people into likely experts? What is the epistemic significance of agreements among experts? Is there a special kind of expert agreement that is indicative of knowledge? Why do the beliefs experts accept seem to enjoy a higher likelihood of being knowledge than the opinions of non-experts?
There are three types of causes of correlations between people and the beliefs they accept: Expertise, in the sense of special knowledge and impartiality; bias; and coincidence. The Neyman-Rubin model is useful for finding which is most likely.
The Neyman-Rubin model infers types of causes of correlated types of effects in two stages: First, it proves or disproves that the correlations between the types of effects are likelier given the hypothetical common cause type than given alternative (unspecified) numerous types of causes. The tested common cause type hypothesis specifies the properties of the type of common cause; but the properties of the alternative numerous types of causes are not specified. If both experimental and control populations are affected by the same types of (unknown or unspecified) variables, but only the experimental group is affected by a particular cause type (the treatment), significant differences between the two populations are likely to result from that cause type. At the second stage, the Neyman-Rubin model attempts to find the exact causal relations or nets, which may be complex, requiring the construction of multicollinear, interactive, and so on models.
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Notes
- 1.
I do not think that social science epistemology can distinguish between rival claims of experts. It can however distinguish likely from unlikely experts with rival claims.
- 2.
It is possible to characterize a type as having a certain property even if not all its tokens have the property. Though many tokens (for example of letters and words) resemble each other and share properties, not all do: “The analogy to zoology is helpful. Not every so-called black bear is black; not every grizzly is four-legged, brown or has a hump … It may be permissible to characterize the species in terms of such properties anyway. In many cases, one extrapolates from properties of the tokens, individually or collectively, to properties of the type. However, … even if the overwhelming majority of the tokens have a property it does not entail that the type has it” (Wetzel 2009, pp. 119–20). Some properties of a type are not shared by any of its tokens; for example, “the grizzly bear is endangered” (Wetzel 2009, p. 120).
- 3.
I emphasize that the inference is of a common cause type rather than common cause token. Confusions between the inferences of common cause types and tokens have been rife and destructive in philosophy since Reichenbach. I have argued that the distinction between the inference of common cause types and tokens distinguished the theoretical from the historical sciences (Tucker 2007, 2012).
- 4.
Solomon suggested that diversity of decision vectors is the best explanation of consensuses on beliefs among scientists. Still, ceteris paribus, even without using the Neyman-Rubin method, the likelihood of consensus is higher given a single common cause type such as expertise or an overwhelming bias for all the opinions of the experts than given many diverse decision vectors because of the low probability that all different biases will cause identical types of beliefs. In Bayesian terms, it would require multiplying the independent likelihoods of the beliefs given each independent type of bias by each other. Even if each likelihood is high to begin with, the result of multiplying many such factors by each other would reduce the total likelihood to close to zero. Miller (2013) noted correctly that this criticism is valid only when the vectors should explain a consensus in the sense of absolute agreement among all. A weaker but still significant correlation may be likely given different causes that generate the correlation with high likelihood.
- 5.
Goldman’s (2001) analyses the position of a layperson deciding between experts in terms borrowed from the epistemology of testimony. Only independent testimonies matter and they should be evaluated according to their reliabilities. Experts have higher reliabilities than lay people about esoteric subjects they specialize in. Despite the Bayesian framework, Goldman left out the prior estimation of the probability of what the experts testify for. As Bovens and Hartmann (2003) demonstrated, when that prior is low enough, two even unreliable but independent testimonies are sufficient for inferring knowledge. This Bayesian epistemology of testimony approach cannot discriminate between competing expert opinions if the experts who testify are independent and have similar credibility and their testimonies-opinions are similarly surprising.
- 6.
Charles Peirce (1877, pp. 132–3) believed that the scientific process will ultimately generate consensus on objective truth. His faith in an unspecified scientific process of investigation was combined with an eschatological rhetoric about consensus as the “destiny”, “destined center”, “foreshadowed Goal”, and “predestined opinion” of the history of science. Consensus for Peirce was not concrete, but a chiliastic ideal to be realized at the end of time, a form of epistemic messianism. Before the promised consensus comes about at the end of the scientific process, it is impossible to know which beliefs are true and which will be forsaken during the future history of science, just as it is impossible to distinguish the righteous from the sinners, the city of god from the city of man, before judgment day.
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Tucker, A. (2014). Epistemology as a Social Science: Applying the Neyman-Rubin Model to Explain Expert Beliefs. In: Martini, C., Boumans, M. (eds) Experts and Consensus in Social Science. Ethical Economy, vol 50. Springer, Cham. https://doi.org/10.1007/978-3-319-08551-7_8
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