Abstract
In earlier chapters various social aspects of scientific knowledge have been explored. These have been aspects which allow for social evidence to provide scientific knowledge to an individual. The focus in this chapter, however, moves beyond the study of individualistic characteristics of scientific knowledge by looking at science itself as an epistemic system. The thoroughgoing social nature of science leads to some characteristics which make it an epistemic system particularly well suited for adding to the store of scientific knowledge. In particular, the social nature of science leads to a division of cognitive labor. This division of cognitive labor both makes it so that trust plays an integral role in the generation of scientific knowledge and so that scientific progress is enhanced by the scientific community hedging its bets by scientists pursuing a wide variety of research projects utilizing a variety of methods. Although the individual scientists who make up the scientific community are not perfect, various social institutions in science help to make good use of their baser motivations.
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Notes
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Thagard (1993) claims that it should be somewhat unsurprising that group rationality and individual rationality diverge in science. He points out that such divergences have long been recognized in decision theory in situations such as the “prisoners’ dilemma” and the “tragedy of the commons”.
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It is worth noting that this situation does assume that scientists do not have perfect knowledge of what every other scientist is doing. If each individual scientist had this knowledge and was altruistic, then some might rationally accept the lower chances of their making the discovery by pursing Project 2 for the greater good of the community. However, the assumptions that scientists do not have perfect knowledge of what each other is doing and that they are not perfectly altruistic are exceedingly plausible.
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See, for example, Brock and Durlaf (1999), Goldman (1999), Goldman and Shaked (1991), Hagstrom (1965), Hull (1988), Kitcher (1993), Latour and Woolgar (1979), Merton (1973), Rescher (1990), and Strevens (2003). For criticisms of economic models of science see Hands (1995, 1997), Muldoon (2013), Muldoon and Weisberg (2011), Weisberg and Muldoon (2009), and Wray (2000). There are other ways of modeling science such as Thagard’s (1993) distributed A.I. approach, the ecological model, which Muldoon and Weisberg (2011) and Weisberg and Muldoon (2009) draw from “hill climbing” models in computer science, and various consensus models such as those put forward by Hegselman and Kraus (2006), Lehrer (1975), Lehrer and Wagner (1981), Wagner (1985), and Zollman (2010). Although these other models are interesting and worthy of careful consideration, we will limit our focus to the economic approach. The reason for this is twofold. First, such models are by far the most widely accepted approaches to modeling science and its distribution of cognitive labor. Second, the other primary ways of modeling science and the distribution of cognitive labor seem to agree with the economic models on the crucial points that hedging its bets is the best way for science to proceed. They also agree with economic models that there are social structures in place which help ensure that science has a diversity of cognitive labor.
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There are, of course, a number of such social institutions present in science. We will limit our focus here to just one of the most prominent ones because doing so is sufficient for our current illustrative purposes.
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Strevens (2003) suggests that the priority rule is perhaps simply a clear representation of a much more general reward scheme which we find in society as a whole. Namely, a reward scheme that rewards in proportion to the benefit provided to society as a whole. The priority rule exemplifies this rule because when it comes to scientific discovery only the first to make the discovery provides a benefit to society, so only the first to make a discovery gets rewarded.
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For example see Merton’s (1973, 1988) discussion of the “Matthew effect” where more well-known scientists receive more credit than less well-known scientists do for the same achievements. The Matthew effect is a social institution in science which many believe is a misallocation of credit, and so, a negative side effect of credit-based motivational structures in science. Though see Strevens (2006) for persuasive arguments for thinking that the Matthew effect does in fact distribute credit fairly.
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McCain, K. (2016). Knowledge in a Scientific Community. In: The Nature of Scientific Knowledge. Springer Undergraduate Texts in Philosophy. Springer, Cham. https://doi.org/10.1007/978-3-319-33405-9_16
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