![]() To this end, he uses computer simulations and mathematical analyzes to investigate how structural properties of these networks influence the communities’ convergence to objectively better theories. ![]() In a series of papers culminating in Zollman ( 2013), Kevin Zollman investigates how scientific networks should be structured in order to provide the optimal basis for a successful division of labor. ![]() One rather new avenue in this area is that of social epistemology, and the investigation of information networks in particular. Although Kuhn may well be right that individual differences contribute to a diversification of research agendas, Kitcher’s and Strevens’ ideas also offer avenues for fruitful investigation into incentive structures and the process of theory choice in scientific communities. For example, Kitcher ( 1990) and Strevens ( 2003) argue that the incentive structures in science contribute to the division of scientific labor. While Kuhn emphasized scientists’ individual differences in assessing scientific theories and weighting their theoretical values, others have advocated models of scientific practice where individuals can share an epistemic strategy yet still scatter among scientific fields and theories. Much has since been learned about these structures, one notable insight being the importance of diversity. At least since the seminal work by Thomas Kuhn ( 1962), philosophers and historians of science have paid more attention to the social and institutional structures that are essential for scientific enterprise. For centuries, the prevalent image of science has been that of solitary men working in secluded studies or laboratories.
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