When should a scientific community be cognitively diverse? This article presents a model for studying how the heterogeneity of learning heuristics used by scientist agents affects the epistemic efficiency of a scientific community. By extending the epistemic landscapes modeling approach introduced by Weisberg and Muldoon, the article casts light on the micro-mechanisms mediating cognitive diversity, coordination, and problem-solving efficiency. The results suggest that social learning and cognitive diversity produce epistemic benefits only when the epistemic community is faced with problems of sufficient difficulty.
KeywordsSocial epistemology Diversity Social learning Division of cognitive labor
I would like to thank the anonymous referees for their useful comments and suggestions on earlier drafts of the paper. I am also thankful to Manuela Fernández Pinto, Marion Godman, Jaakko Kuorikoski, Otto Lappi, Caterina Marchionni, Carlo Martini, and Petri Ylikoski for helpful discussions about the paper and about epistemic landscapes modeling in general. The paper also benefited from comments by the participants at the Agent-based Models in Philosophy conference at LMU Munich, TINT brown bag seminar, and the cognitive science research seminar at University of Helsinki. This research has been financially supported by the Academy of Finland and the University of Helsinki.
- Goldman, A. (2011). A guide to social epistemology. In A. Goldman & D. Whitecomb (Eds.), Social epistemology: Essential readings (pp. 11–37). Oxford: Oxford University Press.Google Scholar
- Johnson, S. (2011). Where good ideas come from: The seven patterns of innovation. London: Penguin.Google Scholar
- Kitcher, P. (1993). The advancement of science: Science without legend, objectivity without illusions. New York, Oxford: Oxford University Press.Google Scholar
- Klarreich, E. (2013). Unheralded mathematician bridges the prime gap. Quanta Magazine https://www.quantamagazine.org/20130519-unheralded-mathematician-bridges-the-prime-gap/
- Longino, H. E. (1990). Science as social knowledge: Values and objectivity in scientific inquiry. Princeton: Princeton University Press.Google Scholar
- Longino, H. E. (2002). The fate of knowledge. Princeton, Oxford: Princeton University Press.Google Scholar
- MacArthur-Foundation (2014). MacArthur fellows: Yitang Zhang. https://www.macfound.org/fellows/927/
- Novak, M. (2014). Tesla and the lone inventor myth. http://www.bbc.com/future/story/20130322-tesla-and-the-lone-inventor-myth
- Page, S. E. (2008). The difference: How the power of diversity creates better groups, firms, schools, and societies (paperback ed.). Princeton, Woodstock: Princeton University Press.Google Scholar
- Russell, S. J., & Norvig, P. (2003). Artificial intelligence: A modern approach (2nd ed.). Prentice Hall series in artificial intelligence. Upper Saddle River, London: Prentice Hall/Pearson Education.Google Scholar
- Weisberg, M. (2010). New approaches to the division of cognitive labor. In J. Busch & P. D. Magnus (Eds.), New waves in philosophy of science. Basingstoke: Palgrave Macmillan.Google Scholar
- Wright, S. (1932). The roles of mutation, inbreeding, crossbreeding, and selection in evolution. In Proceedings of the Sixth International Congress on Genetics, pp. 355–366Google Scholar