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The Communication Structure of Epistemic Communities

Published online by Cambridge University Press:  01 January 2022

Kevin J. S. Zollman
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Abstract

Increasingly, epistemologists are becoming interested in social structures and their effect on epistemic enterprises, but little attention has been paid to the proper distribution of experimental results among scientists. This paper will analyze a model first suggested by two economists, which nicely captures one type of learning situation faced by scientists. The results of a computer simulation study of this model provide two interesting conclusions. First, in some contexts, a community of scientists is, as a whole, more reliable when its members are less aware of their colleagues’ experimental results. Second, there is a robust tradeoff between the reliability of a community and the speed with which it reaches a correct conclusion.

Type
Philosophy of Science
Information
Philosophy of Science , Volume 74 , Issue 5: Proceedings of the 2006 Biennial Meeting of the Philosophy of Science Association. Part I: Contributed Papers. Edited by Cristina Bicchieri and Jason Alexander , December 2007 , pp. 574 - 587
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

‡

The author would like to thank Brian Skyrms, Kyle Stanford, Jeffrey Barrett, Bruce Glymour, and the participants in the Social Dynamics Seminar at University of California–Irvine for their helpful comments. Generous financial support was provided by the School of Social Science and Institute for Mathematical Behavioral Sciences at UCI.

References

Bala, Venkatesh, and Goyal, Sanjeev (1998), “Learning from Neighbours”, Learning from Neighbours 65:565–621.Google Scholar
Bovens, Luc, and Hartmann, Stephan (2003), Bayesian Epistemology. Oxford: Oxford University Press.Google Scholar
Ellison, Gregory, and Fudenberg, Drew (1995), “Word-of-Mouth Communication and Social Learning”, Word-of-Mouth Communication and Social Learning 110:93–125.Google Scholar
Goldman, Alvin (1999), Knowledge in a Social World. Oxford: Clarendon.CrossRefGoogle Scholar
Herron, Timothy, Seidenfeld, Teddy, and Wasserman, Larry (1997), “Divisive Conditioning: Further Results on Dilation”, Divisive Conditioning: Further Results on Dilation 64:411–444.Google Scholar
Hull, David (1988), Science as a Process. Chicago: University of Chicago Press.CrossRefGoogle Scholar
Kitcher, Philip (1990), “The Division of Cognitive Labor”, The Division of Cognitive Labor 87:5–22.Google Scholar
Kitcher, Philip (1993), The Advancement of Science. New York: Oxford University Press.Google Scholar
Laudan, Larry (1996), Beyond Positivism and Relativism: Theory, Method, and Evidence. Boulder, CO: Westview.Google Scholar
Newman, M. E. J., Watts, D. J., and Strogatz, H. J. (2002), “Random Graph Models of Social Networks”, Random Graph Models of Social Networks 99:2566–2572.Google ScholarPubMed
Popper, Karl (1975), “The Rationality of Scientific Revolutions”, in Harré, Rom (ed.), Problems of Scientific Revolution: Progress and Obstacles to Progress. Oxford: Clarendon, 72–101.Google Scholar
Strevens, Michael (2003a), “Further Properties of the Priority Rule”, manuscript. http://www.strevens.org/research/scistruc/MorePrior.pdf.CrossRefGoogle Scholar
Strevens, Michael (2003b), “The Role of the Priority Rule in Science”, The Role of the Priority Rule in Science 100:55–79.Google Scholar
Watts, Duncan (1999), Small Worlds: The Dynamics of Networks between Order and Randomness. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar