Hostname: page-component-76fb5796d-x4r87 Total loading time: 0 Render date: 2024-04-25T14:56:06.209Z Has data issue: false hasContentIssue false
  • English
  • Français

The Independence Thesis: When Individual and Social Epistemology Diverge

Published online by Cambridge University Press:  01 January 2022

Conor Mayo-Wilson ,
Kevin J. S. Zollman  and
David Danks
Get access Rights & Permissions [Opens in a new window]

Abstract

Several philosophers of science have argued that epistemically rational individuals might form epistemically irrational groups and that, conversely, rational groups might be composed of irrational individuals. We call the conjunction of these two claims the Independence Thesis, as they entail that methodological prescriptions for scientific communities and those for individual scientists are logically independent. We defend the inconsistency thesis by characterizing four criteria for epistemic rationality and then proving that, under said criteria, individuals will be judged rational when groups are not and vice versa. We then explain the implications of our results for descriptive history of science and normative epistemology.

Type
Research Article
Information
Philosophy of Science , Volume 78 , Issue 4 , October 2011 , pp. 653 - 677
Copyright
Copyright © The Philosophy of Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

‡

The authors would like to thank three anonymous referees and audiences at Logic and the Foundations of Game and Decision Theory 2010; Logic, Reasoning, and Rationality 2010; the London School of Economics; and the University of Tilburg for their helpful comments. Conor Mayo-Wilson and Kevin Zollman were supported by the National Science Foundation grant SES 1026586. David Danks was partially supported by a James S. McDonnell Foundation Scholar Award. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or the James S. McDonnell Foundation.

References

Bala, Venkatesh, and Goyal, Sanjeev. 2011. “Learning in Networks.” In Handbook of Mathematical Economics, ed. Benhabib, J., Bisin, A., and Jackson, M. O.. Amsterdam: North-Holland.Google Scholar
Beggs, Alan. 2005. “On the Convergence of Reinforcement Learning.” Journal of Economic Theory 122:1–36.CrossRefGoogle Scholar
Berry, Donald A., and Fristedt, Bert. 1985. Bandit Problems: Sequential Allocation of Experiments. London: Chapman & Hall.CrossRefGoogle Scholar
Bishop, Michael A. 2005. “The Autonomy of Social Epistemology.” Episteme 2:65–78.CrossRefGoogle Scholar
Bovens, Luc, and Hartmann, Stephan. 2003. Bayesian Epistemology. Oxford: Oxford University Press.Google Scholar
Carey, Susan. 1985. Conceptual Change in Childhood. Cambridge, MA: MIT Press.Google Scholar
Feyerabend, Paul. 1965. “Problems of Empiricism.” In Beyond the Edge of Certainty: Essays in Contemporary Science and Philosophy, ed. Colodny, Robert G., 145–260. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Feyerabend, Paul. 1968. “How to Be a Good Empiricist: A Plea for Tolerance in Matters Epistemological.” In The Philosophy of Science: Oxford Readings in Philosophy, ed. Nidditch, Peter, 12–39. Oxford: Oxford University Press.Google Scholar
Goldman, Alvin. 1992. Liasons: Philosophy Meets the Cognitive Sciences. Cambridge, MA: MIT Press.Google Scholar
Goldman, Alvin. 1999. Knowledge in a Social World. Oxford: Clarendon.CrossRefGoogle Scholar
Goodin, Robert E. 2006. “The Epistemic Benefits of Multiple Biased Observers.” Episteme 3 (2): 166–74.Google Scholar
Gopnik, Alison, and Meltzoff, Andrew. 1997. Words, Thoughts, and Theories. Cambridge, MA: MIT Press.Google Scholar
Hong, Lu, and Page, Scott. 2001. “Problem Solving by Heterogeneous Agents.” Journal of Economic Theory 97 (1): 123–63.CrossRefGoogle Scholar
Hong, Lu, and Page, Scott. 2004. “Groups of Diverse Problem Solvers Can Outperform Groups of High-Ability Problem Solvers.” Proceedings of the National Academy of Sciences 101 (46): 16385–89.CrossRefGoogle ScholarPubMed
Hull, David. 1988. Science as a Process. Chicago: Cambridge University Press.CrossRefGoogle Scholar
Kitcher, Philip. 1990. “The Division of Cognitive Labor.” Journal of Philosophy 87 (1): 5–22.CrossRefGoogle Scholar
Kitcher, Philip. 1993. The Advancement of Science. New York: Oxford University Press.Google Scholar
Kitcher, Philip. 2002. “Social Psychology and the Theory of Science.” In The Cognitive Basis of Science, ed. Stich, Stephen and Siegal, Michael. Cambridge: Cambridge University Press.Google Scholar
Kuhn, Thomas S. 1977. “Collective Belief and Scientific Change.” In The Essential Tension, 320–39. Chicago: Cambridge University Press.CrossRefGoogle Scholar
Mayo-Wilson, Conor A., Zollman, Kevin J., and Danks, David. 2010. “Wisdom of the Crowds vs. Groupthink: Learning in Groups and in Isolation.” Technical Report 188, Department of Philosophy, Carnegie Mellon University.Google Scholar
Medin, Douglas L., and Schaffer, Marguerite M.. 1978. “Context Theory of Classification Learning.” Psychological Review 85:207–38.CrossRefGoogle Scholar
Minda, John P., and Smith, J. David. 2001. “Prototypes in Category Learning: The Effects of Category Size, Category Structure, and Stimulus Complexity.” Journal of Experimental Psychology: Learning, Memory, and Cognition 27:775–99.Google ScholarPubMed
Nosofsky, Robert M. 1984. “Choice, Similarity, and the Context Theory of Classification.” Journal of Experimental Psychology: Learning, Memory, and Cognition 10:104–14.Google ScholarPubMed
Popper, Karl. 1975. “The Rationality of Scientific Revolutions.” In Problems of Scientific Revolution: Progress and Obstacles to Progress, ed. Harre, R.. Oxford: Clarendon.Google Scholar
Rehder, Bob. 2003a. “Categorization as Causal Reasoning.” Cognitive Science 27:709–48.CrossRefGoogle Scholar
Rehder, Bob. 2003b. “A Causal-Model Theory of Conceptual Representation and Categorization.” Journal of Experimental Psychology: Learning, Memory, and Cognition 29:1141–59.Google Scholar
Smith, J. David, and Minda, John P.. 1998. “Prototypes in the Mist: The Early Epochs of Category Learning.” Journal of Experimental Psychology: Learning, Memory, and Cognition 24:1411–36.Google Scholar
Strevens, Michael. 2003. “The Role of the Priority Rule in Science.” Journal of Philosophy 100 (2): 55–79.CrossRefGoogle Scholar
Surowiecki, James. 2004. The Wisdom of Crowds: Why the Many Are Smarter than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations. New York: Doubleday.Google Scholar
Sutton, Richard S., and Barto, Andrew G.. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press.Google Scholar
Weisberg, Michael, and Muldoon, Ryan. 2009. “Epistemic Landscapes and the Division of Cognitive Labor.” Philosophy of Science 76 (2): 225–52.CrossRefGoogle Scholar
Zollman, Kevin J. 2007. “The Communication Structure of Epistemic Communities.” Philosophy of Science 74 (5): 574–87.CrossRefGoogle Scholar
Zollman, Kevin J.. 2010. “The Epistemic Benefit of Transient Diversity.” Erkenntnis 2 (1): 17–35.CrossRefGoogle Scholar