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A STATISTICAL APPROACH TO EPISTEMIC DEMOCRACY

Published online by Cambridge University Press:  11 July 2012

Marcus Pivato
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Abstract

We briefly review Condorcet's and Young's epistemic interpretations of preference aggregation rules as maximum likelihood estimators. We then develop a general framework for interpreting epistemic social choice rules as maximum likelihood estimators, maximum a posteriori estimators, or expected utility maximizers. We illustrate this framework with several examples. Finally, we critique this program.

Type
Disagreement and Opinion Aggregation
Information
Episteme , Volume 9 , Special Issue 2: Formal Social Epistemology, in memory of Horacio Arló-Costa , June 2012 , pp. 115 - 137
Copyright
Copyright © Cambridge University Press 2012

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References

REFERENCES

Arrow, Kenneth J. 1963. Individual Values and Social Choice. New York: John Wiley & Sons.Google Scholar
Austen-Smith, D., and Banks, J. 1996. ‘Information Aggregation, Rationality, and the Condorcet Jury Theorem.’ American Political Science Review, 90: 34–45.CrossRefGoogle Scholar
Bohman, J., and Rehg, W. (eds). 1997. Deliberative Democracy: Essays on Reason and Politics. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Brams, S. J., and Fishburn, P. C. 1983. Approval Voting. Boston, MA: Birkhauser.Google Scholar
Cohen, J. 1986. ‘An Epistemic Conception of Democracy.’ Ethics, 97(1): 26–38.CrossRefGoogle Scholar
Condorcet, M. d. 1785. Essai sur l'application de l'analyse à la probabilite des décisions rendues à la pluralité des voix. Paris.Google Scholar
Conitzer, V., and Sandholm, T. 2005. ‘Common Voting Rules as Maximum Likelihood Estimatorsn.’ I 21st Annual Conference on Uncertainty in Artificial Intelligence (UAI-05), pp. 145–52. Edinburgh: UAI.Google Scholar
Conitzer, V., Rognlie, M., and Xia, L. 2009. ‘Preference Functions that Score Rankings and Maximum Likelihood Estimation.’ In 21st International Joint Conference on Artificial Intelligence (IJCAI-09), pp. 109–15. Pasadena, CA: IJCAI.Google Scholar
Dietrich, F., and Spiekerman, K. 2011. Epistemic Democracy with Defensible Premises. (preprint).Google Scholar
Estlund, D. 1997. ‘Beyond Fairness and Deliberation: The Epistemic Dimension of Democratic Authority.’ In Bohman and Rehg (1997: 173–204).CrossRefGoogle Scholar
Galton, Francis. 1907. ‘Vox Populi.’ Nature, 75: 450–1.CrossRefGoogle Scholar
Hummel, P. 2010. ‘Jury Theorems with Multiple Alternatives.’ Social Choice and Welfare, 34(1): 65–103.CrossRefGoogle Scholar
Kaniovski, S. 2010. ‘Aggregation of Correlated Votes and Condorcet's Jury Theorem.’ Theory and Decision, 69(3): 453–68.CrossRefGoogle Scholar
Kemeny, J. G. 1959. ‘Math without Numbers.’ Daedalus, 88: 571–91.Google Scholar
List, C., and Goodin, R. E. 2001. ‘Epistemic Democracy: Generalizing the Condorcet Jury Theorem.’ Journal of Political Philosophy, 9(3): 277–306.CrossRefGoogle Scholar
Lorenz, Jan, Rauhut, Heiko, Schweitzer, Frank, and Helbing, Dirk. 2011. ‘How Social Influence Can Undermine the Wisdom of Crowd Effect.’ Proceedings of the National Academy of Science, 16 May, doi: 10.1073/pnas.1008636108.CrossRefGoogle ScholarPubMed
Myerson, R. 1995. ‘Axiomatic Derivation of Scoring Rules without the Ordering Assumption.’ Social Choice and Welfare, 12(1): 59–74.CrossRefGoogle Scholar
Pivato, M. 2011. ‘Variable Population Voting Rules’ (preprint).Google Scholar
Pivato, M. 2012. ‘Voting Rules as Statistical Estimators.’ Social Choice and Welfare.Google Scholar
Smith, Warren D. 2001. ‘Range Voting’: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.32.9150.Google Scholar
Surowiecki, James. 2004. The Wisdom of Crowds. New York: Doubleday.Google Scholar
Xia, L., and Conitzer, V. 2011. ‘A Maximum Likelihood Approach towards Aggregating Partial Orders.’ In 23rd International Joint Conference on Artificial Intelligence (IJCAI-11). Pasadena, CA: IJCAI.Google Scholar
Xia, L., Conitzer, V., and Lang, J. 2010. ‘Aggregating Preferences in Multi-Issue Domains by Using Maximum Likelihood Estimators’. In 9th International Joint Conference on Autonomous Agents and Multi Agent Systems (AAMAS-10), pp. 399–406. Toronto: AAMAS.Google Scholar
Young, H. P. 1986. ‘Optimal Ranking and Choice from Pairwise Decisions.’ In Grofman, B. and Owen, G. (eds), Information Pooling and Group Decision Making., pp. 113–22. Greenwich, CT: JAI Press.Google Scholar
Young, H. P. 1988. ‘Condorcet's Theory of Voting.’ American Political Science Review, 82(4): 1231–44.CrossRefGoogle Scholar
Young, H. P. 1995. ‘Optimal Voting Rules.’ Journal of Economic Perspectives, 9(1): 51–64.CrossRefGoogle Scholar
Young, H. P. 1997. ‘Group Choice and Individual Judgments.’ In Mueller, D. C. (ed.), Perspectives on Public Choice: A Handbook, pp. 181–200. Cambridge: Cambridge University Press.Google Scholar