Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-27T15:47:37.296Z Has data issue: false hasContentIssue false

Two aggregation paradoxes in social decision making: the Ostrogorski paradox and the discursive dilemma

Published online by Cambridge University Press:  03 January 2012

Abstract

The Ostrogorski paradox and the discursive dilemma are seemingly unrelated paradoxes of aggregation. The former is discussed in traditional social choice theory, while the latter is at the core of the new literature on judgment aggregation. Both paradoxes arise when, in a group, each individual consistently makes a judgment, or expresses a preference, (in the form of yes or no) over specific propositions, and the collective outcome is in some respect inconsistent. While the result is logically inconsistent in the case of the discursive paradox, it is not stable with respect to the level of aggregation in the case of the Ostrogorski paradox. In the following I argue that, despite these differences, the two problems have a similar structure. My conclusion will be twofold: on the one hand, the similarities between the paradoxes support the claim that these problems should be tackled using the same aggregation procedure; on the other hand, applying the same procedure to these paradoxes will help clarify the strengths and weaknesses of the aggregation method itself. More specifically, I will show that an operator defined in artificial intelligence to merge belief bases can deal with both paradoxes.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2006

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.)

References

Bezembinder, T., and Van Acker, P. (1985). “The Ostrogorski paradox and its relation to nontransitive choice”, Journal of Mathematical Sociology, 11(2): 131158.CrossRefGoogle Scholar
Bovens, L., and Rabinowicz, W. (2005). “Democratic answers to complex questions. An epistemic perspective”, Synthese, forthcoming.Google Scholar
Brennan, G. (2001). “Collective coherence?”, International Review of Law and Economics, 21 (2), 197211.CrossRefGoogle Scholar
Dalal, M. (1988a). “Updates in propositional databases”, technical report, Rutgers University.Google Scholar
Dalal, M. (1988b). “Investigations into a theory of knowledge base revision: Preliminary report”, in Proceedings of the 7th National Conference of the American Association for Artificial Intelligence, Saint Paul, Minn., 475479.Google Scholar
Daudt, H., and Rae, D.W. (1976). “The Ostrogorski paradox: a peculiarity of compound majority decision”, European Journal of Political Research, 4(4): 391399.Google Scholar
Goldman, A. (1999). Knowledge in a Social World. Oxford: Oxford University Press.CrossRefGoogle Scholar
Goldman, A. (2004a). “Group knowledge versus group rationality: two approaches to social epistemology”, Episteme. A Journal of Social Epistemology, 1: 1122.CrossRefGoogle Scholar
Goldman, A. (2004b). “The need for social epistemology”. In The Future of Philosophy, Leiter, B. (ed.), New York: Oxford University Press, 182207.CrossRefGoogle Scholar
Horty, J.F. (1994). “Some direct theories of nonmonotonic inheritance”. In Handbook of Logic in Artificial Intelligence and Logic Programming, Vol. 3: Nonmonotonic Reasoning and Uncertain Reasoning, Gabbay, D., Hogger, C., and Robinson, J. (eds.), Oxford University Press, 111187.CrossRefGoogle Scholar
Horty, J.F. (2002). “Skepticism and floating conclusions”, Artificial Intelligence, 135: 5572.CrossRefGoogle Scholar
Horty, J.F., Thomason, R.H., and Touretzky, D.S. (1990). “A skeptical theory of inheritance in nonmonotonic semantic networks”, Artificial Intelligence, 42: 311348.CrossRefGoogle Scholar
Kelly, J.S. (1989). “The Ostrogorski paradox”, Social Choice and Welfare, 6: 7176.CrossRefGoogle Scholar
Konieczny, S. (1999). Sur la Logique du Changement: Révision et Fusion de Bases de Connaissance, Ph.D. dissertation, University of Lille, Lille, France.Google Scholar
Konieczny, S., and Pino-Pérez, R. (1998). “On the logic of merging”, in Proceedings of KR'98, Morgan Kaufmann, 488498.Google Scholar
Konieczny, S., and Pino-Pérez, R. (2002). “Merging information under constraints: a logical framework”, Journal of Logic and Computation, 12(5): 773808.CrossRefGoogle Scholar
Kornhauser, L. A. (1992). “Modelling collegial courts. II. Legal doctrine”, Journal of Law, Economics and Organization, 8: 441470.Google Scholar
Kornhauser, L.A., and Sager, L.G. (1986). “Unpacking the court”, Yale Law Journal, 96:82117.CrossRefGoogle Scholar
Kornhauser, L.A., and Sager, L.G. (1993). “The one and the many: Adjudication in collegial courts”, California Law Review, 81(1): 159.CrossRefGoogle Scholar
Liberatore, P, and Schaerf, M. (1998). “Arbitration (or how to merge knowledge bases)”, in IEEE Transactions on Knowledge and Data Engineering, 10(1): 7690.CrossRefGoogle Scholar
Lin, J., and Mendelzon, A. (1996). “Merging databases under constraints“, International Journal of Cooperative Information Systems, 7: 5576.CrossRefGoogle Scholar
Lin, J., and Mendelzon, A. (1999). “Knowledge base merging by majority”, in Dynamic Worlds: From the Frame Problem to Knowledge Management, Pareschi, R. and Fronhoefer, B., (eds), Kluwer.Google Scholar
List, C. (2005a). Judgment Aggregation – A Bibliography on the Discursive Dilemma, the Doctrinal Paradox and Decisions on Multiple Propositions. http://personal.lse.ac.uk/LISTdoctrinalparadox.htmGoogle Scholar
List, C. (2005b). “Group knowledge and group rationality: a judgment aggregation perspective”, Episteme. A Journal of Social Epistemology, 2(1): 2538.CrossRefGoogle Scholar
List, C., and Pettit, P. (2002). “Aggregating sets of judgments: An impossibility result”, Economics and Philosophy, 18: 89110.CrossRefGoogle Scholar
Makinson, D, and Schlechta, K. (1991). “Floating conclusions and zombie paths: two deep difficulties in the ‘directly skeptical’ approach to defeasible inheritance nets”, Artificial Intelligence, 48: 199209.CrossRefGoogle Scholar
Pigozzi, G. (2005). “Collective decision-making without paradoxes: A fusion approach”. Working paper. King's College London. http://www.dcs.kcl.ac.uk/staff/pigozzi/publications.htmlGoogle Scholar
Revesz, P. (1997). “On the semantics of arbitration”, International Journal of Algebra and Computation, 7: 133160.CrossRefGoogle Scholar