Applications of Logic in Social Choice Theory

(Invited Talk)
  • Ulle Endriss
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6814)


Social choice theory studies of how groups of people should and do make collective decisions. In this talk I will argue that modern logic can contribute to the study of social choice theory in many different ways, and I will substantiate this claim with examples from recent work by members of my group at the University of Amsterdam.


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    Chevaleyre, Y., Endriss, U., Lang, J., Maudet, N.: Preference handling in combinatorial domains: From AI to social choice. AI Magazine 29(4), 37–46 (2008)Google Scholar
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    Endriss, U., Grandi, U., Porello, D.: Complexity of judgment aggregation: Safety of the agenda. In: Proc. 9th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS-2010 (2010)Google Scholar
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    Endriss, U., Grandi, U., Porello, D.: Complexity of winner determination and strategic manipulation in judgment aggregation. In: Proc. 3rd International Workshop on Computational Social Choice, COMSOC-2010 (2010)Google Scholar
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    Endriss, U., Pini, M.S., Rossi, F., Venable, K.B.: Preference aggregation over restricted ballot languages: Sincerity and strategy-proofness. In: Proc. 21st International Joint Conference on Artificial Intelligence, IJCAI-2009 (2009)Google Scholar
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    Geist, C., Endriss, U.: Automated search for impossibility theorems in social choice theory: Ranking sets of objects. Journal of Artificial Intelligence Research (JAIR) 40, 143–174 (2011)MathSciNetzbMATHGoogle Scholar
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    Grandi, U., Endriss, U.: First-order logic formalisation of arrow’s theorem. In: He, X., Horty, J., Pacuit, E. (eds.) LORI 2009. LNCS, vol. 5834, pp. 133–146. Springer, Heidelberg (2009)CrossRefGoogle Scholar
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    Grandi, U., Endriss, U.: Lifting rationality assumptions in binary aggregation. In: Proc. 24th AAAI Conference on Artificial Intelligence, AAAI-2010 (2010)Google Scholar
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    Grandi, U., Endriss, U.: Binary aggregation with integrity constraints. In: Proc. 22nd International Joint Conference on Artificial Intelligence, IJCAI-2011 (2011)Google Scholar
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    Porello, D., Endriss, U.: Modelling combinatorial auctions in linear logic. In: Proc. 12th International Conference on the Principles of Knowledge Representation and Reasoning, KR-2010 (2010)Google Scholar
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    Porello, D., Endriss, U.: Ontology merging as social choice. In: Leite, F., Torroni, P., Ågotnes, T., Boella, G., van der Torre, L. (eds.) CLIMA XII 2011. LNCS (LNAI), vol. 6814, pp. 157–170. Springer, Heidelberg (2011)Google Scholar
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    Uckelman, J., Chevaleyre, Y., Endriss, U., Lang, J.: Representing utility functions via weighted goals. Mathematical Logic Quarterly 55(4), 341–361 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
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    Uckelman, J., Endriss, U.: Compactly representing utility functions using weighted goals and the max aggregator. Artificial Intelligence 174(15), 1222–1246 (2010)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ulle Endriss
    • 1
  1. 1.Institute for Logic, Language and Computation (ILLC)University of AmsterdamNetherlands

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