Project Description

One of the most common preference aggregation methods—the one most familiar to Americans—is election
by majority. Other preference aggregation methods are not always recognized as such, for example, (sports)
tournaments. One can view a sports tournament as an election where the best team wins. We can affect the
outcome of a vote or tournament by voting and playing truthfully and to the best of our ability, etc., or by
manipulating the aggregation process.

There are several methods by which aggregation schemes can be manipulated. The most intuitive and
well known is by influencing individual agents (through payments or other means). In real-world systems,
typically not everything (the influence, the vote, the result) is observable by the manipulator. With this project,
we focus on uncertain outcomes: What happens if the manipulator has access only to probabilities of vote
outcomes and/or to probabilities of agents’ responses to attempts to influence them.

We achieve this through new model methods for established problems which take into account an agent's
uncertainty about aspects of the aggregation procedures. Once we have developed these new models
we study the complexity of lobbying and other influence methods in this uncertain world.

Project Details

Research Team

University of Kentucky

Institut Fur Informatik: Heinrich-Heine-Universitat Dusseldorf

University of Trier


  • An Empirical Study of Voting Rules and Manipulation with Large Datasets. Nicholas Mattei, James Forshee, and Judy Goldsmith. Proceedings of the 4th International Conference on Computational Social Choice (COMSOC 2012), Krakow, Poland.
  • The Complexity of Probabilistic Lobbying. G. Erdélyi, H. Fernau, J. Goldsmith, N. Mattei, D. Raible, and J. Rothe. Proceedings of the 1st International Conference on Algorithmic Decision Theory (ADT 2009), Venice, Italy. Springer-Verlag Lecture Notes in Artificial Intelligence 5783, pp. 86-97, October 2009.
  • The Complexity of Probabilistic Lobbying. G. Erdélyi, H. Fernau, J. Goldsmith, N. Mattei, D. Raible, and J. Rothe. Technical Report arXiv:0906.4431v3 [cs.CC], ACM Computing Research Repository (CoRR), 27 pages, June 2009. Revised, November 2009.


  • NSF-EAGER CCF-1049360: Changing Minds, Changing Probabilities
    • Ammount: $144,467
    • Duration: August 2010 - August 2012
Topic revision: r4 - 2012-10-23 - 21:16:01 - LibbyKnouse
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