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I have not really studied voting theory yet, but found some great links on the topic.http://en.wikipedia.org/wiki/Voting_systemhttp://plato.stanford.edu/entries/voting-methods/http://en.wikipedia.org/wiki/Social_choice_theoryThe field seems quite comprehensive.
At the Algorithmic Decision Theory conference (Guerin,Allen, and Goldsmith, 2013), we presented a heuristic algo-rithm, earlier proposed by Guerin (2012), for learning CP-nets from user queries. Our algorithm differs from other ap-proaches in that: 1. It learns through elicitation rather thanpassive observation. 2. It can employ a richer set of queries,such as outcome comparisons that can differ in any numberof variables and attribute comparisons that offer a heuris-tic for faster learning. 3. The algorithm is robust: it will al-ways output a CP-net, never failure. 4. While the algorithmdoes not always recover the original CP-net, the learned CP-net that it outputs is guaranteed to be consistent with theoriginal on all queries encountered in the learning process.5. The algorithm is efficient, assuming a bound on in-degree;that is, if a preference order can be represented as a CP-net,the algorithm learns a CP-net in time O(n p ), where n is thenumber of nodes (features) and p is a bound on the num-ber of parents a node may have. 6. We employed an exper-imental approach to evaluate our algorithm, simulating thequery process using randomly generated CP-nets. For theconference paper, I planned and performed additional ex-periments, including a series that used statistical samplingwhen exhaustive analysis was infeasible. I also modified thealgorithm that randomly generated CP-nets to exclude thepossibility of degenerate CPTs that could conflict with thedependency graph. The resulting set of experiments demon-strated that the learned CP-nets agree with the originals on ahigh percent of non-training preference comparisons.