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A testbed to enable comparisons between competing approaches for computational social choice

1. University of Waterloo & New College of Florida 5800 Bayshore Road Sarasota, FL 34234, USA;
2. University of Waterloo 200 University Avenue West Waterloo, ON N2L 3G1, Canada

Within artificial intelligence, the field of computational social choice studies the application of AI techniques to the problem of group decision making, especially through systems where each agent submits a vote taking the form of a total ordering over the alternatives (a preference). Reaching a reasonable decision becomes more difficult when some agents are unwilling or unable to rank all the alternatives, and appropriate voting systems must be devised to handle the resulting incomplete preference information. In this paper, we present a detailed testbed which can be used to perform information analytics in this domain. We illustrate the testbed in action for the context of determining a winner or putting candidates into ranked order, using data from realworld elections, and demonstrate how to use the results of the testbed to produce effective comparisons between competing algorithms.
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Keywords Information analytics for voting systems; computational social choice; machine learning; partial preferences; testbeds

Citation: John A. Doucette, Robin Cohen. A testbed to enable comparisons between competing approaches for computational social choice. Big Data and Information Analytics, 2016, 1(4): 309-340. doi: 10.3934/bdia.2016013


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