Print Email Facebook Twitter Minimising the Rank Aggregation Error Title Minimising the Rank Aggregation Error: (Extended Abstract) Author de Weerdt, M.M. (TU Delft Algorithmics) Gerding, Enrico (University of Southampton) Stein, Sebastian (University of Southampton) Date 2016 Abstract Rank aggregation is the problem of generating an overall ranking from a set of individual votes which is as close as possible to the (unknown) correct ranking. The challenge is that votes are often both noisy and incomplete. Existing work focuses on the most likely ranking for a particular noise model. Instead, we focus on minimising the error, i.e., the expected distance between the aggregated ranking and the correct one. We show that this results in different rankings, and we show how to compute local improvements of rankings to reduce the error. Extensive experiments on both synthetic data based on Mallows' model and real data show that Copeland has a smaller error than the Kemeny rule, while the latter is the maximum likelihood estimator. Subject Economic paradigmsSocial Choice Theory To reference this document use: http://resolver.tudelft.nl/uuid:440b3bd7-4235-4735-a887-ee91805ed5ba Page numbers 1375-1376 Event AAMAS 2016, 2016-05-09 → 2016-05-13, Singapore, Singapore Bibliographical note Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type abstract Rights © 2016 M.M. de Weerdt, Enrico Gerding, Sebastian Stein Files PDF p1375_1.pdf 1014.4 KB Close viewer /islandora/object/uuid:440b3bd7-4235-4735-a887-ee91805ed5ba/datastream/OBJ/view