Titlebook: Algorithmic Decision Theory; Second International Ronen I. Brafman,Fred S. Roberts,Alexis Tsoukiàs Conference proceedings 2011 Springer-Ver

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Efficiently Eliciting Preferences from a Group of Users,ers, we can use the preferences of the users we have already processed to increase the efficiency of the elicitation process for the remaining users. However, current methods either require strong prior knowledge about the users’ preferences or can be overly cautious and inefficient. Our method, bas

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Vote Elicitation with Probabilistic Preference Models: Empirical Estimation and Cost Tradeoffs,rement that participants provide full preference information in the form of a complete ranking of alternatives is a severe impediment to their practical deployment. Only recently have incremental elicitation schemes been proposed that allow winners to be determined with partial preferences; however,

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Efficient Approximation Algorithms for Multi-objective Constraint Optimization, optimization. Our approach builds upon recent advances in multi-objective heuristic search over weighted AND/OR search spaces and uses an .-dominance relation between cost vectors to significantly reduce the set of non-dominated solutions. Our empirical evaluation on various benchmarks demonstrates

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Empirical Evaluation of Voting Rules with Strictly Ordered Preference Data,rive several million elections (more than all the existing studies combined) from a publicly available data, the Netflix Prize dataset. The Netflix data is derived from millions of Netflix users, who have an incentive to report sincere preferences, unlike random survey takers. We evaluate each of th