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Found 6 papers, 0 papers with code
Optimizing Multiple Simultaneous Objectives for Voting and Facility Location
In particular, we show that for any such pair of objectives, it is always possible to choose an outcome which simultaneously approximates both objectives within a factor of $1+\sqrt{2}$, and give a precise characterization of how this factor improves as the two objectives being optimized become more similar.
Awareness of Voter Passion Greatly Improves the Distortion of Metric Social Choice
Previous work has often assumed that only ordinal preferences of the voters are known (instead of their true costs), and focused on minimizing distortion: the quality of the chosen candidate as compared with the best possible candidate.
Truthful Mechanisms for Matching and Clustering in an Ordinal World
We study truthful mechanisms for matching and related problems in a partial information setting, where the agents' true utilities are hidden, and the algorithm only has access to ordinal preference information.
Randomized Social Choice Functions Under Metric Preferences
We determine the quality of randomized social choice mechanisms in a setting in which the agents have metric preferences: every agent has a cost for each alternative, and these costs form a metric.
Blind, Greedy, and Random: Ordinal Approximation Algorithms for Matching and Clustering
Using our algorithms for matching as a black-box, we also design new approximation algorithms for other closely related problems: these include a a 3. 2-approximation for the problem of clustering agents into equal sized partitions, a 4-approximation algorithm for Densest k-subgraph, and a 2. 14-approximation algorithm for Max TSP.
Computing Stable Coalitions: Approximation Algorithms for Reward Sharing
Consider a setting where selfish agents are to be assigned to coalitions or projects from a fixed set P. Each project k is characterized by a valuation function; v_k(S) is the value generated by a set S of agents working on project k. We study the following classic problem in this setting: "how should the agents divide the value that they collectively create?".
