资源论文Analysis and Optimization of Multi-Dimensional Percentile Mechanisms Xin Sui Craig Boutilier Tuomas Sandholm

Analysis and Optimization of Multi-Dimensional Percentile Mechanisms Xin Sui Craig Boutilier Tuomas Sandholm

2019-11-11 | |  72 |   39 |   0
Abstract We consider the mechanism design problem for agents with single-peaked preferences over multi-dimensional domains when multiple alternatives can be chosen. Facility location and committee selection are classic embodiments of this problem. We propose a class of percentile mechanisms, a form of generalized median mechanisms, that are strategy-proof, and derive worst-case approximation ratios for social cost and maximum load for L1 and L2 cost models. More importantly, we propose a samplebased framework for optimizing the choice of percentiles relative to any prior distribution over preferences, whil maintaining strategy-proofness. Our empirical investigations, using social cost and maximum load as objectives, demonstrate the viability of this approach and the value of such optimized mechanisms vis-a?-vis mechanisms derived through worst-case analysis.

上一篇:Bimodal Switching for Online Planning in Multiagent Settings? Ekhlas Sonu and Prashant Doshi

下一篇:Multi-Dimensional Single-Peaked Consistency and Its Approximations Xin Sui Alex Francois-Nienaber Craig Boutilier

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...