资源Strategy-Proofness of Scoring Allocation Correspondences for Indivisible Goods Nhan-Tam Nguyen Dorothea Baumeister Jo?rg Rothe

Strategy-Proofness of Scoring Allocation Correspondences for Indivisible Goods Nhan-Tam Nguyen Dorothea Baumeister Jo?rg Rothe

2019-11-18 | |  32 |   1 |   0
Abstract We study resource allocation in a model due to Brams and King [2005] and further developed by Baumeister et al. [2014]. Resource allocation deals with the distribution of resources to agents. We assume resources to be indivisible, nonshareable, and of single-unit type. Agents have ordinal preferences over single resources. Using scoring vectors, every ordinal preference induces a utility function. These utility functions are used in conjunction with utilitarian social welfare to assess the quality of allocations of resources to agents. Then allocation correspondences determine the optimal allocations that maximize utilitarian social welfare. Since agents may have an incentive to misreport their true preferences, the question of strategyproofness is important to resource allocation. We assume that a manipulator has a strictly monotonic and strictly separable linear order on the power set of the resources. We use extension principles (from social choice theory, such as the Kelly and the Ga?rdenfors extension) for preferences to study manipulation of allocation correspondences. We characterize strategy-proofness of the utilitarian allocation correspondence: It is Ga?rdenfors/Kellystrategy-proof if and only if the number of different values in the scoring vector is at most two or the number of occurrences of the greatest value in the scoring vector is larger than half the number of goods.

上一篇:Towards City-scale Mobile Crowdsourcing: Task Recommendations under Trajectory Uncertainties?

下一篇:A Hybrid Neural Model for Type Classification of Entity Mentions Li Dong†? Furu Wei‡ Hong Sun$ Ming Zhou‡ Ke Xu†

用户评价
全部评价

热门资源

  • Multi-Source Cros...

    Modern NLP applications have enjoyed a great bo...

  • Reference Network...

    Neural Machine Translation (NMT) has achieved n...

  • Soft Contextual D...

    While data augmentation is an important trick t...

  • Style Transformer...

    Disentangling the content and style in the lat...

  • Towards Fine-grai...

    In this paper, we focus on the task of finegra...