Strategyproof peer selection using randomization, partitioning, and apportionment
Strategyproof peer selection using randomization, partitioning, and apportionment
复制标题
使用随机化、分区和分配进行策略证明的对等选择
DOI：
10.1016/j.artint.2019.06.004
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
T. Walsh
中科院分区：
文献类型：
--
作者：
H. Aziz;Omer Lev;Nicholas Mattei;J. Rosenschein;T. Walsh
同行评议、评估和选择是现代科学的一个基本方面。世界各地的资助机构聘请专家从提交的资助方案中审查和选择最佳方案。然而，同行选择的问题要普遍得多：一个专业协会可能希望根据所有成员的意见给它的一部分成员颁奖；大型在线开放课程（MOOC）或在线课程的讲师可能想要众包评分；或者，一家营销公司可能会根据同行评估，从集体头脑风暴会议中选择创意。我们对同伴选择的研究做出了三个基本贡献，同伴选择是一种特定类型的群体决策问题，在计算机科学、经济学和政治学中都有研究。首先，我们提出了一种新的策略证明机制，即代理人不能通过报告不真实的估值而受益。其次，我们通过与文献中发现的一套机制进行全面的基于模拟的比较来证明我们机制的有效性。最后，我们的机制采用了一种独立的随机四舍五入技术，因为它解决了在需要按比例划分议会代表席位等离散资源的各种设置中出现的分配问题。
Peer reviews, evaluations, and selections are a fundamental aspect of modern science. Funding bodies the world over employ experts to review and select the best proposals from those submitted for funding. The problem of peer selection, however, is much more general: a professional society may want to give a subset of its members awards based on the opinions of all members; an instructor for a Massive Open Online Course (MOOC) or an online course may want to crowdsource grading; or a marketing company may select ideas from group brainstorming sessions based on peer evaluation.We make three fundamental contributions to the study of peer selection, a specific type of group decision-making problem, studied in computer science, economics, and political science. First, we propose a novel mechanism that is strategyproof, i.e., agents cannot benefit by reporting insincere valuations. Second, we demonstrate the effectiveness of our mechanism by a comprehensive simulation-based comparison with a suite of mechanisms found in the literature. Finally, our mechanism employs a randomized rounding technique that is of independent interest, as it solves the apportionment problem that arises in various settings where discrete resources such as parliamentary representation slots need to be divided proportionally.
NIPS 2016审核流程的设计与分析
影响因子：
6
作者：
Shah, N. B.;Tabibian, B.;Muandet, K.;Guyon, I.;Von Luxburg, U.
通讯作者：
Von Luxburg, U.
DOI：
--
发表时间：
2018-06
期刊：
J. Mach. Learn. Res.
影响因子：
--
作者：
Ivan Stelmakh;Nihar B. Shah;Aarti Singh
通讯作者：
Ivan Stelmakh;Nihar B. Shah;Aarti Singh