用于众包评审的并行竞赛:均衡的存在性与质量
Parallel Contests for Crowdsourcing Reviews: Existence and Quality of Equilibria
AI总结:
本文针对区块链财政资金分配场景下的并行众包评审问题,设计了一种始终存在纯纳什均衡的奖励方案,并证明该均衡在评审总质量和获评审提案比例两个指标上均能达到常数因子近似最优。
摘要:
受区块链环境中分配财政资金的复杂性所驱动,我们研究了并行地为许多不同提案众包评审的问题。在评审阶段,每位评审者可以选择要为哪些提案撰写评审,以及每份评审的质量。质量等级遵循某些非常粗略的社区指南,可以取诸如“优秀”或“良好”等值。基于这些分数和评审的分布,每位评审者将因其努力而获得一定奖励。在本文中,我们设计了一种奖励方案,并证明对于任意提案集合和评审者集合,它始终具有纯纳什均衡。此外,我们证明这些均衡能够为两个自然指标保证常数因子近似:与最优结果相比,所有评审的总质量,以及至少收到一份评审的提案所占的比例。
英文摘要:
Motivated by the intricacies of allocating treasury funds in blockchain settings, we study the problem of crowdsourcing reviews for many different proposals, in parallel. During the reviewing phase, every reviewer can select the proposals to write reviews for, as well as the quality of each review. The quality levels follow certain very coarse community guidelines and can have values such as 'excellent' or 'good'. Based on these scores and the distribution of reviews, every reviewer will receive some reward for their efforts. In this paper, we design a reward scheme and show that it always has pure Nash equilibria, for any set of proposals and reviewers. In addition, we show that these equilibria guarantee constant factor approximations for two natural metrics: the total quality of all reviews, as well as the fraction of proposals that received at least one review, compared to the optimal outcome.
机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。