Boomerang：将声誉反馈的后果反弹回众包平台
计算机与社会
2019-04-16 v1 人机交互
综合经济学
经济学
摘要
付费众包平台饱受低质量工作与不公正拒收之苦，但矛盾的是，大多数工作者和请求者都拥有较高的声誉分数。这些膨胀的分数使得高质量工作与工作者难以被找到，其源于避免给出负面反馈的社会压力。我们提出 Boomerang，一种用于众包的声誉系统，它通过将反馈的后果直接反弹回给出反馈者身上，从而引出更准确的反馈。借助 Boomerang，请求者发现其高评分工作者能最早获得其未来任务的访问权，而工作者在其任务流顶部看到来自高评分请求者的任务。现场实验验证，Boomerang 使工作者与请求者提供的反馈更贴近其私下看法。受博弈论中激励相容概念的启发，Boomerang 为交互设计提供了激励诚实报告而非策略性欺骗的机会。
引用
@article{arxiv.1904.06722,
title = {Boomerang: Rebounding the Consequences of Reputation Feedback on Crowdsourcing Platforms},
author = {Snehalkumar and S. Gaikwad and Durim Morina and Adam Ginzberg and Catherine Mullings and Shirish Goyal and Dilrukshi Gamage and Christopher Diemert and Mathias Burton and Sharon Zhou and Mark Whiting and Karolina Ziulkoski and Alipta Ballav and Aaron Gilbee and Senadhipathige S. Niranga and Vibhor Sehgal and Jasmine Lin and Leonardy Kristianto and Angela Richmond-Fuller and Jeff Regino and Nalin Chhibber and Dinesh Majeti and Sachin Sharma and Kamila Mananova and Dinesh Dhakal and William Dai and Victoria Purynova and Samarth Sandeep and Varshine Chandrakanthan and Tejas Sarma and Sekandar Matin and Ahmed Nasser and Rohit Nistala and Alexander Stolzoff and Kristy Milland and Vinayak Mathur and Rajan Vaish and Michael S. Bernstein},
journal= {arXiv preprint arXiv:1904.06722},
year = {2019}
}