在极难问题上的互联网合作:在博学者网站上的研究与奥林匹克问题

Isabel M. Kloumann, Chenhao Tan, J. Kleinberg, Lillian Lee
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引用次数: 6

摘要

尽管在包括开源软件在内的复杂项目上存在着非常成功的互联网合作,但人们对互联网合作如何解决“极其”困难的问题(如开放式研究问题)知之甚少。我们定量调查了一系列被称为“博学者”的项目,这些项目通过开放的在线讨论来解决数学研究问题。一个关键的分析见解是,我们可以将通才项目与迷你通才项目进行对比——迷你通才项目是以与通才项目相同的方式进行的,但旨在解决奥林匹克数学问题,尽管相当困难,但已知是可行的。我们的比较分析在项目的三个要素之间转换:作者的角色和关系,项目如何演变的时间动态,以及讨论本身的语言特性。通过这些分析,我们发现了两个领域之间有趣的差异,并将这些分析作为模板,以方便将Polymath与其他领域的协作和交流进行比较。我们还开发了在区分基于任何特征组的研究级评论方面具有强大性能的模型。最后,我们考察了代表研究突破的评论是否可以基于其内在特征或通过他人的(再)行为更有效地识别,并发现语言特征具有良好的预测能力。
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Internet Collaboration on Extremely Difficult Problems: Research versus Olympiad Questions on the Polymath Site
Despite the existence of highly successful Internet collaborations on complex projects, including open-source software, little is known about how Internet collaborations work for solving "extremely" difficult problems, such as open-ended research questions. We quantitatively investigate a series of efforts known as the Polymath projects, which tackle mathematical research problems through open online discussion. A key analytical insight is that we can contrast the polymath projects with mini-polymaths -- spinoffs that were conducted in the same manner as the polymaths but aimed at addressing math Olympiad questions, which, while quite difficult, are known to be feasible. Our comparative analysis shifts between three elements of the projects: the roles and relationships of the authors, the temporal dynamics of how the projects evolved, and the linguistic properties of the discussions themselves. We find interesting differences between the two domains through each of these analyses, and present these analyses as a template to facilitate comparison between Polymath and other domains for collaboration and communication. We also develop models that have strong performance in distinguishing research-level comments based on any of our groups of features. Finally, we examine whether comments representing research breakthroughs can be recognized more effectively based on their intrinsic features, or by the (re-)actions of others, and find good predictive power in linguistic features.
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