The Knowledge Accelerator: Big Picture Thinking in Small Pieces
The Knowledge Accelerator: Big Picture Thinking in Small Pieces
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知识加速器：小片段的大局思考
DOI：
10.1145/2858036.2858364
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
A. Kittur
中科院分区：
文献类型：
--
作者：
Nathan Hahn;Joseph Chee Chang;Ji Eun Kim;A. Kittur
众包为在线工作提供了强大的新范例。然而，现实世界的任务通常是相互依赖的，需要对所涉及的不同部分有一个大局观。支持此类任务的现有众包方法（从维基百科到 Flash 团队）由于依赖少数人来维护全局而受到瓶颈。在本文中，我们探讨了这样的想法：计算系统可以完全通过个人的微小贡献构建新兴的相互依存的大局观，每个人只看到整体的一部分。为了研究这种方法的可行性、优点和缺点，我们在原型系统中实例化了这个想法，以完成分布式信息合成，并评估其跨各种主题的输出。我们还贡献了一组设计模式，这些模式可能为其他旨在支持小片段的大局思考的系统提供信息。
Crowdsourcing offers a powerful new paradigm for online work. However, real world tasks are often interdependent, requiring a big picture view of the difference pieces involved. Existing crowdsourcing approaches that support such tasks -- ranging from Wikipedia to flash teams -- are bottlenecked by relying on a small number of individuals to maintain the big picture. In this paper, we explore the idea that a computational system can scaffold an emerging interdependent, big picture view entirely through the small contributions of individuals, each of whom sees only a part of the whole. To investigate the viability, strengths, and weaknesses of this approach we instantiate the idea in a prototype system for accomplishing distributed information synthesis and evaluate its output across a variety of topics. We also contribute a set of design patterns that may be informative for other systems aimed at supporting big picture thinking in small pieces.