Center for Collective Intelligence, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
PLoS One. 2018 Oct 11;13(10):e0205335. doi: 10.1371/journal.pone.0205335. eCollection 2018.
Researchers in many disciplines have previously used a variety of mathematical techniques for analyzing group interactions. Here we use a new metric for this purpose, called "integrated information" or "phi." Phi was originally developed by neuroscientists as a measure of consciousness in brains, but it captures, in a single mathematical quantity, two properties that are important in many other kinds of groups as well: differentiated information and integration. Here we apply this metric to the activity of three types of groups that involve people and computers. First, we find that 4-person work groups with higher measured phi perform a wide range of tasks more effectively, as measured by their collective intelligence. Next, we find that groups of Wikipedia editors with higher measured phi create higher quality articles. Last, we find that the measured phi of the collection of people and computers communicating on the Internet increased over a recent six-year period. Together, these results suggest that integrated information can be a useful way of characterizing a certain kind of interactional complexity that, at least sometimes, predicts group performance. In this sense, phi can be viewed as a potential metric of effective group collaboration.
许多学科的研究人员以前曾使用各种数学技术来分析群体互动。在这里，我们为此目的使用了一种新的度量标准，称为“综合信息”或“phi”。Phi 最初是由神经科学家作为大脑意识的一种衡量标准而开发的，但它在单个数学量中捕获了在许多其他类型的群体中也很重要的两个特性：差异化信息和集成。在这里，我们将此度量标准应用于涉及人和计算机的三种类型的群体的活动。首先，我们发现具有更高测量 phi 的 4 人工作组在更广泛的任务中表现更有效，这可以通过他们的集体智慧来衡量。接下来，我们发现具有更高测量 phi 的维基百科编辑群体创建了更高质量的文章。最后，我们发现互联网上人与人、计算机之间的通信集合的测量 phi 在最近六年期间有所增加。总的来说，这些结果表明综合信息可以是一种有用的方法来描述某种交互复杂性，至少在某些时候，它可以预测群体表现。从这个意义上说，phi 可以被视为有效群体协作的潜在度量标准。
