可预测和量化的集体智慧

In order to address issues ranging from climate change to developing complex technologies and curing diseases, science relies on collective intelligence, or the ability of a group to work together and solve a range of problems that vary in complexity.

为了解决从气候变化到开发复杂技术和治疗疾病等各种问题，科学依赖于集体智慧，即一个团体共同努力解决一系列复杂性不同的问题的能力。

To better understand how to measure and predict collective intelligence, researchers used meta-analytic methods to evaluate data collected in 22 studies, including 5,349 individuals in 1,356 groups, and found strong support for a general factor of collective intelligence (CI). Furthermore, the data demonstrated that group collaboration processes were about twice as important for predicting CI than individual skill, and that group composition, including the proportion of women in a group and group member social perceptiveness, are also significant predictors of CI.

为了更好地理解如何衡量和预测集体智慧，研究人员使用元分析方法对22项研究中收集的数据进行了评估，其中包括1,356个组中的5,349个人，并为集体智慧(CI)的一般因素提供了有力支持。此外，数据表明，小组协作过程对于预测CI的重要性是个人技能的两倍，并且小组组成(包括组中女性的比例和小组成员的社会感知力)也是CI的重要预测因子。

The paper, "Quantifying Collective Intelligence in Human Groups," by Christoph Riedl (Northeastern University), Young Ji Kim (University of California, Santa Barbara), Pranav Gupta (Carnegie Mellon University), Thomas W. Malone (MIT Sloan School of Management), and Williams Woolley, Anita (Carnegie Mellon University) will be published in Proceedings of the National Academy of Sciences of the United States of America.

这篇名为《量化人类群体中的集体智慧》的论文将由 Christoph Riedl (东北大学)、 Young Ji Kim (卡内基梅隆大学)、 Pranav Gupta (卡内基梅隆大学)、 Thomas w. Malone (MIT斯隆管理学院)和 Williams Woolley，Anita (卡内基梅隆大学)撰写，发表在《美国国家科学院院刊》杂志上。

"This paper introduces some computational metrics for evaluating collaboration processes that could be foundational for studying collaboration moving forward," says Anita Williams Woolley, Associate Professor of Organizational Behavior and Theory at Carnegie Mellon's Tepper School of Business, who co-authored the paper. "We also continue to find that having more women in the group raises collective intelligence, and in the supplement we specifically compare face-to-face and online collaborators and find few differences in the elements that lead to collective intelligence."

“这篇论文介绍了一些评估协作过程的计算量度，这些量度可能是进一步研究协作的基础,”Anita Williams Woolley 说，她是卡内基梅隆大学组织行为学和理论泰珀商学院的副教授，也是这篇论文的合著者之一。“我们还发现，团队中有更多的女性会提高集体智慧，在补充材料中，我们特别比较了面对面和在线合作者，发现导致集体智慧的因素几乎没有差异。”

In previous research, Woolley and colleagues built on the approach informing research on general intelligence in individuals and found that a group's ability to perform a wide range of tasks could also be predicted by a single statistical factor, which they labeled collective intelligence. They further demonstrated that this CI factor was weakly correlated with the group members' individual intelligence, but more strongly correlated with members' social sensitivity, and the proportion of females in the group. Since that research was published, other papers have confirmed the results while a few have challenged whether or not there is a general CI factor, and have asserted that individual intelligence is the only real predictor of it.

在之前的研究中，Woolley 和他的同事们建立了一种研究个体一般智力的方法，他们发现一个团队执行广泛任务的能力也可以通过一个单一的统计因素来预测，他们称之为集体智力。他们进一步证明，这个 CI 因子与群体成员的个人智力相关性很弱，但与群体成员的社会敏感性和女性比例相关性更强。自从这项研究发表以来，其他论文已经证实了这一结果，而少数论文质疑是否存在一个普遍的 CI 因素，并断言个人智力是唯一真正的预测因素。

In this new paper, the researchers evaluate these questions by drawing on accumulated data from 22 different samples, involving 5,349 individuals working together in 1,356 groups of various settings, including online, face-to-face, people who know and work together as well as strangers. Using a meta-analytic approach, the researchers analyzed each sample and quantified indicators of group collaboration. Using machine learning techniques, they determined the relative importance of different variables for predicting CI, observing that group collaboration process measures were about twice as important as individual member skill; other important predictors were social perceptiveness, group composition (particularly female proportion and age diversity) and group size.

在这篇新论文中，研究人员利用22个不同样本的累积数据来评估这些问题，这些样本涉及5349个人，他们在1356个不同的设置共同工作，包括在线、面对面、认识和共事的人以及陌生人。研究人员使用元分析方法，分析了每个样本和团队合作的量化指标。利用机器学习技术，他们确定了不同变量对于预测 CI 的相对重要性，观察到团队合作过程测量的重要性大约是个人成员技能的两倍; 其他重要的预测因素是社会认知度、团队组成(特别是女性比例和年龄多样性)和团队规模。

The research advances the science of collective performance both conceptually and methodologically. By using a metric of collective intelligence based on a variety of tasks, a group's score should predict future performance in a broader range of settings Additionally, by focusing on a more robust measure of a group's capability to work together, researchers can more confidently identify the group composition and collaboration behaviors that will enable people to assemble and structure groups for high collective intelligence.

本研究从概念和方法两个方面推动了集体绩效的科学发展。通过使用基于各种任务的集体智慧度量标准，一个团队的得分应该可以预测未来在更广泛的环境中的表现。研究人员可以更自信地确定团体的组成和协作方式，使人们能够为高集体智慧而整合和调整自己的小组。