可預測和量化的集體智慧

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.

為了解決從氣候變化到開發(fā)復雜技術(shù)和治療疾病等各種問題，科學依賴于集體智慧，即一個團體共同努力解決一系列復雜性不同的問題的能力。

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.

為了更好地理解如何衡量和預測集體智慧，研究人員使用元分析方法對22項研究中收集的數(shù)據(jù)進行了評估，其中包括1,356個組中的5,349個人，并為集體智慧(CI)的一般因素提供了有力支持。此外，數(shù)據(jù)表明，小組協(xié)作過程對于預測CI的重要性是個人技能的兩倍，并且小組組成(包括組中女性的比例和小組成員的社會感知力)也是CI的重要預測因子。

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.

這篇名為《量化人類群體中的集體智慧》的論文將由 Christoph Riedl (東北大學)、 Young Ji Kim (卡內(nèi)基梅隆大學)、 Pranav Gupta (卡內(nèi)基梅隆大學)、 Thomas w. Malone (MIT斯隆管理學院)和 Williams Woolley，Anita (卡內(nèi)基梅隆大學)撰寫，發(fā)表在《美國國家科學院院刊》雜志上。

"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."

“這篇論文介紹了一些評估協(xié)作過程的計算量度，這些量度可能是進一步研究協(xié)作的基礎(chǔ),”Anita Williams Woolley 說，她是卡內(nèi)基梅隆大學組織行為學和理論泰珀商學院的副教授，也是這篇論文的合著者之一。“我們還發(fā)現(xiàn)，團隊中有更多的女性會提高集體智慧，在補充材料中，我們特別比較了面對面和在線合作者，發(fā)現(xiàn)導致集體智慧的因素幾乎沒有差異。”

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 和他的同事們建立了一種研究個體一般智力的方法，他們發(fā)現(xiàn)一個團隊執(zhí)行廣泛任務的能力也可以通過一個單一的統(tǒng)計因素來預測，他們稱之為集體智力。他們進一步證明，這個 CI 因子與群體成員的個人智力相關(guān)性很弱，但與群體成員的社會敏感性和女性比例相關(guān)性更強。自從這項研究發(fā)表以來，其他論文已經(jīng)證實了這一結(jié)果，而少數(shù)論文質(zhì)疑是否存在一個普遍的 CI 因素，并斷言個人智力是唯一真正的預測因素。

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.

在這篇新論文中，研究人員利用22個不同樣本的累積數(shù)據(jù)來評估這些問題，這些樣本涉及5349個人，他們在1356個不同的設(shè)置共同工作，包括在線、面對面、認識和共事的人以及陌生人。研究人員使用元分析方法，分析了每個樣本和團隊合作的量化指標。利用機器學習技術(shù)，他們確定了不同變量對于預測 CI 的相對重要性，觀察到團隊合作過程測量的重要性大約是個人成員技能的兩倍; 其他重要的預測因素是社會認知度、團隊組成(特別是女性比例和年齡多樣性)和團隊規(guī)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.

本研究從概念和方法兩個方面推動了集體績效的科學發(fā)展。通過使用基于各種任務的集體智慧度量標準，一個團隊的得分應該可以預測未來在更廣泛的環(huán)境中的表現(xiàn)。研究人員可以更自信地確定團體的組成和協(xié)作方式，使人們能夠為高集體智慧而整合和調(diào)整自己的小組。