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通信技术的进步以较低的间接成本促使了全球公司的兴起。互联网遍布全球，因此一家全球整合的公司打破了地域限制，他们可以访问任何新市场，新思想和新技术。

通信技术的进步以较低的间接成本促使了全球公司的兴起。互联网遍布全球，因此一家全球整合的公司打破了地域限制，他们可以访问任何新市场，新思想和新技术。

== Collective intelligence factor ''c'' ==

== Collective intelligence factor ''c'' 集体智力因子c ==

[[File:Scree plot showing percent of explained variance for the first five factors in Woolley et al.'s (2010) two original studies as well as the individual intelligence test for all participants (assessed with Wonderlic Personnel Test).png|thumb|[[Scree plot]] showing percent of explained variance for the first factors in Woolley et al.'s (2010) two original studies.]]

[[文件:陡坡图显示了伍利等人（2010）的两项初始研究成果，其中包括第一因素的可释方差百分比。.png|缩略图|右|陡坡图显示了伍利等人（2010）的两项初始研究成果，其中包括第一因素的可释方差百分比。]]

A new scientific understanding of collective intelligence defines it as a group's general ability to perform a wide range of tasks. Definition, operationalization and statistical methods are similar to the [[G factor (psychometrics)|psychometric approach of general individual intelligence]]. Hereby, an individual's performance on a given set of cognitive tasks is used to measure general cognitive ability indicated by the general intelligence [[G factor (psychometrics)|factor ''g'']] extracted via [[factor analysis]]. In the same vein as ''g'' serves to display between-individual performance differences on cognitive tasks, collective intelligence research aims to find a parallel intelligence factor for groups) displaying between-group differences on task performance. The collective intelligence score then is used to predict how this same group will perform on any other similar task in the future. Yet tasks, hereby, refer to mental or intellectual tasks performed by small groups even though the concept is hoped to be transferable to other performances and any groups or crowds reaching from families to companies and even whole cities. Since individuals' ''g'' factor scores are highly correlated with full-scale [[Intelligence quotient|IQ]] scores, which are in turn regarded as good estimates of ''g'', this measurement of collective intelligence can also be seen as an intelligence indicator or quotient respectively for a group (Group-IQ) parallel to an individual's intelligence quotient (IQ) even though the score is not a quotient per se.

 

 

A new scientific understanding of collective intelligence defines it as a group's general ability to perform a wide range of tasks.<ref name=":0" /> Definition, operationalization and statistical methods are similar to the [[G factor (psychometrics)|psychometric approach of general individual intelligence]]. Hereby, an individual's performance on a given set of cognitive tasks is used to measure general cognitive ability indicated by the general intelligence [[G factor (psychometrics)|factor ''g'']] extracted via [[factor analysis]].<ref>{{Cite journal|last=Spearman|first=Charles, E.|date=1904|title="General intelligence," objectively determined and measured|url=|journal=American Journal of Psychology |volume=15 |issue=2|pages=201–293|doi=10.2307/1412107|pmid=|jstor=1412107}}</ref> In the same vein as ''g'' serves to display between-individual performance differences on cognitive tasks, collective intelligence research aims to find a parallel intelligence factor for groups {{'}}''c'' factor'<ref name=":0" /> (also called 'collective intelligence factor' (''CI'')<ref name=":4">{{Cite journal|author1=Engel, D. |author2=Woolley, A. W. |author3=Jing, L. X. |author4=Chabris, C. F. |author5= Malone, T. W. |last-author-amp=yes |date=2014|title=Reading the Mind in the Eyes or reading between the lines? Theory of Mind predicts collective intelligence equally well online and face-to-face|journal=PLOS One |volume=9 |issue=12 |pages=e115212|doi=10.1371/journal.pone.0115212|pmid=25514387 |pmc=4267836|bibcode=2014PLoSO...9k5212E }}</ref>) displaying between-group differences on task performance. The collective intelligence score then is used to predict how this same group will perform on any other similar task in the future. Yet tasks, hereby, refer to mental or intellectual tasks performed by small groups<ref name=":0" /> even though the concept is hoped to be transferable to other performances and any groups or crowds reaching from families to companies and even whole cities.<ref name=":7">{{Cite journal|author1=Woolley, A. |author2= Malone, T. |last-author-amp=yes |date=June 2011|title=Defend your research: What makes a team smarter? More women|url=https://www.researchgate.net/publication/51453001|journal=Harvard Business Review |volume=89 |issue=6 |pages=32–33|doi=|pmid=}}</ref> Since individuals' ''g'' factor scores are highly correlated with full-scale [[Intelligence quotient|IQ]] scores, which are in turn regarded as good estimates of ''g'',<ref name=":1" /><ref name=":2" /> this measurement of collective intelligence can also be seen as an intelligence indicator or quotient respectively for a group (Group-IQ) parallel to an individual's intelligence quotient (IQ) even though the score is not a quotient per se.

A new scientific understanding of collective intelligence defines it as a group's general ability to perform a wide range of tasks. In the same vein as g serves to display between-individual performance differences on cognitive tasks, collective intelligence research aims to find a parallel intelligence factor for groups c factor') displaying between-group differences on task performance. The collective intelligence score then is used to predict how this same group will perform on any other similar task in the future. Yet tasks, hereby, refer to mental or intellectual tasks performed by small groups Since individuals' g factor scores are highly correlated with full-scale IQ scores, which are in turn regarded as good estimates of g, this measurement of collective intelligence can also be seen as an intelligence indicator or quotient respectively for a group (Group-IQ) parallel to an individual's intelligence quotient (IQ) even though the score is not a quotient per se.

A new scientific understanding of collective intelligence defines it as a group's general ability to perform a wide range of tasks. In the same vein as g serves to display between-individual performance differences on cognitive tasks, collective intelligence research aims to find a parallel intelligence factor for groups c factor') displaying between-group differences on task performance. The collective intelligence score then is used to predict how this same group will perform on any other similar task in the future. Yet tasks, hereby, refer to mental or intellectual tasks performed by small groups Since individuals' g factor scores are highly correlated with full-scale IQ scores, which are in turn regarded as good estimates of g, this measurement of collective intelligence can also be seen as an intelligence indicator or quotient respectively for a group (Group-IQ) parallel to an individual's intelligence quotient (IQ) even though the score is not a quotient per se.

对集体智慧的一种新的科学理解将它定义为一个群体执行广泛任务的一般能力。集体智力研究的目的在于找到一个平行的智力因素来表现群体间的任务绩效差异。然后集体智慧分数被用来预测同一组人在未来任何其他类似任务中的表现。由于个人的 g 因子得分与全面的 IQ 得分高度相关，而全面的 IQ 得分又被认为是对 g 的良好估计，因此这种集体智力的测量也可以分别被视为一个与个人的智商 / 智商(IQ)平行的群体(group-IQ)的智力指标或商，即使这个分数本身并不是商数。

对集体智能最新的科学理解，是将其定义为一个团队执行各种任务的综合能力。定义，可操作性和统计方法类似于常规个人智力的计量方法。因此，在给定的一组认知任务上的个人表现被用于计量综合认知能力，通过因子分析法算出其智力因子g。同理，g用于表达认知任务与个体之间的表现差异，集体智能研究的目的是为群体“c因子”（也称为“集体智力因子”（CI））找到一个类似的智力因子，以显示任务表现上群体间的差异。然后，将集体智力得分用于预测该组将来执行其他类似任务的表现。然而，目前任务的内容设置还局限在针对小团体的心智任务，尽管一开始的概念是希望能涉及的更广泛，比如说从家庭到公司甚至整个城市的任何团体或人群。由于个体的g因子得分与全方位IQ得分密切相关，并且后者还可以恰当的估计g因子，因此集体智能测量的结果同样可以被视为是一个群体的智力指标或商（Group-IQ），类似于个人智商（IQ），虽然该分数本身不是商。

Mathematically, ''c'' and ''g'' are both variables summarizing positive correlations among different tasks supposing that performance on one task is comparable with performance on other similar tasks.<ref name=":5">{{Cite book|title=A history of intelligence test interpretation. In D.P. Flanagan and P.L. Harrison (Eds.), Contemporary intellectual assessment: Theories, tests, and issues (2nd Ed.)|author1=Kamphaus, R.W. |author2=Winsor, A.P. |author3=Rowe, E.W. |author4= Kim, S. |last-author-amp=yes |publisher=Guilford|year=2005|isbn=|location=New York, NY|pages=23–38}}</ref> ''c'' thus is a source of variance among groups and can only be considered as a group's standing on the ''c'' factor compared to other groups in a given relevant population.<ref name=":2" /><ref>{{Cite journal|last=van der Maas|first=Han L. J.|last2=Dolan|first2=Conor V.|last3=Grasman|first3=Raoul P. P. P.|last4=Wicherts|first4=Jelte M.|last5=Huizenga|first5=Hilde M.|last6=Raijmakers|first6=Maartje E. J.|date=2006-10-01|title=A dynamical model of general intelligence: the positive manifold of intelligence by mutualism|journal=Psychological Review|volume=113|issue=4|pages=842–861|doi=10.1037/0033-295X.113.4.842|pmid=17014305}}</ref> The concept is in contrast to competing hypotheses including other correlational structures to explain group intelligence,<ref name=":0" /> such as a composition out of several equally important but independent factors as found in [[Big Five personality traits|individual personality research]].<ref>{{Cite journal|author1=McCrae, R. R.  |author2=Costa Jr., P. T.|date=1987|title=Validation of the Five-Factor Model of Personality Across Instruments and Observers|url=http://webs.wofford.edu/steinmetzkr/teaching/Psy150/Lecture%20PDFs/FiveFactorModel.pdf|journal=Journal of Personality and Social Psychology |volume=52 |issue=1 |pages=81–90|doi=10.1037/0022-3514.52.1.81|pmid=3820081|access-date=}}</ref>

Mathematically, ''c'' and ''g'' are both variables summarizing positive correlations among different tasks supposing that performance on one task is comparable with performance on other similar tasks. ''c'' thus is a source of variance among groups and can only be considered as a group's standing on the ''c'' factor compared to other groups in a given relevant population. The concept is in contrast to competing hypotheses including other correlational structures to explain group intelligence, such as a composition out of several equally important but independent factors as found in [[Big Five personality traits|individual personality research]].

Mathematically, c and g are both variables summarizing positive correlations among different tasks supposing that performance on one task is comparable with performance on other similar tasks. c thus is a source of variance among groups and can only be considered as a group's standing on the c factor compared to other groups in a given relevant population. The concept is in contrast to competing hypotheses including other correlational structures to explain group intelligence,

Mathematically, c and g are both variables summarizing positive correlations among different tasks supposing that performance on one task is comparable with performance on other similar tasks. c thus is a source of variance among groups and can only be considered as a group's standing on the c factor compared to other groups in a given relevant population. The concept is in contrast to competing hypotheses including other correlational structures to explain group intelligence,

数学上，c 和 g 都是变量，总结了不同任务之间的正相关性，假设一项任务的表现与其他类似任务的表现是可比的。因此，c 是各群体之间差异的来源，只能被视为一个群体在 c 系数上与某一特定相关人口中的其他群体相比的地位。这个概念与包括其他相关结构在内的竞争性假设形成对比，以解释群体智力,

从数学上讲，c和g都是变量，假设不同团队或个人在不同任务（但相似）中的表现具有可比性,这两个变量均概述了该团队或个人在不同任务之间的正相关性。因此，c表示的是团队之间的差异，与给定相关人口设置的其他组相比，它仅被视为该组在c因子上的设置结果。需要注意的是，该概念与竞争假设（包括其他可以解释群体智能的相关结构）形成对比，例如由个体人格研究中发现的一些同样重要但相互独立的因素组合。

Besides, this scientific idea also aims to explore the causes affecting collective intelligence, such as group size, collaboration tools or group members' interpersonal skills.<ref name=":3">{{Cite web|url=http://cci.mit.edu/research_developing.html|title=MIT Center for Collective Intelligence|website=cci.mit.edu|access-date=2016-04-26|archive-url=https://web.archive.org/web/20160330091237/http://cci.mit.edu/research_developing.html|archive-date=30 March 2016|url-status=dead}}</ref> The [[MIT Center for Collective Intelligence]], for instance, announced the detection of ''The Genome of Collective Intelligence''<ref name=":3" /> as one of its main goals aiming to develop a ''taxonomy of organizational building blocks, or genes, that can be combined and recombined to harness the intelligence of crowds''.<ref name=":3" />

Besides, this scientific idea also aims to explore the causes affecting collective intelligence, such as group size, collaboration tools or group members' interpersonal skills. The [[MIT Center for Collective Intelligence]], for instance, announced the detection of ''The Genome of Collective Intelligence'' as one of its main goals aiming to develop a ''taxonomy of organizational building blocks, or genes, that can be combined and recombined to harness the intelligence of crowds''.

Besides, this scientific idea also aims to explore the causes affecting collective intelligence, such as group size, collaboration tools or group members' interpersonal skills. The MIT Center for Collective Intelligence, for instance, announced the detection of The Genome of Collective Intelligence as one of its main goals aiming to develop a taxonomy of organizational building blocks, or genes, that can be combined and recombined to harness the intelligence of crowds.

Besides, this scientific idea also aims to explore the causes affecting collective intelligence, such as group size, collaboration tools or group members' interpersonal skills. The MIT Center for Collective Intelligence, for instance, announced the detection of The Genome of Collective Intelligence as one of its main goals aiming to develop a taxonomy of organizational building blocks, or genes, that can be combined and recombined to harness the intelligence of crowds.

=== Causes ===

=== Causes 原因 ===

Individual intelligence is shown to be genetically and environmentally influenced.<ref>{{Cite journal|last=Briley|first=Daniel A.|last2=Tucker-Drob|first2=Elliot M.|date=2014-09-01|title=Genetic and environmental continuity in personality development: a meta-analysis|journal=Psychological Bulletin|volume=140|issue=5|pages=1303–1331|doi=10.1037/a0037091|pmc=4152379|pmid=24956122}}</ref><ref>{{Cite journal|last=Deary|first=Ian J.|last2=Spinath|first2=Frank M.|last3=Bates|first3=Timothy C.|date=2006-01-01|title=Genetics of intelligence|journal=European Journal of Human Genetics|volume=14|issue=6|pages=690–700|doi=10.1038/sj.ejhg.5201588|pmid=16721405|doi-access=free}}</ref> Analogously, collective intelligence research aims to explore reasons why certain groups perform more intelligent than other groups given that ''c'' is just moderately correlated with the intelligence of individual group members.<ref name=":0" /> According to Woolley et al.'s results, neither team cohesion nor motivation or satisfaction is correlated with ''c''. However, they claim that three factors were found as significant correlates: the variance in the number of speaking turns, group members' average social sensitivity and the proportion of females. All three had similar predictive power for ''c'', but only social sensitivity was statistically significant (b=0.33, P=0.05).<ref name=":0" />

Individual intelligence is shown to be genetically and environmentally influenced. Analogously, collective intelligence research aims to explore reasons why certain groups perform more intelligent than other groups given that ''c'' is just moderately correlated with the intelligence of individual group members. According to Woolley et al.'s results, neither team cohesion nor motivation or satisfaction is correlated with ''c''. However, they claim that three factors were found as significant correlates: the variance in the number of speaking turns, group members' average social sensitivity and the proportion of females. All three had similar predictive power for ''c'', but only social sensitivity was statistically significant (b=0.33, P=0.05).

Individual intelligence is shown to be genetically and environmentally influenced. Analogously, collective intelligence research aims to explore reasons why certain groups perform more intelligent than other groups given that c is just moderately correlated with the intelligence of individual group members. According to Woolley et al.'s results, neither team cohesion nor motivation or satisfaction is correlated with c. However, they claim that three factors were found as significant correlates: the variance in the number of speaking turns, group members' average social sensitivity and the proportion of females. All three had similar predictive power for c, but only social sensitivity was statistically significant (b=0.33, P=0.05).

Individual intelligence is shown to be genetically and environmentally influenced. Analogously, collective intelligence research aims to explore reasons why certain groups perform more intelligent than other groups given that c is just moderately correlated with the intelligence of individual group members. According to Woolley et al.'s results, neither team cohesion nor motivation or satisfaction is correlated with c. However, they claim that three factors were found as significant correlates: the variance in the number of speaking turns, group members' average social sensitivity and the proportion of females. All three had similar predictive power for c, but only social sensitivity was statistically significant (b=0.33, P=0.05).

个人的智力被证明受到遗传和环境的影响。类似地，集体智力研究的目的是探索为什么某些群体比其他群体表现得更聪明，因为 c 只是与个体群体成员的智力适度相关。根据伍利等人的研究。研究结果表明，团队凝聚力、团队动机和团队满意度与 c 均无相关性，但有三个因素显著相关: 说话次数的差异、团队成员的平均社会敏感度和女性比例。三者对 c 的预测能力相似，但只有社会敏感性具有统计学意义(b0.33，p0.05)。

个体智力被证明具有遗传性且易受环境影响。类似地，集体智力的研究目的是探索为什么某些群体的表现要比其他群体更聪明的原因，因为因子c仅与群体单个成员的智力适度相关。根据伍利等人的结果，团队凝聚力，动机或满意度都与因子c无关。但是，他们声称发现了三个非常重要的相关因素：成员发表意见的次数，成员社会敏感度平均值和女性比例。这三者对因子c具有相似的预测能力，但目前只有社会敏感度具有统计学意义（b = 0.33，P = 0.05）。

The number speaking turns indicates that "groups where a few people dominated the conversation were less collectively intelligent than those with a more equal distribution of conversational turn-taking".<ref name=":4" /> Hence, providing multiple team members the chance to speak up made a group more intelligent.<ref name=":0" />

The number speaking turns indicates that "groups where a few people dominated the conversation were less collectively intelligent than those with a more equal distribution of conversational turn-taking". Hence, providing multiple team members the chance to speak up made a group more intelligent.

The number speaking turns indicates that "groups where a few people dominated the conversation were less collectively intelligent than those with a more equal distribution of conversational turn-taking". Hence, providing multiple team members the chance to speak up made a group more intelligent.

The number speaking turns indicates that "groups where a few people dominated the conversation were less collectively intelligent than those with a more equal distribution of conversational turn-taking". Hence, providing multiple team members the chance to speak up made a group more intelligent.

Group members' social sensitivity was measured via the Reading the Mind in the Eyes Test<ref name=":6">{{Cite journal|vauthors=Baron-Cohen S, Wheelwright S, Hill J, Raste Y, Plumb I |date=2001|title=The ''Reading the Mind in the Eyes'' Test revised version: a study with normal adults, and adults with Asperger syndrome or high-functioning autism|url=|journal=Journal of Child Psychology and Psychiatry |volume=42 |issue=2|pages=241–251|doi=10.1017/s0021963001006643|pmid=}}</ref> (RME) and correlated .26 with ''c''.<ref name=":0" /> Hereby, participants are asked to detect thinking or feeling expressed in other peoples' eyes presented on pictures and assessed in a multiple choice format. The test aims to measure peoples' [[Theory of mind|theory of mind (ToM)]], also called 'mentalizing'<ref>{{Cite journal|last=Apperly|first=Ian A.|date=2012-05-01|title=What is "theory of mind"? Concepts, cognitive processes and individual differences|journal=The Quarterly Journal of Experimental Psychology|volume=65|issue=5|pages=825–839|doi=10.1080/17470218.2012.676055|pmid=22533318}}</ref><ref>{{cite journal | last1 = Baron-Cohen | first1 = Simon | last2 = Leslie | first2 = Alan M. | last3 = Frith | first3 = Uta | title = Does the autistic child have a "theory of mind"? | journal = [[Cognition (journal)|Cognition]] | volume = 21 | issue = 1 | pages = 37&ndash;46 | doi = 10.1016/0010-0277(85)90022-8 | pmid = 2934210 | date = October 1985 | ref = harv }} [https://web.archive.org/web/20170928145836/http://ruccs.rutgers.edu/images/personal-alan-leslie/publications/Baron-Cohen%20Leslie%20%26%20Frith%201985.pdf Pdf.]</ref><ref>{{Cite journal|last=Flavell|first=J. H.|date=1999-01-01|title=Cognitive development: children's knowledge about the mind|journal=Annual Review of Psychology|volume=50|pages=21–45|doi=10.1146/annurev.psych.50.1.21|pmid=10074674}}</ref><ref>{{Cite journal|last=Premack|first=David|last2=Woodruff|first2=Guy|date=1978-12-01|title=Does the chimpanzee have a theory of mind?|url=http://journals.cambridge.org/article_S0140525X00076512|journal=Behavioral and Brain Sciences|volume=1|issue=4|pages=515–526|doi=10.1017/S0140525X00076512|doi-access=free}}</ref> or 'mind reading',<ref>{{Cite journal|last=Heyes|first=Cecilia M.|last2=Frith|first2=Chris D.|date=2014-06-20|title=The cultural evolution of mind reading|journal=Science|volume=344|issue=6190|pages=1243091|doi=10.1126/science.1243091|pmid=24948740}}</ref> which refers to the ability to attribute mental states, such as beliefs, desires or intents, to other people and in how far people understand that others have beliefs, desires, intentions or perspectives different from their own ones.<ref name=":6" /> RME is a ToM test for adults<ref name=":6" /> that shows sufficient test-retest reliability<ref>{{Cite journal|last=Hallerbäck|first=Maria Unenge|last2=Lugnegård|first2=Tove|last3=Hjärthag|first3=Fredrik|last4=Gillberg|first4=Christopher|date=2009-03-01|title=The Reading the Mind in the Eyes Test: Test–retest reliability of a Swedish version|journal=Cognitive Neuropsychiatry|volume=14|issue=2|pages=127–143|doi=10.1080/13546800902901518|pmid=19370436}}</ref> and constantly differentiates control groups from individuals with functional [[autism]] or [[Asperger syndrome|Asperger Syndrome]].<ref name=":6" /> It is one of the most widely accepted and well-validated tests for ToM within adults.<ref>{{Cite journal|last=Pinkham|first=Amy E.|last2=Penn|first2=David L.|last3=Green|first3=Michael F.|last4=Buck|first4=Benjamin|last5=Healey|first5=Kristin|last6=Harvey|first6=Philip D.|date=2014-07-01|title=The Social Cognition Psychometric Evaluation Study: Results of the Expert Survey and RAND Panel|url=http://schizophreniabulletin.oxfordjournals.org/content/40/4/813|journal=Schizophrenia Bulletin|volume=40|issue=4|pages=813–823|doi=10.1093/schbul/sbt081|pmc=4059426|pmid=23728248}}</ref> ToM can be regarded as an associated subset of skills and abilities within the broader concept of [[emotional intelligence]].<ref name=":4" /><ref name="Yip 48–55">{{Cite journal|last=Yip|first=Jeremy A.|last2=Côté|first2=Stéphane|date=2013-01-01|title=The Emotionally Intelligent Decision Maker Emotion-Understanding Ability Reduces the Effect of Incidental Anxiety on Risk Taking|journal=Psychological Science|volume=24|issue=1|pages=48–55|doi=10.1177/0956797612450031|pmid=23221020}}</ref>

Group members' social sensitivity was measured via the Reading the Mind in the Eyes Test (RME) and correlated .26 with c. or 'mind reading', which refers to the ability to attribute mental states, such as beliefs, desires or intents, to other people and in how far people understand that others have beliefs, desires, intentions or perspectives different from their own ones. and constantly differentiates control groups from individuals with functional autism or Asperger Syndrome. ToM can be regarded as an associated subset of skills and abilities within the broader concept of emotional intelligence.

Group members' social sensitivity was measured via the Reading the Mind in the Eyes Test (RME) and correlated .26 with c. or 'mind reading', which refers to the ability to attribute mental states, such as beliefs, desires or intents, to other people and in how far people understand that others have beliefs, desires, intentions or perspectives different from their own ones. and constantly differentiates control groups from individuals with functional autism or Asperger Syndrome. ToM can be regarded as an associated subset of skills and abilities within the broader concept of emotional intelligence.

Group members' social sensitivity was measured via the Reading the Mind in the Eyes Test (RME) and correlated .26 with c. or 'mind reading', which refers to the ability to attribute mental states, such as beliefs, desires or intents, to other people and in how far people understand that others have beliefs, desires, intentions or perspectives different from their own ones. and constantly differentiates control groups from individuals with functional autism or Asperger Syndrome. ToM can be regarded as an associated subset of skills and abilities within the broader concept of emotional intelligence.

The proportion of females as a predictor of ''c'' was '''largely mediated by social sensitivity ([[Sobel test|Sobel]] z = 1.93, P= 0.03)'''<ref name=":0" /> which is in vein with previous research showing that women score higher on social sensitivity tests.<ref name=":6" /> While a [[Mediation (statistics)|mediation]], statistically speaking, clarifies the mechanism underlying the relationship between a dependent and an independent variable,<ref>{{Cite book|title=Introduction to Statistical Mediation Analysis|last=MacKinnon, D. P.|publisher=Erlbaum|year=2008|isbn=|location=New York, NY|pages=}}</ref> Wolley agreed in an interview with the ''[[Harvard Business Review]]'' that these findings are '''saying that groups of women are smarter than groups of men'''.<ref name=":7" /> However, she relativizes this stating that the actual important thing is the high social sensitivity of group members.<ref name=":7" />

The proportion of females as a predictor of ''c'' was '''largely mediated by social sensitivity ([[Sobel test|Sobel]] z = 1.93, P= 0.03)''' which is in vein with previous research showing that women score higher on social sensitivity tests. While a [[Mediation (statistics)|mediation]], statistically speaking, clarifies the mechanism underlying the relationship between a dependent and an independent variable,Wolley agreed in an interview with the Harvard Business Review that these findings are saying that groups of women are smarter than groups of men.[46] However, she relativizes this stating that the actual important thing is the high social sensitivity of group members.[46]

The proportion of females as a predictor of c was largely mediated by social sensitivity (Sobel z = 1.93, P= 0.03) Wolley agreed in an interview with the Harvard Business Review that these findings are saying that groups of women are smarter than groups of men. However, she relativizes this stating that the actual important thing is the high social sensitivity of group members.

The proportion of females as a predictor of c was largely mediated by social sensitivity (Sobel z = 1.93, P= 0.03) Wolley agreed in an interview with the Harvard Business Review that these findings are saying that groups of women are smarter than groups of men. However, she relativizes this stating that the actual important thing is the high social sensitivity of group members.

 

It is theorized that the collective intelligence factor ''c'' is an emergent property resulting from bottom-up as well as top-down processes. Hereby, bottom-up processes cover aggregated group-member characteristics. Top-down processes cover group structures and norms that influence a group's way of collaborating and coordinating.

It is theorized that the collective intelligence factor ''c'' is an emergent property resulting from bottom-up as well as top-down processes.<ref name=":11">{{Cite journal|last=Woolley|first=Anita Williams|last2=Aggarwal|first2=Ishani|last3=Malone|first3=Thomas W.|date=2015-12-01|title=Collective Intelligence and Group Performance|journal=Current Directions in Psychological Science|volume=24|issue=6|pages=420–424|doi=10.1177/0963721415599543}}</ref> Hereby, bottom-up processes cover aggregated group-member characteristics. Top-down processes cover group structures and norms that influence a group's way of collaborating and coordinating.<ref name=":11" />

It is theorized that the collective intelligence factor c is an emergent property resulting from bottom-up as well as top-down processes. Hereby, bottom-up processes cover aggregated group-member characteristics. Top-down processes cover group structures and norms that influence a group's way of collaborating and coordinating.

It is theorized that the collective intelligence factor c is an emergent property resulting from bottom-up as well as top-down processes. Hereby, bottom-up processes cover aggregated group-member characteristics. Top-down processes cover group structures and norms that influence a group's way of collaborating and coordinating.

=== Processes ===

=== Processes 处理程序===

[[File:Causes for c.png|thumb|Predictors for the collective intelligence factor ''c''. Suggested by Woolley, Aggarwal & Malone<ref name=":11"/> (2015)]]

[[文件:集体智能因子c的预测。Woolley，Aggarwal和Malone建议（2015）.png|缩略图|右|集体智能因子c的预测。Woolley，Aggarwal和Malone建议（2015）]]

Top-down processes cover group interaction, such as structures, processes, and norms. Research further suggest that collectively intelligent groups communicate more in general as well as more equally; same applies for participation and is shown for face-to-face as well as online groups communicating only via writing.

 

 

 

 

Top-down processes cover group interaction, such as structures, processes, and norms.<ref name="Woolley 420–424">{{Cite journal|last=Woolley|first=A. W.|last2=Aggarwal|first2=I.|last3=Malone|first3=T. W.|date=2015-12-01|title=Collective Intelligence and Group Performance|url=https://www.researchgate.net/publication/286512331|journal=Current Directions in Psychological Science|volume=24|issue=6|pages=420–424|doi=10.1177/0963721415599543}}</ref> An example of such top-down processes is conversational turn-taking.<ref name=":0" /> Research further suggest that collectively intelligent groups communicate more in general as well as more equally; same applies for participation and is shown for face-to-face as well as online groups communicating only via writing.<ref name=":4" /><ref name=":9" />

Top-down processes cover group interaction, such as structures, processes, and norms. An example of such top-down processes is conversational turn-taking. Research further suggest that collectively intelligent groups communicate more in general as well as more equally; same applies for participation and is shown for face-to-face as well as online groups communicating only via writing.

Top-down processes cover group interaction, such as structures, processes, and norms. An example of such top-down processes is conversational turn-taking. Research further suggest that collectively intelligent groups communicate more in general as well as more equally; same applies for participation and is shown for face-to-face as well as online groups communicating only via writing.

==== Bottom-up processes ====

==== Bottom-up processes 自下而上 ====

Bottom-up processes include group composition,<ref name="Woolley 420–424"/> namely the characteristics of group members which are aggregated to the team level.<ref name=":11" /> An example of such bottom-up processes is the average social sensitivity or the average and maximum intelligence scores of group members.<ref name=":0" /> Furthermore, collective intelligence was found to be related to a group's cognitive diversity<ref name=":12">{{Cite journal|author1=Aggarwal, I. |author2=Woolley, A. W. |author3=Chabris, C. F. |author4= Malone, T. W. |last-author-amp=yes |date=2015|title=Cognitive diversity, collective intelligence, and learning in teams.|url=|journal=Paper Presented at the 2015 Collective Intelligence Conference, Santa Clara, CA.|doi=|pmid=}}</ref> including thinking styles and perspectives.<ref>{{Cite journal|author1=Kozhevnikov, M. |author2=Evans, C. |author3= Kosslyn, S. M. |last-author-amp=yes|date=2014|title=Cognitive style as environmentally sensitive individual differences in cognition: A modern synthesis and applications in education, business, and management|url=|journal=Psychological Science in the Public Interest |volume=15 |issue=1 |pages=3–33|doi=10.1177/1529100614525555|pmid=26171827}}</ref> Groups that are moderately diverse in [[cognitive style]] have higher collective intelligence than those who are very similar in cognitive style or very different. Consequently, groups where members are too similar to each other lack the variety of perspectives and skills needed to perform well. On the other hand, groups whose members are too different seem to have difficulties to communicate and coordinate effectively.<ref name=":12" />

Bottom-up processes include group composition, namely the characteristics of group members which are aggregated to the team level. An example of such bottom-up processes is the average social sensitivity or the average and maximum intelligence scores of group members. Furthermore, collective intelligence was found to be related to a group's cognitive diversity including thinking styles and perspectives. Groups that are moderately diverse in [[cognitive style]] have higher collective intelligence than those who are very similar in cognitive style or very different. Consequently, groups where members are too similar to each other lack the variety of perspectives and skills needed to perform well. On the other hand, groups whose members are too different seem to have difficulties to communicate and coordinate effectively.

Bottom-up processes include group composition, including thinking styles and perspectives. Groups that are moderately diverse in cognitive style have higher collective intelligence than those who are very similar in cognitive style or very different. Consequently, groups where members are too similar to each other lack the variety of perspectives and skills needed to perform well. On the other hand, groups whose members are too different seem to have difficulties to communicate and coordinate effectively.

Bottom-up processes include group composition, including thinking styles and perspectives. Groups that are moderately diverse in cognitive style have higher collective intelligence than those who are very similar in cognitive style or very different. Consequently, groups where members are too similar to each other lack the variety of perspectives and skills needed to perform well. On the other hand, groups whose members are too different seem to have difficulties to communicate and coordinate effectively.

 

 

For most of human history, collective intelligence was confined to small tribal groups in which opinions were aggregated through real-time parallel interactions among members.<ref>{{Cite book|title=Moral tribes : emotion, reason, and the gap between us and them|last=1974–|first=Greene, Joshua David|isbn=978-0143126058|oclc=871336785|date=2014-12-30|url-access=registration|url=https://archive.org/details/moraltribesemoti0000gree}}</ref>  In modern times, mass communication, mass media, and networking technologies have enabled collective intelligence to span massive groups, distributed across continents and time-zones.  To accommodate this shift in scale, collective intelligence in large-scale groups been dominated by serialized polling processes such as aggregating up-votes, likes, and ratings over time.  While modern systems benefit from larger group size, the serialized process has been found to introduce substantial noise that distorts the collective output of the group.  In one significant study of serialized collective intelligence, it was found that the first vote contributed to a serialized voting system can distort the final result by 34%.<ref>{{Cite journal|last=Muchnik|first=Lev|last2=Aral|first2=Sinan|last3=Taylor|first3=Sean J.|date=2013-08-09|title=Social Influence Bias: A Randomized Experiment|journal=Science|volume=341|issue=6146|pages=647–651|doi=10.1126/science.1240466|issn=0036-8075|pmid=23929980|bibcode=2013Sci...341..647M}}</ref>

For most of human history, collective intelligence was confined to small tribal groups in which opinions were aggregated through real-time parallel interactions among members. In modern times, mass communication, mass media, and networking technologies have enabled collective intelligence to span massive groups, distributed across continents and time-zones.  To accommodate this shift in scale, collective intelligence in large-scale groups been dominated by serialized polling processes such as aggregating up-votes, likes, and ratings over time.  While modern systems benefit from larger group size, the serialized process has been found to introduce substantial noise that distorts the collective output of the group.  In one significant study of serialized collective intelligence, it was found that the first vote contributed to a serialized voting system can distort the final result by 34%.

For most of human history, collective intelligence was confined to small tribal groups in which opinions were aggregated through real-time parallel interactions among members.  In modern times, mass communication, mass media, and networking technologies have enabled collective intelligence to span massive groups, distributed across continents and time-zones.  To accommodate this shift in scale, collective intelligence in large-scale groups been dominated by serialized polling processes such as aggregating up-votes, likes, and ratings over time.  While modern systems benefit from larger group size, the serialized process has been found to introduce substantial noise that distorts the collective output of the group.  In one significant study of serialized collective intelligence, it was found that the first vote contributed to a serialized voting system can distort the final result by 34%.

For most of human history, collective intelligence was confined to small tribal groups in which opinions were aggregated through real-time parallel interactions among members.  In modern times, mass communication, mass media, and networking technologies have enabled collective intelligence to span massive groups, distributed across continents and time-zones.  To accommodate this shift in scale, collective intelligence in large-scale groups been dominated by serialized polling processes such as aggregating up-votes, likes, and ratings over time.  While modern systems benefit from larger group size, the serialized process has been found to introduce substantial noise that distorts the collective output of the group.  In one significant study of serialized collective intelligence, it was found that the first vote contributed to a serialized voting system can distort the final result by 34%.

To address the problems of serialized aggregation of input among large-scale groups, recent advancements collective intelligence have worked to replace serialized votes, polls, and markets, with parallel systems such as "[[Swarm intelligence|human swarms]]" modeled after synchronous swarms in nature.<ref>{{Cite web|url=http://www.bbc.com/future/story/20161215-why-bees-could-be-the-secret-to-superhuman-intelligence|title=Why bees could be the secret to superhuman intelligence|last=Oxenham|first=Simon|access-date=2017-05-23}}</ref><ref>{{Cite book|last=Rosenberg|first=L.|last2=Baltaxe|first2=D.|last3=Pescetelli|first3=N.|date=2016-10-01|title=Crowds vs swarms, a comparison of intelligence|journal=2016 Swarm/Human Blended Intelligence Workshop (SHBI)|pages=1–4|doi=10.1109/SHBI.2016.7780278|isbn=978-1-5090-3502-1}}</ref>  Based on natural process of [[Swarm Intelligence]], these artificial swarms of networked humans enable participants to work together in parallel to answer questions and make predictions as an emergent collective intelligence.<ref>{{Cite journal|last=Metcalf|first=Lynn|last2=Askay|first2=David A.|last3=Rosenberg|first3=Louis B.|date=2019|title=Keeping Humans in the Loop: Pooling Knowledge through Artificial Swarm Intelligence to Improve Business Decision Making|journal=California Management Review|language=en|volume=61|issue=4|pages=84–109|doi=10.1177/0008125619862256|issn=0008-1256}}</ref>  In one high-profile example, a human swarm challenge by CBS Interactive to predict the Kentucky Derby.  The swarm correctly predicted the first four horses, in order, defying 542–1 odds and turning a $20 bet into $10,800.<ref>{{Cite news|url=http://www.newsweek.com/artificial-intelligence-turns-20-11000-kentucky-derby-bet-457783|title=Artificial intelligence turns $20 into $11,000 in Kentucky Derby bet|date=2016-05-10|work=Newsweek|access-date=2017-05-23}}</ref>

To address the problems of serialized aggregation of input among large-scale groups, recent advancements collective intelligence have worked to replace serialized votes, polls, and markets, with parallel systems such as "[[Swarm intelligence|human swarms]]" modeled after synchronous swarms in nature. Based on natural process of [[Swarm Intelligence]], these artificial swarms of networked humans enable participants to work together in parallel to answer questions and make predictions as an emergent collective intelligence. In one high-profile example, a human swarm challenge by CBS Interactive to predict the Kentucky Derby.  The swarm correctly predicted the first four horses, in order, defying 542–1 odds and turning a $20 bet into $10,800.

To address the problems of serialized aggregation of input among large-scale groups, recent advancements collective intelligence have worked to replace serialized votes, polls, and markets, with parallel systems such as "human swarms" modeled after synchronous swarms in nature.  Based on natural process of Swarm Intelligence, these artificial swarms of networked humans enable participants to work together in parallel to answer questions and make predictions as an emergent collective intelligence.  In one high-profile example, a human swarm challenge by CBS Interactive to predict the Kentucky Derby.  The swarm correctly predicted the first four horses, in order, defying 542–1 odds and turning a $20 bet into $10,800.

To address the problems of serialized aggregation of input among large-scale groups, recent advancements collective intelligence have worked to replace serialized votes, polls, and markets, with parallel systems such as "human swarms" modeled after synchronous swarms in nature.  Based on natural process of Swarm Intelligence, these artificial swarms of networked humans enable participants to work together in parallel to answer questions and make predictions as an emergent collective intelligence.  In one high-profile example, a human swarm challenge by CBS Interactive to predict the Kentucky Derby.  The swarm correctly predicted the first four horses, in order, defying 542–1 odds and turning a $20 bet into $10,800.

为了解决大规模群体之间连续集合输入的问题，最近的进展集体智慧致力于用类似“人类群体”的并行系统取代连续投票、投票和市场，这些系统是仿照自然界的同步群体建立的。基于群体智能的自然过程，这些网络化的人类群使得参与者能够并行工作回答问题，并作为一个新兴的集体智慧进行预测。在一个引人注目的例子中，哥伦比亚广播公司互动公司(CBS Interactive)发起了一场群体挑战赛，来预测肯塔基赛马会。蜂群正确地预测了前四匹马，排序，排除了542-1的赔率，把20美元的赌注变成了10800美元。

为了解决大规模群体之间因为输入序列化汇总的问题，目前的进展是，集体智能已经淘汰了序列化的选票，民意测验和市场，进而采用了以自然群体为蓝本的并行系统，例如“人类集群Human swarms”。基于群体智能Swarm Intelligence（注意区分Collective intelligence）的自然执行过程，这些由人类联网组成的人工集群使参与者可以并行工作来解决问题，并为涌现集体智能做出预测。在一个引人注目的示例中，CBS Interactive（美国著名媒体公司）进行了人类集群的挑战以预测肯塔基德比（美国著名跑马赛）。这群人正确地预测了前四匹马，顺次击败了542-1的赔率，将20美元的赌注变成了10,800美元。

The value of parallel collective intelligence was demonstrated in medical applications by researchers at [[Stanford University School of Medicine]] and [[Unanimous A.I.|Unanimous AI]] in a set of published studies wherein groups of human doctors were connected by real-time swarming algorithms and tasked with diagnosing chest x-rays for the presence of pneumonia.<ref>{{Cite web|url=https://spectrum.ieee.org/the-human-os/biomedical/diagnostics/ai-human-hive-mind-diagnoses-pneumonia|title=AI-Human "Hive Mind" Diagnoses Pneumonia|last=Scudellari|first=Megan|date=2018-09-13|website=IEEE Spectrum: Technology, Engineering, and Science News|access-date=2019-07-20}}</ref><ref>{{Cite web|url=https://www.stanforddaily.com/2018/09/27/artificial-swarm-intelligence-diagnoses-pneumonia-better-than-individual-computer-or-doctor/|title=Artificial swarm intelligence diagnoses pneumonia better than individual computer or doctor|last=Liu|first=Fan|date=2018-09-27|website=The Stanford Daily|access-date=2019-07-20}}</ref> When working together as "human swarms," the groups of experienced radiologists demonstrated a 33% reduction in diagnostic errors as compared to traditional methods.<ref>{{Cite web|url=https://www.radiologytoday.net/archive/rt0119p12.shtml|title=A Swarm of Insight - Radiology Today Magazine|website=www.radiologytoday.net|access-date=2019-07-20}}</ref><ref>{{Cite journal|last=Rosenberg|first=Louis|last2=Lungren|first2=Matthew|last3=Halabi|first3=Safwan|last4=Willcox|first4=Gregg|last5=Baltaxe|first5=David|last6=Lyons|first6=Mimi|date=November 2018|title=Artificial Swarm Intelligence employed to Amplify Diagnostic Accuracy in Radiology|journal=2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)|location=Vancouver, BC|publisher=IEEE|pages=1186–1191|doi=10.1109/IEMCON.2018.8614883|isbn=9781538672662}}</ref>

The value of parallel collective intelligence was demonstrated in medical applications by researchers at [[Stanford University School of Medicine]] and [[Unanimous A.I.|Unanimous AI]] in a set of published studies wherein groups of human doctors were connected by real-time swarming algorithms and tasked with diagnosing chest x-rays for the presence of pneumonia. When working together as "human swarms," the groups of experienced radiologists demonstrated a 33% reduction in diagnostic errors as compared to traditional methods.

The value of parallel collective intelligence was demonstrated in medical applications by researchers at Stanford University School of Medicine and Unanimous AI in a set of published studies wherein groups of human doctors were connected by real-time swarming algorithms and tasked with diagnosing chest x-rays for the presence of pneumonia. When working together as "human swarms," the groups of experienced radiologists demonstrated a 33% reduction in diagnostic errors as compared to traditional methods.

The value of parallel collective intelligence was demonstrated in medical applications by researchers at Stanford University School of Medicine and Unanimous AI in a set of published studies wherein groups of human doctors were connected by real-time swarming algorithms and tasked with diagnosing chest x-rays for the presence of pneumonia. When working together as "human swarms," the groups of experienced radiologists demonstrated a 33% reduction in diagnostic errors as compared to traditional methods.

=== Evidence ===

=== Evidence 证据 ===

[[File:Standardized Regression Coefficients.png|alt=Standardized Regression Coefficients for the collective intelligence factor ''c'' and group member intelligence regressed on the two criterion tasks as found in Woolley et al.'s (2010) two original studies.|thumb|Standardized Regression Coefficients for the collective intelligence factor ''c'' as found in Woolley et al.'s<ref name=":0"/> (2010) two original studies. ''c'' and average (maximum) member intelligence  scores are regressed on the criterion tasks.]]

[[File:Standardized Regression Coefficients.png|alt=Standardized Regression Coefficients for the collective intelligence factor ''c'' and group member intelligence regressed on the two criterion tasks as found in Woolley et al.'s (2010) two original studies.|thumb|Standardized Regression Coefficients for the collective intelligence factor ''c'' as found in Woolley et al.'s<ref name=":0"/> (2010) two original studies. ''c'' and average (maximum) member intelligence  scores are regressed on the criterion tasks.]]

值得注意的是，参与研究的研究人员在确认研究结果时，彼此之间以及参与最初围绕安妮塔 · 伍利进行的第一次研究的作者之间都有广泛的重叠。

值得注意的是，参与研究的研究人员在确认研究结果时，彼此之间以及参与最初围绕安妮塔 · 伍利进行的第一次研究的作者之间都有广泛的重叠。

== Alternative mathematical techniques ==

== Alternative mathematical techniques ==