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有时候我们会采用另一种度量方式表达，称为“集体智商Collective intelligence quotient” （或“合作商Cooperation quotient”），它特别受到以人工智能为研究重点的理论家的青睐。它可以由“个体”智商归一化处理后得到。因此可以进一步确定参加集体行动的新增组员所带来的额外边际智商，还可以使用度量标准来避免由群体愚蠢思维带来的危险。

有时候我们会采用另一种度量方式表达，称为“集体智商Collective intelligence quotient” （或“合作商Cooperation quotient”），它特别受到以人工智能为研究重点的理论家的青睐。它可以由“个体”智商归一化处理后得到。因此可以进一步确定参加集体行动的新增组员所带来的额外边际智商，还可以使用度量标准来避免由群体愚蠢思维带来的危险。

== Applications ==

== Applications 应用 ==

 

==== Elicitation of point estimates 评估点提取 ====

 

==== Elicitation of point estimates ====

Here, the goal is to get an estimate (in a single value) of something. For example, estimating the weight of an object, or the release date of a product or probability of success of a project etc. as seen in prediction markets like Intrade, HSX or InklingMarkets and also in several implementations of crowdsourced estimation of a numeric outcome. Essentially, we try to get the average value of the estimates provided by the members in the crowd.

Here, the goal is to get an estimate (in a single value) of something. For example, estimating the weight of an object, or the release date of a product or probability of success of a project etc. as seen in prediction markets like Intrade, HSX or InklingMarkets and also in several implementations of crowdsourced estimation of a numeric outcome. Essentially, we try to get the average value of the estimates provided by the members in the crowd.

Here, the goal is to get an estimate (in a single value) of something. For example, estimating the weight of an object, or the release date of a product or probability of success of a project etc. as seen in prediction markets like Intrade, HSX or InklingMarkets and also in several implementations of crowdsourced estimation of a numeric outcome. Essentially, we try to get the average value of the estimates provided by the members in the crowd.

Here, the goal is to get an estimate (in a single value) of something. For example, estimating the weight of an object, or the release date of a product or probability of success of a project etc. as seen in prediction markets like Intrade, HSX or InklingMarkets and also in several implementations of crowdsourced estimation of a numeric outcome. Essentially, we try to get the average value of the estimates provided by the members in the crowd.

==== Opinion aggregation ====

==== Opinion aggregation 意见汇总 ====

In this situation, opinions are gathered from the crowd regarding an idea, issue or product. For example, trying to get a rating (on some scale) of a product sold online (such as Amazon's star rating system). Here, the emphasis is to collect and simply aggregate the ratings provided by customers/users.

In this situation, opinions are gathered from the crowd regarding an idea, issue or product. For example, trying to get a rating (on some scale) of a product sold online (such as Amazon's star rating system). Here, the emphasis is to collect and simply aggregate the ratings provided by customers/users.

In this situation, opinions are gathered from the crowd regarding an idea, issue or product. For example, trying to get a rating (on some scale) of a product sold online (such as Amazon's star rating system). Here, the emphasis is to collect and simply aggregate the ratings provided by customers/users.

In this situation, opinions are gathered from the crowd regarding an idea, issue or product. For example, trying to get a rating (on some scale) of a product sold online (such as Amazon's star rating system). Here, the emphasis is to collect and simply aggregate the ratings provided by customers/users.

在这种情况下，从人群中收集关于一个想法、问题或产品的意见。例如，试图获得在线销售产品的评级(在一定规模上)(如亚马逊的星级评级系统)。这里的重点是收集并简单地汇总客户 / 用户提供的评级。

在这种场景下，集体智能可用于收集人群中相关的不同想法，问题或产品的意见。例如，尝试对在线销售的产品（例如亚马逊的星级评分系统）进行某种程度的评级。这里重点是收集并简单地汇总客户/用户提供的评级。

==== Idea Collection ====

==== Idea Collection 想法收集 ====

In these problems, someone solicits ideas for projects, designs or solutions from the crowd. For example, ideas on solving a [[data science]] problem (as in [[Kaggle]]) or getting a good design for a T-shirt (as in [[Threadless]]) or in getting answers to simple problems that only humans can do well (as in Amazon's Mechanical Turk). The objective is to gather the ideas and devise some selection criteria to choose the best ideas.

In these problems, someone solicits ideas for projects, designs or solutions from the crowd. For example, ideas on solving a [[data science]] problem (as in [[Kaggle]]) or getting a good design for a T-shirt (as in [[Threadless]]) or in getting answers to simple problems that only humans can do well (as in Amazon's Mechanical Turk). The objective is to gather the ideas and devise some selection criteria to choose the best ideas.

In these problems, someone solicits ideas for projects, designs or solutions from the crowd. For example, ideas on solving a data science problem (as in Kaggle) or getting a good design for a T-shirt (as in Threadless) or in getting answers to simple problems that only humans can do well (as in Amazon's Mechanical Turk). The objective is to gather the ideas and devise some selection criteria to choose the best ideas.

In these problems, someone solicits ideas for projects, designs or solutions from the crowd. For example, ideas on solving a data science problem (as in Kaggle) or getting a good design for a T-shirt (as in Threadless) or in getting answers to simple problems that only humans can do well (as in Amazon's Mechanical Turk). The objective is to gather the ideas and devise some selection criteria to choose the best ideas.

[[James Surowiecki]] divides the advantages of disorganized decision-making into three main categories, which are cognition, cooperation and coordination.<ref name="Surowiecki"/>{{full citation needed|date=November 2017}}

[[James Surowiecki]] divides the advantages of disorganized decision-making into three main categories, which are cognition, cooperation and coordination.

James Surowiecki divides the advantages of disorganized decision-making into three main categories, which are cognition, cooperation and coordination.

James Surowiecki divides the advantages of disorganized decision-making into three main categories, which are cognition, cooperation and coordination.

=== Cognition ===

=== Cognition 认知 ===

==== Market judgment ====

==== Market judgment 市场判断 ====

Because of the Internet's ability to rapidly convey large amounts of information throughout the world, the use of collective intelligence to predict stock prices and stock price direction has become increasingly viable.<ref name=":22">{{Cite book|last=Kaplan|first=Craig A.|year=2001|url=http://www.iqco.com/consulting/Kaplan_TESADI_Final.pdf|journal=Proceedings of the 2001 IEEE Systems, Man, and Cybernetics Conference|volume=5|pages=2893–2898|via=|doi=10.1109/ICSMC.2001.971949|isbn=978-0-7803-7087-6|title=Collective intelligence: A new approach to stock price forecasting}}</ref> Websites aggregate stock market information that is as current as possible so professional or amateur stock analysts can publish their viewpoints, enabling amateur investors to submit their financial opinions and create an aggregate opinion.<ref name=":22" /> The opinion of all investor can be weighed equally so that a pivotal premise of the effective application of collective intelligence can be applied: the masses, including a broad spectrum of stock market expertise, can be utilized to more accurately predict the behavior of financial markets.<ref>{{cite book|doi=10.1007/978-3-642-15420-1|last1=Ma|first1=Ying|last2=Li|first2=Guanyi|last3=Dong|first3=Yingsai|last4=Qin|first4=Zengchang|title=Minority Game Data Mining for Stock Market Predictions|journal=Agents and Data Mining Interaction, 6th International Workshopon Agents and Data Mining Interaction, ADMI 2010|volume=5980|year=2010|url=http://icmll.buaa.edu.cn/publications/Conference%20Papers/LectureNotesCS/ADMI.pdf|series=Lecture Notes in Computer Science|isbn=978-3-642-15419-5|bibcode=2010LNCS.5980.....C|access-date=2 March 2012|archive-url=https://web.archive.org/web/20121021040736/http://icmll.buaa.edu.cn/publications/Conference%20Papers/LectureNotesCS/ADMI.pdf|archive-date=21 October 2012|url-status=dead|df=dmy-all}}</ref><ref>{{cite book|doi=10.1007/978-3-642-23935-9_24|last1=Yu|first1=Du|last2=Dong|first2=Yingsai|last3=Qin|first3=Zengchang|last4=Wan|first4=Tao|title=Exploring Market Behaviors with Evolutionary Mixed-Games Learning Model|journal=Computational Collective Intelligence. Technologies and Applications&nbsp;– Third International Conference, ICCCI 2011|volume=6922|year=2011|url=http://dsd.future-lab.cn/research/publications/2011/ICCCI-springer.pdf|pages=244–253|series=Lecture Notes in Computer Science|isbn=978-3-642-23934-2}}</ref>

Because of the Internet's ability to rapidly convey large amounts of information throughout the world, the use of collective intelligence to predict stock prices and stock price direction has become increasingly viable. Websites aggregate stock market information that is as current as possible so professional or amateur stock analysts can publish their viewpoints, enabling amateur investors to submit their financial opinions and create an aggregate opinion. The opinion of all investor can be weighed equally so that a pivotal premise of the effective application of collective intelligence can be applied: the masses, including a broad spectrum of stock market expertise, can be utilized to more accurately predict the behavior of financial markets.

Because of the Internet's ability to rapidly convey large amounts of information throughout the world, the use of collective intelligence to predict stock prices and stock price direction has become increasingly viable. Websites aggregate stock market information that is as current as possible so professional or amateur stock analysts can publish their viewpoints, enabling amateur investors to submit their financial opinions and create an aggregate opinion.

Because of the Internet's ability to rapidly convey large amounts of information throughout the world, the use of collective intelligence to predict stock prices and stock price direction has become increasingly viable. Websites aggregate stock market information that is as current as possible so professional or amateur stock analysts can publish their viewpoints, enabling amateur investors to submit their financial opinions and create an aggregate opinion.The opinion of all investor can be weighed equally so that a pivotal premise of the effective application of collective intelligence can be applied: the masses, including a broad spectrum of stock market expertise, can be utilized to more accurately predict the behavior of financial markets.

Collective intelligence underpins the [[efficient-market hypothesis]] of [[Eugene Fama]]<ref>{{cite journal | last1 = Fama | first1 = E.F. | year = 1970 | title = Efficient Capital Markets: A Review of Theory and Empirical Work | url = | journal = Journal of Finance | volume = 25 | issue = 2| pages = 383–417 | doi=10.2307/2325486| jstor = 2325486 }}</ref>&nbsp;– although the term collective intelligence is not used explicitly in his paper. Fama cites research conducted by [[Michael C. Jensen|Michael Jensen]]<ref name=":23">{{cite journal | last1 = Jensen | first1 = M.C | year = 1967 | title = The Performance of Mutual Funds in the Period 1945–1964 | journal = Journal of Finance | volume = 23 | issue = 2| pages = 389–416 | doi=10.1111/j.1540-6261.1968.tb00815.x| hdl = 10.1111/j.1540-6261.1968.tb00815.x | hdl-access = free }}</ref> in which 89 out of 115 selected funds underperformed relative to the index during the period from 1955 to 1964. But after removing the loading charge (up-front fee) only 72 underperformed while after removing brokerage costs only 58 underperformed. On the basis of such evidence [[index fund]]s became popular investment vehicles using the collective intelligence of the market, rather than the judgement of professional fund managers, as an investment strategy.<ref name=":23" />

Collective intelligence underpins the [[efficient-market hypothesis]] of [[Eugene Fama]] in which 89 out of 115 selected funds underperformed relative to the index during the period from 1955 to 1964. But after removing the loading charge (up-front fee) only 72 underperformed while after removing brokerage costs only 58 underperformed. On the basis of such evidence [[index fund]]s became popular investment vehicles using the collective intelligence of the market, rather than the judgement of professional fund managers, as an investment strategy.

Collective intelligence underpins the efficient-market hypothesis of Eugene Fama&nbsp;– although the term collective intelligence is not used explicitly in his paper. Fama cites research conducted by Michael Jensen in which 89 out of 115 selected funds underperformed relative to the index during the period from 1955 to 1964. But after removing the loading charge (up-front fee) only 72 underperformed while after removing brokerage costs only 58 underperformed. On the basis of such evidence index funds became popular investment vehicles using the collective intelligence of the market, rather than the judgement of professional fund managers, as an investment strategy.

Collective intelligence underpins the efficient-market hypothesis of Eugene Fama&nbsp;– although the term collective intelligence is not used explicitly in his paper. Fama cites research conducted by Michael Jensen in which 89 out of 115 selected funds underperformed relative to the index during the period from 1955 to 1964. But after removing the loading charge (up-front fee) only 72 underperformed while after removing brokerage costs only 58 underperformed. On the basis of such evidence index funds became popular investment vehicles using the collective intelligence of the market, rather than the judgement of professional fund managers, as an investment strategy.

==== Predictions in politics and technology ====

==== Predictions in politics and technology 政治和技术预测 ====

Political parties mobilize large numbers of people to form policy, select candidates and finance and run election campaigns.<ref name=":24">{{Cite web|url=http://politics.stackexchange.com/questions/11168/vote-amount-according-to-intelligence|title=Vote amount according to "intelligence"|website=politics.stackexchange.com|access-date=2016-12-12}}</ref> Knowledge focusing through various [[voting]] methods allows perspectives to converge through the assumption that uninformed voting is to some degree random and can be filtered from the decision process leaving only a residue of informed consensus.<ref name=":24" /> Critics point out that often bad ideas, misunderstandings, and misconceptions are widely held, and that structuring of the decision process must favor experts who are presumably less prone to random or misinformed voting in a given context.<ref>{{Cite web|url=http://press.princeton.edu/titles/9907.html|title=Landemore, H.: Democratic Reason: Politics, Collective Intelligence, and the Rule of the Many. (eBook and Paperback)|website=press.princeton.edu|access-date=2016-12-04}}</ref>

Political parties mobilize large numbers of people to form policy, select candidates and finance and run election campaigns.<ref name=":24">{{Cite web|url=http://politics.stackexchange.com/questions/11168/vote-amount-according-to-intelligence|title=Vote amount according to "intelligence"|website=politics.stackexchange.com|access-date=2016-12-12}}</ref> Knowledge focusing through various [[voting]] methods allows perspectives to converge through the assumption that uninformed voting is to some degree random and can be filtered from the decision process leaving only a residue of informed consensus.<ref name=":24" /> Critics point out that often bad ideas, misunderstandings, and misconceptions are widely held, and that structuring of the decision process must favor experts who are presumably less prone to random or misinformed voting in a given context.<ref>{{Cite web|url=http://press.princeton.edu/titles/9907.html|title=Landemore, H.: Democratic Reason: Politics, Collective Intelligence, and the Rule of the Many. (eBook and Paperback)|website=press.princeton.edu|access-date=2016-12-04}}</ref>

群体集体智慧是通过自下而上和自上而下过程的协调而产生的一种属性。在自下而上的过程中，每个成员的不同特点都参与促进和加强协调。自上而下的程序更加严格，有规范、小组结构和惯例，以自己的方式加强小组的集体工作。

群体集体智慧是通过自下而上和自上而下过程的协调而产生的一种属性。在自下而上的过程中，每个成员的不同特点都参与促进和加强协调。自上而下的程序更加严格，有规范、小组结构和惯例，以自己的方式加强小组的集体工作。

== Alternative views ==

== Alternative views ==