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第452行： − + − + − − 第462行：
第460行： − 在这里，目标是得到一个估计(在一个单一的价值)的东西。例如，估计一个对象的重量，或者一个产品的发布日期，或者一个项目的成功概率等等。在 Intrade、 HSX 或 InklingMarkets 等预测市场，以及数字结果的众包估计实现中都可以看到这一点。本质上，我们试图得到人群中成员提供的估计值的平均值。+ − + 第472行：
第470行： − 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。+ − + 第482行：
第480行： − 在这些问题中，有人从人群中征求项目、设计或解决方案的想法。例如，解决数据科学问题的想法(比如在 Kaggle) ，为 t 恤设计好的想法(比如在 Threadless) ，或者为只有人类才能解决的简单问题找到答案的想法(比如在亚马逊的土耳其机器人上)。目的是收集想法，并设计一些选择标准，以选择最好的想法。+ − + − James Surowiecki 将无组织决策的优势分为三大类: 认知型、合作型和协调型。+ − + − + − + − + − 由于互联网能够在全世界迅速传递大量信息，利用集体智慧预测股票价格和股票价格走向已变得越来越可行。网站聚合尽可能最新的股票市场信息，这样专业或业余股票分析师可以发表他们的观点，使业余投资者提交他们的金融意见和创造一个聚合的意见。+ − + − 集体智慧支撑着 Eugene Fama 的效率市场假说，尽管在他的论文中并没有明确使用集体智慧这个术语。法玛引用了迈克尔•詹森(Michael Jensen)进行的一项研究。在1955年至1964年期间，选定的115只基金中，有89只表现不及指数。但是在去除加载费(预付费)之后，只有72个股票表现不佳，而去除经纪成本之后，只有58个股票表现不佳。在这些证据的基础上，指数基金成为流行的投资工具，利用市场的集体智慧，而不是专业基金经理的判断，作为一种投资策略。+ − + − [[File:U.S. states (and territories) by election methods, 2016.svg|thumb|Voting methods used in the United States 2016]] − Voting methods used in the United States 2016 − − 美国2016年的投票方式 第799行：
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→Applications
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.
关于集体智能，其应用目标之一是获得某种事务的估计值（单个值）。例如估算物体的重量，产品的发布日期或项目成功的概率等。其应用场景可以是在Intrade，HSX或InklingMarkets等预测市场中，亦或在对数字结果进行众包估计的几种实操过程中。从本质上讲是尝试获取指定群体中成员提供的估计平均值。
==== 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.
在处理问题的时候，集体智能也可以用于从人群中收集相关项目的想法，设计或解决方案。例如，关于解决数据科学问题的想法（类似在Kaggle中），获得T恤衫良好设计的想法（类似在Threadless中），或者收集仅人类能处理的简单问题的答案（类似在Amazon的Mechanical Turk中）。目的是收集各种想法并从中设计选择标准来筛选最佳方案。
[[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.
纽约客商业专栏作家詹姆斯·苏洛维奇James Surowiecki将无组织决策的优势分为三个主要类别，即认知，合作和协调。
=== 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 – 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.
由于英特网具有在全球范围内快速传递大量信息的能力，因此使用集体智能来预测股票价格和股票价格方向已变得越来越可行。网站汇总了尽可能最新的股票市场信息，以便专业或业余股票分析师可以发布其观点，从而使业余投资者可以提交其金融见解并创建汇总意见。这些投资者的意见可以加权平均，以便将有效地运用集体智能作为关键前提：利用群众，包括广泛的股市专业知识，来更准确地预测金融市场的行为。
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> – 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 – 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 – 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.
集体智能巩固了尤金·法玛Eugene Fama的有效市场假说，尽管集体智能这个词在他的论文中并未明确使用。法玛引用了迈克尔·詹森Michael Jensen的研究，在1955年至1964年期间，115个精选基金中有89个相对于该指数表现不佳。但是，在取消了加载费用（前期费用）之后，只有72个基金表现不佳，而在去除经纪费用之后，剩下了58个。在这些证据的基础上，指数基金成为了市场投资工具，使用市场的集体智能而不是专业基金经理的判断作为投资策略。
==== 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 ==