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第88行： − − − 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. − + − − − − + − − 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. 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 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. − + − − − − − 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. 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. − − − − 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. − + − − − − − Woolley, Chabris, Pentland, Hashmi, & Malone (2010), the originators of this scientific understanding of collective intelligence, found a single statistical factor for collective intelligence in their research across 192 groups with people randomly recruited from the public. In Woolley et al.'s two initial studies, groups worked together on different tasks from the [[The Circumplex Model of Group Tasks|McGrath Task Circumplex]], a well-established taxonomy of group tasks. Tasks were chosen from all four quadrants of the circumplex and included visual puzzles, brainstorming, making collective moral judgments, and negotiating over limited resources. The results in these tasks were taken to conduct a [[factor analysis]]. Both studies showed support for a general collective intelligence factor ''c'' underlying differences in group performance with an initial eigenvalue accounting for 43% (44% in study 2) of the variance, whereas the next factor accounted for only 18% (20%). That fits the range normally found in research regarding a [[G factor (psychometrics)|general individual intelligence factor ''g'']] typically accounting for 40% to 50% percent of between-individual performance differences on cognitive tests. − − Woolley, Chabris, Pentland, Hashmi, & Malone (2010), a well-established taxonomy of group tasks. Tasks were chosen from all four quadrants of the circumplex and included visual puzzles, brainstorming, making collective moral judgments, and negotiating over limited resources. The results in these tasks were taken to conduct a factor analysis. Both studies showed support for a general collective intelligence factor c underlying differences in group performance with an initial eigenvalue accounting for 43% (44% in study 2) of the variance, whereas the next factor accounted for only 18% (20%). That fits the range normally found in research regarding a general individual intelligence factor g typically accounting for 40% to 50% percent of between-individual performance differences on cognitive tests. − − − − Afterwards, a more complex criterion task was absolved by each group measuring whether the extracted ''c'' factor had predictive power for performance outside the original task batteries. Criterion tasks were playing [[Draughts|checkers (draughts)]] against a standardized computer in the first and a complex architectural design task in the second study. In a [[regression analysis]] using both individual intelligence of group members and ''c'' to predict performance on the criterion tasks, ''c'' had a significant effect, but average and maximum individual intelligence had not. While average (r=0.15, P=0.04) and maximum intelligence (r=0.19, P=0.008) of individual group members were moderately correlated with ''c'', ''c'' was still a much better predictor of the criterion tasks. According to Woolley et al., this supports the existence of a collective intelligence factor ''c,'' because it demonstrates an effect over and beyond group members' individual intelligence and thus that ''c'' is more than just the aggregation of the individual IQs or the influence of the group member with the highest IQ. − − Afterwards, a more complex criterion task was absolved by each group measuring whether the extracted c factor had predictive power for performance outside the original task batteries. Criterion tasks were playing checkers (draughts) against a standardized computer in the first and a complex architectural design task in the second study. In a regression analysis using both individual intelligence of group members and c to predict performance on the criterion tasks, c had a significant effect, but average and maximum individual intelligence had not. While average (r=0.15, P=0.04) and maximum intelligence (r=0.19, P=0.008) of individual group members were moderately correlated with c, c was still a much better predictor of the criterion tasks. According to Woolley et al., this supports the existence of a collective intelligence factor c, because it demonstrates an effect over and beyond group members' individual intelligence and thus that c is more than just the aggregation of the individual IQs or the influence of the group member with the highest IQ. − − − − Engel et al. (2014) replicated Woolley et al.'s findings applying an accelerated battery of tasks with a first factor in the factor analysis explaining 49% of the between-group variance in performance with the following factors explaining less than half of this amount. Moreover, they found a similar result for groups working together online communicating only via text and confirmed the role of female proportion and social sensitivity in causing collective intelligence in both cases. Similarly to Wolley et al., they also measured social sensitivity with the RME which is actually meant to measure people's ability to detect mental states in other peoples' eyes. The online collaborating participants, however, did neither know nor see each other at all. The authors conclude that scores on the RME must be related to a broader set of abilities of social reasoning than only drawing inferences from other people's eye expressions. − − Engel et al. (2014) replicated Woolley et al.'s findings applying an accelerated battery of tasks with a first factor in the factor analysis explaining 49% of the between-group variance in performance with the following factors explaining less than half of this amount. Moreover, they found a similar result for groups working together online communicating only via text and confirmed the role of female proportion and social sensitivity in causing collective intelligence in both cases. Similarly to Wolley et al., they also measured social sensitivity with the RME which is actually meant to measure people's ability to detect mental states in other peoples' eyes. The online collaborating participants, however, did neither know nor see each other at all. The authors conclude that scores on the RME must be related to a broader set of abilities of social reasoning than only drawing inferences from other people's eye expressions. − − − − A collective intelligence factor ''c'' in the sense of Woolley et al. was further found in groups of MBA students working together over the course of a semester, as well as in groups from different cultures and groups in different contexts in terms of short-term versus long-term groups. None of these investigations considered team members' individual intelligence scores as control variables. − − A collective intelligence factor c in the sense of Woolley et al. in online gaming groups as well as in groups from different cultures and groups in different contexts in terms of short-term versus long-term groups. None of these investigations considered team members' individual intelligence scores as control variables. − − − − Note as well that the field of collective intelligence research is quite young and published empirical evidence is relatively rare yet. However, various proposals and working papers are in progress or already completed but (supposedly) still in a [[scholarly peer review]]ing publication process. − − Note as well that the field of collective intelligence research is quite young and published empirical evidence is relatively rare yet. However, various proposals and working papers are in progress or already completed but (supposedly) still in a scholarly peer reviewing publication process. − + − − Next to predicting a group's performance on more complex criterion tasks as shown in the original experiments, the collective intelligence factor ''c'' was also found to predict group performance in diverse tasks in MBA classes lasting over several months. Thereby, highly collectively intelligent groups earned significantly higher scores on their group assignments although their members did not do any better on other individually performed assignments. Moreover, highly collective intelligent teams improved performance over time suggesting that more collectively intelligent teams learn better. This is another potential parallel to individual intelligence where more intelligent people are found to acquire new material quicker. − − Next to predicting a group's performance on more complex criterion tasks as shown in the original experiments, the collective intelligence factor ''c'' was also found to predict group performance in diverse tasks in MBA classes lasting over several months. Thereby, highly collectively intelligent groups earned significantly higher scores on their group assignments although their members did not do any better on other individually performed assignments. Moreover, highly collective intelligent teams improved performance over time suggesting that more collectively intelligent teams learn better. This is another potential parallel to individual intelligence where more intelligent people are found to acquire new material quicker. − − − − Individual intelligence can be used to predict plenty of life outcomes from school attainment to health outcomes Whether collective intelligence is able to predict other outcomes besides group performance on mental tasks has still to be investigated. − − Individual intelligence can be used to predict plenty of life outcomes from school attainment and career success to health outcomes and even mortality. Whether collective intelligence is able to predict other outcomes besides group performance on mental tasks has still to be investigated. − + − − − − − Gladwell (2008) showed that the relationship between individual IQ and success works only to a certain point and that additional IQ points over an estimate of IQ 120 do not translate into real life advantages. If a similar border exists for Group-IQ or if advantages are linear and infinite, has still to be explored. Similarly, demand for further research on possible connections of individual and collective intelligence exists within plenty of other potentially transferable logics of individual intelligence, such as, for instance, the development over time a group's collective intelligence potentially offers simpler opportunities for improvement by exchanging team members or implementing structures and technologies. as well as watching drama movies.In how far such training ultimately improves collective intelligence through social sensitivity remains an open question. − − Gladwell (2008) showed that the relationship between individual IQ and success works only to a certain point and that additional IQ points over an estimate of IQ 120 do not translate into real life advantages. If a similar border exists for Group-IQ or if advantages are linear and infinite, has still to be explored. Similarly, demand for further research on possible connections of individual and collective intelligence exists within plenty of other potentially transferable logics of individual intelligence, such as, for instance, the development over time or the question of improving intelligence. Whereas it is controversial whether human intelligence can be enhanced via training, as well as watching drama movies. In how far such training ultimately improves collective intelligence through social sensitivity remains an open question. − − − − There are further more advanced concepts and factor models attempting to explain individual cognitive ability including the categorization of intelligence in fluid and crystallized intelligence or the hierarchical model of intelligence differences. Further supplementing explanations and conceptualizations for the factor structure of the Genomes of collective intelligence besides a general c factor', though, are missing yet. − − There are further more advanced concepts and factor models attempting to explain individual cognitive ability including the categorization of intelligence in fluid and crystallized intelligence or the hierarchical model of intelligence differences. Further supplementing explanations and conceptualizations for the factor structure of the Genomes of collective intelligence besides a general c factor', though, are missing yet. − + − − − − − Other scholars explain team performance by aggregating team members' general intelligence to the team level instead of building an own overall collective intelligence measure. Devine and Philips (2001) showed in a meta-analysis that mean cognitive ability predicts team performance in laboratory settings (.37) as well as field settings (.14) – note that this is only a small effect. Suggesting a strong dependence on the relevant tasks, other scholars showed that tasks requiring a high degree of communication and cooperation are found to be most influenced by the team member with the lowest cognitive ability. Tasks in which selecting the best team member is the most successful strategy, are shown to be most influenced by the member with the highest cognitive ability. − − Other scholars explain team performance by aggregating team members' general intelligence to the team level instead of building an own overall collective intelligence measure. Devine and Philips (2001) showed in a meta-analysis that mean cognitive ability predicts team performance in laboratory settings (.37) as well as field settings (.14) – note that this is only a small effect. Suggesting a strong dependence on the relevant tasks, other scholars showed that tasks requiring a high degree of communication and cooperation are found to be most influenced by the team member with the lowest cognitive ability. Tasks in which selecting the best team member is the most successful strategy, are shown to be most influenced by the member with the highest cognitive ability. − − − − Since Woolley et al.'s results do not show any influence of group satisfaction, [[group cohesiveness]], or motivation, they, at least implicitly, challenge these concepts regarding the importance for group performance in general and thus contrast meta-analytically proven evidence concerning the positive effects of [[Group cohesiveness|group cohesion]], motivation and satisfaction on group performance. − − Since Woolley et al.'s[9] results do not show any influence of group satisfaction, group cohesiveness, or motivation, they, at least implicitly, challenge these concepts regarding the importance for group performance in general and thus contrast meta-analytically proven evidence concerning the positive effects of group cohesion,[106][107][108] motivation[109][110] and satisfaction[111] on group performance. − − − − Noteworthy is also that the involved researchers among the confirming findings widely overlap with each other and with the authors participating in the original first study around Anita Woolley. − − Noteworthy is also that the involved researchers among the confirming findings widely overlap with each other and with the authors participating in the original first study around Anita Woolley. + + − + − == Alternative mathematical techniques 其他数学替代技术 == − − === Computational collective intelligence 计算集体智能 === − − − − In 2001, Tadeusz (Tad) Szuba from the [[Akademia Górniczo-Hutnicza|AGH University]] in Poland proposed a formal model for the phenomenon of collective intelligence. It is assumed to be an unconscious, random, parallel, and distributed computational process, run in mathematical logic by the social structure. − − In 2001, Tadeusz (Tad) Szuba from the AGH University in Poland proposed a formal model for the phenomenon of collective intelligence. It is assumed to be an unconscious, random, parallel, and distributed computational process, run in mathematical logic by the social structure. − − − − In this model, beings and information are modeled as abstract information molecules carrying expressions of mathematical logic. They are quasi-randomly displacing due to their interaction with their environments with their intended displacements. Their interaction in abstract computational space creates multi-thread inference process which we perceive as collective intelligence. Thus, a non-[[Alan Turing|Turing]] model of computation is used. This theory allows simple formal definition of collective intelligence as the property of [[social structure]] and seems to be working well for a wide spectrum of beings, from bacterial colonies up to human social structures. Collective intelligence considered as a specific computational process is providing a straightforward explanation of several social phenomena. For this model of collective intelligence, the formal definition of IQS (IQ Social) was proposed and was defined as "the probability function over the time and domain of N-element inferences which are reflecting inference activity of the social structure". While IQS seems to be computationally hard, modeling of social structure in terms of a computational process as described above gives a chance for approximation. Prospective applications are optimization of companies through the maximization of their IQS, and the analysis of drug resistance against collective intelligence of bacterial colonies. − − In this model, beings and information are modeled as abstract information molecules carrying expressions of mathematical logic. They are quasi-randomly displacing due to their interaction with their environments with their intended displacements. Their interaction in abstract computational space creates multi-thread inference process which we perceive as collective intelligence. Thus, a non-Turing model of computation is used. This theory allows simple formal definition of collective intelligence as the property of social structure and seems to be working well for a wide spectrum of beings, from bacterial colonies up to human social structures. Collective intelligence considered as a specific computational process is providing a straightforward explanation of several social phenomena. For this model of collective intelligence, the formal definition of IQS (IQ Social) was proposed and was defined as "the probability function over the time and domain of N-element inferences which are reflecting inference activity of the social structure". While IQS seems to be computationally hard, modeling of social structure in terms of a computational process as described above gives a chance for approximation. Prospective applications are optimization of companies through the maximization of their IQS, and the analysis of drug resistance against collective intelligence of bacterial colonies. − + − − − − − One measure sometimes applied, especially by more artificial intelligence focused theorists, is a "collective intelligence quotient" (or "cooperation quotient") – which can be normalized from the "individual" [[intelligence quotient]] (IQ) – thus making it possible to determine the marginal intelligence added by each new individual participating in the [[collective action]], thus using [[Metric (mathematics)|metrics]] to avoid the hazards of [[group think]] and [[stupidity]]. − − One measure sometimes applied, especially by more artificial intelligence focused theorists, is a "collective intelligence quotient" (or "cooperation quotient") – which can be normalized from the "individual" intelligence quotient (IQ) – thus making it possible to determine the marginal intelligence added by each new individual participating in the collective action, thus using metrics to avoid the hazards of group think and stupidity − + − − − + − − 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. − + − − − − − 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 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. − − James Surowiecki divides the advantages of disorganized decision-making into three main categories, which are cognition, cooperation and coordination. − + − + − − 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.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]] 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. − + − − − − − Political parties mobilize large numbers of people to form policy, select candidates and finance and run election campaigns. 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. 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. − − Political parties mobilize large numbers of people to form policy, select candidates and finance and run election campaigns. 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.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. − − − − Companies such as Affinnova (acquired by Nielsen), [[Google]], [[InnoCentive]], [[Marketocracy]], and [[Threadless]] have successfully employed the concept of collective intelligence in bringing about the next generation of technological changes through their research and development (R&D), customer service, and knowledge management. An example of such application is Google's Project Aristotle in 2012, where the effect of collective intelligence on team makeup was examined in hundreds of the company's R&D teams. − − Companies such as Affinnova (acquired by Nielsen), Google, InnoCentive, Marketocracy, and Threadless have successfully employed the concept of collective intelligence in bringing about the next generation of technological changes through their research and development (R&D), customer service, and knowledge management. An example of such application is Google's Project Aristotle in 2012, where the effect of collective intelligence on team makeup was examined in hundreds of the company's R&D teams. − + − + − − In 2012, the ''Global Futures Collective Intelligence System'' (GFIS) was created by [[The Millennium Project]], which epitomizes collective intelligence as the synergistic intersection among data/information/knowledge, software/hardware, and expertise/insights that has a recursive learning process for better decision-making than the individual players alone. − − In 2012, the Global Futures Collective Intelligence System (GFIS) was created by The Millennium Project, which epitomizes collective intelligence as the synergistic intersection among data/information/knowledge, software/hardware, and expertise/insights that has a recursive learning process for better decision-making than the individual players alone. − − − − [[New media]] are often associated with the promotion and enhancement of collective intelligence. The ability of new media to easily store and retrieve information, predominantly through databases and the Internet, allows for it to be shared without difficulty. Thus, through interaction with new media, knowledge easily passes between sources {{Harv|Flew|2008}} resulting in a form of collective intelligence. The use of interactive new media, particularly the internet, promotes online interaction and this distribution of knowledge between users. − − New media are often associated with the promotion and enhancement of collective intelligence. The ability of new media to easily store and retrieve information, predominantly through databases and the Internet, allows for it to be shared without difficulty. Thus, through interaction with new media, knowledge easily passes between sources resulting in a form of collective intelligence. The use of interactive new media, particularly the internet, promotes online interaction and this distribution of knowledge between users. − − − − [[Francis Heylighen]], [[Valentin Turchin]], and Gottfried Mayer-Kress are among those who view collective intelligence through the lens of computer science and [[cybernetics]]. In their view, the Internet enables collective intelligence at the widest, planetary scale, thus facilitating the emergence of a [[global brain]]. − − Francis Heylighen, Valentin Turchin, and Gottfried Mayer-Kress are among those who view collective intelligence through the lens of computer science and cybernetics. In their view, the Internet enables collective intelligence at the widest, planetary scale, thus facilitating the emergence of a global brain. − − − − The developer of the World Wide Web, [[Tim Berners-Lee]], aimed to promote sharing and publishing of information globally. Later his employer opened up the technology for free use. In the early '90s, the Internet's potential was still untapped, until the mid-1990s when 'critical mass', as termed by the head of the Advanced Research Project Agency (ARPA), Dr. [[J.C.R. Licklider]], demanded more accessibility and utility. The driving force of this Internet-based collective intelligence is the digitization of information and communication. [[Henry Jenkins]], a key theorist of new media and media convergence draws on the theory that collective intelligence can be attributed to media convergence and participatory culture {{Harv|Flew|2008}}. He criticizes contemporary education for failing to incorporate online trends of collective problem solving into the classroom, stating "whereas a collective intelligence community encourages ownership of work as a group, schools grade individuals". Jenkins argues that interaction within a knowledge community builds vital skills for young people, and teamwork through collective intelligence communities contribute to the development of such skills. − − The developer of the World Wide Web, Tim Berners-Lee, aimed to promote sharing and publishing of information globally. Later his employer opened up the technology for free use. In the early '90s, the Internet's potential was still untapped, until the mid-1990s when 'critical mass', as termed by the head of the Advanced Research Project Agency (ARPA), Dr. J.C.R. Licklider, demanded more accessibility and utility. The driving force of this Internet-based collective intelligence is the digitization of information and communication. Henry Jenkins, a key theorist of new media and media convergence draws on the theory that collective intelligence can be attributed to media convergence and participatory culture . He criticizes contemporary education for failing to incorporate online trends of collective problem solving into the classroom, stating "whereas a collective intelligence community encourages ownership of work as a group, schools grade individuals". Jenkins argues that interaction within a knowledge community builds vital skills for young people, and teamwork through collective intelligence communities contribute to the development of such skills. Collective intelligence is not merely a quantitative contribution of information from all cultures, it is also qualitative. − − − − [[Pierre Lévy|Lévy]] and [[Derrick de Kerckhove|de Kerckhove]] consider CI from a mass communications perspective, focusing on the ability of networked information and communication technologies to enhance the community knowledge pool. They suggest that these communications tools enable humans to interact and to share and collaborate with both ease and speed (Flew 2008). With the development of the [[Internet]] and its widespread use, the opportunity to contribute to knowledge-building communities, such as [[Wikipedia]], is greater than ever before. These computer networks give participating users the opportunity to store and to retrieve knowledge through the collective access to these databases and allow them to "harness the hive" Press.|year=2008|isbn=|location=Melbourne|pages=|quote=|via=}}</ref> Researchers at the [[MIT Center for Collective Intelligence]] research and explore collective intelligence of groups of people and computers. − − Lévy and de Kerckhove consider CI from a mass communications perspective, focusing on the ability of networked information and communication technologies to enhance the community knowledge pool. They suggest that these communications tools enable humans to interact and to share and collaborate with both ease and speed (Flew 2008). With the development of the Internet and its widespread use, the opportunity to contribute to knowledge-building communities, such as Wikipedia, is greater than ever before. These computer networks give participating users the opportunity to store and to retrieve knowledge through the collective access to these databases and allow them to "harness the hive" Researchers at the MIT Center for Collective Intelligence research and explore collective intelligence of groups of people and computers. − + − − In this context collective intelligence is often confused with [[shared knowledge]]. The former is the sum total of information held individually by members of a community while the latter is information that is believed to be true and known by all members of the community. Collective intelligence as represented by [[Web 2.0]] has less user engagement than [[collaborative intelligence]]. An art project using Web 2.0 platforms is "Shared Galaxy", an experiment developed by an anonymous artist to create a collective identity that shows up as one person on several platforms like MySpace, Facebook, YouTube and Second Life. The password is written in the profiles and the accounts named "Shared Galaxy" are open to be used by anyone. In this way many take part in being one. Another art project using collective intelligence to produce artistic work is Curatron, where a large group of artists together decides on a smaller group that they think would make a good collaborative group. The process is used based on an algorithm computing the collective preferences In creating what he calls 'CI-Art', Nova Scotia based artist Mathew Aldred follows Pierry Lévy's definition of collective intelligence. Aldred's CI-Art event in March 2016 involved over four hundred people from the community of Oxford, Nova Scotia, and internationally. Later work developed by Aldred used the UNU [[swarm intelligence]] system to create digital drawings and paintings. The Oxford Riverside Gallery (Nova Scotia) held a public CI-Art event in May 2016, which connected with online participants internationally. − − In this context collective intelligence is often confused with shared knowledge. The former is the sum total of information held individually by members of a community while the latter is information that is believed to be true and known by all members of the community. Collective intelligence as represented by Web 2.0 has less user engagement than collaborative intelligence. An art project using Web 2.0 platforms is "Shared Galaxy", an experiment developed by an anonymous artist to create a collective identity that shows up as one person on several platforms like MySpace, Facebook, YouTube and Second Life. The password is written in the profiles and the accounts named "Shared Galaxy" are open to be used by anyone. In this way many take part in being one. Another art project using collective intelligence to produce artistic work is Curatron, where a large group of artists together decides on a smaller group that they think would make a good collaborative group. The process is used based on an algorithm computing the collective preferences In creating what he calls 'CI-Art', Nova Scotia based artist Mathew Aldred follows Pierry Lévy's definition of collective intelligence. Aldred's CI-Art event in March 2016 involved over four hundred people from the community of Oxford, Nova Scotia, and internationally. Later work developed by Aldred used the UNU swarm intelligence system to create digital drawings and paintings. The Oxford Riverside Gallery (Nova Scotia) held a public CI-Art event in May 2016, which connected with online participants internationally. − − − − − − − − In [[social bookmarking]] (also called collaborative tagging), users assign tags to resources shared with other users, which gives rise to a type of information organisation that emerges from this [[crowdsourcing]] process. The resulting information structure can be seen as reflecting the collective knowledge (or collective intelligence) of a community of users and is commonly called a "[[Folksonomy]]", and the process can be captured by [[models of collaborative tagging]]. − − In social bookmarking (also called collaborative tagging), users assign tags to resources shared with other users, which gives rise to a type of information organisation that emerges from this crowdsourcing process. The resulting information structure can be seen as reflecting the collective knowledge (or collective intelligence) of a community of users and is commonly called a "Folksonomy", and the process can be captured by models of collaborative tagging. − − − − Recent research using data from the social bookmarking website [[Delicious (website)|Delicious]], has shown that collaborative tagging systems exhibit a form of [[complex system]]s (or [[Self-organization|self-organizing]]) dynamics.Harry Halpin, Valentin Robu, Hana Shepherd Although there is no central controlled vocabulary to constrain the actions of individual users, the distributions of tags that describe different resources has been shown to converge over time to a stable [[power law]] distributions. Once such stable distributions form, examining the [[correlation]]s between different tags can be used to construct simple folksonomy graphs, which can be efficiently partitioned to obtained a form of community or shared vocabularies.Valentin Robu, Harry Halpin, − − Recent research using data from the social bookmarking website Delicious, has shown that collaborative tagging systems exhibit a form of complex systems (or self-organizing) dynamics. Although there is no central controlled vocabulary to constrain the actions of individual users, the distributions of tags that describe different resources has been shown to converge over time to a stable power law distributions. Such vocabularies can be seen as a form of collective intelligence, emerging from the decentralised actions of a community of users. The Wall-it Project is also an example of social bookmarking. − + − − − − − Research performed by Tapscott and Williams has provided a few examples of the benefits of collective intelligence to business: − − Research performed by Tapscott and Williams has provided a few examples of the benefits of collective intelligence to business: − + − − − − Talent utilization − − − − :At the rate technology is changing, no firm can fully keep up in the innovations needed to compete. Instead, smart firms are drawing on the power of mass collaboration to involve participation of the people they could not employ. This also helps generate continual interest in the firm in the form of those drawn to new idea creation as well as investment opportunities.<ref name="Tapscott, D. 2008" /> − − At the rate technology is changing, no firm can fully keep up in the innovations needed to compete. Instead, smart firms are drawing on the power of mass collaboration to involve participation of the people they could not employ. This also helps generate continual interest in the firm in the form of those drawn to new idea creation as well as investment opportunities. + + − ;Demand creation − − Demand creation − − 需求创造 − − :Firms can create a new market for complementary goods by engaging in open source community. Firms also are able to expand into new fields that they previously would not have been able to without the addition of resources and collaboration from the community. This creates, as mentioned before, a new market for complementary goods for the products in said new fields.<ref name="Tapscott, D. 2008" /> − − Firms can create a new market for complementary goods by engaging in open source community. Firms also are able to expand into new fields that they previously would not have been able to without the addition of resources and collaboration from the community. This creates, as mentioned before, a new market for complementary goods for the products in said new fields. − − 企业可以通过参与开放源代码社区来创建互补商品的新市场。即使没有社区的资源和协作，企业也可以扩展到以前无法实现的新领域。如前所述，这为所述新领域中商品的互补产品创造了新市场。
无编辑摘要
女性占比作为因子c的预测因素主要是通过社会敏感性<font color=“#32CD32”>介导</font>（Sobel z = 1.93，P = 0.03）<ref name=":09">{{Cite journal|last1=Woolley|first1=Anita Williams|last2=Chabris|first2=Christopher F.|last3=Pentland|first3=Alex|last4=Hashmi|first4=Nada|last5=Malone|first5=Thomas W.|s2cid=74579|date=2010-10-29|title=Evidence for a Collective Intelligence Factor in the Performance of Human Groups|journal=Science|volume=330|issue=6004|pages=686–688|doi=10.1126/science.1193147|pmid=20929725|bibcode=2010Sci...330..686W}}</ref>，这与之前的研究结果相符，即女性在社会敏感性测试中得分更高<ref name=":63">{{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|journal=Journal of Child Psychology and Psychiatry |volume=42 |issue=2|pages=241–251|doi=10.1017/s0021963001006643|pmid=11280420}}</ref>。从统计学上讲，<font color=“#32CD32”>介导</font>，从统计学上讲，澄清了因变量和自变量之间关系的基本机制<ref>{{Cite book|title=Introduction to Statistical Mediation Analysis|last=MacKinnon, D. P.|publisher=Erlbaum|year=2008|location=New York, NY}}</ref>。伍利在接受《哈佛商业评论》采访时曾表示这个发现说明了女性群体比男性群体更聪明。但是，她也就这个结论<font color=“#32CD32”>做了相对化的陈述</font>，实际上重要的是团体成员的高度社会敏感性<ref name=":72">{{Cite journal|author1=Woolley, A. |author2= Malone, T. |name-list-style=amp |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}}</ref>。
女性占比作为因子c的预测因素主要是通过社会敏感性<font color=“#32CD32”>介导</font>（Sobel z = 1.93，P = 0.03）<ref name=":09">{{Cite journal|last1=Woolley|first1=Anita Williams|last2=Chabris|first2=Christopher F.|last3=Pentland|first3=Alex|last4=Hashmi|first4=Nada|last5=Malone|first5=Thomas W.|s2cid=74579|date=2010-10-29|title=Evidence for a Collective Intelligence Factor in the Performance of Human Groups|journal=Science|volume=330|issue=6004|pages=686–688|doi=10.1126/science.1193147|pmid=20929725|bibcode=2010Sci...330..686W}}</ref>，这与之前的研究结果相符，即女性在社会敏感性测试中得分更高<ref name=":63">{{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|journal=Journal of Child Psychology and Psychiatry |volume=42 |issue=2|pages=241–251|doi=10.1017/s0021963001006643|pmid=11280420}}</ref>。从统计学上讲，<font color=“#32CD32”>介导</font>，从统计学上讲，澄清了因变量和自变量之间关系的基本机制<ref>{{Cite book|title=Introduction to Statistical Mediation Analysis|last=MacKinnon, D. P.|publisher=Erlbaum|year=2008|location=New York, NY}}</ref>。伍利在接受《哈佛商业评论》采访时曾表示这个发现说明了女性群体比男性群体更聪明。但是，她也就这个结论<font color=“#32CD32”>做了相对化的陈述</font>，实际上重要的是团体成员的高度社会敏感性<ref name=":72">{{Cite journal|author1=Woolley, A. |author2= Malone, T. |name-list-style=amp |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}}</ref>。
从理论上讲，集体智力因子c是由自下而上和自上而下共同产生的<font color=“#ff8000”> 涌现特性</font>。因此，自下而上的过程涉及聚合组成员的特征，自上而下的过程涉及团队结构，以及协作协调方式对团队风格的影响<ref name=":113">{{Cite journal|last1=Woolley|first1=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|s2cid=146673541}}</ref>。
从理论上讲，集体智力因子c是由自下而上和自上而下共同产生的<font color=“#ff8000”> 涌现特性</font>。因此，自下而上的过程涉及聚合组成员的特征，自上而下的过程涉及团队结构，以及协作协调方式对团队风格的影响<ref name=":113">{{Cite journal|last1=Woolley|first1=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|s2cid=146673541}}</ref>。
=== 处理程序===
=== Processes 处理程序===
[[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）]]
[[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 自上而下 ====
==== 自上而下 ====
自上而下的处理包括团队交互分析，涉及例如结构，程序和规范<ref name="Woolley 420–424">{{Cite journal|last1=Woolley|first1=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|s2cid=146673541}}</ref>。这种自上而下的过程的一个例子是话轮转换机制。研究进一步表明，集体智慧的群体大体上能进行平等地交流。此过程同样适用于参与形式的沟通，类似面对面以及通过书面形式进行的在线小组交流<ref name=":44">{{Cite journal|author1=Engel, D. |author2=Woolley, A. W. |author3=Jing, L. X. |author4=Chabris, C. F. |author5= Malone, T. W. |name-list-style=amp |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><ref name=":9">{{Cite journal|author1=Kim, Y. J. |author2=Engel, D. |author3=Woolley, A. W. |author4=Lin, J. |author5=McArthur, N. |author6= Malone, T. W. |name-list-style=amp |date=2015|title=Work together, play smart: Collective intelligence in League of Legends teams|journal=Paper Presented at the 2015 Collective Intelligence Conference, Santa Clara, CA.}}</ref>。
自上而下的处理包括团队交互分析，涉及例如结构，程序和规范<ref name="Woolley 420–424">{{Cite journal|last1=Woolley|first1=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|s2cid=146673541}}</ref>。这种自上而下的过程的一个例子是话轮转换机制。研究进一步表明，集体智慧的群体大体上能进行平等地交流。此过程同样适用于参与形式的沟通，类似面对面以及通过书面形式进行的在线小组交流<ref name=":44">{{Cite journal|author1=Engel, D. |author2=Woolley, A. W. |author3=Jing, L. X. |author4=Chabris, C. F. |author5= Malone, T. W. |name-list-style=amp |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><ref name=":9">{{Cite journal|author1=Kim, Y. J. |author2=Engel, D. |author3=Woolley, A. W. |author4=Lin, J. |author5=McArthur, N. |author6= Malone, T. W. |name-list-style=amp |date=2015|title=Work together, play smart: Collective intelligence in League of Legends teams|journal=Paper Presented at the 2015 Collective Intelligence Conference, Santa Clara, CA.}}</ref>。
==== 自下而上 ====
==== Bottom-up processes 自下而上 ====
自下而上的处理包括小组组成分析，即小组成员的特征，这些特征汇总直接影响到团队级别<ref name=":114">{{Cite journal|last1=Woolley|first1=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|s2cid=146673541}}</ref>。例子之一包括社会敏感度平均值或小组成员的平均和最大智力得分。此外，人们发现集体智能与一个群体的认知多样性有关，包括思维方式和观点<ref>{{Cite journal|author1=Kozhevnikov, M. |author2=Evans, C. |author3= Kosslyn, S. M. |name-list-style=amp|date=2014|title=Cognitive style as environmentally sensitive individual differences in cognition: A modern synthesis and applications in education, business, and management|journal=Psychological Science in the Public Interest |volume=15 |issue=1 |pages=3–33|doi=10.1177/1529100614525555|pmid=26171827|s2cid=20559112 }}</ref>。认知风格适度的群体，相比较认知风格非常相似或非常不同的群体，具有更高的集体智能。因为成员彼此之间过于相似会造成该群体缺乏不同的观点（往往团队任务表现好的具有各种观点）和技能。另一方面，成员差异太大的团体可能会难以有效地沟通和协调<ref name=":122">{{Cite journal|author1=Aggarwal, I. |author2=Woolley, A. W. |author3=Chabris, C. F. |author4= Malone, T. W. |name-list-style=amp |date=2015|title=Cognitive diversity, collective intelligence, and learning in teams.|journal=Paper Presented at the 2015 Collective Intelligence Conference, Santa Clara, CA.}}</ref>。
自下而上的处理包括小组组成分析，即小组成员的特征，这些特征汇总直接影响到团队级别<ref name=":114">{{Cite journal|last1=Woolley|first1=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|s2cid=146673541}}</ref>。例子之一包括社会敏感度平均值或小组成员的平均和最大智力得分。此外，人们发现集体智能与一个群体的认知多样性有关，包括思维方式和观点<ref>{{Cite journal|author1=Kozhevnikov, M. |author2=Evans, C. |author3= Kosslyn, S. M. |name-list-style=amp|date=2014|title=Cognitive style as environmentally sensitive individual differences in cognition: A modern synthesis and applications in education, business, and management|journal=Psychological Science in the Public Interest |volume=15 |issue=1 |pages=3–33|doi=10.1177/1529100614525555|pmid=26171827|s2cid=20559112 }}</ref>。认知风格适度的群体，相比较认知风格非常相似或非常不同的群体，具有更高的集体智能。因为成员彼此之间过于相似会造成该群体缺乏不同的观点（往往团队任务表现好的具有各种观点）和技能。另一方面，成员差异太大的团体可能会难以有效地沟通和协调<ref name=":122">{{Cite journal|author1=Aggarwal, I. |author2=Woolley, A. W. |author3=Chabris, C. F. |author4= Malone, T. W. |name-list-style=amp |date=2015|title=Cognitive diversity, collective intelligence, and learning in teams.|journal=Paper Presented at the 2015 Collective Intelligence Conference, Santa Clara, CA.}}</ref>。
==== 串行与并行 ====
==== Serial vs Parallel processes 串行与并行 ====
在大多数人类历史中，集体智能都局限于少数部落群体，它们通过成员之间的实时并行互动来收集意见。而现代，因为大众传播，媒体和网络技术的发展使集体智能可以跨越各大洲和时区，这是一个极其庞大的群体。为了适应规模上的这种变化，大规模集体智能被序列化投票过程所控制，例如随着时间的推移去汇总投票，赞赏和评级。在工程领域中，汇总各种工程决策可以识别分析优秀的经典设计<ref>{{Cite journal|last1=Bruch|first1=Marcel|last2=Bodden|first2=Eric|last3=Monperrus|first3=Martin|last4=Mezini|first4=Mira|date=2010|title=IDE 2.0: collective intelligence in software development|url=https://hal.archives-ouvertes.fr/hal-01575346/file/bbmm10ide.pdf|journal=Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research - FoSER '10|doi=10.1145/1882362.1882374|s2cid=7637561}}</ref>。尽管现代系统受益于更大的群规模，但事实上发现串行化处理过程会引入大量噪声，从而使群组的集体输出失真。在一项有关序列化集体智能的重要研究中发现，对序列化投票系统做出贡献的第一票可能使最终结果失真34％<ref>{{Cite journal|last1=Muchnik|first1=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|s2cid=15775672}}</ref>。
在大多数人类历史中，集体智能都局限于少数部落群体，它们通过成员之间的实时并行互动来收集意见。而现代，因为大众传播，媒体和网络技术的发展使集体智能可以跨越各大洲和时区，这是一个极其庞大的群体。为了适应规模上的这种变化，大规模集体智能被序列化投票过程所控制，例如随着时间的推移去汇总投票，赞赏和评级。在工程领域中，汇总各种工程决策可以识别分析优秀的经典设计<ref>{{Cite journal|last1=Bruch|first1=Marcel|last2=Bodden|first2=Eric|last3=Monperrus|first3=Martin|last4=Mezini|first4=Mira|date=2010|title=IDE 2.0: collective intelligence in software development|url=https://hal.archives-ouvertes.fr/hal-01575346/file/bbmm10ide.pdf|journal=Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research - FoSER '10|doi=10.1145/1882362.1882374|s2cid=7637561}}</ref>。尽管现代系统受益于更大的群规模，但事实上发现串行化处理过程会引入大量噪声，从而使群组的集体输出失真。在一项有关序列化集体智能的重要研究中发现，对序列化投票系统做出贡献的第一票可能使最终结果失真34％<ref>{{Cite journal|last1=Muchnik|first1=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|s2cid=15775672}}</ref>。
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”<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|archive-url=https://web.archive.org/web/20170525175211/http://www.bbc.com/future/story/20161215-why-bees-could-be-the-secret-to-superhuman-intelligence|archive-date=25 May 2017|url-status=live}}</ref><ref>{{Cite book|last1=Rosenberg|first1=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|s2cid=12725324}}</ref>。基于<font color="#ff8000"> 群体智能Swarm Intelligence</font>（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<ref>{{Cite journal|last1=Metcalf|first1=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|s2cid=202323483|issn=0008-1256}}</ref>。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<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|archive-url=https://web.archive.org/web/20160604063846/http://www.newsweek.com/artificial-intelligence-turns-20-11000-kentucky-derby-bet-457783|archive-date=4 June 2016|url-status=live}}</ref>。
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”<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|archive-url=https://web.archive.org/web/20170525175211/http://www.bbc.com/future/story/20161215-why-bees-could-be-the-secret-to-superhuman-intelligence|archive-date=25 May 2017|url-status=live}}</ref><ref>{{Cite book|last1=Rosenberg|first1=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|s2cid=12725324}}</ref>。基于<font color="#ff8000"> 群体智能Swarm Intelligence</font>（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<ref>{{Cite journal|last1=Metcalf|first1=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|s2cid=202323483|issn=0008-1256}}</ref>。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<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|archive-url=https://web.archive.org/web/20160604063846/http://www.newsweek.com/artificial-intelligence-turns-20-11000-kentucky-derby-bet-457783|archive-date=4 June 2016|url-status=live}}</ref>。
斯坦福大学医学院和Unanimous A.I.的研究人员证明了在医学应用中并行集体智能的价值，在已发表的研究中，它们采用了实时集群算法将一组人类医生联系在一起，运用胸部X射线来诊断肺炎的存在<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|archive-url=https://web.archive.org/web/20190720160349/https://spectrum.ieee.org/the-human-os/biomedical/diagnostics/ai-human-hive-mind-diagnoses-pneumonia|archive-date=20 July 2019|url-status=live}}</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|archive-url=https://web.archive.org/web/20190720160348/https://www.stanforddaily.com/2018/09/27/artificial-swarm-intelligence-diagnoses-pneumonia-better-than-individual-computer-or-doctor/|archive-date=20 July 2019|url-status=live}}</ref>。当作为“人类集群”一起工作时，经验丰富的放射科医生小组相比较传统方法，诊断错误减少了33％.<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|archive-url=https://web.archive.org/web/20190720160350/https://www.radiologytoday.net/archive/rt0119p12.shtml|archive-date=20 July 2019|url-status=live}}</ref><ref>{{Cite journal|last1=Rosenberg|first1=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|s2cid=58675679}}</ref>。
斯坦福大学医学院和Unanimous A.I.的研究人员证明了在医学应用中并行集体智能的价值，在已发表的研究中，它们采用了实时集群算法将一组人类医生联系在一起，运用胸部X射线来诊断肺炎的存在<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|archive-url=https://web.archive.org/web/20190720160349/https://spectrum.ieee.org/the-human-os/biomedical/diagnostics/ai-human-hive-mind-diagnoses-pneumonia|archive-date=20 July 2019|url-status=live}}</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|archive-url=https://web.archive.org/web/20190720160348/https://www.stanforddaily.com/2018/09/27/artificial-swarm-intelligence-diagnoses-pneumonia-better-than-individual-computer-or-doctor/|archive-date=20 July 2019|url-status=live}}</ref>。当作为“人类集群”一起工作时，经验丰富的放射科医生小组相比较传统方法，诊断错误减少了33％.<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|archive-url=https://web.archive.org/web/20190720160350/https://www.radiologytoday.net/archive/rt0119p12.shtml|archive-date=20 July 2019|url-status=live}}</ref><ref>{{Cite journal|last1=Rosenberg|first1=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|s2cid=58675679}}</ref>。
=== 证据 ===
=== Evidence 证据 ===
[[文件:Standardized Regression Coefficients.png|缩略图|伍利等人（2010年）的两项初始研究中发现了集体智力因子c的标准化回归系数。c和平均（最高）成员智力得分在判据任务上得到回归。]]
[[文件:Standardized Regression Coefficients.png|缩略图|伍利等人（2010年）的两项初始研究中发现了集体智力因子c的标准化回归系数。c和平均（最高）成员智力得分在判据任务上得到回归。]]
伍利，察布里斯，彭特兰，哈什米（2010）是集体智能这一科学概念的创始人<ref name=":010">{{Cite journal|last1=Woolley|first1=Anita Williams|last2=Chabris|first2=Christopher F.|last3=Pentland|first3=Alex|last4=Hashmi|first4=Nada|last5=Malone|first5=Thomas W.|s2cid=74579|date=2010-10-29|title=Evidence for a Collective Intelligence Factor in the Performance of Human Groups|journal=Science|volume=330|issue=6004|pages=686–688|doi=10.1126/science.1193147|pmid=20929725|bibcode=2010Sci...330..686W}}</ref>。他们在192个群体的研究中发现了集体智能的单一统计因子，这192个群体的成员均是从公众中随机招募的。研究中，每个组群都是基于<font color="#ff8000"> 麦格拉思任务环McGrath Task Circumplex</font>（一种完善的小组任务分类法）<ref>{{Cite book|title=Groups: Interaction and Performance|last=McGrath, J. E.|publisher=Prentice-Hall|year=1984|location=Englewood Cliffs, NJ}}</ref>进行合作。这些任务是从四个象限中选择的，包括视觉难题，头脑风暴，集体道德判断以及就有限的资源进行谈判。将这些任务中的结果用于因子分析。两项研究均显示出了综合集群智力因子c的特征，并且根据群体的不同表现出了一定的差异，其初始特征值约占这些差异的43％（研究2中为44％），而另一个因子仅占18％（20％）。该数据与综合个体智力因子g的范围相符，通常在认知测验中占个体间性能差异的40％至50％<ref name=":52">{{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. |name-list-style=amp |publisher=Guilford|year=2005|location=New York, NY|pages=23–38}}</ref>。
伍利，察布里斯，彭特兰，哈什米（2010）是集体智能这一科学概念的创始人<ref name=":010">{{Cite journal|last1=Woolley|first1=Anita Williams|last2=Chabris|first2=Christopher F.|last3=Pentland|first3=Alex|last4=Hashmi|first4=Nada|last5=Malone|first5=Thomas W.|s2cid=74579|date=2010-10-29|title=Evidence for a Collective Intelligence Factor in the Performance of Human Groups|journal=Science|volume=330|issue=6004|pages=686–688|doi=10.1126/science.1193147|pmid=20929725|bibcode=2010Sci...330..686W}}</ref>。他们在192个群体的研究中发现了集体智能的单一统计因子，这192个群体的成员均是从公众中随机招募的。研究中，每个组群都是基于<font color="#ff8000"> 麦格拉思任务环McGrath Task Circumplex</font>（一种完善的小组任务分类法）<ref>{{Cite book|title=Groups: Interaction and Performance|last=McGrath, J. E.|publisher=Prentice-Hall|year=1984|location=Englewood Cliffs, NJ}}</ref>进行合作。这些任务是从四个象限中选择的，包括视觉难题，头脑风暴，集体道德判断以及就有限的资源进行谈判。将这些任务中的结果用于因子分析。两项研究均显示出了综合集群智力因子c的特征，并且根据群体的不同表现出了一定的差异，其初始特征值约占这些差异的43％（研究2中为44％），而另一个因子仅占18％（20％）。该数据与综合个体智力因子g的范围相符，通常在认知测验中占个体间性能差异的40％至50％<ref name=":52">{{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. |name-list-style=amp |publisher=Guilford|year=2005|location=New York, NY|pages=23–38}}</ref>。
后来每个小组进行测试，验证提取c因子是否具有预测原始任务以外的能力，进而解决了更为复杂的判据任务。在第一个研究中，判据任务是在标准计算机上玩跳棋（国际跳棋），在第二个研究中则是复杂的建筑设计任务。在使用组员个人智力和c因子来预测判据任务执行情况的回归分析中，c具有显著作用，而平均和最大的个人智力则没有。虽然单个组成员的平均智力（r = 0.15，P = 0.04）和最高智力（r = 0.19，P = 0.008）与c有中等程度的相关性，但是c仍然是判据任务更好的预测指标。根据伍利等人的说法，该结果支持了集群智力因子c的存在，因为它证明了超出小组成员个人智力外的影响，因此c不仅仅是个人智商的累加，或单纯受到智商最高组员的影响<ref name=":011">{{Cite journal|last1=Woolley|first1=Anita Williams|last2=Chabris|first2=Christopher F.|last3=Pentland|first3=Alex|last4=Hashmi|first4=Nada|last5=Malone|first5=Thomas W.|s2cid=74579|date=2010-10-29|title=Evidence for a Collective Intelligence Factor in the Performance of Human Groups|journal=Science|volume=330|issue=6004|pages=686–688|doi=10.1126/science.1193147|pmid=20929725|bibcode=2010Sci...330..686W}}</ref>。
后来每个小组进行测试，验证提取c因子是否具有预测原始任务以外的能力，进而解决了更为复杂的判据任务。在第一个研究中，判据任务是在标准计算机上玩跳棋（国际跳棋），在第二个研究中则是复杂的建筑设计任务。在使用组员个人智力和c因子来预测判据任务执行情况的回归分析中，c具有显著作用，而平均和最大的个人智力则没有。虽然单个组成员的平均智力（r = 0.15，P = 0.04）和最高智力（r = 0.19，P = 0.008）与c有中等程度的相关性，但是c仍然是判据任务更好的预测指标。根据伍利等人的说法，该结果支持了集群智力因子c的存在，因为它证明了超出小组成员个人智力外的影响，因此c不仅仅是个人智商的累加，或单纯受到智商最高组员的影响<ref name=":011">{{Cite journal|last1=Woolley|first1=Anita Williams|last2=Chabris|first2=Christopher F.|last3=Pentland|first3=Alex|last4=Hashmi|first4=Nada|last5=Malone|first5=Thomas W.|s2cid=74579|date=2010-10-29|title=Evidence for a Collective Intelligence Factor in the Performance of Human Groups|journal=Science|volume=330|issue=6004|pages=686–688|doi=10.1126/science.1193147|pmid=20929725|bibcode=2010Sci...330..686W}}</ref>。
恩格尔等人的研究（2014）在重复了伍利组员之前的研究发现<ref name=":45">{{Cite journal|author1=Engel, D. |author2=Woolley, A. W. |author3=Jing, L. X. |author4=Chabris, C. F. |author5= Malone, T. W. |name-list-style=amp |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>，将加速任务组合与因子分析中的第一因素结合在一起，可以解释组间表现差异的49％，而其他因素解释占该比例一半以下。此外，他们在仅通过文本进行在线交流的小组中发现了相似的结果，并证实了女性比例和社会敏感性在两种情况下引起集体智能的作用<ref name=":012">{{Cite journal|last1=Woolley|first1=Anita Williams|last2=Chabris|first2=Christopher F.|last3=Pentland|first3=Alex|last4=Hashmi|first4=Nada|last5=Malone|first5=Thomas W.|s2cid=74579|date=2010-10-29|title=Evidence for a Collective Intelligence Factor in the Performance of Human Groups|journal=Science|volume=330|issue=6004|pages=686–688|doi=10.1126/science.1193147|pmid=20929725|bibcode=2010Sci...330..686W}}</ref>。他们还模仿伍利小组使用RME来衡量社会敏感度，为了衡测试者感受他人眼中心理状态的能力。但是，在线合作参与者根本不认识也不见面。作者得出的结论是，RME的分数必须与更广泛的社会推理能力相关，而不仅仅是从其他人的眼神表情中得出推论<ref name=":13">{{Cite book|last1=Engel|first1=David|last2=Woolley|first2=Anita Williams|last3=Aggarwal|first3=Ishani|last4=Chabris|first4=Christopher F.|last5=Takahashi|first5=Masamichi|last6=Nemoto|first6=Keiichi|last7=Kaiser|first7=Carolin|last8=Kim|first8=Young Ji|last9=Malone|first9=Thomas W.|date=2015-01-01|title=Collective Intelligence in Computer-Mediated Collaboration Emerges in Different Contexts and Cultures|journal=Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems|series=CHI '15|location=New York, NY, USA|publisher=ACM|pages=3769–3778|doi=10.1145/2702123.2702259|isbn=9781450331456|s2cid=14303201}}</ref>。
恩格尔等人的研究（2014）在重复了伍利组员之前的研究发现<ref name=":45">{{Cite journal|author1=Engel, D. |author2=Woolley, A. W. |author3=Jing, L. X. |author4=Chabris, C. F. |author5= Malone, T. W. |name-list-style=amp |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>，将加速任务组合与因子分析中的第一因素结合在一起，可以解释组间表现差异的49％，而其他因素解释占该比例一半以下。此外，他们在仅通过文本进行在线交流的小组中发现了相似的结果，并证实了女性比例和社会敏感性在两种情况下引起集体智能的作用<ref name=":012">{{Cite journal|last1=Woolley|first1=Anita Williams|last2=Chabris|first2=Christopher F.|last3=Pentland|first3=Alex|last4=Hashmi|first4=Nada|last5=Malone|first5=Thomas W.|s2cid=74579|date=2010-10-29|title=Evidence for a Collective Intelligence Factor in the Performance of Human Groups|journal=Science|volume=330|issue=6004|pages=686–688|doi=10.1126/science.1193147|pmid=20929725|bibcode=2010Sci...330..686W}}</ref>。他们还模仿伍利小组使用RME来衡量社会敏感度，为了衡测试者感受他人眼中心理状态的能力。但是，在线合作参与者根本不认识也不见面。作者得出的结论是，RME的分数必须与更广泛的社会推理能力相关，而不仅仅是从其他人的眼神表情中得出推论<ref name=":13">{{Cite book|last1=Engel|first1=David|last2=Woolley|first2=Anita Williams|last3=Aggarwal|first3=Ishani|last4=Chabris|first4=Christopher F.|last5=Takahashi|first5=Masamichi|last6=Nemoto|first6=Keiichi|last7=Kaiser|first7=Carolin|last8=Kim|first8=Young Ji|last9=Malone|first9=Thomas W.|date=2015-01-01|title=Collective Intelligence in Computer-Mediated Collaboration Emerges in Different Contexts and Cultures|journal=Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems|series=CHI '15|location=New York, NY, USA|publisher=ACM|pages=3769–3778|doi=10.1145/2702123.2702259|isbn=9781450331456|s2cid=14303201}}</ref>。
伍利他们进一步在MBA学生群体中（时间跨度为一学期）<ref name=":013">{{Cite journal|last1=Woolley|first1=Anita Williams|last2=Chabris|first2=Christopher F.|last3=Pentland|first3=Alex|last4=Hashmi|first4=Nada|last5=Malone|first5=Thomas W.|s2cid=74579|date=2010-10-29|title=Evidence for a Collective Intelligence Factor in the Performance of Human Groups|journal=Science|volume=330|issue=6004|pages=686–688|doi=10.1126/science.1193147|pmid=20929725|bibcode=2010Sci...330..686W}}</ref>，在线游戏玩家群体中以及来自不同文化和不同背景的其他群体中（时间跨度分别为短期和长期组）发现了集体智力因子c。这些调查均未将团队成员的个人智力得分视为控制变量<ref name=":92">{{Cite journal|author1=Kim, Y. J. |author2=Engel, D. |author3=Woolley, A. W. |author4=Lin, J. |author5=McArthur, N. |author6= Malone, T. W. |name-list-style=amp |date=2015|title=Work together, play smart: Collective intelligence in League of Legends teams|journal=Paper Presented at the 2015 Collective Intelligence Conference, Santa Clara, CA.}}</ref><ref name=":8">{{Cite journal|author1=Aggarwal, I. |author2= Woolley, A.W. |name-list-style=amp |date=2014|title=The effects of cognitive diversity on collective intelligence and team learning.|journal=Symposium Presented at the 50th Meeting of the Society of Experimental Social Psychology, Columbus, OH.}}</ref><ref name=":10">{{Cite journal|author1=Engel, D. |author2=Woolley, A. W. |author3=Aggarwal, I. |author4=Chabris, C. F. |author5=Takahashi, M. |author6=Nemoto, K. |author7=Malone, T. W. |date=2015|title=Collective intelligence in computer-mediates collaboration emerges in different contexts and cultures.|url=https://dl.acm.org/ft_gateway.cfm?id=2702259&type=pdf|journal=In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15) (Pp. 3769–3778). New York, NY: ACM}}</ref>。
伍利他们进一步在MBA学生群体中（时间跨度为一学期）<ref name=":013">{{Cite journal|last1=Woolley|first1=Anita Williams|last2=Chabris|first2=Christopher F.|last3=Pentland|first3=Alex|last4=Hashmi|first4=Nada|last5=Malone|first5=Thomas W.|s2cid=74579|date=2010-10-29|title=Evidence for a Collective Intelligence Factor in the Performance of Human Groups|journal=Science|volume=330|issue=6004|pages=686–688|doi=10.1126/science.1193147|pmid=20929725|bibcode=2010Sci...330..686W}}</ref>，在线游戏玩家群体中以及来自不同文化和不同背景的其他群体中（时间跨度分别为短期和长期组）发现了集体智力因子c。这些调查均未将团队成员的个人智力得分视为控制变量<ref name=":92">{{Cite journal|author1=Kim, Y. J. |author2=Engel, D. |author3=Woolley, A. W. |author4=Lin, J. |author5=McArthur, N. |author6= Malone, T. W. |name-list-style=amp |date=2015|title=Work together, play smart: Collective intelligence in League of Legends teams|journal=Paper Presented at the 2015 Collective Intelligence Conference, Santa Clara, CA.}}</ref><ref name=":8">{{Cite journal|author1=Aggarwal, I. |author2= Woolley, A.W. |name-list-style=amp |date=2014|title=The effects of cognitive diversity on collective intelligence and team learning.|journal=Symposium Presented at the 50th Meeting of the Society of Experimental Social Psychology, Columbus, OH.}}</ref><ref name=":10">{{Cite journal|author1=Engel, D. |author2=Woolley, A. W. |author3=Aggarwal, I. |author4=Chabris, C. F. |author5=Takahashi, M. |author6=Nemoto, K. |author7=Malone, T. W. |date=2015|title=Collective intelligence in computer-mediates collaboration emerges in different contexts and cultures.|url=https://dl.acm.org/ft_gateway.cfm?id=2702259&type=pdf|journal=In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15) (Pp. 3769–3778). New York, NY: ACM}}</ref>。
注意的是，集体智能研究领域仍处在初始阶段，而且公开的经验证据还很少。各种提议和文章正在进行或已经完成，但（据说）仍处于学术同行评审出版过程中<ref>{{Cite web|url=https://sites.google.com/a/stern.nyu.edu/collective-intelligence-conference/|title=Collective Intelligence 2016|website=sites.google.com|access-date=2016-04-27|archive-url=https://web.archive.org/web/20160805043627/https://sites.google.com/a/stern.nyu.edu/collective-intelligence-conference/|archive-date=5 August 2016|url-status=live}}</ref><ref>{{Cite web|url=https://sites.lsa.umich.edu/collectiveintelligence/posters/|title=Posters {{!}} Collective Intelligence 2015|website=sites.lsa.umich.edu|access-date=2016-04-27|archive-url=https://web.archive.org/web/20160731181615/http://sites.lsa.umich.edu/collectiveintelligence/posters/|archive-date=31 July 2016|url-status=live}}</ref><ref>{{Cite web|url=http://collective.mech.northwestern.edu/?page_id=217|title=Proceedings {{!}} Collective Intelligence 2014|website=collective.mech.northwestern.edu|access-date=2016-04-27|archive-url=https://web.archive.org/web/20160404215108/http://collective.mech.northwestern.edu/?page_id=217|archive-date=4 April 2016|url-status=dead}}</ref><ref>{{Cite arxiv|eprint=1204.2991|last1= Malone|first1= Thomas W.|title= Collective Intelligence 2012: Proceedings|author2= Luis von Ahn|class= cs.SI|year= 2012}}</ref>。
注意的是，集体智能研究领域仍处在初始阶段，而且公开的经验证据还很少。各种提议和文章正在进行或已经完成，但（据说）仍处于学术同行评审出版过程中<ref>{{Cite web|url=https://sites.google.com/a/stern.nyu.edu/collective-intelligence-conference/|title=Collective Intelligence 2016|website=sites.google.com|access-date=2016-04-27|archive-url=https://web.archive.org/web/20160805043627/https://sites.google.com/a/stern.nyu.edu/collective-intelligence-conference/|archive-date=5 August 2016|url-status=live}}</ref><ref>{{Cite web|url=https://sites.lsa.umich.edu/collectiveintelligence/posters/|title=Posters {{!}} Collective Intelligence 2015|website=sites.lsa.umich.edu|access-date=2016-04-27|archive-url=https://web.archive.org/web/20160731181615/http://sites.lsa.umich.edu/collectiveintelligence/posters/|archive-date=31 July 2016|url-status=live}}</ref><ref>{{Cite web|url=http://collective.mech.northwestern.edu/?page_id=217|title=Proceedings {{!}} Collective Intelligence 2014|website=collective.mech.northwestern.edu|access-date=2016-04-27|archive-url=https://web.archive.org/web/20160404215108/http://collective.mech.northwestern.edu/?page_id=217|archive-date=4 April 2016|url-status=dead}}</ref><ref>{{Cite arxiv|eprint=1204.2991|last1= Malone|first1= Thomas W.|title= Collective Intelligence 2012: Proceedings|author2= Luis von Ahn|class= cs.SI|year= 2012}}</ref>。
=== Predictive validity 预测有效性 ===
=== 预测有效性 ===
集体智力因子c除了能预测团队在判据任务（初始实验中相对较复杂任务）上的表现外，还能够预测持续数月的MBA课程中各种任务的团队绩效<ref name=":014">{{Cite journal|last1=Woolley|first1=Anita Williams|last2=Chabris|first2=Christopher F.|last3=Pentland|first3=Alex|last4=Hashmi|first4=Nada|last5=Malone|first5=Thomas W.|s2cid=74579|date=2010-10-29|title=Evidence for a Collective Intelligence Factor in the Performance of Human Groups|journal=Science|volume=330|issue=6004|pages=686–688|doi=10.1126/science.1193147|pmid=20929725|bibcode=2010Sci...330..686W}}</ref>。因此，尽管组员在其他单独执行任务上没有做得很好，但具有高度集体智能的小组在团队任务上得分明显更高。此外，具有高度集体智能的团队会随着时间推移逐渐提高能力，这表明团队智力的集合性越高，其本身的学习能力约好。这类似于个人智力的性质，即聪明人越多，团队可以更快地获取新材料<ref name=":24">{{Cite book|title=The g factor: The science of mental ability.|last=Jensen|first=Arthur, R.|publisher=Praeger|year=1998|location=Westport, CT}}</ref><ref>{{Cite journal|author1=Schmidt, F.L. |author2= Hunter, J.E. |name-list-style=amp |date=1998|title=The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings|journal=Psychological Bulletin |volume=124 |issue= 2 |pages=262–274|doi=10.1037/0033-2909.124.2.262|citeseerx= 10.1.1.172.1733 }}</ref>。
集体智力因子c除了能预测团队在判据任务（初始实验中相对较复杂任务）上的表现外，还能够预测持续数月的MBA课程中各种任务的团队绩效<ref name=":014">{{Cite journal|last1=Woolley|first1=Anita Williams|last2=Chabris|first2=Christopher F.|last3=Pentland|first3=Alex|last4=Hashmi|first4=Nada|last5=Malone|first5=Thomas W.|s2cid=74579|date=2010-10-29|title=Evidence for a Collective Intelligence Factor in the Performance of Human Groups|journal=Science|volume=330|issue=6004|pages=686–688|doi=10.1126/science.1193147|pmid=20929725|bibcode=2010Sci...330..686W}}</ref>。因此，尽管组员在其他单独执行任务上没有做得很好，但具有高度集体智能的小组在团队任务上得分明显更高。此外，具有高度集体智能的团队会随着时间推移逐渐提高能力，这表明团队智力的集合性越高，其本身的学习能力约好。这类似于个人智力的性质，即聪明人越多，团队可以更快地获取新材料<ref name=":24">{{Cite book|title=The g factor: The science of mental ability.|last=Jensen|first=Arthur, R.|publisher=Praeger|year=1998|location=Westport, CT}}</ref><ref>{{Cite journal|author1=Schmidt, F.L. |author2= Hunter, J.E. |name-list-style=amp |date=1998|title=The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings|journal=Psychological Bulletin |volume=124 |issue= 2 |pages=262–274|doi=10.1037/0033-2909.124.2.262|citeseerx= 10.1.1.172.1733 }}</ref>。
个体智力可以用来预测从学业事业的成功到健康甚至死亡的大量生活场景<ref name=":14">{{Cite journal |author1=Deary, I.J. |author2=Weiss, A. |author3=Batty, D.G. |name-list-style=amp |date=2010 |title=Intelligence and Personality as Predictors of Illness and Death. How Researchers in Differential Psychology and Chronic Disease Epidemiology Are Collaborating to Understand and Address Health Inequalities |journal=Psychological Science in the Public Interest |volume=11 |issue=2 |pages=53–79 |doi=10.1177/1529100610387081 |pmid=26168413 |hdl=20.500.11820/134d66d9-98db-447a-a8b2-5b019b96a7bb |s2cid=13106622 |url=https://www.pure.ed.ac.uk/ws/files/8895401/intelligence_and_personality_as_predictors.pdf |access-date=9 December 2019 |archive-url=https://web.archive.org/web/20180719215714/https://www.pure.ed.ac.uk/ws/files/8895401/intelligence_and_personality_as_predictors.pdf |archive-date=19 July 2018 |url-status=live }}</ref>。除了在智力任务上的表现外，集体智能是否能够预测其他结果尚待研究。
个体智力可以用来预测从学业事业的成功到健康甚至死亡的大量生活场景<ref name=":14">{{Cite journal |author1=Deary, I.J. |author2=Weiss, A. |author3=Batty, D.G. |name-list-style=amp |date=2010 |title=Intelligence and Personality as Predictors of Illness and Death. How Researchers in Differential Psychology and Chronic Disease Epidemiology Are Collaborating to Understand and Address Health Inequalities |journal=Psychological Science in the Public Interest |volume=11 |issue=2 |pages=53–79 |doi=10.1177/1529100610387081 |pmid=26168413 |hdl=20.500.11820/134d66d9-98db-447a-a8b2-5b019b96a7bb |s2cid=13106622 |url=https://www.pure.ed.ac.uk/ws/files/8895401/intelligence_and_personality_as_predictors.pdf |access-date=9 December 2019 |archive-url=https://web.archive.org/web/20180719215714/https://www.pure.ed.ac.uk/ws/files/8895401/intelligence_and_personality_as_predictors.pdf |archive-date=19 July 2018 |url-status=live }}</ref>。除了在智力任务上的表现外，集体智能是否能够预测其他结果尚待研究。
=== 与个人智力的潜在联系 ===
=== Potential connections to individual intelligence 与个人智力的潜在联系 ===
格拉德威尔Gladwell（2008）<ref>{{Cite book|title=Outliers. The Story of Success|last=Gladwell, M.|publisher=Little, Brown and Company|year=2008|isbn=978-0-316-01792-3|location=New York, NY|url=https://archive.org/details/outliersstoryofs00glad}}</ref>指出，个人智商与成功之间的关系仅在一定程度上起作用，而且智商超过120以上的其他智力点并不能转化为现实生活中的优势。是否Group-IQ存在相似的边界？或者优势是线性的和无限的？这仍然有待探索。同样，对个人智力和集体智力之间的联系也需要进一步探究，是否存在个人智力其他潜在因素可以转移到集体智力中？例如随着时间的推移自我进化或智力提高<ref name=":15">{{Cite journal|last1=Shipstead|first1=Zach|last2=Redick|first2=Thomas S|last3=Engle|first3=Randall W.|date=2010-10-01|title=Does working memory training generalize?|journal=Psychologica Belgica|volume=50|issue=3–4|doi=10.5334/pb-50-3-4-245|pages=245|doi-access=free}}</ref><ref name=":16">{{Cite journal|author1=Buschkuehl, M. |author2=Jaeggi, S.M.|date=2010|title=Improving intelligence a literature review|journal=Swiss Medical Weekly |volume=140 |issue=19 |pages=266–72|pmid=20349365}}</ref>。尽管目前对于是否能通过培训来增强人类智力这一论点存在争议，但一个团队的集体智力是可以潜在性地通过交换组员或实施结构和技术上的提升来改进的。此外，人们发现阅读文学小说<ref>{{Cite journal|last1=Kidd|first1=David Comer|last2=Castano|first2=Emanuele|date=2013-10-18|title=Reading Literary Fiction Improves Theory of Mind|journal=Science|volume=342|issue=6156|pages=377–380|doi=10.1126/science.1239918|pmid=24091705|bibcode=2013Sci...342..377K|s2cid=5929573}}</ref>以及看戏曲电影<ref>{{Cite journal|last1=Black|first1=Jessica|last2=Barnes|first2=Jennifer L.|title=Fiction and social cognition: The effect of viewing award-winning television dramas on theory of mind|journal=Psychology of Aesthetics, Creativity, and the Arts|volume=9|issue=4|pages=423–429|doi=10.1037/aca0000031|year=2015}}</ref>至少可以暂时改善社会敏感性。但是社会敏感性培训最终是否能提高集体智力以及在多大程度上提高集体智力，这仍然是一个悬而未决的问题<ref>{{Cite book|title=Handbook of Collective Intelligence|author1=Malone, T. W. |author2= Bernstein, M.S. |name-list-style=amp |publisher=MIT Press|year=2015|location=Cambridge, MA}}</ref>。
格拉德威尔Gladwell（2008）<ref>{{Cite book|title=Outliers. The Story of Success|last=Gladwell, M.|publisher=Little, Brown and Company|year=2008|isbn=978-0-316-01792-3|location=New York, NY|url=https://archive.org/details/outliersstoryofs00glad}}</ref>指出，个人智商与成功之间的关系仅在一定程度上起作用，而且智商超过120以上的其他智力点并不能转化为现实生活中的优势。是否Group-IQ存在相似的边界？或者优势是线性的和无限的？这仍然有待探索。同样，对个人智力和集体智力之间的联系也需要进一步探究，是否存在个人智力其他潜在因素可以转移到集体智力中？例如随着时间的推移自我进化或智力提高<ref name=":15">{{Cite journal|last1=Shipstead|first1=Zach|last2=Redick|first2=Thomas S|last3=Engle|first3=Randall W.|date=2010-10-01|title=Does working memory training generalize?|journal=Psychologica Belgica|volume=50|issue=3–4|doi=10.5334/pb-50-3-4-245|pages=245|doi-access=free}}</ref><ref name=":16">{{Cite journal|author1=Buschkuehl, M. |author2=Jaeggi, S.M.|date=2010|title=Improving intelligence a literature review|journal=Swiss Medical Weekly |volume=140 |issue=19 |pages=266–72|pmid=20349365}}</ref>。尽管目前对于是否能通过培训来增强人类智力这一论点存在争议，但一个团队的集体智力是可以潜在性地通过交换组员或实施结构和技术上的提升来改进的。此外，人们发现阅读文学小说<ref>{{Cite journal|last1=Kidd|first1=David Comer|last2=Castano|first2=Emanuele|date=2013-10-18|title=Reading Literary Fiction Improves Theory of Mind|journal=Science|volume=342|issue=6156|pages=377–380|doi=10.1126/science.1239918|pmid=24091705|bibcode=2013Sci...342..377K|s2cid=5929573}}</ref>以及看戏曲电影<ref>{{Cite journal|last1=Black|first1=Jessica|last2=Barnes|first2=Jennifer L.|title=Fiction and social cognition: The effect of viewing award-winning television dramas on theory of mind|journal=Psychology of Aesthetics, Creativity, and the Arts|volume=9|issue=4|pages=423–429|doi=10.1037/aca0000031|year=2015}}</ref>至少可以暂时改善社会敏感性。但是社会敏感性培训最终是否能提高集体智力以及在多大程度上提高集体智力，这仍然是一个悬而未决的问题<ref>{{Cite book|title=Handbook of Collective Intelligence|author1=Malone, T. W. |author2= Bernstein, M.S. |name-list-style=amp |publisher=MIT Press|year=2015|location=Cambridge, MA}}</ref>。
还有更多更高级的概念和因子模型试图解释个体的认知能力，包括流体智力和晶体智力<ref>{{Cite book|title=Models of intelligence. In R.L. Linn (Ed.), Intelligence: Measurement, theory, and public policy (pp. 29–73).|last=Horn, J.|publisher=University of Illinois Press.|year=1989|location=Urbana, IL}}</ref><ref>{{Cite book|title=Abilities: Their structure, growth, and action.|last=Cattell, R. B.|publisher=New York, NY|year=1971|location=Houghton Mifflin}}</ref>或智力差异的分层模型.<ref>{{Cite book|title=Human cognitive abilities: A survey of factor analytic studies.|last=Carroll, J.B.|publisher=Cambridge University Press.|year=1993|isbn=9780521387125|location=Cambridge, England|url=https://books.google.com/books?id=jp9dt4_0_cIC}}</ref><ref>{{Cite journal|last1=Johnson|first1=Wendy|last2=Bouchard Jr.|first2=Thomas J.|date=2005-07-01|title=The structure of human intelligence: It is verbal, perceptual, and image rotation (VPR), not fluid and crystallized|journal=Intelligence|volume=33|issue=4|pages=393–416|doi=10.1016/j.intell.2004.12.002}}</ref>。但是，除了通用的“c因子”外，目前并没有对集体智力基因组的因子结构采取进一步补充说明和概念化<ref>{{Cite web|url=http://cci.mit.edu/research_developing.html|title=MIT Center for Collective Intelligence|website=cci.mit.edu|access-date=2016-04-27|archive-url=https://web.archive.org/web/20160330091237/http://cci.mit.edu/research_developing.html|archive-date=30 March 2016|url-status=dead}}</ref>。
还有更多更高级的概念和因子模型试图解释个体的认知能力，包括流体智力和晶体智力<ref>{{Cite book|title=Models of intelligence. In R.L. Linn (Ed.), Intelligence: Measurement, theory, and public policy (pp. 29–73).|last=Horn, J.|publisher=University of Illinois Press.|year=1989|location=Urbana, IL}}</ref><ref>{{Cite book|title=Abilities: Their structure, growth, and action.|last=Cattell, R. B.|publisher=New York, NY|year=1971|location=Houghton Mifflin}}</ref>或智力差异的分层模型.<ref>{{Cite book|title=Human cognitive abilities: A survey of factor analytic studies.|last=Carroll, J.B.|publisher=Cambridge University Press.|year=1993|isbn=9780521387125|location=Cambridge, England|url=https://books.google.com/books?id=jp9dt4_0_cIC}}</ref><ref>{{Cite journal|last1=Johnson|first1=Wendy|last2=Bouchard Jr.|first2=Thomas J.|date=2005-07-01|title=The structure of human intelligence: It is verbal, perceptual, and image rotation (VPR), not fluid and crystallized|journal=Intelligence|volume=33|issue=4|pages=393–416|doi=10.1016/j.intell.2004.12.002}}</ref>。但是，除了通用的“c因子”外，目前并没有对集体智力基因组的因子结构采取进一步补充说明和概念化<ref>{{Cite web|url=http://cci.mit.edu/research_developing.html|title=MIT Center for Collective Intelligence|website=cci.mit.edu|access-date=2016-04-27|archive-url=https://web.archive.org/web/20160330091237/http://cci.mit.edu/research_developing.html|archive-date=30 March 2016|url-status=dead}}</ref>。
=== 争议 ===
=== Controversies 争议 ===
有的学者通过将团队成员的综合智力进行汇总到团队级别来解释团队能力，而不是建立团队自身的集群智力指标<ref>{{Cite journal|last=LePine|first=Jeffery A.|title=Adaptation of Teams in Response to Unforeseen Change: Effects of Goal Difficulty and Team Composition in Terms of Cognitive Ability and Goal Orientation|journal=Journal of Applied Psychology|volume=90|issue=6|pages=1153–1167|doi=10.1037/0021-9010.90.6.1153|year=2005|pmid=16316271}}</ref><ref>{{Cite journal|last1=Tziner|first1=Aharon|last2=Eden|first2=Dov|title=Effects of crew composition on crew performance: Does the whole equal the sum of its parts?|journal=Journal of Applied Psychology|volume=70|issue=1|pages=85–93|doi=10.1037/0021-9010.70.1.85|year=1985}}</ref>。迪瓦恩Devine和飞利浦Philips（2001）在一项Meta综合分析中表明<ref>{{Cite journal|last1=Devine|first1=Dennis J.|last2=Philips|first2=Jennifer L.|date=2001-10-01|title=Do Smarter Teams Do Better A Meta-Analysis of Cognitive Ability and Team Performance|journal=Small Group Research|volume=32|issue=5|pages=507–532|doi=10.1177/104649640103200501|s2cid=145635205}}</ref>，认知能力可以预测团队在实验室环境（.37）和现场环境（.14）中的表现，但是请注意，这只是很小的影响。其他学者认为这相当依赖于不同的相关任务，他们表示那些需要高度沟通与合作的任务其实受认知能力最低组员的影响最大<ref>{{Cite journal|author1=O'Brien, G. |author2=Owens, A.|date=1969|title=Effects of organizational structure on correlations between member abilities and group productivity|journal=Journal of Applied Psychology |volume=53 |issue=6|pages=525–530|doi=10.1037/h0028659}}</ref>。因此选择最佳组员是成功的关键策略，这些任务受认知能力最高的成员影响最大<ref name="Yip 48–552">{{Cite journal|last1=Yip|first1=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|s2cid=33438475}}</ref>。
有的学者通过将团队成员的综合智力进行汇总到团队级别来解释团队能力，而不是建立团队自身的集群智力指标<ref>{{Cite journal|last=LePine|first=Jeffery A.|title=Adaptation of Teams in Response to Unforeseen Change: Effects of Goal Difficulty and Team Composition in Terms of Cognitive Ability and Goal Orientation|journal=Journal of Applied Psychology|volume=90|issue=6|pages=1153–1167|doi=10.1037/0021-9010.90.6.1153|year=2005|pmid=16316271}}</ref><ref>{{Cite journal|last1=Tziner|first1=Aharon|last2=Eden|first2=Dov|title=Effects of crew composition on crew performance: Does the whole equal the sum of its parts?|journal=Journal of Applied Psychology|volume=70|issue=1|pages=85–93|doi=10.1037/0021-9010.70.1.85|year=1985}}</ref>。迪瓦恩Devine和飞利浦Philips（2001）在一项Meta综合分析中表明<ref>{{Cite journal|last1=Devine|first1=Dennis J.|last2=Philips|first2=Jennifer L.|date=2001-10-01|title=Do Smarter Teams Do Better A Meta-Analysis of Cognitive Ability and Team Performance|journal=Small Group Research|volume=32|issue=5|pages=507–532|doi=10.1177/104649640103200501|s2cid=145635205}}</ref>，认知能力可以预测团队在实验室环境（.37）和现场环境（.14）中的表现，但是请注意，这只是很小的影响。其他学者认为这相当依赖于不同的相关任务，他们表示那些需要高度沟通与合作的任务其实受认知能力最低组员的影响最大<ref>{{Cite journal|author1=O'Brien, G. |author2=Owens, A.|date=1969|title=Effects of organizational structure on correlations between member abilities and group productivity|journal=Journal of Applied Psychology |volume=53 |issue=6|pages=525–530|doi=10.1037/h0028659}}</ref>。因此选择最佳组员是成功的关键策略，这些任务受认知能力最高的成员影响最大<ref name="Yip 48–552">{{Cite journal|last1=Yip|first1=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|s2cid=33438475}}</ref>。
由于伍利等人的结果并未显示出团队满意度，团队凝聚力或动机的任何影响，因此他们仅隐含地挑战了这些观点，并表示了其总体上对团队绩效的重要性。通过Meta综合分析，他们证明了团队凝聚力<ref>{{Cite journal|last1=Evans|first1=Charles R.|last2=Dion|first2=Kenneth L.|date=1991-05-01|title=Group Cohesion and Performance A Meta-Analysis|journal=Small Group Research|volume=22|issue=2|pages=175–186|doi=10.1177/1046496491222002|s2cid=145344583}}</ref><ref>{{Cite journal|last1=Gully|first1=Stanley M.|last2=Devine|first2=Dennis J.|last3=Whitney|first3=David J.|date=2012-12-01|title=A Meta-Analysis of Cohesion and Performance Effects of Level of Analysis and Task Interdependence|journal=Small Group Research|volume=43|issue=6|pages=702–725|doi=10.1177/1046496412468069|s2cid=220319732}}</ref><ref>{{Cite journal|last1=Beal|first1=Daniel J.|last2=Cohen|first2=Robin R.|last3=Burke|first3=Michael J.|last4=McLendon|first4=Christy L.|title=Cohesion and Performance in Groups: A Meta-Analytic Clarification of Construct Relations.|journal=Journal of Applied Psychology|volume=88|issue=6|pages=989–1004|doi=10.1037/0021-9010.88.6.989|pmid=14640811|date=December 2003}}</ref>，动机<ref>{{Cite journal|last1=O'leary-kelly|first1=Anne M.|last2=Martocchio|first2=Joseph J.|last3=Frink|first3=Dwight D.|date=1994-10-01|title=A Review of the Influence of Group Goals on Group Performance|url=http://amj.aom.org/content/37/5/1285|journal=Academy of Management Journal|volume=37|issue=5|pages=1285–1301|doi=10.2307/256673|jstor=256673}}</ref><ref>{{Cite journal|last1=Kleingeld|first1=Ad|last2=Mierlo|first2=Heleen van|last3=Arends|first3=Lidia|title=The effect of goal setting on group performance: A meta-analysis|journal=Journal of Applied Psychology|volume=96|issue=6|pages=1289–1304|doi=10.1037/a0024315|pmid=21744940|year=2011}}</ref>和满意度<ref>{{Cite journal|author1=Duffy, M. K. |author2=Shaw, J. D. |author3= Stark, E. M. |name-list-style=amp |date=2000|title=Performance and satisfaction in conflicted interdependent groups: When and how does selfesteem make a difference?|journal=Academy of Management Journal |volume=43 |issue=4 |pages=772–782|doi=10.2307/1556367|jstor=1556367 }}</ref>对团队绩效的积极影响。
由于伍利等人的结果并未显示出团队满意度，团队凝聚力或动机的任何影响，因此他们仅隐含地挑战了这些观点，并表示了其总体上对团队绩效的重要性。通过Meta综合分析，他们证明了团队凝聚力<ref>{{Cite journal|last1=Evans|first1=Charles R.|last2=Dion|first2=Kenneth L.|date=1991-05-01|title=Group Cohesion and Performance A Meta-Analysis|journal=Small Group Research|volume=22|issue=2|pages=175–186|doi=10.1177/1046496491222002|s2cid=145344583}}</ref><ref>{{Cite journal|last1=Gully|first1=Stanley M.|last2=Devine|first2=Dennis J.|last3=Whitney|first3=David J.|date=2012-12-01|title=A Meta-Analysis of Cohesion and Performance Effects of Level of Analysis and Task Interdependence|journal=Small Group Research|volume=43|issue=6|pages=702–725|doi=10.1177/1046496412468069|s2cid=220319732}}</ref><ref>{{Cite journal|last1=Beal|first1=Daniel J.|last2=Cohen|first2=Robin R.|last3=Burke|first3=Michael J.|last4=McLendon|first4=Christy L.|title=Cohesion and Performance in Groups: A Meta-Analytic Clarification of Construct Relations.|journal=Journal of Applied Psychology|volume=88|issue=6|pages=989–1004|doi=10.1037/0021-9010.88.6.989|pmid=14640811|date=December 2003}}</ref>，动机<ref>{{Cite journal|last1=O'leary-kelly|first1=Anne M.|last2=Martocchio|first2=Joseph J.|last3=Frink|first3=Dwight D.|date=1994-10-01|title=A Review of the Influence of Group Goals on Group Performance|url=http://amj.aom.org/content/37/5/1285|journal=Academy of Management Journal|volume=37|issue=5|pages=1285–1301|doi=10.2307/256673|jstor=256673}}</ref><ref>{{Cite journal|last1=Kleingeld|first1=Ad|last2=Mierlo|first2=Heleen van|last3=Arends|first3=Lidia|title=The effect of goal setting on group performance: A meta-analysis|journal=Journal of Applied Psychology|volume=96|issue=6|pages=1289–1304|doi=10.1037/a0024315|pmid=21744940|year=2011}}</ref>和满意度<ref>{{Cite journal|author1=Duffy, M. K. |author2=Shaw, J. D. |author3= Stark, E. M. |name-list-style=amp |date=2000|title=Performance and satisfaction in conflicted interdependent groups: When and how does selfesteem make a difference?|journal=Academy of Management Journal |volume=43 |issue=4 |pages=772–782|doi=10.2307/1556367|jstor=1556367 }}</ref>对团队绩效的积极影响。
值得一提的是，确认结果中涉及的研究人员之间，以及与参与有关Anita Woolley最初第一项研究的作者之间也存在广泛的重叠<ref name=":015">{{Cite journal|last1=Woolley|first1=Anita Williams|last2=Chabris|first2=Christopher F.|last3=Pentland|first3=Alex|last4=Hashmi|first4=Nada|last5=Malone|first5=Thomas W.|s2cid=74579|date=2010-10-29|title=Evidence for a Collective Intelligence Factor in the Performance of Human Groups|journal=Science|volume=330|issue=6004|pages=686–688|doi=10.1126/science.1193147|pmid=20929725|bibcode=2010Sci...330..686W}}</ref><ref name=":115">{{Cite journal|last1=Woolley|first1=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|s2cid=146673541}}</ref><ref name=":46">{{Cite journal|author1=Engel, D. |author2=Woolley, A. W. |author3=Jing, L. X. |author4=Chabris, C. F. |author5= Malone, T. W. |name-list-style=amp |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><ref name=":123">{{Cite journal|author1=Aggarwal, I. |author2=Woolley, A. W. |author3=Chabris, C. F. |author4= Malone, T. W. |name-list-style=amp |date=2015|title=Cognitive diversity, collective intelligence, and learning in teams.|journal=Paper Presented at the 2015 Collective Intelligence Conference, Santa Clara, CA.}}</ref><ref name=":132">{{Cite book|last1=Engel|first1=David|last2=Woolley|first2=Anita Williams|last3=Aggarwal|first3=Ishani|last4=Chabris|first4=Christopher F.|last5=Takahashi|first5=Masamichi|last6=Nemoto|first6=Keiichi|last7=Kaiser|first7=Carolin|last8=Kim|first8=Young Ji|last9=Malone|first9=Thomas W.|date=2015-01-01|title=Collective Intelligence in Computer-Mediated Collaboration Emerges in Different Contexts and Cultures|journal=Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems|series=CHI '15|location=New York, NY, USA|publisher=ACM|pages=3769–3778|doi=10.1145/2702123.2702259|isbn=9781450331456|s2cid=14303201}}</ref>。
值得一提的是，确认结果中涉及的研究人员之间，以及与参与有关Anita Woolley最初第一项研究的作者之间也存在广泛的重叠<ref name=":015">{{Cite journal|last1=Woolley|first1=Anita Williams|last2=Chabris|first2=Christopher F.|last3=Pentland|first3=Alex|last4=Hashmi|first4=Nada|last5=Malone|first5=Thomas W.|s2cid=74579|date=2010-10-29|title=Evidence for a Collective Intelligence Factor in the Performance of Human Groups|journal=Science|volume=330|issue=6004|pages=686–688|doi=10.1126/science.1193147|pmid=20929725|bibcode=2010Sci...330..686W}}</ref><ref name=":115">{{Cite journal|last1=Woolley|first1=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|s2cid=146673541}}</ref><ref name=":46">{{Cite journal|author1=Engel, D. |author2=Woolley, A. W. |author3=Jing, L. X. |author4=Chabris, C. F. |author5= Malone, T. W. |name-list-style=amp |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><ref name=":123">{{Cite journal|author1=Aggarwal, I. |author2=Woolley, A. W. |author3=Chabris, C. F. |author4= Malone, T. W. |name-list-style=amp |date=2015|title=Cognitive diversity, collective intelligence, and learning in teams.|journal=Paper Presented at the 2015 Collective Intelligence Conference, Santa Clara, CA.}}</ref><ref name=":132">{{Cite book|last1=Engel|first1=David|last2=Woolley|first2=Anita Williams|last3=Aggarwal|first3=Ishani|last4=Chabris|first4=Christopher F.|last5=Takahashi|first5=Masamichi|last6=Nemoto|first6=Keiichi|last7=Kaiser|first7=Carolin|last8=Kim|first8=Young Ji|last9=Malone|first9=Thomas W.|date=2015-01-01|title=Collective Intelligence in Computer-Mediated Collaboration Emerges in Different Contexts and Cultures|journal=Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems|series=CHI '15|location=New York, NY, USA|publisher=ACM|pages=3769–3778|doi=10.1145/2702123.2702259|isbn=9781450331456|s2cid=14303201}}</ref>。
== 其他数学替代技术 ==
=== 计算集体智能 ===
[[文件:计算集体智能.jpg|缩略图|右|计算集体智能|335x335像素]]
[[文件:计算集体智能.jpg|缩略图|右|计算集体智能]]
2001年，来自波兰AGH科技大学的Tadeusz（Tad）Szuba提出了一种具有集体智能现象的正式模型。模型假定是一个无意识，随机，并行和分布式的计算程序，其社会结构以数学逻辑方式运行<ref name="szuba">Szuba T., ''Computational Collective Intelligence'', 420 pages, Wiley NY, 2001</ref>。
2001年，来自波兰AGH科技大学的Tadeusz（Tad）Szuba提出了一种具有集体智能现象的正式模型。模型假定是一个无意识，随机，并行和分布式的计算程序，其社会结构以数学逻辑方式运行<ref name="szuba">Szuba T., ''Computational Collective Intelligence'', 420 pages, Wiley NY, 2001</ref>。
在此模型中，将真实环境中的生物和信息两部分进行建模，表示为带有数学逻辑表达式的抽象信息分子。由于它们与环境之间的相互作用，以及它们自身带有的目标位移属性，会准随机地进行挪动。随后，它们会在抽象的计算空间中交互，进而创建多线程推导处理程序，其过程则被视为集体智能。因此，非图灵计算模型被采用。该理论将集体智能简单定义为社会结构的属性，而且似乎对于各类生物（从细菌菌落到人类社会结构）均适用。集体智能被视为是一种特定的计算过程，它为几种社会现象提供了直接的解释。对于这种集体智能模型，科学家们提出了IQS（即IQ社会）的正式定义，并将其定义为“在时间和N元素推理域（反映社会结构推理活动）上的概率函数”。IQS在计算上似乎很难，但是根据如上所述的计算过程对社会结构进行建模的话，可以得到近似的结果。通过最大化IQS，公司可以优化其潜在的应用，另外医学上，也可以对细菌菌落的集体智能进行建模，来分析耐药性。
在此模型中，将真实环境中的生物和信息两部分进行建模，表示为带有数学逻辑表达式的抽象信息分子。由于它们与环境之间的相互作用，以及它们自身带有的目标位移属性，会准随机地进行挪动。随后，它们会在抽象的计算空间中交互，进而创建多线程推导处理程序，其过程则被视为集体智能。因此，非图灵计算模型被采用。该理论将集体智能简单定义为社会结构的属性，而且似乎对于各类生物（从细菌菌落到人类社会结构）均适用。集体智能被视为是一种特定的计算过程，它为几种社会现象提供了直接的解释。对于这种集体智能模型，科学家们提出了IQS（即IQ社会）的正式定义，并将其定义为“在时间和N元素推理域（反映社会结构推理活动）上的概率函数”。IQS在计算上似乎很难，但是根据如上所述的计算过程对社会结构进行建模的话，可以得到近似的结果。通过最大化IQS，公司可以优化其潜在的应用，另外医学上，也可以对细菌菌落的集体智能进行建模，来分析耐药性。
=== 集体智商 ===
=== Collective intelligence quotient 集体智商 ===
有时候我们会采用另一种度量方式表达，称为“<font color="#ff8000"> 集体智商Collective intelligence quotient</font>” （或“<font color="#ff8000"> 合作商Cooperation quotient</font>”）<ref name="auto">{{Cite book|url=https://books.google.com/books?id=_tHmKrpSeEQC&q=collective+intelligence+quotient&pg=PA141|title=Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems: First International Conference, ICCCI 2009, Wroclaw, Poland, October 5–7, 2009, Proceedings|last=Kowalczyk|first=Ryszard|date=2009-09-23|publisher=Springer Science & Business Media|isbn=9783642044403}}</ref>，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<ref>{{Cite web|url=http://www.dougengelbart.org/about/collective-iq.html|title=About Collective IQ - Doug Engelbart Institute|last=Administrator|website=www.dougengelbart.org|access-date=2016-12-11|archive-url=https://web.archive.org/web/20161229024342/http://www.dougengelbart.org/about/collective-iq.html|archive-date=29 December 2016|url-status=live}}</ref>。
有时候我们会采用另一种度量方式表达，称为“<font color="#ff8000"> 集体智商Collective intelligence quotient</font>” （或“<font color="#ff8000"> 合作商Cooperation quotient</font>”）<ref name="auto">{{Cite book|url=https://books.google.com/books?id=_tHmKrpSeEQC&q=collective+intelligence+quotient&pg=PA141|title=Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems: First International Conference, ICCCI 2009, Wroclaw, Poland, October 5–7, 2009, Proceedings|last=Kowalczyk|first=Ryszard|date=2009-09-23|publisher=Springer Science & Business Media|isbn=9783642044403}}</ref>，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<ref>{{Cite web|url=http://www.dougengelbart.org/about/collective-iq.html|title=About Collective IQ - Doug Engelbart Institute|last=Administrator|website=www.dougengelbart.org|access-date=2016-12-11|archive-url=https://web.archive.org/web/20161229024342/http://www.dougengelbart.org/about/collective-iq.html|archive-date=29 December 2016|url-status=live}}</ref>。
== 应用 ==
== Applications 应用 ==
集体智慧最近有许多应用，包括在众包、公民科学和预测市场等领域。内斯塔集体智慧设计中心<ref>https://www.nesta.org.uk/project/centre-collective-intelligence-design/</ref>于2018年成立，已经完成了许多申请调查以及资助实验
集体智慧最近有许多应用，包括在众包、公民科学和预测市场等领域。内斯塔集体智慧设计中心<ref>https://www.nesta.org.uk/project/centre-collective-intelligence-design/</ref>于2018年成立，已经完成了许多申请调查以及资助实验
==== Elicitation of point estimates 评估点提取 ====
==== 评估点提取 ====
关于集体智能，其应用目标之一是获得某个变量的估计值（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。
关于集体智能，其应用目标之一是获得某个变量的估计值（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。
==== 意见汇总 ====
==== Opinion aggregation 意见汇总 ====
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。
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。
==== 想法收集 ====
==== Idea Collection 想法收集 ====
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）。这里的目标是收集各种想法并设计选择标准来从中筛选出最佳方案。
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）。这里的目标是收集各种想法并设计选择标准来从中筛选出最佳方案。
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<ref name="Surowiecki">Surowiecki, J., 2007 'The Wisdom of crowds. Why the Many Are Smarter Than the Few'</ref>。
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<ref name="Surowiecki">Surowiecki, J., 2007 'The Wisdom of crowds. Why the Many Are Smarter Than the Few'</ref>。
=== Cognition 认知 ===
=== 认知 ===
==== Market judgment 市场判断 ====
==== 市场判断 ====
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<ref name=":222">{{Cite book|last=Kaplan|first=Craig A.|title=2001 IEEE International Conference on Systems, Man and Cybernetics. E-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236)|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|doi=10.1109/ICSMC.2001.971949|isbn=978-0-7803-7087-6|chapter=Collective intelligence: A new approach to stock price forecasting|s2cid=4836176|access-date=12 December 2016|archive-url=https://web.archive.org/web/20170610153216/http://www.iqco.com/consulting/Kaplan_TESADI_Final.pdf|archive-date=10 June 2017|url-status=live}}</ref>。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<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|access-date=10 May 2019|archive-url=https://web.archive.org/web/20170919180051/http://dsd.future-lab.cn/research/publications/2011/ICCCI-springer.pdf|archive-date=19 September 2017|url-status=live}}</ref>。
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<ref name=":222">{{Cite book|last=Kaplan|first=Craig A.|title=2001 IEEE International Conference on Systems, Man and Cybernetics. E-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236)|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|doi=10.1109/ICSMC.2001.971949|isbn=978-0-7803-7087-6|chapter=Collective intelligence: A new approach to stock price forecasting|s2cid=4836176|access-date=12 December 2016|archive-url=https://web.archive.org/web/20170610153216/http://www.iqco.com/consulting/Kaplan_TESADI_Final.pdf|archive-date=10 June 2017|url-status=live}}</ref>。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<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|access-date=10 May 2019|archive-url=https://web.archive.org/web/20170919180051/http://dsd.future-lab.cn/research/publications/2011/ICCCI-springer.pdf|archive-date=19 September 2017|url-status=live}}</ref>。
集体智能巩固了尤金·法玛Eugene Fama的有效市场假说<ref>{{cite journal | last1 = Fama | first1 = E.F. | year = 1970 | title = Efficient Capital Markets: A Review of Theory and Empirical Work | journal = Journal of Finance | volume = 25 | issue = 2| pages = 383–417 | doi=10.2307/2325486| jstor = 2325486 }}</ref>，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<ref name=":232">{{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>。
集体智能巩固了尤金·法玛Eugene Fama的有效市场假说<ref>{{cite journal | last1 = Fama | first1 = E.F. | year = 1970 | title = Efficient Capital Markets: A Review of Theory and Empirical Work | journal = Journal of Finance | volume = 25 | issue = 2| pages = 383–417 | doi=10.2307/2325486| jstor = 2325486 }}</ref>，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<ref name=":232">{{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>。
==== 政治和技术预测 ====
==== Predictions in politics and technology 政治和技术预测 ====
[[文件:美国2016年使用的投票方法.svg|缩略图|右|美国2016年使用的投票方法]]
[[文件:美国2016年使用的投票方法.svg|缩略图|右|美国2016年使用的投票方法]]
政党动员了大量人力制定政策，选拔候选人和资助并开展竞选活动<ref name=":242">{{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|archive-url=https://web.archive.org/web/20161220093535/http://politics.stackexchange.com/questions/11168/vote-amount-according-to-intelligence|archive-date=20 December 2016|url-status=live}}</ref>。通过各种投票方法集中信息，使观点融合假设，不知情者的投票在某种程度上可视为是随机的，可以从决策过程中过滤掉，仅留下有共识的知情者的投票。批评家指出，坏主意，误解和谬见通常会广泛存在，因此决策过程的结构必须有利于那些在给定背景下，不大可能出现随机或者误导投票的专家<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|archive-url=https://web.archive.org/web/20161220050449/http://press.princeton.edu/titles/9907.html|archive-date=20 December 2016|url-status=live}}</ref>。
政党动员了大量人力制定政策，选拔候选人和资助并开展竞选活动<ref name=":242">{{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|archive-url=https://web.archive.org/web/20161220093535/http://politics.stackexchange.com/questions/11168/vote-amount-according-to-intelligence|archive-date=20 December 2016|url-status=live}}</ref>。通过各种投票方法集中信息，使观点融合假设，不知情者的投票在某种程度上可视为是随机的，可以从决策过程中过滤掉，仅留下有共识的知情者的投票。批评家指出，坏主意，误解和谬见通常会广泛存在，因此决策过程的结构必须有利于那些在给定背景下，不大可能出现随机或者误导投票的专家<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|archive-url=https://web.archive.org/web/20161220050449/http://press.princeton.edu/titles/9907.html|archive-date=20 December 2016|url-status=live}}</ref>。
诸如Affinnova（被尼尔森收购），Google，InnoCentive，Marketocracy和Threadless<ref name=":17">{{Cite journal|last=Bonabeau|first=E|year=2009|title=The power of collective intelligence|journal=MIT Sloan Management Review|volume=50|pages=45–52|id={{ProQuest|224962498}}}}</ref>等公司已经成功地采用了集体智能的概念<ref>{{Cite report|last1=Malone|first1=Thomas W.|last2=Laubacher|first2=Robert|last3=Dellarocas|first3=Chrysanthos|date=2009-02-03|title=Harnessing Crowds: Mapping the Genome of Collective Intelligence|ssrn=1381502|location=Rochester, NY|publisher=Social Science Research Network|id=MIT Sloan Research Paper No. 4732-09}}</ref>，通过其研发（R＆D），客户服务和知识管理实现了下一代技术变革<ref>{{Cite news|url=https://www.nytimes.com/2016/02/28/magazine/what-google-learned-from-its-quest-to-build-the-perfect-team.html|title=What Google Learned From Its Quest to Build the Perfect Team|last=Duhigg|first=Charles|date=2016-02-25|newspaper=The New York Times|access-date=2016-12-11|archive-url=https://web.archive.org/web/20170223084955/https://www.nytimes.com/2016/02/28/magazine/what-google-learned-from-its-quest-to-build-the-perfect-team.html|archive-date=23 February 2017|url-status=live}}</ref>。这种应用的一个例子是2012年谷歌的亚里士多德项目，在该项目中，集体智慧对团队组成的影响在数百个公司的研发团队中进行了研究<ref>{{Cite news|url=https://www.nytimes.com/2016/02/28/magazine/what-google-learned-from-its-quest-to-build-the-perfect-team.html|title=What Google Learned From Its Quest to Build the Perfect Team|last=Duhigg|first=Charles|date=2016-02-25|newspaper=The New York Times|access-date=2016-12-11|archive-url=https://web.archive.org/web/20170223084955/https://www.nytimes.com/2016/02/28/magazine/what-google-learned-from-its-quest-to-build-the-perfect-team.html|archive-date=23 February 2017|url-status=live}}</ref>。
诸如Affinnova（被尼尔森收购），Google，InnoCentive，Marketocracy和Threadless<ref name=":17">{{Cite journal|last=Bonabeau|first=E|year=2009|title=The power of collective intelligence|journal=MIT Sloan Management Review|volume=50|pages=45–52|id={{ProQuest|224962498}}}}</ref>等公司已经成功地采用了集体智能的概念<ref>{{Cite report|last1=Malone|first1=Thomas W.|last2=Laubacher|first2=Robert|last3=Dellarocas|first3=Chrysanthos|date=2009-02-03|title=Harnessing Crowds: Mapping the Genome of Collective Intelligence|ssrn=1381502|location=Rochester, NY|publisher=Social Science Research Network|id=MIT Sloan Research Paper No. 4732-09}}</ref>，通过其研发（R＆D），客户服务和知识管理实现了下一代技术变革<ref>{{Cite news|url=https://www.nytimes.com/2016/02/28/magazine/what-google-learned-from-its-quest-to-build-the-perfect-team.html|title=What Google Learned From Its Quest to Build the Perfect Team|last=Duhigg|first=Charles|date=2016-02-25|newspaper=The New York Times|access-date=2016-12-11|archive-url=https://web.archive.org/web/20170223084955/https://www.nytimes.com/2016/02/28/magazine/what-google-learned-from-its-quest-to-build-the-perfect-team.html|archive-date=23 February 2017|url-status=live}}</ref>。这种应用的一个例子是2012年谷歌的亚里士多德项目，在该项目中，集体智慧对团队组成的影响在数百个公司的研发团队中进行了研究<ref>{{Cite news|url=https://www.nytimes.com/2016/02/28/magazine/what-google-learned-from-its-quest-to-build-the-perfect-team.html|title=What Google Learned From Its Quest to Build the Perfect Team|last=Duhigg|first=Charles|date=2016-02-25|newspaper=The New York Times|access-date=2016-12-11|archive-url=https://web.archive.org/web/20170223084955/https://www.nytimes.com/2016/02/28/magazine/what-google-learned-from-its-quest-to-build-the-perfect-team.html|archive-date=23 February 2017|url-status=live}}</ref>。
=== Cooperation 合作 ===
=== 合作 ===
==== Networks of trust 信任网络====
==== 信任网络====
[[文件:集体智能在千年计划中的应用.png|缩略图|左|集体智能在千年计划中的应用]]
[[文件:集体智能在千年计划中的应用.png|缩略图|左|集体智能在千年计划中的应用]]
2012年，千年计划<ref>{{Cite web|url=http://www.millennium-project.org/millennium/GFIS.html|title=Global Futures Intelligence System|website=www.millennium-project.org|access-date=2016-12-07|archive-url=https://web.archive.org/web/20161225025210/http://www.millennium-project.org/millennium/GFIS.html|archive-date=25 December 2016|url-status=live}}</ref>创建了<font color="#ff8000"> 全球集体智能系统Global Futures Collective Intelligence System（GFIS）</font>，因为它将数据/信息/知识，软件/硬件以及技术/见解进行了协同处理，使其成为了集体智能最贴切的代表。与单独的各项参与模块相比，它具有递归学习的处理能力，可以更好地进行决策<ref>{{Cite web|url=http://www.millennium-project.org/millennium/GFIS.html|title=Global Futures Intelligence System|website=www.millennium-project.org|access-date=2016-12-11|archive-url=https://web.archive.org/web/20161225025210/http://www.millennium-project.org/millennium/GFIS.html|archive-date=25 December 2016|url-status=live}}</ref>。
2012年，千年计划<ref>{{Cite web|url=http://www.millennium-project.org/millennium/GFIS.html|title=Global Futures Intelligence System|website=www.millennium-project.org|access-date=2016-12-07|archive-url=https://web.archive.org/web/20161225025210/http://www.millennium-project.org/millennium/GFIS.html|archive-date=25 December 2016|url-status=live}}</ref>创建了<font color="#ff8000"> 全球集体智能系统Global Futures Collective Intelligence System（GFIS）</font>，因为它将数据/信息/知识，软件/硬件以及技术/见解进行了协同处理，使其成为了集体智能最贴切的代表。与单独的各项参与模块相比，它具有递归学习的处理能力，可以更好地进行决策<ref>{{Cite web|url=http://www.millennium-project.org/millennium/GFIS.html|title=Global Futures Intelligence System|website=www.millennium-project.org|access-date=2016-12-11|archive-url=https://web.archive.org/web/20161225025210/http://www.millennium-project.org/millennium/GFIS.html|archive-date=25 December 2016|url-status=live}}</ref>。
另外新媒体也可以促进增强集体智能。其通过数据库和英特网轻松存储和检索信息的能力使得信息共享毫无困难。因此，通过与新媒体的互动，知识很容易在资源之间传递（Flew 2008），从而形成了集体智能。交互式新媒体（尤其是互联网）的使用促进了在线互动以及用户之间的知识分配。
另外新媒体也可以促进增强集体智能。其通过数据库和英特网轻松存储和检索信息的能力使得信息共享毫无困难。因此，通过与新媒体的互动，知识很容易在资源之间传递（Flew 2008），从而形成了集体智能。交互式新媒体（尤其是互联网）的使用促进了在线互动以及用户之间的知识分配。
弗朗西斯·海里格森Francis Heylighen，瓦伦丁·图尔钦Valentin Turchin和Gottfried Mayer-Kress都是通过计算机科学和控制论的视角看待集体智能。他们认为，互联网可以在最广泛的地球尺度上实现集体智能，从而促进全球大脑的出现<ref>[http://cci.mit.edu/people/index.html MIT Center for Collective Intelligence] {{webarchive|url=https://web.archive.org/web/20100611041615/http://cci.mit.edu/people/index.html |date=11 June 2010 }}. Cci.mit.edu. Retrieved on 2013-07-13.</ref>。
弗朗西斯·海里格森Francis Heylighen，瓦伦丁·图尔钦Valentin Turchin和Gottfried Mayer-Kress都是通过计算机科学和控制论的视角看待集体智能。他们认为，互联网可以在最广泛的地球尺度上实现集体智能，从而促进全球大脑的出现<ref>[http://cci.mit.edu/people/index.html MIT Center for Collective Intelligence] {{webarchive|url=https://web.archive.org/web/20100611041615/http://cci.mit.edu/people/index.html |date=11 June 2010 }}. Cci.mit.edu. Retrieved on 2013-07-13.</ref>。
万维网创始人蒂姆·伯纳斯·李Tim Berners-Lee，曾以促进全球信息共享和发作为目标开发了万维网。后来，他的雇主开放了该技术以供大家免费使用。在90年代初期，互联网的潜力一直没有得到开发，直到1990年代中期，高级研究计划局（ARPA）负责人J.C.R. Licklider博士将其称为“临界质量”，并要求其具有更强的可访问性和实用性<ref name="Weiss, A. 2005 pp. 19-232">Weiss, A. (2005). The Power of Collective Intelligence. Collective Intelligence, pp. 19–23</ref>。这种基于互联网的集体智能驱动力是信息和通信的数字化。研究新媒体出现和媒体融合的关键理论家Henry Jenkins借鉴了其概念，认为集体智能可以归因于媒体融合和参与性文化（Flew 2008）。他批判当代教育未能将集体智能理念的趋势纳入课堂，比如说可以通过在线集群智慧解决问题这一思想。并指出“通过集体智能社区鼓励以集体为单位进行工作学习，而学校则需要对个人评分”。詹金斯认为，知识社区内的互动为年轻人创造了至关重要的技能，而通过集体智能社区的团队合作则有助于此类技能的发展。集体智能不仅是来自所有文化信息的定量贡献，同样也是定性存在<ref name=":252">{{Cite web|url=http://labweb.education.wisc.edu/curric606/readings/Jenkins2002.pdf|title=INTERACTIVE AUDIENCES? THE 'COLLECTIVE INTELLIGENCE' OF MEDIA FANS|last=Henry|first=Jenkins|access-date=11 December 2016|archive-url=https://web.archive.org/web/20180426232104/https://labweb.education.wisc.edu/curric606/readings/Jenkins2002.pdf|archive-date=26 April 2018|url-status=dead}}</ref>。
万维网创始人蒂姆·伯纳斯·李Tim Berners-Lee，曾以促进全球信息共享和发作为目标开发了万维网。后来，他的雇主开放了该技术以供大家免费使用。在90年代初期，互联网的潜力一直没有得到开发，直到1990年代中期，高级研究计划局（ARPA）负责人J.C.R. Licklider博士将其称为“临界质量”，并要求其具有更强的可访问性和实用性<ref name="Weiss, A. 2005 pp. 19-232">Weiss, A. (2005). The Power of Collective Intelligence. Collective Intelligence, pp. 19–23</ref>。这种基于互联网的集体智能驱动力是信息和通信的数字化。研究新媒体出现和媒体融合的关键理论家Henry Jenkins借鉴了其概念，认为集体智能可以归因于媒体融合和参与性文化（Flew 2008）。他批判当代教育未能将集体智能理念的趋势纳入课堂，比如说可以通过在线集群智慧解决问题这一思想。并指出“通过集体智能社区鼓励以集体为单位进行工作学习，而学校则需要对个人评分”。詹金斯认为，知识社区内的互动为年轻人创造了至关重要的技能，而通过集体智能社区的团队合作则有助于此类技能的发展。集体智能不仅是来自所有文化信息的定量贡献，同样也是定性存在<ref name=":252">{{Cite web|url=http://labweb.education.wisc.edu/curric606/readings/Jenkins2002.pdf|title=INTERACTIVE AUDIENCES? THE 'COLLECTIVE INTELLIGENCE' OF MEDIA FANS|last=Henry|first=Jenkins|access-date=11 December 2016|archive-url=https://web.archive.org/web/20180426232104/https://labweb.education.wisc.edu/curric606/readings/Jenkins2002.pdf|archive-date=26 April 2018|url-status=dead}}</ref>。
莱维Lévy和德克霍夫de Kerckhove从大众传播的角度考虑了CI，特别是专注用网络信息和通信技术来增强社区知识库的能力。他们认为，这些通信工具可以使人们能够轻松快捷地进行交互，共享和协作（Flew 2008）。随着互联网的发展及其广泛使用，为诸如Wikipedia之类的知识社区做出贡献的机会比以往任何时候都要大。这些计算机网络使参与活动的用户有机会通过对这些数据库的集体式访问来存储和检索知识，同时还允许他们“驾驭蜂巢”，这是麻省理工学院集体智能中心的研究人员的任务，它们一直在探索人和计算机群体的集体智能<ref>[http://cci.mit.edu/people/index.html MIT Center for Collective Intelligence] {{webarchive|url=https://web.archive.org/web/20100611041615/http://cci.mit.edu/people/index.html |date=11 June 2010 }}. Cci.mit.edu. Retrieved on 2013-07-13.</ref>。
莱维Lévy和德克霍夫de Kerckhove从大众传播的角度考虑了CI，特别是专注用网络信息和通信技术来增强社区知识库的能力。他们认为，这些通信工具可以使人们能够轻松快捷地进行交互，共享和协作（Flew 2008）。随着互联网的发展及其广泛使用，为诸如Wikipedia之类的知识社区做出贡献的机会比以往任何时候都要大。这些计算机网络使参与活动的用户有机会通过对这些数据库的集体式访问来存储和检索知识，同时还允许他们“驾驭蜂巢”，这是麻省理工学院集体智能中心的研究人员的任务，它们一直在探索人和计算机群体的集体智能<ref>[http://cci.mit.edu/people/index.html MIT Center for Collective Intelligence] {{webarchive|url=https://web.archive.org/web/20100611041615/http://cci.mit.edu/people/index.html |date=11 June 2010 }}. Cci.mit.edu. Retrieved on 2013-07-13.</ref>。
在这种情况下，集体智能常常与共享知识相混淆。前者是社区成员单独持有的信息的总和，而后者是社区所有成员都认为是真实且已知的信息<ref>Jenkins, H. 2006. ''[https://archive.org/details/isbn_9780814742815 Convergence Culture]''. New York: New York University Press.</ref>。以Web 2.0为代表的集体智能比协作智能具有更少的用户参与度。使用Web 2.0平台的艺术项目“共享银河”，是一个由匿名艺术家开发的实验，目的是创建一个集体身份，并在MySpace，Facebook，YouTube和Second Life等多个平台上以这个集体身份出现。密码将写在配置文件中，并且名为“ Shared Galaxy”的帐户开放给任何人使用。通过这种方式，许多人成为一体<ref>{{Cite journal|last1=Scardamalia|first1=Marlene|last2=Bereiter|first2=Carl|date=1994-07-01|title=Computer Support for Knowledge-Building Communities|journal=Journal of the Learning Sciences|volume=3|issue=3|pages=265–283|doi=10.1207/s15327809jls0303_3|citeseerx=10.1.1.600.463}}</ref>。Curatron是另一个利用集体智能创作艺术作品的艺术项目，其中一大批艺术家共同决定建立一个较小的团队，他们对其团队的协作表现非常自信。该项目基于一种计算集体偏好的算法<ref>{{cite web|url=http://www.vocativ.com/culture/art-culture/math-takes-guessing-art-curation/|title=Math Takes the Guessing Out of Artistic Collaboration|date=9 July 2014|access-date=30 April 2015|archive-url=https://web.archive.org/web/20141001052839/http://www.vocativ.com/culture/art-culture/math-takes-guessing-art-curation/|archive-date=1 October 2014|url-status=live}}</ref>。在创建他所谓的“ CI艺术”时，新斯科舍省的艺术家马修·阿尔德雷德Mathew Aldred遵循了皮耶·列维对集体智能的定义<ref>Mathew Aldred, May 2016. {{cite web|title=What is Collective Intelligence Art?|year=2016|url=http://www.collectiveintelligenceart.ca/collective-intelligence-art.html|access-date=1 October 2016|archive-url=https://web.archive.org/web/20161002024912/http://www.collectiveintelligenceart.ca/collective-intelligence-art.html|archive-date=2 October 2016|url-status=dead}}</ref>。2016年3月，奥尔德雷德的CI-Art活动吸引了来自牛津，新斯科舍省和全球的400多人参加。奥尔德雷德后来开发的工作使用联合国大学群体智能系统来创建数字绘图。牛津河畔画廊（新斯科舍省）于2016年5月举办了一次公共CI艺术活动，与国际在线参与者建立联系<ref>Oxford Riverside Gallery News, May 2016. {{cite web|title=CI-Art event at Oxford Riverside Gallery 'Nexus' opening|year=2016|url=http://www.oxfordriversidegallery.ca/news/ci-art-event-at-oxford-riverside-gallery-nexus-opening|access-date=1 October 2016|archive-url=https://web.archive.org/web/20161002104037/http://www.oxfordriversidegallery.ca/news/ci-art-event-at-oxford-riverside-gallery-nexus-opening|archive-date=2 October 2016|url-status=live}}</ref>。[[文件:Collaborative tagging.png|缩略图|左|育儿社交网络和协作标签是自动IPTV内容阻止系统的基础|317x317像素]]
在这种情况下，集体智能常常与共享知识相混淆。前者是社区成员单独持有的信息的总和，而后者是社区所有成员都认为是真实且已知的信息<ref>Jenkins, H. 2006. ''[https://archive.org/details/isbn_9780814742815 Convergence Culture]''. New York: New York University Press.</ref>。以Web 2.0为代表的集体智能比协作智能具有更少的用户参与度。使用Web 2.0平台的艺术项目“共享银河”，是一个由匿名艺术家开发的实验，目的是创建一个集体身份，并在MySpace，Facebook，YouTube和Second Life等多个平台上以这个集体身份出现。密码将写在配置文件中，并且名为“ Shared Galaxy”的帐户开放给任何人使用。通过这种方式，许多人成为一体<ref>{{Cite journal|last1=Scardamalia|first1=Marlene|last2=Bereiter|first2=Carl|date=1994-07-01|title=Computer Support for Knowledge-Building Communities|journal=Journal of the Learning Sciences|volume=3|issue=3|pages=265–283|doi=10.1207/s15327809jls0303_3|citeseerx=10.1.1.600.463}}</ref>。Curatron是另一个利用集体智能创作艺术作品的艺术项目，其中一大批艺术家共同决定建立一个较小的团队，他们对其团队的协作表现非常自信。该项目基于一种计算集体偏好的算法<ref>{{cite web|url=http://www.vocativ.com/culture/art-culture/math-takes-guessing-art-curation/|title=Math Takes the Guessing Out of Artistic Collaboration|date=9 July 2014|access-date=30 April 2015|archive-url=https://web.archive.org/web/20141001052839/http://www.vocativ.com/culture/art-culture/math-takes-guessing-art-curation/|archive-date=1 October 2014|url-status=live}}</ref>。在创建他所谓的“ CI艺术”时，新斯科舍省的艺术家马修·阿尔德雷德Mathew Aldred遵循了皮耶·列维对集体智能的定义<ref>Mathew Aldred, May 2016. {{cite web|title=What is Collective Intelligence Art?|year=2016|url=http://www.collectiveintelligenceart.ca/collective-intelligence-art.html|access-date=1 October 2016|archive-url=https://web.archive.org/web/20161002024912/http://www.collectiveintelligenceart.ca/collective-intelligence-art.html|archive-date=2 October 2016|url-status=dead}}</ref>。2016年3月，奥尔德雷德的CI-Art活动吸引了来自牛津，新斯科舍省和全球的400多人参加。奥尔德雷德后来开发的工作使用联合国大学群体智能系统来创建数字绘图。牛津河畔画廊（新斯科舍省）于2016年5月举办了一次公共CI艺术活动，与国际在线参与者建立联系<ref>Oxford Riverside Gallery News, May 2016. {{cite web|title=CI-Art event at Oxford Riverside Gallery 'Nexus' opening|year=2016|url=http://www.oxfordriversidegallery.ca/news/ci-art-event-at-oxford-riverside-gallery-nexus-opening|access-date=1 October 2016|archive-url=https://web.archive.org/web/20161002104037/http://www.oxfordriversidegallery.ca/news/ci-art-event-at-oxford-riverside-gallery-nexus-opening|archive-date=2 October 2016|url-status=live}}</ref>。
[[文件:Collaborative tagging.png|缩略图|左|育儿社交网络和协作标签是自动IPTV内容阻止系统的基础]]
在社交书签（也称为协作标签）中，用户将标签分配给与其他用户共享的资源中，继而从这种众包过程中产生了一种信息组织。最终的信息结构可以看作反映用户社区的集体知识（或集体智能），通常被称为“大众分类”，这个过程可以通过协作标记模型来捕获<ref name=":26">{{Cite book|last1=Millen|first1=David R.|last2=Feinberg|first2=Jonathan|last3=Kerr|first3=Bernard|date=2006-01-01|title=Dogear: Social Bookmarking in the Enterprise|journal=Proceedings of the SIGCHI Conference on Human Factors in Computing Systems|series=CHI '06|location=New York, NY, USA|publisher=ACM|pages=111–120|doi=10.1145/1124772.1124792|isbn=978-1595933720|s2cid=18423803}}</ref>。
在社交书签（也称为协作标签）中，用户将标签分配给与其他用户共享的资源中，继而从这种众包过程中产生了一种信息组织。最终的信息结构可以看作反映用户社区的集体知识（或集体智能），通常被称为“大众分类”，这个过程可以通过协作标记模型来捕获<ref name=":26">{{Cite book|last1=Millen|first1=David R.|last2=Feinberg|first2=Jonathan|last3=Kerr|first3=Bernard|date=2006-01-01|title=Dogear: Social Bookmarking in the Enterprise|journal=Proceedings of the SIGCHI Conference on Human Factors in Computing Systems|series=CHI '06|location=New York, NY, USA|publisher=ACM|pages=111–120|doi=10.1145/1124772.1124792|isbn=978-1595933720|s2cid=18423803}}</ref>。
近期，通过对社会书签网站Delicious的数据的研究表明，协作标签系统表现出一种复杂的系统（或自组织）动态形式。尽管没有中央控制来约束单个用户的操作，但是不同资源标签的分布已显示出会随着时间推移，逐渐收敛到稳定的幂律分布<ref name="WWW07-ref">Harry Halpin, Valentin Robu, Hana Shepherd [http://portal.acm.org/citation.cfm?id=1242572.1242602 The Complex Dynamics of Collaborative Tagging], Proceedings 6th International Conference on the World Wide Web (WWW'07), Banff, Canada, pp. 211–220, ACM Press, 2007.</ref>。一旦这种稳定的分布形成，就可以利用不同标签之间的相关性来构建简单的大众分类图，进而可以对其有效的划分，以获得社区或共享词汇表的形式<ref name="TWEB-ref">Valentin Robu, Harry Halpin, Hana Shepherd [http://portal.acm.org/citation.cfm?id=1594173.1594176 Emergence of consensus and shared vocabularies in collaborative tagging systems], ACM Transactions on the Web (TWEB), Vol. 3(4), article 14, ACM Press, September 2009.</ref>。这些词汇可以看作是集体智能的一种形式，它源于用户社区的分散行动。Wall-it项目也是社交书签的一个示例<ref>Carlos J. Costa, January 2012. {{cite web|title=Article on Wall-it project|year=2012|url=http://masteropensource.wordpress.com/2012/01/21/wall-it/|access-date=23 January 2012|archive-url=https://web.archive.org/web/20131218122446/http://masteropensource.wordpress.com/2012/01/21/wall-it/|archive-date=18 December 2013|url-status=live}}</ref>。
近期，通过对社会书签网站Delicious的数据的研究表明，协作标签系统表现出一种复杂的系统（或自组织）动态形式。尽管没有中央控制来约束单个用户的操作，但是不同资源标签的分布已显示出会随着时间推移，逐渐收敛到稳定的幂律分布<ref name="WWW07-ref">Harry Halpin, Valentin Robu, Hana Shepherd [http://portal.acm.org/citation.cfm?id=1242572.1242602 The Complex Dynamics of Collaborative Tagging], Proceedings 6th International Conference on the World Wide Web (WWW'07), Banff, Canada, pp. 211–220, ACM Press, 2007.</ref>。一旦这种稳定的分布形成，就可以利用不同标签之间的相关性来构建简单的大众分类图，进而可以对其有效的划分，以获得社区或共享词汇表的形式<ref name="TWEB-ref">Valentin Robu, Harry Halpin, Hana Shepherd [http://portal.acm.org/citation.cfm?id=1594173.1594176 Emergence of consensus and shared vocabularies in collaborative tagging systems], ACM Transactions on the Web (TWEB), Vol. 3(4), article 14, ACM Press, September 2009.</ref>。这些词汇可以看作是集体智能的一种形式，它源于用户社区的分散行动。Wall-it项目也是社交书签的一个示例<ref>Carlos J. Costa, January 2012. {{cite web|title=Article on Wall-it project|year=2012|url=http://masteropensource.wordpress.com/2012/01/21/wall-it/|access-date=23 January 2012|archive-url=https://web.archive.org/web/20131218122446/http://masteropensource.wordpress.com/2012/01/21/wall-it/|archive-date=18 December 2013|url-status=live}}</ref>。
==== P2P业务 ====
==== P2P business P2P业务 ====
Tapscott和Williams进行的研究提供了一些示例，说明了集体智能对企业的好处<ref name="Tapscott, D. 20082">Tapscott, D., & Williams, A. D. (2008). ''[https://books.google.com/books?id=DVomiOeBg_YC&printsec=frontcover#v=onepage&q=%22collective%20intelligence%22&f=false Wikinomics: How Mass Collaboration Changes Everything] {{Webarchive|url=https://web.archive.org/web/20111110041604/http://books.google.com/books?id=DVomiOeBg_YC&printsec=frontcover#v=onepage&q=%22collective%20intelligence%22&f=false |date=10 November 2011 }}'', USA: Penguin Group</ref>：
Tapscott和Williams进行的研究提供了一些示例，说明了集体智能对企业的好处<ref name="Tapscott, D. 20082">Tapscott, D., & Williams, A. D. (2008). ''[https://books.google.com/books?id=DVomiOeBg_YC&printsec=frontcover#v=onepage&q=%22collective%20intelligence%22&f=false Wikinomics: How Mass Collaboration Changes Everything] {{Webarchive|url=https://web.archive.org/web/20111110041604/http://books.google.com/books?id=DVomiOeBg_YC&printsec=frontcover#v=onepage&q=%22collective%20intelligence%22&f=false |date=10 November 2011 }}'', USA: Penguin Group</ref>：
;人才利用
;Talent utilization
人才利用
随着技术发展速率的变化，没有一家公司能够完全跟上竞争所需的创新。相反，聪明的公司正在利用大规模协作的力量来吸引他们无法雇用的人员。这也有助于公司持续地有兴趣去吸引新创意和投资机会的出现。
随着技术发展速率的变化，没有一家公司能够完全跟上竞争所需的创新。相反，聪明的公司正在利用大规模协作的力量来吸引他们无法雇用的人员。这也有助于公司持续地有兴趣去吸引新创意和投资机会的出现。
;需求创造
企业可以通过参与开放源代码社区来创建互补商品的新市场。即使没有社区的资源和协作，企业也可以扩展到以前无法实现的新领域。如前所述，这为所述新领域中商品的互补产品创造了新市场。