Wednesday, 21 September 2016

Collective intelligence

 

From Wikipedia, the free encyclopedia/ Blogger Ref  http://www.p2pfoundation.net/Universal_Debating_Project
Jump to: navigation, search


Types of collective intelligence
Collective intelligence is shared or group intelligence that emerges from the collaboration, collective efforts, and competition of many individuals and appears in consensus decision making. The term appears in sociobiology, political science and in context of mass peer review and crowdsourcing applications. It may involve consensus, social capital and formalisms such as voting systems, social media and other means of quantifying mass activity. Collective IQ is a measure of collective intelligence, although it is often used interchangeably with the term collective intelligence. Collective intelligence has also been attributed to bacteria[1] and animals.[2]
It can be understood as an emergent property from the synergies among: 1) data-information-knowledge; 2) software-hardware; and 3) experts (those with new insights as well as recognized authorities) that continually learns from feedback to produce just-in-time knowledge for better decisions than these three elements acting alone.[3] Or more narrowly as an emergent property between people and ways of processing information.[4] This notion of collective intelligence is referred to as Symbiotic intelligence by Norman Lee Johnson.[5] The concept is used in sociology, business, computer science and mass communications: it also appears in science fiction. Pierre Lévy defines collective intelligence as, "It is a form of universally distributed intelligence, constantly enhanced, coordinated in real time, and resulting in the effective mobilization of skills. I'll add the following indispensable characteristic to this definition: The basis and goal of collective intelligence is mutual recognition and enrichment of individuals rather than the cult of fetishized or hypostatized communities."[6] According to researchers Lévy and Kerckhove, it refers to capacity of networked ICTs (Information communication technologies) to enhance the collective pool of social knowledge by simultaneously expanding the extent of human interactions.[7]
Collective intelligence strongly contributes to the shift of knowledge and power from the individual to the collective. According to Eric S. Raymond (1998) and JC Herz (2005), open source intelligence will eventually generate superior outcomes to knowledge generated by proprietary software developed within corporations (Flew 2008). Media theorist Henry Jenkins sees collective intelligence as an 'alternative source of media power', related to convergence culture. He draws attention to education and the way people are learning to participate in knowledge cultures outside formal learning settings. Henry Jenkins criticizes schools which promote 'autonomous problem solvers and self-contained learners' while remaining hostile to learning through the means of collective intelligence.[8] Both Pierre Lévy (2007) and Henry Jenkins (2008) support the claim that collective intelligence is important for democratization, as it is interlinked with knowledge-based culture and sustained by collective idea sharing, and thus contributes to a better understanding of diverse society.
Similar to the g factor for general individual intelligence, a new scientific understanding of collective intelligence aims to extract a general collective intelligence factor c factor for groups indicating a group's ability to perform a wide range of tasks.[9] Definition, operationalization and statistical methods are derived from g. Similarly as g is highly interrelated with the concept of IQ,[10][11] this measurement of collective intelligence can be interpreted as intelligence quotient for groups (Group-IQ) even though the score is not a quotient per se. Causes for c and predictive validity are investigated as well.
Writers who have influenced the idea of collective intelligence include Douglas Hofstadter (1979), Peter Russell (1983), Tom Atlee (1993), Pierre Lévy (1994), Howard Bloom (1995), Francis Heylighen (1995), Douglas Engelbart, Cliff Joslyn, Ron Dembo, Gottfried Mayer-Kress (2003).


History[edit]

The concept (although not so named) originated in 1785 with the Marquis de Condorcet, whose "jury theorem" states that if each member of a voting group is more likely than not to make a correct decision, the probability that the highest vote of the group is the correct decision increases with the number of members of the group (see Condorcet's jury theorem).[12] Many theorists have interpreted Aristotle's statement in the Politics that "a feast to which many contribute is better than a dinner provided out of a single purse" to mean that just as many may bring different dishes to the table, so in a deliberation many may contribute different pieces of information to generate a better decision.[13][14] Recent scholarship,[15] however, suggests that this was probably not what Aristotle meant but is a modern interpretation based on what we now know about team intelligence.[16]
A precursor of the concept is found in entomologist William Morton Wheeler's observation that seemingly independent individuals can cooperate so closely as to become indistinguishable from a single organism (1911).[17] Wheeler saw this collaborative process at work in ants that acted like the cells of a single beast he called a superorganism.
In 1912 Émile Durkheim identified society as the sole source of human logical thought. He argued, in "The Elementary Forms of Religious Life" that society constitutes a higher intelligence because it transcends the individual over space and time.[18] Other antecedents are Vladimir Vernadsky's concept of "noosphere" and H.G. Wells's concept of "world brain" (see also the term "global brain"). Peter Russell, Elisabet Sahtouris, and Barbara Marx Hubbard (originator of the term "conscious evolution")[citation needed] are inspired by the visions of a noosphere – a transcendent, rapidly evolving collective intelligence – an informational cortex of the planet. The notion has more recently been examined by the philosopher Pierre Lévy. In a 1962 research report, Douglas Engelbart linked collective intelligence to organizational effectiveness, and predicted that pro-actively 'augmenting human intellect' would yield a multiplier effect in group problem solving: "Three people working together in this augmented mode [would] seem to be more than three times as effective in solving a complex problem as is one augmented person working alone".[19] In 1994, he coined the term 'collective IQ' as a measure of collective intelligence, to focus attention on the opportunity to significantly raise collective IQ in business and society.[20]
The idea of collective intelligence also forms the framework for contemporary democratic theories often referred to as epistemic democracy. Epistemic democratic theories refer to the capacity of the populous, either through deliberation or aggregation of knowledge, to track the truth and relies on mechanisms to synthesize and apply collective intelligence.[21]

Dimensions[edit]

Howard Bloom has discussed mass behavior—collective behavior from the level of quarks to the level of bacterial, plant, animal, and human societies. He stresses the biological adaptations that have turned most of this earth's living beings into components of what he calls "a learning machine". In 1986 Bloom combined the concepts of apoptosis, parallel distributed processing, group selection, and the superorganism to produce a theory of how collective intelligence works.[22] Later he showed how the collective intelligences of competing bacterial colonies and human societies can be explained in terms of computer-generated "complex adaptive systems" and the "genetic algorithms", concepts pioneered by John Holland.[23]
Bloom traced the evolution of collective intelligence to our bacterial ancestors 1 billion years ago and demonstrated how a multi-species intelligence has worked since the beginning of life.[23] Ant societies exhibit more intelligence, in terms of technology, than any other animal except for humans and co-operate in keeping livestock, for example aphids for "milking". Leaf cutters care for fungi and carry leaves to feed the fungi.
David Skrbina[24] cites the concept of a 'group mind' as being derived from Plato's concept of panpsychism (that mind or consciousness is omnipresent and exists in all matter). He develops the concept of a 'group mind' as articulated by Thomas Hobbes in "Leviathan" and Fechner's arguments for a collective consciousness of mankind. He cites Durkheim as the most notable advocate of a "collective consciousness" and Teilhard de Chardin as a thinker who has developed the philosophical implications of the group mind.
Tom Atlee focuses primarily on humans and on work to upgrade what Howard Bloom calls "the group IQ". Atlee feels that collective intelligence can be encouraged "to overcome 'groupthink' and individual cognitive bias in order to allow a collective to cooperate on one process—while achieving enhanced intellectual performance." George Pór defined the collective intelligence phenomenon as "the capacity of human communities to evolve towards higher order complexity and harmony, through such innovation mechanisms as differentiation and integration, competition and collaboration."[25] Atlee and Pór state that "collective intelligence also involves achieving a single focus of attention and standard of metrics which provide an appropriate threshold of action". Their approach is rooted in Scientific Community Metaphor.
Atlee and Pór suggest that the field of collective intelligence should primarily be seen as a human enterprise in which mind-sets, a willingness to share and an openness to the value of distributed intelligence for the common good are paramount, though group theory and artificial intelligence have something to offer. Individuals who respect collective intelligence are confident of their own abilities and recognize that the whole is indeed greater than the sum of any individual parts.[citation needed] Maximizing collective intelligence relies on the ability of an organization to accept and develop "The Golden Suggestion", which is any potentially useful input from any member. Groupthink often hampers collective intelligence by limiting input to a select few individuals or filtering potential Golden Suggestions without fully developing them to implementation.
Robert David Steele Vivas in The New Craft of Intelligence portrayed all citizens as "intelligence minutemen," drawing only on legal and ethical sources of information, able to create a "public intelligence" that keeps public officials and corporate managers honest, turning the concept of "national intelligence" (previously concerned about spies and secrecy) on its head.
According to Don Tapscott and Anthony D. Williams, collective intelligence is mass collaboration. In order for this concept to happen, four principles need to exist;
Openness
Sharing ideas and intellectual property: though these resources provide the edge over competitors more benefits accrue from allowing others to share ideas and gain significant improvement and scrutiny through collaboration.
Peering
Horizontal organization as with the 'opening up' of the Linux program where users are free to modify and develop it provided that they make it available for others. Peering succeeds because it encourages self-organization – a style of production that works more effectively than hierarchical management for certain tasks.
Sharing
Companies have started to share some ideas while maintaining some degree of control over others, like potential and critical patent rights. Limiting all intellectual property shuts out opportunities, while sharing some expands markets and brings out products faster.
Acting Globally
The advancement in communication technology has prompted the rise of global companies at low overhead costs. The internet is widespread, therefore a globally integrated company has no geographical boundaries and may access new markets, ideas and technology.[26]

Examples[edit]

The Global Futures Collective Intelligence System (GFIS) was created by The Millennium Project in 2012.
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.[citation needed]
Military united, trade unions, and corporations satisfy some definitions of CI — the most rigorous definition would require a capacity to respond to very arbitrary conditions without orders or guidance from "law" or "customers" to constrain actions.Online advertising companies are using collective intelligence to bypass traditional marketing and creative agencies.[27]
The UNU platform for "human swarming" (or "social swarming") establishes real-time closed-loop systems around groups of networked users molded after biological swarms, enabling human participants to behave as a unified collective intelligence.[28][29] When connected to UNU, groups of distributed users collectively answer questions and make predictions in real-time.[30] Early testing shows that human swarms can out-predict individuals.[31] In 2016, an UNU swarm was challenged by a reporter to predict the winners of the Kentucky Derby, and successfully picked the first four horses, in order, beating 540 to 1 odds.[32][33]
In Learner generated context a group of users marshal resources to create an ecology that meets their needs often (but not only) in relation to the co-configuration, co-creation and co-design of a particular learning space that allows learners to create their own context.[34][35][36] Learner generated contexts represent an ad hoc community that facilitates coordination of collective action in a network of trust. An example of Learner generated context is found on the Internet when collaborative users pool knowledge in a "shared intelligence space" such as Wikipedia. As the Internet has developed so has the concept of CI as a shared public forum. The global accessibility and availability of the Internet has allowed more people than ever to contribute and access ideas. (Flew 2008)
Improvisational actors also experience a type of collective intelligence which they term 'Group Mind'. A further example of collective intelligence is found in idea competitions.[37]
Specialized information site such as Digital Photography Review or Camera Labs is an example of collective intelligence. Anyone who has an access to the internet can contribute to distributing their knowledge over the world through the specialized information sites.

Mathematical techniques[edit]

One measure sometimes applied, especially by more artificial intelligence focused theorists, is a "collective intelligence quotient" (or "cooperation quotient")—which presumably can be measured like the "individual" intelligence quotient (IQ)—thus making it possible to determine the marginal extra intelligence added by each new individual participating in the collective, thus using metrics to avoid the hazards of group think and stupidity.
In 2001, Tadeusz (Ted) 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.[38]
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.[38]
Another approach of measuring collective intelligence, not directly related to the field of artificial intelligence, is the collective intelligence factor c.[9]

The collective intelligence factor c[edit]

A new scientific understanding of collective intelligence defines it as a group's general ability to perform a wide range of tasks.[9] Definition, operationalization and statistical methods are similar to the psychometric approach of general individual intelligence. Hereby, an individual's performance on a given set of cognitive tasks is used to measure general cognitive ability indicated by the general intelligence factor g extracted via factor analysis.[39] In the same vein as g serves to display between-individual performance differences on cognitive tasks, collective intelligence research aims to find a parallel intelligence factor for groups 'c factor'[9] (also called 'collective intelligence factor' (CI)[40]) displaying between-group differences on task performance. The collective intelligence score then is used to predict how this same group will perform on any other similar task in the future. Yet tasks, hereby, refer to mental or intellectual tasks performed by small groups[9] even though the concept is hoped to be transferrable to other performances and any groups or crowds reaching from families to companies and even whole cities.[41] Since individuals' g factor scores are highly correlated with full-scale IQ scores, which are in turn regarded as good estimates of g,[10][11] this measurement of collective intelligence can also be seen as an intelligence indicator or quotient respectively for a group (Group-IQ) parallel to an individual's intelligence quotient (IQ) even though the score is not a quotient per se.
Mathematically, c and g are both variables summarizing positive correlations among different tasks supposing that performance on one task is comparable with performance on other similar tasks.[42] c thus is a source of variance among groups and can only be considered as a group's standing on the c factor compared to other groups in a given relevant population.[11][43] The concept is in contrast to competing hypotheses including other correlational structures to explain group intelligence,[9] such as a composition out of several equally important but independent factors as found in individual personality research.[44]
Besides, this scientific idea also aims to explore the causes affecting collective intelligence, such as group size, collaboration tools or group members' interpersonal skills.[45] The MIT Center for Collective Intelligence, for instance, announced the detection of 'The Genome of Collective Intelligence'[45] as one of its main goals aiming to develop a taxonomy of organizational building blocks, or genes, that can be combined and recombined to harness the intelligence of crowds.[45]

Original evidence for the c factor[edit]

Woolley, Chabris, Pentland, Hashmi, & Malone (2010),[9] 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 McGrath Task Circumplex,[46] 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.[42]


Scree plot showing percent of explained variance for the first factors in Woolley et al.'s (2010) two original studies.
Standardized Regression Coefficients for the collective intelligence factor c and group member intelligence regressed on the two criterion tasks as found in Woolley et al.'s (2010) two original studies.

Standardized Regression Coefficients for the collective intelligence factor c as found in Woolley et al.'s[9] (2010) two original studies. c and average (maximum) member intelligence scores are regressed on the criterion tasks.
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.[9]

Causes for c[edit]

Individual intelligence is shown to be genetically and environmentally influenced.[47][48] Analogously, collective intelligence research aims to explore reasons why certain groups perform more intelligent than other groups given that c is just moderately correlated with the intelligence of individual group members.[9] According to Woolley et al.'s results, neither team cohesion nor motivation or satisfaction correlated with c. However, they claim that three factors were found as significant correlates: the variance in the number of speaking turns, group members' average social sensitivity and the proportion of females. All three had similar predictive power for c, but only social sensitivity was statistically significant (b=0.33, P=0.05).[9]
The number speaking turns indicates that "groups where a few people dominated the conversation were less collectively intelligent than those with a more equal distribution of conversational turn-taking".[40] Hence, providing multiple team members the chance to speak up made a group more intelligent.[9]
Group members' social sensitivity was measured via the Reading the Mind in the Eyes Test[49] (RME) and correlated .26 with c.[9] Hereby, participants are asked to detect thinking or feeling expressed in other peoples' eyes presented on pictures and assessed in a multiple choice format. The test aims to measure peoples' Theory of Mind (ToM), also called 'mentalizing'[50][51][52][53] or 'mind reading',[54] which refers to the ability to attribute mental states, such as beliefs, desires or intents, to other people and in how far people understand that others have beliefs, desires, intentions or perspectives different from their own ones.[49] RME is a ToM test for adults[49] that shows sufficient test-retest reliability[55] and constantly differentiates control groups from individuals with functional autism or Asperger Syndrome.[49] It is one of the most widely accepted and well-validated tests for ToM within adults.[56] ToM can be regarded as an associated subset of skills and abilities within the broader concept of emotional intelligence.[40][57]
The proportion of females as a predictor of c was largely mediated by social sensitivity (Sobel z = 1.93, P= 0.03)[9] which is in vein with previous research showing that women score higher on social sensitivity tests.[49] While a mediation, statistically speaking, clarifies the mechanism underlying the relationship between a dependent and an independent variable,[58] Wolley agreed in an interview with the Harvard Business Review that these findings are saying that groups of women are smarter than groups of men[41]. However, she relativizes this stating that the actual important thing is the high social sensitivity of group members.[41]

Further evidence[edit]

Engel et al.[40] (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.,[9] 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.[59]
A collective intelligence factor c in the sense of Woolley et al.[9] was further found in groups of MBA students working together over the course of a semester,[60] in online gaming groups[61] as well as in groups from different cultures[62] and groups in different contexts in terms of short-term versus long-term groups.[62] None of these investigations considered team members' individual intelligence scores as control variables.[60][61][62]
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.[63][64][65][66]

Further causes for c[edit]

It is theorized that the collective intelligence factor c is an emergent property resulting from bottom-up as well as top-down processes.[67] 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.[67]
Bottom-up processes


Predictors for the collective intelligence factor c. Suggested by Woolley, Aggarwal & Malone[67] (2015)
Bottom-up processes include characteristics of group members which are aggregated to the team level[67] encompassing, for example, the average social sensitivity or the average and maximum intelligence scores of group members.[9] Furthermore, collective intelligence was found to be related to a group's cognitive diversity[68] including thinking styles and perspectives.[69] 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.[68]
Top-down processes
Top-down processes cover, for example, conversational turn-taking.[9] 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.[40][61]

Predictive validity and further potential connections to individual intelligence[edit]

Next to predicting a group's performance on more complex criterion tasks as shown in the original experiments,[9] the collective intelligence factor c was also found to predict group performance in diverse tasks in MBA classes lasting over several months.[60] 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.[60] This is another potential parallel to individual intelligence where more intelligent people are found to acquire new material quicker.[11][70]
Individual intelligence can be used to predict plenty of life outcomes from school attainment[71] and career success[72] to health outcomes[73] and even mortality.[73] Whether collective intelligence is able to predict other outcomes besides group performance on mental tasks has still to be investigated.
Gladwell[74] (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[75] or the question of improving intelligence.[76][77] Whereas it is controversial whether human intelligence can be enhanced via training,[76][77] a group's collective intelligence potentially offers simpler opportunities for improvement by exchanging team members or implementing structures and technologies.[41] Moreover, social sensitivity was found to be, at least temporarily, improvable by reading literary fiction[78] as well as watching drama movies.[79] In how far such training ultimately improves collective intelligence through social sensitivity remains an open question.[80]
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[81][82] or the hierarchical model of intelligence differences.[83][84] Further supplementing explanations and conceptualizations for the factor structure of the Genomes'[85] of collective intelligence besides a general c factor', though, are missing yet.

Contradicting results and controversies[edit]

Other scholars explain team performance by aggregating team members' general intelligence to the team level[86][87] instead of building an own overall collective intelligence measure. Devine and Philips[88] (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.[89] 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.[57]
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,[90][91][92] motivation[93][94] and satisfaction[95] 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.[9][40][59][67][67][68]

Digital media[edit]

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 (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.
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.[96] The driving force of this form of collective intelligence[which?] 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 (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. Collective intelligence is not merely a quantitative contribution of information from all cultures, it is also qualitative.
Levy 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 community-based knowledge forums[clarification needed], 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" (Raymond 1998; JC Herz 2005 in Flew 2008).[citation needed] Researchers at the MIT Center for Collective Intelligence research and explore collective intelligence of groups of people and computers.[97]
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.[98] 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.[citation needed] 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[99]
Growth of the Internet and mobile telecom has also produced "swarming" or "rendezvous" events that enable meetings or even dates on demand. The full impact has yet to be felt but the anti-globalization movement, for example, relies heavily on e-mail, cell phones, pagers, SMS and other means of organizing. Atlee discusses the connections between these events and the political views that drive them.[citation needed] The Indymedia organization does this in a more journalistic way. Such resources could combine into a form of collective intelligence accountable only to the current participants yet with some strong moral or linguistic guidance from generations of contributors – or even take on a more obviously democratic form to advance shared goals.

Social bookmarking[edit]

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, has shown that collaborative tagging systems exhibit a form of complex systems (or self-organizing) dynamics.[100][101][102] 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.[100] Once such stable distributions form, examining the correlations 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.[103] 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.[104]

Video games[edit]

Games such as The Sims Series, and Second Life are designed to be non-linear and to depend on collective intelligence for expansion. This way of sharing is gradually evolving and influencing the mindset of the current and future generations.[96] For them, collective intelligence has become a norm. In Terry Flew's discussion of 'interactivity' in the online games environment, the ongoing interactive dialogue between users and game developers,[105] he refers to Pierre Levy's concept of Collective Intelligence (Levy 1998) and argues this is active in videogames as clans or guilds in MMORPG constantly work to achieve goals. Henry Jenkins proposes that the participatory cultures emerging between games producers, media companies, and the end-users mark a fundamental shift in the nature of media production and consumption. Jenkins argues that this new participatory culture arises at the intersection of three broad new media trends.[106] Firstly, the development of new media tools/technologies enabling the creation of content. Secondly, the rise of subcultures promoting such creations, and lastly, the growth of value adding media conglomerates, which foster image, idea and narrative flow. Cultural theorist and online community developer, John Banks considered the contribution of online fan communities in the creation of the Trainz product. He argued that its commercial success was fundamentally dependent upon "the formation and growth of an active and vibrant online fan community that would both actively promote the product and create content- extensions and additions to the game software".[107]
The increase in user created content and interactivity gives rise to issues of control over the game itself and ownership of the player-created content. This gives rise to fundamental legal issues, highlighted by Lessig[108] and Bray and Konsynski,[109] such as Intellectual Property and property ownership rights.
Gosney extends this issue of Collective Intelligence in videogames one step further in his discussion of Alternate Reality Gaming. This genre, he describes as an "across-media game that deliberately blurs the line between the in-game and out-of-game experiences"[110] as events that happen outside the game reality "reach out" into the player's lives in order to bring them together. Solving the game requires "the collective and collaborative efforts of multiple players"; thus the issue of collective and collaborative team play is essential to ARG. Gosney argues that the Alternate Reality genre of gaming dictates an unprecedented level of collaboration and "collective intelligence" in order to solve the mystery of the game.

Stock market predictions[edit]

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.[111][112]
Collective intelligence underpins the efficient-market hypothesis of Eugene Fama[113] – although the term collective intelligence is not used explicitly in his paper. Fama cites research conducted by Michael Jensen[114] 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.

Views[edit]

Tom Atlee reflects that, although humans have an innate ability to gather and analyze data, they are affected by culture, education and social institutions. A single person tends to make decisions motivated by self-preservation. In addition, humans lack a way to make choices that balance innovation and reality.[dubious – discuss] Therefore, without collective intelligence, humans may drive themselves into extinction based on their selfish needs.[115]
Phillip Brown and Hugh Lauder quotes Bowles and Gintis (1976) that in order to truly define collective intelligence, it is crucial to separate 'intelligence' from IQism. They go on to argue that intelligence is an achievement and can only be developed if allowed to. For example, earlier on, groups from the lower levels of society are severely restricted from aggregating and pooling their intelligence. This is because the elites fear that the collective intelligence would convince the people to rebel. If there is no such capacity and relations, there would be no infrastructure on which collective intelligence is built (Brown & Lauder 2000, p. 230). This reflects how powerful collective intelligence can be if left to develop.
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.
Demand Creation
Firms can create a new market for complementary goods by engaging in open source community.
Costs Reduction
Mass collaboration can help to reduce costs dramatically. Firms can release a specific software or product to be evaluated or debugged by online communities. The results will be more personal, robust and error-free products created in a short amount of time and costs.[26]
Skeptics, especially those critical of artificial intelligence and more inclined to believe that risk of bodily harm and bodily action are the basis of all unity between people, are more likely to emphasize the capacity of a group to take action and withstand harm as one fluid mass mobilization, shrugging off harms the way a body shrugs off the loss of a few cells. This strain of thought is most obvious in the anti-globalization movement and characterized by the works of John Zerzan, Carol Moore, and Starhawk, who typically shun academics. These theorists are more likely to refer to ecological and collective wisdom and to the role of consensus process in making ontological distinctions than to any form of "intelligence" as such, which they often argue does not exist, or is mere "cleverness".
Harsh critics of artificial intelligence on ethical grounds are likely to promote collective wisdom-building methods, such as the new tribalists and the Gaians. Whether these can be said to be collective intelligence systems is an open question. Some, e.g. Bill Joy, simply wish to avoid any form of autonomous artificial intelligence and seem willing to work on rigorous collective intelligence in order to remove any possible niche for AI.

See also[edit]

Recommender systems
Concepts
Methods and challenges
Implementations
Research

Notes and references[edit]

  1. Jump up ^ Ngoc Thanh Nguyen (25 July 2011). Transactions on Computational Collective Intelligence III. Springer. p. 63. ISBN 978-3-642-19967-7. Retrieved 11 June 2013. 
  2. Jump up ^ Ngoc Thanh Nguyen (25 July 2011). Transactions on Computational Collective Intelligence III. Springer. p. 69. ISBN 978-3-642-19967-7. Retrieved 11 June 2013. 
  3. Jump up ^ Glenn, Jerome C. Collective Intelligence – One of the Next Big Things, Futura 4/2009, Finnish Society for Futures Studies, Helsinki, Finland
  4. Jump up ^ Glenn, Jerome C. Chapter 5, 2008 State of the Future. The Millennium Project, Washington, DC 2008
  5. Jump up ^ Norman Lee Johnson, Collective Science site
  6. Jump up ^ Pierre Lévy, Collective Intelligence: Mankind's Emerging World in Cyberspace, 1994, p. 13
  7. Jump up ^ Flew, Terry New Media: An Introduction. Oxford University Press, 2007, p. 21
  8. Jump up ^ Jenkins, Henry Convergence Culture: Where old and new media collide. New York: New York University Press, 2006, p. 259
  9. ^ Jump up to: a b c d e f g h i j k l m n o p q r s t u Woolley, Anita Williams; Chabris, Christopher F.; Pentland, Alex; Hashmi, Nada; Malone, Thomas W. (2010-10-29). "Evidence for a Collective Intelligence Factor in the Performance of Human Groups". Science. 330 (6004): 686–688. doi:10.1126/science.1193147. ISSN 0036-8075. PMID 20929725. 
  10. ^ Jump up to: a b Jensen, Arthur, R. (1992). "Understanding g in terms of information processing". Educational Psychology Review. 4: 271–308. doi:10.1007/bf01417874. 
  11. ^ Jump up to: a b c d Jensen, Arthur, R. (1998). The g factor: The science of mental ability. Westport, CT: Praeger. 
  12. Jump up ^ Landemore, Hélène (2012). Landemore, Democratic Reason: Politics, Collective Intelligence, and the Rule of the Many. Princeton: Princeton University Press. 
  13. Jump up ^ Waldron, Jeremy (1995). "The Wisdom of the Multitude: Some Reflections on Book 3, Chapter 11 of Aristotle's Politics". Political Theory. 23 (4): 563–584;. doi:10.1177/0090591795023004001. 
  14. Jump up ^ Ober, Josiah (2008). Democracy and Knowledge. Princeton, N.J.: Princeton University Press. pp. 110–14. 
  15. Jump up ^ Cammack, Daniela (2013). "Aristotle and the Virtue of the Multitude". Political Theory. 41: 175–202. doi:10.1177/0090591712470423. 
  16. Jump up ^ Page, Scott (2008). The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies. Princeton: Princeton University Press. 
  17. Jump up ^ Its source can be found in this link http://skccblog.tistory.com/716. wroten by story teller who belongs to SK C&C, story teller explains history of collective intelligence. also, it mentioned principle of collective intelligence. especially, it says William Morton Wheeler studied it at first time. The title is 'collective intelligence that made us who are smarter than me'
  18. Jump up ^ Émile Durkheim, The Elementary Forms of Religious Life, 1912.
  19. Jump up ^ Engelbart, Douglas (1962) Augmenting Human Intellect: A Conceptual Framework - section on Team Cooperation
  20. Jump up ^ Engelbart, Douglas (1994)Boosting Collective IQ (Slide Handouts) – 'Collective IQ' defined on Slide 4; also (1994) BBN Distinguished Guest Lecture (Video) – 'Collective IQ' defined @16:56 "CoDIAK"
  21. Jump up ^ Landemore, Helene (2013). Democratic Reason: Politics, Collective Intelligence, and the Rule of the Many. Princeton University Press. 
  22. Jump up ^ Howard Bloom, The Lucifer Principle: A Scientific Expedition Into the Forces of History, 1995
  23. ^ Jump up to: a b Howard Bloom, Global Brain: The Evolution of Mass Mind from the Big Bang to the 21st Century, 2000
  24. Jump up ^ Skrbina, D., 2001, Participation, Organization, and Mind: Toward a Participatory Worldview, ch. 8, Doctoral Thesis, Centre for Action Research in Professional Practice, School of Management, University of Bath: England
  25. Jump up ^ George Pór, Blog of Collective Intelligence
  26. ^ Jump up to: a b Tapscott, D., & Williams, A. D. (2008). Wikinomics: How Mass Collaboration Changes Everything, USA: Penguin Group
  27. Jump up ^ Lee,Sang M.,et al. "Success factors of platform leadership in web 2.0 service business." Service Business 4.2 (2010): 89-103.
  28. Jump up ^ http://news.discovery.com/human/life/swarms-of-humans-power-a-i-platform-150603.htm
  29. Jump up ^ https://mitpress.mit.edu/sites/default/files/titles/content/ecal2015/ch117.html
  30. Jump up ^ Rosenberg, L.B., "Human swarming, a real-time method for parallel distributed intelligence," in Swarm/Human Blended Intelligence Workshop (SHBI), 2015 , vol., no., pp.1-7, 28-29 Sept. 2015 doi: 10.1109/SHBI.2015.7321685
  31. Jump up ^ http://sites.lsa.umich.edu/collectiveintelligence/wp-content/uploads/sites/176/2015/05/Rosenberg-CI-2015-Abstract.pdf
  32. Jump up ^ http://www.newsweek.com/artificial-intelligence-turns-20-11000-kentucky-derby-bet-457783
  33. Jump up ^ https://www.washingtonpost.com/news/the-intersect/wp/2016/06/02/what-happened-when-an-ai-hive-mind-answered-reddits-burning-politics-questions/
  34. Jump up ^ Luckin, R., du Boulay, B., Smith, H., Underwood, J., Fitzpatrick, G., Holmberg, J., Kerawalla, L., Tunley, H., Brewster, D. and Pearce, D. (2005), 'Using Mobile Technology to Create Flexible Learning Contexts '. Journal of Interactive Media in Education, 22.
  35. Jump up ^ Luckin, R. (2006), Understanding Learning Contexts as Ecologies of Resources: From the Zone of Proximal Development to Learner Generated Contexts. Paper presented at the Proceedings of World Conference on Elearning in Corporate, Government, Healthcare, and Higher Education 2006.
  36. Jump up ^ Luckin, R., Shurville, S. and Browne, T. (2007), 'Initiating elearning by stealth, participation and consultation in a late majority institution'. Organisational Transformation and Social Change, 3, 4, 317–332.
  37. Jump up ^ Jan Marco Leimeister, Michael Huber, Ulrich Bretschneider, Helmut Krcmar (2009): Leveraging Crowdsourcing: Activation-Supporting Components for IT-Based Ideas Competition. In: Journal of Management Information Systems (2009), Volume: 26, Issue: 1, Publisher: M.E. Sharpe Inc., Pages: 197–224, ISSN 0742-1222, doi:10.2753/MIS0742-1222260108 [1], Winfried Ebner; Jan Marco Leimeister; Helmut Krcmar (2009): Community Engineering for Innovations – The Ideas Competition as a method to nurture a Virtual Community for Innovations. In: R&D Management, 39 (4),pp 342–356 doi:10.1111/j.1467-9310.2009.00564.x [2]
  38. ^ Jump up to: a b Szuba T., Computational Collective Intelligence, 420 pages, Wiley NY, 2001
  39. Jump up ^ Spearman, Charles, E. (1904). ""General intelligence," objectively determined and measured". American Journal of Psychology. 15: 201–293. doi:10.2307/1412107. 
  40. ^ Jump up to: a b c d e f Engel, D.; Woolley, A. W.; Jing, L. X.; Chabris, C. F. & Malone, T. W. (2014). "Reading the Mind in the Eyes or reading between the lines? Theory of Mind predicts collective intelligence equally well online and face-to-face". PLOS ONE. 9 (12): e115212. doi:10.1371/journal.pone.0115212. 
  41. ^ Jump up to: a b c d Woolley, A. & Malone, T. (June 2011). "Defend your research: What makes a team smarter? More women". Harvard Business Review. 89 (6): 32–33. 
  42. ^ Jump up to: a b Kamphaus, R.W.; Winsor, A.P.; Rowe, E.W. & Kim, S. (2005). A history of intelligence test interpretation. In D.P. Flanagan and P.L. Harrison (Eds.), Contemporary intellectual assessment: Theories, tests, and issues (2nd Ed.). New York, NY: Guilford. pp. 23–38. 
  43. Jump up ^ van der Maas, Han L. J.; Dolan, Conor V.; Grasman, Raoul P. P. P.; Wicherts, Jelte M.; Huizenga, Hilde M.; Raijmakers, Maartje E. J. (2006-10-01). "A dynamical model of general intelligence: the positive manifold of intelligence by mutualism". Psychological Review. 113 (4): 842–861. doi:10.1037/0033-295X.113.4.842. ISSN 0033-295X. PMID 17014305. 
  44. Jump up ^ McCrae, R. R.; Costa Jr., P. T. (1987). "Validation of the Five-Factor Model of Personality Across Instruments and Observers" (PDF). Journal of Personality and Social Psychology. 52 (1): 81–90. doi:10.1037/0022-3514.52.1.81. 
  45. ^ Jump up to: a b c "MIT Center for Collective Intelligence". cci.mit.edu. Retrieved 2016-04-26. 
  46. Jump up ^ McGrath, J. E. (1984). Groups: Interaction and Performance. Englewood Cliffs, NJ: Prentice-Hall. 
  47. Jump up ^ Briley, Daniel A.; Tucker-Drob, Elliot M. (2014-09-01). "Genetic and environmental continuity in personality development: a meta-analysis". Psychological Bulletin. 140 (5): 1303–1331. doi:10.1037/a0037091. ISSN 1939-1455. PMC 4152379free to read. PMID 24956122. 
  48. Jump up ^ Deary, Ian J.; Spinath, Frank M.; Bates, Timothy C. (2006-01-01). "Genetics of intelligence". European Journal of Human Genetics. 14 (6): 690–700. doi:10.1038/sj.ejhg.5201588. ISSN 1018-4813. 
  49. ^ Jump up to: a b c d e Baron-Cohen S, Wheelwright S, Hill J, Raste Y, Plumb I (2001). "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 of Child Psychology and Psychiatry. 42: 241–251. doi:10.1017/s0021963001006643. 
  50. Jump up ^ Apperly, Ian A. (2012-05-01). "What is "theory of mind"? Concepts, cognitive processes and individual differences". The Quarterly Journal of Experimental Psychology. 65 (5): 825–839. doi:10.1080/17470218.2012.676055. ISSN 1747-0218. PMID 22533318. 
  51. Jump up ^ Baron-Cohen, S., Leslie, A.M,, Frith, U. (1985). "Does the autistic child have a theory of mind?". Cognition. 21: 37–46. doi:10.1016/0010-0277(85)90022-8. 
  52. Jump up ^ Flavell, J. H. (1999-01-01). "Cognitive development: children's knowledge about the mind". Annual Review of Psychology. 50: 21–45. doi:10.1146/annurev.psych.50.1.21. ISSN 0066-4308. PMID 10074674. 
  53. Jump up ^ Premack, David; Woodruff, Guy (1978-12-01). "Does the chimpanzee have a theory of mind?". Behavioral and Brain Sciences. 1 (04): 515–526. doi:10.1017/S0140525X00076512. ISSN 1469-1825. 
  54. Jump up ^ Heyes, Cecilia M.; Frith, Chris D. (2014-06-20). "The cultural evolution of mind reading". Science. 344 (6190): 1243091. doi:10.1126/science.1243091. ISSN 0036-8075. PMID 24948740. 
  55. Jump up ^ Hallerbäck, Maria Unenge; Lugnegård, Tove; Hjärthag, Fredrik; Gillberg, Christopher (2009-03-01). "The Reading the Mind in the Eyes Test: Test–retest reliability of a Swedish version". Cognitive Neuropsychiatry. 14 (2): 127–143. doi:10.1080/13546800902901518. ISSN 1354-6805. PMID 19370436. 
  56. Jump up ^ Pinkham, Amy E.; Penn, David L.; Green, Michael F.; Buck, Benjamin; Healey, Kristin; Harvey, Philip D. (2014-07-01). "The Social Cognition Psychometric Evaluation Study: Results of the Expert Survey and RAND Panel". Schizophrenia Bulletin. 40 (4): 813–823. doi:10.1093/schbul/sbt081. ISSN 0586-7614. PMC 4059426free to read. PMID 23728248. 
  57. ^ Jump up to: a b Yip, Jeremy A.; Côté, Stéphane (2013-01-01). "The Emotionally Intelligent Decision Maker Emotion-Understanding Ability Reduces the Effect of Incidental Anxiety on Risk Taking". Psychological Science. 24 (1): 48–55. doi:10.1177/0956797612450031. ISSN 0956-7976. PMID 23221020. 
  58. Jump up ^ MacKinnon, D. P. (2008). Introduction to Statistical Mediation Analysis. New York, NY: Erlbaum. 
  59. ^ Jump up to: a b Engel, David; Woolley, Anita Williams; Aggarwal, Ishani; Chabris, Christopher F.; Takahashi, Masamichi; Nemoto, Keiichi; Kaiser, Carolin; Kim, Young Ji; Malone, Thomas W. (2015-01-01). "Collective Intelligence in Computer-Mediated Collaboration Emerges in Different Contexts and Cultures". Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. CHI '15. New York, NY, USA: ACM: 3769–3778. doi:10.1145/2702123.2702259. ISBN 9781450331456. 
  60. ^ Jump up to: a b c d Aggarwal, I. & Woolley, A.W. (2014). "The effects of cognitive diversity on collective intelligence and team learning.". Symposium presented at the 50th Meeting of the Society of Experimental Social Psychology, Columbus, OH. 
  61. ^ Jump up to: a b c Kim, Y. J.; Engel, D.; Woolley, A. W.; Lin, J.; McArthur, N. & Malone, T. W. (2015). "Work together, play smart: Collective intelligence in League of Legends teams". Paper presented at the 2015 Collective Intelligence Conference, Santa Clara, CA. 
  62. ^ Jump up to: a b c Engel, D.; Woolley, A. W.; Aggarwal, I.; Chabris, C. F.; Takahashi, M.; Nemoto, K.; Malone, T. W. (2015). "Collective intelligence in computer-mediates collaboration emerges in different contexts and cultures.". In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15) (pp. 3769–3778). New York, NY: ACM. 
  63. Jump up ^ "Collective Intelligence 2016". sites.google.com. Retrieved 2016-04-27. 
  64. Jump up ^ "Posters | Collective Intelligence 2015". sites.lsa.umich.edu. Retrieved 2016-04-27. 
  65. Jump up ^ "Proceedings | Collective Intelligence 2014". collective.mech.northwestern.edu. Retrieved 2016-04-27. 
  66. Jump up ^ "Collective Intelligence 2012". arxiv.org. Retrieved 2016-04-27. 
  67. ^ Jump up to: a b c d e f Woolley, Anita Williams; Aggarwal, Ishani; Malone, Thomas W. (2015-12-01). "Collective Intelligence and Group Performance". Current Directions in Psychological Science. 24 (6): 420–424. doi:10.1177/0963721415599543. ISSN 0963-7214. 
  68. ^ Jump up to: a b c Aggarwal, I.; Woolley, A. W.; Chabris, C. F. & Malone, T. W. (2015). "Cognitive diversity, collective intelligence, and learning in teams.". Paper presented at the 2015 Collective Intelligence Conference, Santa Clara, CA. 
  69. Jump up ^ Kozhevnikov, M.; Evans, C. & Kosslyn, S. M. (2014). "Cognitive style as environmentally sensitive individual differences in cognition: A modern synthesis and applications in education, business, and management". Psychological Science in the Public Interest. 15: 3–33. doi:10.1177/1529100614525555. 
  70. Jump up ^ Schmidt, F.L. & Hunter, J.E. (1998). "The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings". Psychological Bulletin. 124: 262–274. doi:10.1037/0033-2909.124.2.262. 
  71. Jump up ^ Nathan, B. (1997). "Intelligence, Schooling, and Society". American Psychologist. 52 (10): 1046–1050. 
  72. Jump up ^ Strenze, Tarmo (2007-09-01). "Intelligence and socioeconomic success: A meta-analytic review of longitudinal research". Intelligence. 35 (5): 401–426. doi:10.1016/j.intell.2006.09.004. 
  73. ^ Jump up to: a b Deary, I.J.; Weiss, A. & Batty, D.G. (2010). "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". Psychological Science in the Public Interest. 11 (2): 53–79. doi:10.1177/1529100610387081. 
  74. Jump up ^ Gladwell, M. (2008). Outliers. The Story of Success. New York, NY: Little, Brown and Company. ISBN 978-0-316-01792-3. 
  75. Jump up ^ Hedden, Trey; Gabrieli, John D. E. (2004-02-01). "Insights into the ageing mind: a view from cognitive neuroscience". Nature Reviews. Neuroscience. 5 (2): 87–96. doi:10.1038/nrn1323. ISSN 1471-003X. PMID 14735112. 
  76. ^ Jump up to: a b Shipstead, Zach; Redick, Thomas S; Engle, Randall W. (2010-10-01). "Does working memory training generalize?". Psychologica Belgica. 50 (3-4): 245. doi:10.5334/pb-50-3-4-245. ISSN 2054-670X. 
  77. ^ Jump up to: a b Buschkuehl, M.; Jaeggi, S.M. (2010). "Improving intelligence a literature review". Swiss Medical Weekly. 140 (19): 266–72. 
  78. Jump up ^ Kidd, David Comer; Castano, Emanuele (2013-10-18). "Reading Literary Fiction Improves Theory of Mind". Science. 342 (6156): 377–380. doi:10.1126/science.1239918. ISSN 0036-8075. PMID 24091705. 
  79. Jump up ^ Black, Jessica; Barnes, Jennifer L. "Fiction and social cognition: The effect of viewing award-winning television dramas on theory of mind.". Psychology of Aesthetics, Creativity, and the Arts. 9 (4): 423–429. doi:10.1037/aca0000031. 
  80. Jump up ^ Malone, T. W. & Bernstein, M.S. (2015). Handbook of Collective Intelligence. Cambridge, MA: MIT Press. 
  81. Jump up ^ Horn, J. (1989). Models of intelligence. In R.L. Linn (Ed.), Intelligence: Measurement, theory, and public policy (pp. 29–73). Urbana, IL: University of Illinois Press. 
  82. Jump up ^ Cattell, R. B. (1971). Abilities: Their structure, growth, and action. Houghton Mifflin: New York, NY. 
  83. Jump up ^ Carroll, J.B. (1993). Human cognitive abilities: A survey of factor analytic studies. Cambridge, England: Cambridge University Press. 
  84. Jump up ^ Johnson, Wendy; Bouchard Jr., Thomas J. (2005-07-01). "The structure of human intelligence: It is verbal, perceptual, and image rotation (VPR), not fluid and crystallized". Intelligence. 33 (4): 393–416. doi:10.1016/j.intell.2004.12.002. 
  85. Jump up ^ "MIT Center for Collective Intelligence". cci.mit.edu. Retrieved 2016-04-27. 
  86. Jump up ^ LePine, Jeffery A. "Adaptation of Teams in Response to Unforeseen Change: Effects of Goal Difficulty and Team Composition in Terms of Cognitive Ability and Goal Orientation.". Journal of Applied Psychology. 90 (6): 1153–1167. doi:10.1037/0021-9010.90.6.1153. 
  87. Jump up ^ Tziner, Aharon; Eden, Dov. "Effects of crew composition on crew performance: Does the whole equal the sum of its parts?". Journal of Applied Psychology. 70 (1): 85–93. doi:10.1037/0021-9010.70.1.85. 
  88. Jump up ^ Devine, Dennis J.; Philips, Jennifer L. (2001-10-01). "Do Smarter Teams Do Better A Meta-Analysis of Cognitive Ability and Team Performance". Small Group Research. 32 (5): 507–532. doi:10.1177/104649640103200501. ISSN 1046-4964. 
  89. Jump up ^ O'Brien, G. & Owens, A. (1969). "Effects of organizational structure on correlations between member abilities and group productivity". Journal of Applied Psychology. 53: 525–530. doi:10.1037/h0028659. 
  90. Jump up ^ Evans, Charles R.; Dion, Kenneth L. (1991-05-01). "Group Cohesion and Performance A Meta-Analysis". Small Group Research. 22 (2): 175–186. doi:10.1177/1046496491222002. ISSN 1046-4964. 
  91. Jump up ^ Gully, Stanley M.; Devine, Dennis J.; Whitney, David J. (2012-12-01). "A Meta-Analysis of Cohesion and Performance Effects of Level of Analysis and Task Interdependence". Small Group Research. 43 (6): 702–725. doi:10.1177/1046496412468069. ISSN 1046-4964. 
  92. Jump up ^ Beal, Daniel J.; Cohen, Robin R.; Burke, Michael J.; McLendon, Christy L. "Cohesion and Performance in Groups: A Meta-Analytic Clarification of Construct Relations.". Journal of Applied Psychology. 88 (6): 989–1004. doi:10.1037/0021-9010.88.6.989. 
  93. Jump up ^ O'leary-kelly, Anne M.; Martocchio, Joseph J.; Frink, Dwight D. (1994-10-01). "A Review of the Influence of Group Goals on Group Performance". Academy of Management Journal. 37 (5): 1285–1301. doi:10.2307/256673. ISSN 0001-4273. 
  94. Jump up ^ Kleingeld, Ad; Mierlo, Heleen van; Arends, Lidia. "The effect of goal setting on group performance: A meta-analysis.". Journal of Applied Psychology. 96 (6): 1289–1304. doi:10.1037/a0024315. 
  95. Jump up ^ Duffy, M. K.; Shaw, J. D. & Stark, E. M. (2000). "Performance and satisfaction in conflicted interdependent groups: When and how does selfesteem make a difference?". Academy of Management Journal. 43: 772–782. doi:10.2307/1556367. 
  96. ^ Jump up to: a b Weiss, A. (2005). The Power of Collective Intelligence. Collective Intelligence, pp. 19–23
  97. Jump up ^ MIT Center for Collective Intelligence. Cci.mit.edu. Retrieved on 2013-07-13.
  98. Jump up ^ Jenkins, H. 2006. Convergence Culture. New York: New York University Press.
  99. Jump up ^ Math Takes the Guessing Out of Artistic Collaboration, Vocativ.com, 07/09/2014
  100. ^ Jump up to: a b Harry Halpin, Valentin Robu, Hana Shepherd 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.
  101. Jump up ^ Fu, Wai-Tat (2010). "Semantic imitation in social tagging". ACM Transactions on Computer-Human Interaction. doi:10.1145/1460563.1460600. 
  102. Jump up ^ Fu, Wai-Tat (Aug 2009). "A Semantic Imitation Model of Social Tagging.". Proceedings of the IEEE conference on Social Computing: 66–72. 
  103. Jump up ^ Valentin Robu, Harry Halpin, Hana Shepherd 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.
  104. Jump up ^ Carlos J. Costa, January 2012. "Article on Wall-it project". 2012. 
  105. Jump up ^ Flew, Terry and Humphreys, Sal (2005) "Games: Technology, Industry, Culture" in Terry Flew, New Media: An Introduction (2nd edn), Oxford University Press, South Melbourne 101-114.
  106. Jump up ^ Henry Jenkins (2002) in Flew, Terry and Humphreys, Sal (2005) Games: Technology, Industry, Culture in Terry Flew, New Media: An Introduction (2nd edn), Oxford University Press, South Melbourne 101-114.
  107. Jump up ^ John A.L. Banks. Negotiating Participatory Culture in the New Media Environment: Auran and the Trainz Online Community – An (Im)possible Relation, The University of Queensland. School of English, Media Studies and Art History. MelbourneDAC2003
  108. Jump up ^ L, Lessig,(2006)Code Version 2.0 (2nd ed.). New York: Basic Books.
  109. Jump up ^ Bray, DA & Konsynski, BR, 2007, Virtual Worlds, Virtual Economies, Virtual Institutions, viewed 10 October 2008, p. 1-27 <http://ssrn.com/abstract=962501>
  110. Jump up ^ Gosney, J.W, 2005, Beyond Reality: A Guide to Alternate Reality Gaming, Thomson Course Technology, Boston.
  111. Jump up ^ Ma, Ying; Li, Guanyi; Dong, Yingsai; Qin, Zengchang (2010). "Minority Game Data Mining for Stock Market Predictions" (PDF). Agents and Data Mining Interaction, 6th International Workshopon Agents and Data Mining Interaction, ADMI 2010. doi:10.1007/978-3-642-15420-1. 
  112. Jump up ^ Yu, Du; Dong, Yingsai; Qin, Zengchang; Wan, Tao (2011). "Exploring Market Behaviors with Evolutionary Mixed-Games Learning Model" (PDF). Computational Collective Intelligence. Technologies and Applications – Third International Conference, ICCCI 2011: 244–253. doi:10.1007/978-3-642-23935-9_24. 
  113. Jump up ^ Fama, E.F. (1970). "Efficient Capital Markets: A Review of Theory and Empirical Work". Journal of Finance. 25 (2): 383–417. doi:10.2307/2325486. 
  114. Jump up ^ Jensen, M.C (1967). "The Performance of Mutual Funds in the Period 1945–1964". Journal of Finance. 23 (2): 389–416. doi:10.1111/j.1540-6261.1968.tb00815.x. 
  115. Jump up ^ Atlee, T. (2008). Reflections on the evolution of choice and collective intelligence, Retrieved 26 August 2008

Bibliography[edit]

External links[edit]

Retrieved from "https://en.wikipedia.org/w/index.php?title=Collective_intelligence&oldid=740065994"
at
Labels:

No comments:

Post a Comment

Subscribe to: Post Comments (Atom)

Welcome to Precision Universal Debate

  IMPORTANT Though the title of this p2p entry is the Universal Debating Project (at present) it has now been re-named the Precision Univers...

  • Committee
    From Wikipedia, the free encyclopedia    Blogger Ref Universal Debating Project Jump to: navigation , search For othe...
  • Consensus decion-making
    Blogger Ref Universal Debating Project Consensus decision-making is a group decision-making process in which group members develop, and ...
  • Reliability of Wikipedia
      From Wikipedia, the free encyclopedia/Blogger Ref   http://www.p2pfoundation.net/Universal_Debating_Project     (Redirected from W...