Collective Intelligence
Collective Intelligence
Collective Intelligence
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.[2] Or more narrowly as an emergent property between people
and ways of processing information.[3] This notion of collective intelligence is referred to as "symbiotic intelligence" by Norman Lee
Johnson.[4] The concept is used insociology, business, computer science and mass communications: it also appears inscience 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."[5] According to researchers Pierre Lévy and Derrick de 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.[6]
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.[7] 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 (g) 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.[8] Definition,
operationalization and statistical methods are derived from g. Similarly as g is highly interrelated with the concept of IQ,[9][10] 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 forc and predictive validity are investigated as well.
Writers who have influenced the idea of collective intelligence include Francis Galton, Douglas Hofstadter (1979), Peter Russell
(1983), Tom Atlee (1993), Pierre Lévy (1994), Howard Bloom (1995), Francis Heylighen (1995), Douglas Engelbart, Louis
Rosenberg, Cliff Joslyn, Ron Dembo, Gottfried Mayer-Kress (2003).
Contents
History
Dimensions
Collective intelligence factorc
Causes
Processes
Evidence
Predictive validity
Potential connections to individual intelligence
Controversies
Alternative mathematical techniques
Computational collective intelligence
Collective intelligence quotient
Applications
Cognition
Cooperation
Coordination
Alternative views
A tool for combating self-preservation
Separation from IQism
Artificial intelligence views
Solving climate change
See also
Similar concepts and applications
Computation and computer science
Others
Notes and references
Bibliography
External links
History
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).[11] 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.[12][13] Recent scholarship,[14] however, suggests that this was probably not what Aristotle
[15]
meant but is a modern interpretation based on what we now know about team intelligence.
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).[16] Wheeler saw this collaborative process at
work in ants that acted like the cells of a single beast he called asuperorganism.
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.[17] 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")[18] 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
H.G. Wells World Brain (1936–1938) 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 populace,
either through deliberation or aggregation of knowledge, to track the truth and relies on mechanisms to synthesize and apply
collective intelligence.[21]
Collective intelligence was introduced into the machine learning community in the late 20th century,[22] and matured into a broader
consideration of how to "design collectives" of self-interested adaptive agents to meet a system-wide goal.[23][24] This was related to
single-agent work on "reward shaping"[25] and has been taken forward by numerous researchers in the game theory and engineering
communities.[26]
Dimensions
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.[27] 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 byJohn Holland.[28] Complex adaptive systems model
David Skrbina[29] 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 consciousnessof mankind. He cites Durkheim as the most notable advocate of a
"collective consciousness"[30] and Teilhard de Chardin as a thinker who has developed the philosophical implications of the group
mind.[31]
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."[32] 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".[33] Their approach is
rooted in scientific community metaphor.[33]
The term group intelligence is sometimes used interchangeably with the term collective intelligence. Anita Woolley presents
Collective intelligence as a measure of group intelligence and group creativity.[8] The idea is that a measure of collective intelligence
covers a broad range of features of the group, mainly group composition and group interaction.[34] The features of composition that
lead to increased levels of collective intelligence in groups include criteria such as higher numbers of women in the group as well as
increased diversity of the group.[34]
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.[33] 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.[35] 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.[36] Groupthink often hampers collective intelligence by limiting input to a select few individuals or filtering
[33]
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,
[37]
turning the concept of "national intelligence" (previously concerned about spies and secrecy) on its head.
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.[38]
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.[38]
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.[38]
Collective intelligence factor c
A new scientific understanding of collective intelligence defines it as a group's
general ability to perform a wide range of tasks.[8] 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'[8] (also called
'collective intelligence factor' (CI)[40] ) displaying between-group differences on task Scree plot showing percent of
performance. The collective intelligence score then is used to predict how this same explained variance for the first
group will perform on any other similar task in the future. Yet tasks, hereby, refer to factors in Woolley et al.'s (2010) two
mental or intellectual tasks performed by small groups[8] even though the concept is original studies.
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,[9][10] 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.[10][43] The concept is in
contrast to competing hypotheses including other correlational structures to explain group intelligence,[8] such as a composition out
of several equally important but independent factors as found inindividual 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]
Causes
Individual intelligence is shown to be genetically and environmentally influenced.[46][47] 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.[8] 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
[8]
predictive power for c, but only social sensitivity was statistically significant (b=0.33, P=0.05).
The number speaking turns indicates that "groups where a few people dominated the conversation were less collectively intelligent
than those with a more equal distribution of conversational turn-taking".[40] Hence, providing multiple team members the chance to
speak up made a group more intelligent.[8]
Group members' social sensitivity was measured via the Reading the Mind in the Eyes Test[48] (RME) and correlated .26 with c.[8]
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'[49][50][51][52] or 'mind
reading',[53] 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.[48] RME is a ToM test
for adults[48] that shows sufficient test-retest reliability[54] and constantly differentiates control groups from individuals with
functional autism or Asperger Syndrome.[48] It is one of the most widely accepted and well-validated tests for ToM within adults.[55]
emotional intelligence.[40][56]
ToM can be regarded as an associated subset of skills and abilities within the broader concept of
The proportion of females as a predictor ofc was largely mediated by social sensitivity S( obel z = 1.93, P= 0.03)[8] which is in vein
with previous research showing that women score higher on social sensitivity tests.[48] While a mediation, statistically speaking,
clarifies the mechanism underlying the relationship between a dependent and an independent variable,[57] 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]
It is theorized that the collective intelligence factor c is an emergent property resulting from bottom-up as well as top-down
processes.[34] Hereby, bottom-up processes cover aggregated group-member characteristics. Top-down processes cover group
[34]
structures and norms that influence a group's way of collaborating and coordinating.
Processes
Top-down processes
Top-down processes cover group interaction, such as structures, processes, and
norms.[58] An example of such top-down processes is conversational turn-taking.[8]
Research further suggest that collectively intelligent groups communicate more in
general as well as more equally; same applies for participation and is shown for
[40][59]
face-to-face as well as online groups communicating only via writing.
Bottom-up processes
Bottom-up processes include group composition,[58] namely the characteristics of Predictors for the collective
intelligence factor c. Suggested by
group members which are aggregated to the team level[34] encompassing. An
Woolley, Aggarwal & Malone[34]
example of such bottom-up processes is the average social sensitivity or the average
(2015)
and maximum intelligence scores of group members.[8] Furthermore, collective
intelligence was found to be related to a group's cognitive diversity[60] including
thinking styles and perspectives.[61] 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.[60]
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.[64][65] 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
[66]
predicted the first four horses, in order, defying 542–1 odds and turning a $20 bet into $10,800.
Evidence
Woolley, Chabris, Pentland, Hashmi, & Malone (2010),[8] 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,[67] 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 Standardized Regression
Coefficients for the collective
tests.[42] Afterwards, a more complex criterion task was absolved by each group
intelligence factor c as found in
measuring whether the extracted c factor had predictive power for performance
Woolley et al.'s[8] (2010) two original
outside the original task batteries. Criterion tasks were playing checkers (draughts) studies. c and average (maximum)
against a standardized computer in the first and a complex architectural design task member intelligence scores are
in the second study. In a regression analysis using both individual intelligence of regressed on the criterion tasks.
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
[8]
more than just the aggregation of the individual IQs or the influence of the group member with the highest IQ.
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.,[8] 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.[68]
A collective intelligence factorc in the sense of Woolley et al.[8] was further found in groups of MBA students working together over
the course of a semester,[69] in online gaming groups[59] as well as in groups from different cultures[70] and groups in different
contexts in terms of short-term versus long-term groups.[70] None of these investigations considered team members' individual
intelligence scores as control variables.[59][69][70]
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.[71][72][73][74]
Predictive validity
Next to predicting a group's performance on more complex criterion tasks as shown in the original experiments,[8] the collective
intelligence factor c was also found to predict group performance in diverse tasks in MBA classes lasting over several months.[69]
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.[69] This is another potential parallel to
.[10][75]
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[76] and career success[77] to health
outcomes[78] and even mortality.[78] Whether collective intelligence is able to predict other outcomes besides group performance on
mental tasks has still to be investigated.
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[86][87] or the hierarchical model of intelligence differences.[88][89]
Further supplementing explanations and conceptualizations for the factor structure of the Genomes' of collective intelligence besides
a general c factor', though, are missing yet.[90]
Controversies
Other scholars explain team performance by aggregating team members' general intelligence to the team level[91][92] instead of
[93] (2001) showed in a meta-analysis that mean cognitive
building an own overall collective intelligence measure. Devine and Philips
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.[94] 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.[56]
Since Woolley et al.'s[8] 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,[95][96][97] motivation[98][99] and satisfaction[100] on group
performance.
Noteworthy is also that the involved researchers among the confirming findings widely overlap with each other and with the authors
oolley.[8][34][34][40][60][68]
participating in the original first study around Anita W
In this model, beings and information are modeled as abstract information molecules
carrying expressions of mathematical logic.[101] They are quasi-randomly displacing
due to their interaction with their environments with their intended
displacements.[101] Their interaction in abstract computational space creates multi-
thread inference process which we perceive as collective intelligence.[101] 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
Computational Collective
structure".[101] While IQS seems to be computationally hard, modeling of social
Intelligence, by Tadeusz Szuba
structure in terms of a computational process as described above gives a chance for
approximation.[101] Prospective applications are optimization of companies through
[101]
the maximization of their IQS, and the analysis of drug resistance against collective intelligence of bacterial colonies.
Applications
Elicitation of point estimates– Here, we try 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 are seen in prediction markets like Intrade,
HSX or InklingMarkets and also in several implementations of crowdsourced estimation of a numeric outcome. Essentially, we try to
get the average value of the estimates provided by the members in the crowd.
Opinion Aggregation – In this situation, we gather opinions from the crowd regarding some 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.
Idea Collection – 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). Here, 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.[104]
Cognition
Market judgment
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.[105] 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.[105] 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
[106][107]
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[108] – although the term collective intelligence is
not used explicitly in his paper. Fama cites research conducted by Michael Jensen[109] 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.[109]
Cooperation
Networks of trust
In 2012, the Global Futures Collective Intelligence System (GFIS) was created by The Millennium Project,[115] which epitomizes
collective intelligence as the synergistic intersection among data/information/knowledge, software/hardware, and expertise/insights
[116]
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 (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.
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"[119] Researchers at the MIT Center for Collective Intelligence research and
[120]
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.[121] 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.[122]
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[123] In creating what he calls 'CI-Art', Nova Scotia based artist Mathew Aldred follows Pierry Lévy's
definition of collective intelligence.[124] Aldred's CI-Art event in March 2016 involved over four hundred people from the
community of Oxford, Nova Scotia, and internationally.[125][126] Later work developed by Aldred used the UNU swarm intelligence
system to create digital drawings and paintings.[127] The Oxford Riverside Gallery (Nova Scotia) held a public CI-Art event in May
.[128]
2016, which connected with online participants internationally
In social bookmarking (also called collaborative tagging),[129] 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 bymodels of collaborative tagging.[129]
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.[130][131][132] 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.[130] 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.[133] Such vocabularies can be seen as a form of collective
Parenting social network and
intelligence, emerging from the decentralised actions of a community of users. The collaborative tagging as pillars for
[134]
Wall-it Project is also an example of social bookmarking. automatic IPTV content blocking
system
P2P business
[38]
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.[38]
Demand creation
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.[38]
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.
New ideas can also be generated and explored by collaboration of online communities
creating opportunities for free R&D outside the confines of the company.[38]
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[136] and Bray and Konsynski,[137] 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"[138]
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
[138]
"collective intelligence" in order to solve the mystery of the game.
Benefits of co-operation
Co-operation helps to solve most important and most interesting multi-science problems. In his book, James Surowiecki mentioned
that most scientists think that benefits of co-operation have much more value when compared to potential costs. Co-operation works
also because at best it guarantees number of different viewpoints. Because of the possibilities of technology global co-operation is
nowadays much easier and productive than before. It is clear that, when co-operation goes from university level to global it has
significant benefits.
For example, why do scientists co-operate? Science has become more and more isolated and each science field has spread even more
and it is impossible for one person to be aware of all developments. This is true especially in experimental research where highly
advanced equipment requires special skills. With co-operation scientists can use information from different fields and use it
effectively instead of gathering all the informationjust by reading by themselves."[104]
Coordination
Ad-hoc communities
Military, 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
[139]
companies are using collective intelligence to bypass traditional marketing and creative agencies.
The UNU open 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.[140][141]
When connected to UNU, groups of distributed users collectively answer questions and make predictions in real-time.[142] Early
testing shows that human swarms can out-predict individuals.[140] In 2016, an UNU swarm was challenged by a reporter to predict
, beating 540 to 1 odds.[143][144]
the winners of the Kentucky Derby, and successfully picked the first four horses, in order
Specialized information sites such as Digital Photography Review[145] or Camera Labs[146] 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.
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.[147][148][149] 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". 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)
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.[117] 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,[150] he refers to Pierre Lévy's concept of Collective Intelligence (Lévy 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.[151] 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.
Growth of the Internet and mobile telecom has also produced "swarming" or
"rendezvous" events that enable meetings or even dates on demand.[22] The full
impact has yet to be felt but the anti-globalization movement, for example, relies
ganizing.[154] The
heavily on e-mail, cell phones, pagers, SMS and other means of or
Indymedia organization does this in a more journalistic way.[155] Such resources The cast of After School Improv
could combine into a form of collective intelligence accountable only to the current learns an important lesson about
participants yet with some strong moral or linguistic guidance from generations of improvisation and life
contributors – or even take on a more obviously democratic form to advance shared
goal.[155]
A further application of collective intelligence is found in the "Community Engineering for Innovations".[156] In such an integrated
framework proposed by Ebner et al., idea competitions and virtual communities are combined to better realize the potential of the
[157]
collective intelligence of the participants, particularly in open-source R&D.
Group collective intelligence is a property that emerges through coordination from both bottom-up and top-down processes. In a
bottom-up process the different characteristics of each member are involved in contributing and enhancing coordination. Top-down
processes are more strict and fixed with norms, group structures and routines that in their own way enhance the group's collective
work.[159]
Alternative views
Harsh critics of artificial intelligence on ethical grounds are likely to promote collective wisdom-building methods, such as the new
tribalists and the Gaians.[164] 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.[165]
In contrast to these views, Artificial Intelligence companies such as Amazon Mechanical Turk and CrowdFlower are using collective
intelligence and crowdsourcing or consensus-based assessment to collect the enormous amounts of data for machine learning
algorithms such as Keras and IBM Watson.
See also
Recommendation system Enterprise bookmarking
Smart mob Human-based computation
Similar concepts and
Social commerce Open-source software
applications Social information processing Organismic computing
Civic intelligence Stigmergy Preference elicitation
Collaborative filtering Syntality
Collaborative innovation network The Wisdom of Crowds
Collective decision-making Others
Think tank
Collective effervescence Customer engagement
Wiki
Collective memory Dispersed knowledge
Collective problem solving Distributed cognition
Crowd psychology Computation and Facilitation (business)
Global Consciousness Project computer science Facilitator
Group behaviour Bees algorithm Hundredth monkey effect
Group mind (science fiction) Cellular automaton Keeping up with the Joneses
Knowledge ecosystem Collaborative human interpreter Library
Noogenesis Collaborative software Library of Alexandria
Open source intelligence Connectivity (graph theory) Meme
Open-space meeting
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External links
Blog of Collective Intelligence
GFIS – Global Futures Intelligence System
CIRI – the Collective Intelligence Research Institute– a R&D non-profit organization on collective intelligence
An application of Collective Intelligence for the Global Climate Change Situation Room designed and implemented
by The Millennium Project in Gimcheon, South Korea in 2009.
MIT Handbook of Collective Intelligence
Cultivating Society's Civic IntelligenceDoug Schuler Journal of Society, Information and Communication, vol 4 No. 2.
Jennifer H. Watkins (2007). Prediction Markets as an Aggregation Mechanism for Collective Intelligence Los Alamos
National Laboratory article on Collective Intelligence
Hideyasu Sasaki (2010). International Journal of Organizational and Collective Intelligence (IJOCI)
, vol 1 No. 1.
Olivier Zara, Managing Collective Intelligence, Toward a New Corporate Governance, Axiopole editions, 2004
The collective intelligence framework, open-source framework for leveraging collective intelligence
Raimund Minichbauer (2012).Fragmented Collectives. On the Politics of "Collective Intelligence" in Electronic
Networks, transversal 01 12, 'unsettling knowledges'
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