The Institute for the Future (IFTF) is a Palo Alto, California, US–based not-for-profit think tank. It was established, in 1968, as a spin-off from the RAND Corporation to help organizations plan for the long-term future, a subject known as futures studies.
History
Genesis
First references to the idea of an Institute for the Future may be found in a 1966 Prospectus by Olaf Helmer and others. While at RAND Corporation, Helmer had already been involved with developing the Delphi method of futures studies.
He, and others, wished to extend the work further with an emphasis on
examining multiple scenarios. This can be seen in the prospectus
summary:
To explore systematically the possible futures for our [USA] nation and for the international community.
To ascertain which among these possible futures seems desirable, and why.
To seek means by which the probability of their occurrence can be enhanced through appropriate purposeful action.
The Institute's work initially relied on the forecasting methods built upon by Helmer while at RAND. The Delphi method was used to glean information from multiple anonymous sources. It was augmented by Cross Impact Analysis, which encouraged analysts to consider multiple future scenarios.
While precise and powerful, the methods that had been developed
in a corporate environment were oriented to providing business and
economic analyses. At a 1971 conference on mathematical modelling Helmer noted the need for similar improvements in societal modelling.
Early attempts at doing so included a "Future State of the Union"
report, formatted according to the traditional US Presidential address
to the Nation.
Despite establishing an excellent reputation for painstaking
analysis of future analyses and forecasting methods, various problems
meant that the Institute struggled to find its footing at first. In 1970
Helmer took over the leadership from Davidson, and the Institute
shifted its headquarters to Menlo Park, California.
In 1971 Roy Amara took over from Helmer, who continued to run the Middletown office until his departure in 1973.3
Amara held this position until 1990. During Amara's presidency, the
Institute conducted some of the earliest studies of the impact of the ARPANET
on collaborative work and scientific research, and was notable for its
research on computer mediated communications, also known as groupware.
In
1975 the Corporate Associates Program was started to assist private
organisations interpret emerging trends and the long-term consequences.
Although this program operated until 2001, its role as the Institute's
main reporting tool was superseded by the Ten Year Forecast in 1978.
In 1984 the sociologist
Herbert L Smith noted that, by the late 1970s, the idea of an open
Union reporting format had given way to the proprietary Ten Year
Forecast. Smith interpreted this as a renewed focus on business
forecasting as public funds became scarce.
It is not clear how pertinent Smith's observations were to how
the Institute was operating in this period. Sociologists such as Bob
Johansen continued to be active in the Institute's projects. Having
taken part in early ARPANET development, Institute staff were well aware
of the impact that computer networking would have on society and its
inclusion in policy making.
However, in a 1984 essay, Roy Amara appeared to acknowledge some form
of crisis, and a renewed interest in societal forecasting.
Evolution of societal forecasting
New
ways of presenting studies to a less specialised audience were adopted,
or developed. As an aid to memory retention, 'Vignetting' presented
future scenarios as short stories; to illustrate the point of the
scenario, and engage the reader's attention. Later initiatives showed an increasing emphasis on narrative engagement, e.g. 'Artifacts of the future', and 'Human-future interaction'.
Ethnographic
forecasting was adopted as it became recognised that "society" was
actually a myriad of sub-cultures, each with its own outlook.
While older forecasting methods sought the advice of field
experts, newer techniques sought the statistical input from all members
of society. Public interaction, provided via the internet and social
media, made it possible to engage in "bottom up forecasting".
While roleplaying and simulation games had long been part of a
forecaster's tools, they could now be scaled up into "massively
multiplayer forecasting games" such as Superstruct. This game enlisted
the blogs and wikis of over 5,000 people to discuss life 10 years in
the future; presenting them with a set of hypothetical, overlapping
social threats, and encouraging them to seek collaborative "superstruct"
solutions. The concept of the superstruct was subsequently incorporated into the Institute's 'Foresight Engine' tool.
Work
The Institute
maintains research programs on the futures of technology, health, and
organizations. It publishes a variety of reports and maps, as well as Future Now, a blog on emerging technologies. It offers three programs to its clients:
The Ten year forecast is the Institute's signature piece, having operated since 1978. It tracks today's latent signals, and forecasts what they might mean for business in ten years' time.
The Technology Horizons program, beginning around 2004,escribed by the Institute as "combining a deep understanding of
technology and societal forces to identify and evaluate discontinuities
and innovations in the next three to ten years".
The Health horizons program has operated since 2005. The Institute describes its purpose as "seeking more resilient responses for the complex challenges facing global health".
In 2014 the Institute moved its headquarters to 201 Hamilton Avenue, Palo Alto, California.
The Institute's annual publication Future Now is intended
to provide summaries of the Institute's body of research. The inaugural
edition was published in February 2017. Its theme The New Body Language concentrated on the Technology Horizons Program's studies on human and machine symbiosis.
Guidestar, the largest information source on nonprofit organizations and private foundations in the United States, gave Institute for the Future a Gold Transparency rating for 2023. Charity Navigator, the world's largest evaluator of nonprofits,
gave it a score of 83% earning it a rating of three out of four stars,
stating, "If this organization aligns with your passions and values, you
can give with confidence."
Collective intelligence (CI) is shared or group intelligence (GI) 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 and animals.
individuals (those with new insights as well as recognized
authorities) that continually learn from feedback to produce
just-in-time knowledge for better decisions than these three elements
acting alone
Or it can be more narrowly understood as an emergent property between people and ways of processing information. This notion of collective intelligence is referred to as "symbiotic intelligence" by Norman Lee Johnson. 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." 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. A broader definition was provided by Geoff Mulgan in a series of lectures and reports from 2006 onwards and in the book Big Mind which proposed a framework for analysing any thinking system, including
both human and machine intelligence, in terms of functional elements
(observation, prediction, creativity, judgement etc.), learning loops
and forms of organisation. The aim was to provide a way to diagnose, and
improve, the collective intelligence of a city, business, NGO or
parliament.
Collective intelligence strongly contributes to the shift of
knowledge and power from the individual to the collective. According to Eric S. Raymond in 1998 and JC Herz in 2005, open-source intelligence will eventually generate superior outcomes to knowledge generated by proprietary software developed within corporations. 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. Both Pierre Lévy and Henry Jenkins 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. Definition, operationalization and statistical methods are derived from g. Similarly as g is highly interrelated with the concept of IQ,
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.
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). 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.Recent scholarship,
however, suggests that this was probably not what Aristotle 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 in 1910 that seemingly independent individuals can
cooperate so closely as to become indistinguishable from a single
organism. 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. Other antecedents are Vladimir Vernadsky and Pierre Teilhard de Chardin's concept of "noosphere" and H.G. Wells's concept of "world brain". Peter Russell, Elisabet Sahtouris, and Barbara Marx Hubbard (originator of the term "conscious evolution")
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".
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.
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.
Collective intelligence was introduced into the machine learning community in the late 20th century,
and matured into a broader consideration of how to design "collectives"
of self-interested adaptive agents to meet a system-wide goal. This was related to single-agent work on "reward shaping" and has been taken forward by numerous researchers in the game theory and engineering communities.
Dimensions
Complex adaptive systems model
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.
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.
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. 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 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."
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.
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.
The idea is that a measure of collective intelligence covers a broad
range of features of the group, mainly group composition and group
interaction.
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.
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.
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.
Stigmergic Collaboration: a theoretical framework for mass collaboration
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.
Collective intelligence factor c
Scree plot showing percent of explained variance for the first factors in Woolley et al.'s two original studies in 2010
A new scientific understanding of collective intelligence defines it
as a group's general ability to perform a wide range of tasks.[17] 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 proposed by English psychologist Charles Spearman and extracted via factor analysis. 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' (also called 'collective intelligence factor' (CI))
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
even though the concept is hoped to be transferable to other
performances and any groups or crowds reaching from families to
companies and even whole cities. Since individuals' g factor scores are highly correlated with full-scale IQ scores, which are in turn regarded as good estimates of 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. 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. The concept is in contrast to competing hypotheses including other correlational structures to explain group intelligence, such as a composition out of several equally important but independent factors as found in individual personality research.
Besides, this scientific idea also aims to explore the causes
affecting collective intelligence, such as group size, collaboration
tools or group members' interpersonal skills. The MIT Center for Collective Intelligence, for instance, announced the detection of The Genome of Collective Intelligence
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".
Causes
Individual intelligence is shown to be genetically and environmentally influenced.
Analogously, collective intelligence research aims to explore reasons
why certain groups perform more intelligently than other groups given
that c is just moderately correlated with the intelligence of individual group members. According to Woolley et al.'s results, neither team cohesion nor motivation or satisfaction is 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).
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". Hence, providing multiple team members the chance to speak up made a group more intelligent.
Group members' social sensitivity was measured via the Reading the Mind in the Eyes Test (RME) and correlated .26 with c.
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' or 'mind reading',
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. RME is a ToM test for adults that shows sufficient test-retest reliability and constantly differentiates control groups from individuals with functional autism or Asperger Syndrome. It is one of the most widely accepted and well-validated tests for ToM within adults. ToM can be regarded as an associated subset of skills and abilities within the broader concept of emotional intelligence.
The proportion of females as a predictor of c was largely mediated by social sensitivity (Sobel z = 1.93, P= 0.03) which is in vein with previous research showing that women score higher on social sensitivity tests. While a mediation, statistically speaking, clarifies the mechanism underlying the relationship between a dependent and an independent variable, 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. However, she relativizes this stating that the actual important thing is the high social sensitivity of group members.
It is theorized that the collective intelligence factor c is an emergent property resulting from bottom-up as well as top-down processes.
Hereby, bottom-up processes cover aggregated group-member
characteristics. Top-down processes cover group structures and norms
that influence a group's way of collaborating and coordinating.
Processes
Predictors for the collective intelligence factor c. Suggested by Woolley, Aggarwal & Malone (2015)
Top-down processes
Top-down processes cover group interaction, such as structures, processes, and norms. An example of such top-down processes is conversational turn-taking.
Research further suggest that collectively intelligent groups
communicate more in general as well as more equally; same applies for
participation and is shown for face-to-face as well as online groups
communicating only via writing.
Bottom-up processes
Bottom-up processes include group composition, namely the characteristics of group members which are aggregated to the team level.
An example of such bottom-up processes is the average social
sensitivity or the average and maximum intelligence scores of group
members. Furthermore, collective intelligence was found to be related to a group's cognitive diversity including thinking styles and perspectives. Groups that are moderately diverse in cognitive style
have higher collective intelligence than those who are very similar in
cognitive style or very different. Consequently, groups where members
are too similar to each other lack the variety of perspectives and
skills needed to perform well. On the other hand, groups whose members
are too different seem to have difficulties to communicate and
coordinate effectively.
Serial vs Parallel processes
For
most of human history, collective intelligence was confined to small
tribal groups in which opinions were aggregated through real-time
parallel interactions among members.
In modern times, mass communication, mass media, and networking
technologies have enabled collective intelligence to span massive
groups, distributed across continents and time-zones. To accommodate
this shift in scale, collective intelligence in large-scale groups been
dominated by serialized polling processes such as aggregating up-votes,
likes, and ratings over time. While modern systems benefit from larger
group size, the serialized process has been found to introduce
substantial noise that distorts the collective output of the group. In
one significant study of serialized collective intelligence, it was
found that the first vote contributed to a serialized voting system can
distort the final result by 34%.
To address the problems of serialized aggregation of input among
large-scale groups, recent advancements collective intelligence have
worked to replace serialized votes, polls, and markets, with parallel
systems such as "human swarms" modeled after synchronous swarms in nature. Based on natural process of Swarm Intelligence,
these artificial swarms of networked humans enable participants to work
together in parallel to answer questions and make predictions as an
emergent collective intelligence.
In one high-profile example, a human swarm challenge by CBS Interactive
to predict the Kentucky Derby. The swarm correctly predicted the first
four horses, in order, defying 542–1 odds and turning a $20 bet into
$10,800.
The value of parallel collective intelligence was demonstrated in medical applications by researchers at Stanford University School of Medicine and Unanimous AI
in a set of published studies wherein groups of human doctors were
connected by real-time swarming algorithms and tasked with diagnosing
chest x-rays for the presence of pneumonia.
When working together as "human swarms," the groups of experienced
radiologists demonstrated a 33% reduction in diagnostic errors as
compared to traditional methods.
Evidence
Standardized Regression Coefficients for the collective intelligence factor c as found in Woolley et al.'s (2010) two original studies. c and average (maximum) member intelligence scores are regressed on the criterion tasks.
Woolley, Chabris, Pentland, Hashmi, & Malone (2010),
the originators of this scientific understanding of collective
intelligence, found a single statistical factor for collective
intelligence in their research across 192 groups with people randomly
recruited from the public. In Woolley et al.'s two initial studies,
groups worked together on different tasks from the McGrath Task Circumplex,
a well-established taxonomy of group tasks. Tasks were chosen from all
four quadrants of the circumplex and included visual puzzles,
brainstorming, making collective moral judgments, and negotiating over
limited resources. The results in these tasks were taken to conduct a factor analysis. Both studies showed support for a general collective intelligence factor c
underlying differences in group performance with an initial eigenvalue
accounting for 43% (44% in study 2) of the variance, whereas the next
factor accounted for only 18% (20%). That fits the range normally found
in research regarding a general individual intelligence factor g typically accounting for 40% to 50% percent of between-individual performance differences on cognitive tests.
Afterwards, a more complex task was solved by each group to determine whether c factor scores predict performance on tasks beyond the original test. Criterion tasks were playing checkers (draughts) against a standardized computer in the first and a complex architectural design task in the second study. In a regression analysis using both individual intelligence of group members and c to predict performance on the criterion tasks, c
had a significant effect, but average and maximum individual
intelligence had not. While average (r=0.15, P=0.04) and maximum
intelligence (r=0.19, P=0.008) of individual group members were
moderately correlated with c, c was still a much better
predictor of the criterion tasks. According to Woolley et al., this
supports the existence of a collective intelligence factor c, because it demonstrates an effect over and beyond group members' individual intelligence and thus that c is more than just the aggregation of the individual IQs or the influence of the group member with the highest IQ.
Engel et al.
(2014) replicated Woolley et al.'s findings applying an accelerated
battery of tasks with a first factor in the factor analysis explaining
49% of the between-group variance in performance with the following
factors explaining less than half of this amount. Moreover, they found a
similar result for groups working together online communicating only
via text and confirmed the role of female proportion and social
sensitivity in causing collective intelligence in both cases. Similarly
to Wolley et al.,
they also measured social sensitivity with the RME which is actually
meant to measure people's ability to detect mental states in other
peoples' eyes. The online collaborating participants, however, did
neither know nor see each other at all. The authors conclude that scores
on the RME must be related to a broader set of abilities of social
reasoning than only drawing inferences from other people's eye
expressions.
A collective intelligence factor c in the sense of Woolley et al. was further found in groups of MBA students working together over the course of a semester, in online gaming groups as well as in groups from different cultures and groups in different contexts in terms of short-term versus long-term groups. None of these investigations considered team members' individual intelligence scores as control variables.
Note as well that the field of collective intelligence research
is quite young and published empirical evidence is relatively rare yet.
However, various proposals and working papers are in progress or already
completed but (supposedly) still in a scholarly peer reviewing publication process.
Predictive validity
Individual intelligence can be used to predict plenty of life outcomes from school attainment and career success to health outcomes and even mortality.
Whether collective intelligence is able to predict other outcomes
besides group performance on mental tasks has still to be investigated.
Potential connections to individual intelligence
Gladwell
(2008) showed that the relationship between individual IQ and success
works only to a certain point and that additional IQ points over an
estimate of IQ 120 do not translate into real life advantages. If a
similar border exists for Group-IQ or if advantages are linear and
infinite, has still to be explored. Similarly, demand for further
research on possible connections of individual and collective
intelligence exists within plenty of other potentially transferable
logics of individual intelligence, such as, for instance, the
development over time or the question of improving intelligence. Whereas it is controversial whether human intelligence can be enhanced via training,
a group's collective intelligence potentially offers simpler
opportunities for improvement by exchanging team members or implementing
structures and technologies. Moreover, social sensitivity was found to be, at least temporarily, improvable by reading literary fiction as well as watching drama movies. In how far such training ultimately improves collective intelligence through social sensitivity remains an open question.
There are further more advanced concepts and factor models
attempting to explain individual cognitive ability including the
categorization of intelligence in fluid and crystallized intelligence or the hierarchical model of intelligence differences. Further supplementing explanations and conceptualizations for the factor structure of the Genomes of collective intelligence besides a general 'c factor', though, are missing yet.
Controversies
Other scholars explain team performance by aggregating team members' general intelligence to the team level instead of building an own overall collective intelligence measure. Devine and Philips
(2001) showed in a meta-analysis that mean cognitive ability predicts
team performance in laboratory settings (0.37) as well as field settings
(0.14) – note that this is only a small effect. Suggesting a strong
dependence on the relevant tasks, other scholars showed that tasks
requiring a high degree of communication and cooperation are found to be
most influenced by the team member with the lowest cognitive ability.
Tasks in which selecting the best team member is the most successful
strategy, are shown to be most influenced by the member with the highest
cognitive ability.
Since Woolley et al.'s results do not show any influence of group satisfaction, group cohesiveness,
or motivation, they, at least implicitly, challenge these concepts
regarding the importance for group performance in general and thus
contrast meta-analytically proven evidence concerning the positive
effects of group cohesion,motivation and satisfaction 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.
Alternative mathematical techniques
Computational collective intelligence
Computational Collective Intelligence, by Tadeusz Szuba
In 2001, Tadeusz (Tad) Szuba from the AGH University
in Poland proposed a formal model for the phenomenon of collective
intelligence. It is assumed to be an unconscious, random, parallel, and
distributed computational process, run in mathematical logic by the
social structure.
In this model, beings and information are modeled as abstract information molecules carrying expressions of mathematical logic. They are quasi-randomly displacing due to their interaction with their environments with their intended displacements.
Their interaction in abstract computational space creates multi-thread
inference process which we perceive as collective intelligence. Thus, a non-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.
Collective intelligence quotient
One
measure sometimes applied, especially by more artificial intelligence
focused theorists, is a "collective intelligence quotient" (or "cooperation quotient") – which can be normalized from the "individual" intelligence quotient (IQ) – thus making it possible to determine the marginal intelligence added by each new individual participating in the collective action, thus using metrics to avoid the hazards of group think and stupidity.
Applications
There
have been many recent applications of collective intelligence,
including in fields such as crowd-sourcing, citizen science and
prediction markets. The Nesta Centre for Collective Intelligence Design
was launched in 2018 and has produced many surveys of applications as
well as funding experiments. In 2020 the UNDP Accelerator Labs began using collective intelligence methods in their work to accelerate innovation for the Sustainable Development Goals.
Elicitation of point estimates
Here,
the goal is to get an estimate (in a single value) of something. For
example, estimating the weight of an object, or the release date of a
product or probability of success of a project etc. as seen in
prediction markets like Intrade, HSX or InklingMarkets and also in
several implementations of crowdsourced estimation of a numeric outcome
such as the Delphi method. Essentially, we try to get the average value of the estimates provided by the members in the crowd.
Opinion aggregation
In
this situation, opinions are gathered from the crowd regarding an idea,
issue or product. For example, trying to get a rating (on some scale)
of a product sold online (such as Amazon's star rating system). Here,
the emphasis is to collect and simply aggregate the ratings provided by
customers/users.
A similar approach is used in political science, where the
opinions collected from different media such as Facebook, Twitter,
Twitter Sentiment, YouTube, Google are aggregated via simple averaging
or factor analysis to study or predict elections such as Obama's in 2012
or Trump's in 2016.
Opinion aggregation is also the basis of other election prediction
studies, which adopt this approach to assess the forecasting accuracy of
groups consisting of political scientists, journalists, citizens, and
the wider public.
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). 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.
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.
Websites aggregate stock market information that is as current as
possible so professional or amateur stock analysts can publish their
viewpoints, enabling amateur investors to submit their financial
opinions and create an aggregate opinion.
The opinion of all investor can be weighed equally so that a pivotal
premise of the effective application of collective intelligence can be
applied: the masses, including a broad spectrum of stock market
expertise, can be utilized to more accurately predict the behavior of
financial markets.
Collective intelligence underpins the efficient-market hypothesis of Eugene Fama – although the term collective intelligence is not used explicitly in his paper. Fama cites research conducted by Michael Jensen
in which 89 out of 115 selected funds underperformed relative to the
index during the period from 1955 to 1964. But after removing the
loading charge (up-front fee) only 72 underperformed while after
removing brokerage costs only 58 underperformed. On the basis of such
evidence index funds
became popular investment vehicles using the collective intelligence of
the market, rather than the judgement of professional fund managers, as
an investment strategy.
Predictions in politics and technology
Voting methods used in the United States 2016
Political parties mobilize large numbers of people to form policy, select candidates, and finance and run election campaigns. Knowledge focusing through various voting
methods allows perspectives to converge through the assumption that
uninformed voting is to some degree random and can be filtered from the
decision process leaving only a residue of informed consensus.
Critics point out that often bad ideas, misunderstandings, and
misconceptions are widely held, and that structuring of the decision
process must favor experts who are presumably less prone to random or
misinformed voting in a given context.
Companies such as Affinnova (acquired by Nielsen), Google, InnoCentive, Marketocracy, and Threadless
have successfully employed the concept of collective intelligence in
bringing about the next generation of technological changes through
their research and development (R&D), customer service, and
knowledge management.
An example of such application is Google's Project Aristotle in 2012,
where the effect of collective intelligence on team makeup was examined
in hundreds of the company's R&D teams.
Cooperation
Networks of trust
Application of collective intelligence in the Millennium Project
In 2012, the Global Futures Collective Intelligence System (GFIS) was created by The Millennium Project,
which epitomizes collective intelligence as the synergistic
intersection among data/information/knowledge, software/hardware, and
expertise/insights that has a recursive learning process for better
decision-making than the individual players alone.
New media
are often associated with the promotion and enhancement of collective
intelligence. The ability of new media to easily store and retrieve
information, predominantly through databases and the Internet, allows
for it to be shared without difficulty. Thus, through interaction with
new media, knowledge easily passes between sources
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. The driving force of this Internet-based collective intelligence is the digitization of information and communication. Henry Jenkins,
a key theorist of new media and media convergence draws on the theory
that collective intelligence can be attributed to media convergence and
participatory culture.
He criticizes contemporary education for failing to incorporate online
trends of collective problem solving into the classroom, stating
"whereas a collective intelligence community encourages ownership of
work as a group, schools grade individuals". Jenkins argues that
interaction within a knowledge community builds vital skills for young
people, and teamwork through collective intelligence communities
contribute to the development of such skills. Collective intelligence is not merely a quantitative contribution of information from all cultures, it is also qualitative.
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. With the development of the Internet and its widespread use, the opportunity to contribute to knowledge-building communities, such as Wikipedia,
is greater than ever before. These computer networks give participating
users the opportunity to store and to retrieve knowledge through the
collective access to these databases and allow them to "harness the
hive" Researchers at the MIT Center for Collective Intelligence research and explore collective intelligence of groups of people and computers.
In this context collective intelligence is often confused with shared knowledge.
The former is the sum total of information held individually by members
of a community while the latter is information that is believed to be
true and known by all members of the community. Collective intelligence as represented by Web 2.0 has less user engagement than collaborative intelligence.
An art project using Web 2.0 platforms is "Shared Galaxy", an
experiment developed by an anonymous artist to create a collective
identity that shows up as one person on several platforms like MySpace,
Facebook, YouTube and Second Life. The password is written in the
profiles and the accounts named "Shared Galaxy" are open to be used by
anyone. In this way many take part in being one.
Another art project using collective intelligence to produce artistic
work is Curatron, where a large group of artists together decides on a
smaller group that they think would make a good collaborative group. The
process is used based on an algorithm computing the collective
preferences
In creating what he calls 'CI-Art', Nova Scotia based artist Mathew
Aldred follows Pierry Lévy's definition of collective intelligence.
Aldred's CI-Art event in March 2016 involved over four hundred people
from the community of Oxford, Nova Scotia, and internationally. Later work developed by Aldred used the UNU swarm intelligence system to create digital drawings and paintings.
The Oxford Riverside Gallery (Nova Scotia) held a public CI-Art event
in May 2016, which connected with online participants internationally.
Parenting social network and collaborative tagging as pillars for automatic IPTV content blocking system
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.
Although there is no central controlled vocabulary to constrain the
actions of individual users, the distributions of tags that describe
different resources has been shown to converge over time to a stable power law distributions. Once such stable distributions form, examining the 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.
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.
P2P business
Research performed by Tapscott and Williams has provided a few examples of the benefits of collective intelligence to business:
Talent utilization
At the rate technology is changing, no firm can fully keep up in the
innovations needed to compete. Instead, smart firms are drawing on the
power of mass collaboration to involve participation of the people they
could not employ. This also helps generate continual interest in the
firm in the form of those drawn to new idea creation as well as
investment opportunities.
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.
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.
Open-source software
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".
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 and Bray and Konsynski, 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"
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.
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 information just by reading by themselves."
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 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. When connected to UNU, groups of distributed users collectively answer questions and make predictions in real-time. Early testing shows that human swarms can out-predict individuals.
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.
Specialized information sites 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.
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.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.
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. 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, he refers to Pierre Lévy's concept of Collective Intelligence 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.
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.
Coordinating collective actions
The cast of After School Improv learns an important lesson about improvisation and life.
Improvisational actors also experience a type of collective
intelligence which they term "group mind", as theatrical improvisation
relies on mutual cooperation and agreement, leading to the unity of "group mind".
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. 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 goal.
A further application of collective intelligence is found in the "Community Engineering for Innovations".
In such an integrated framework proposed by Ebner et al., idea
competitions and virtual communities are combined to better realize the
potential of the collective intelligence of the participants,
particularly in open-source R&D.
In management theory the use of collective intelligence and crowd
sourcing leads to innovations and very robust answers to quantitative
issues.
Therefore, collective intelligence and crowd sourcing is not
necessarily leading to the best solution to economic problems, but to a
stable, good solution.
Coordination in different types of tasks
Collective
actions or tasks require different amounts of coordination depending on
the complexity of the task. Tasks vary from being highly independent
simple tasks that require very little coordination to complex
interdependent tasks that are built by many individuals and require a
lot of coordination. In the article written by Kittur, Lee and Kraut the
writers introduce a problem in cooperation: "When tasks require high
coordination because the work is highly interdependent, having more
contributors can increase process losses, reducing the effectiveness of
the group below what individual members could optimally accomplish".
Having a team too large the overall effectiveness may suffer even when
the extra contributors increase the resources. In the end the overall
costs from coordination might overwhelm other costs.
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.
Alternative views
A tool for combating self-preservation
Tom
Atlee reflects that, although humans have an innate ability to gather
and analyze data, they are affected by culture, education and social
institutions.[177][self-published source?]
A single person tends to make decisions motivated by self-preservation.
Therefore, without collective intelligence, humans may drive themselves
into extinction based on their selfish needs.[46]
Separation from IQism
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. This reflects how powerful collective intelligence can be if left to develop.
Artificial intelligence views
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 train 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.
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