Collective intelligence
Collective intelligence or group intelligence is the emergent ability of groups, whether composed of humans alone, animals, or networks of humans and artificial agents, to solve problems, make decisions, or generate knowledge more effectively than individuals alone, through either cooperation or by aggregation of diverse information, perspectives, and behaviors. The term swarm intelligence is sometimes used interchangeably with collective intelligence but is simply one instance of it.
Collective intelligence encompasses not only complex adaptive systems, which self-organize and adapt in dynamic environments, but also creative and cognitive processes observed in social groups, which are often referred to as the wisdom of crowds. In this context, collective judgments, sometimes from non-experts, often exceed the accuracy of expert predictions, as illustrated by Francis Galton's famous experiment on estimating the weight of an ox. Contemporary theorists have posited that intelligence can be interpreted as an emergent collective process that manifests across various biological and social scales, including neural, organismal, and group levels.
The term appears in sociobiology, political science and in the context of mass peer review and crowdsourcing applications. It may involve consensus decision-making, social capital and formalisms such as voting systems, social media and other means of quantifying mass activity. Collective intelligence should not be conflated with metaphysical theories of panpsychism or with claims about group consciousness. These concern the fundamental nature of the mind, and the possibility that consciousness is a ubiquitous or emergent property. The empirical study of collective intelligence, however, focuses on observable mechanisms by which groups coordinate information, tasks or problem-solving.
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.
Collective intelligence is attributed to bacteria and animals, but also algorithmic governance. It can be understood as an emergent property from the synergies among:
- data-information-knowledge
- software-hardware
- individuals that continually learn from feedback to produce just-in-time knowledge for better decisions than these three elements acting alone
Pierre Lévy defines collective intelligence as "a form of universally distributed intelligence, constantly enhanced, coordinated in real time, and resulting in the effective mobilization of skills. 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 Lévy and Derrick de Kerckhove, it refers to capacity of networked ICTs 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, 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 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, although the score is not a quotient per se.
History
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. Melissa Schwartzberg reinterprets Aristotle's discussion, arguing that Politics III.11 should be read not as a claim that collective judgement is superior in quality, but as a defence of democratic rule grounded in an equal capacity for political judgment among citizens. Her "equal judgment" reading shifts emphasis from epistemic arguments about the wisdom of crowds toward Aristotle's concern with the political value of equal participation, and cautions against using Aristotle as straightforward support for contemporary claims of superior collective accuracy.Modern collective intelligence theory began 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.
The concept is also 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 are inspired by the visions of a noosphere – a transcendent, rapidly evolving collective intelligence – an informational cortex of the planet.
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 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.
Brown and Lauder framed collective intelligence as a form of social capital: a social capacity rooted in institutions, cooperation, and shared problem-solving, rather than as an attribute of individuals alone. They presented it as an counterweight to ‘market individualism.’ Drawing on earlier critiques of IQ-based conceptions of intelligence, Brown and Lauder emphasised that intelligence is an achievement shaped by social conditions, and that structural inequalities can restrict groups’ ability to develop and mobilise collective intelligence, even when cognitive potential is widely distributed. They recommended deliberately embedding it: “The struggle for collective intelligence involves more than changing the way we think about our own capacities and our relationship to society, it also includes weaving the potential of collective intelligence into the very fabric of our society.”
Work in the early 2000s examined the informational conditions required for collective intelligence to emerge in digitally networked groups. Jean-François Noubel introduced the concept of holopticism to describe communication architectures in which participants have shared visibility of one another's contributions and of the group's evolving state, arguing that such structures enhance a group's capacity to integrate dispersed knowledge and coordinate collective problem-solving. This line of thinking parallels other theoretical accounts that emphasise common awareness, transparent feedback and shared cognitive artefacts as enabling mechanisms for collective intelligence.
Pierre Lévy was an influential early theorist who reframed collective intelligence as a civilisation-level phenomenon enabled by networked media and shared symbolic tools. In his work he argued that the emerging information environment makes possible new forms of distributed cognition and collective sense-making, and he proposed a methodological program of "positive interpretation" to study how collectively produced meanings and problem-solving capacities can be cultivated and governed in cyberspace. Lévy's account emphasises cultural and semantic infrastructure, i.e. common languages, ontologies and mediation tools, that allow dispersed individuals to coordinate, accumulate knowledge and engage in large-scale cooperative intelligence. His ideas, first widely circulated in his 1990s writings and reiterated in later essays and reviews, have been influential in shaping debates about the digital foundations of collective intelligence and about how institutions and technologies can be designed to support collective reflection and decision-making.
Howard Bloom discussed mass behavior – collective behavior –
from the level of quarks to the level of bacterial, plant, animal, and human societies. He stressed the biological adaptations that have turned most of this earth's living beings into components of what he calls "a learning machine". 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.
Tom Atlee focused primarily on humans and on work to upgrade what Howard Bloom calls "the group IQ". Atlee felt 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 stated that "collective intelligence also involves achieving a single focus of attention and standard of metrics which provide an appropriate threshold of action". Their approach is rooted in scientific community metaphor.
Atlee and Pór suggested 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.
In 2008, Don Tapscott and Anthony D. Williams, proposed that collective intelligence is mass collaboration, and four conditions are needed to enable it.:
- 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 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.