Collective intelligence

Types of collective intelligence

Collective intelligence is a shared or group intelligence that emerges from the collaboration and competition of many individuals and appears in consensus decision making in bacteria, animals, humans and computer networks.

The idea emerged from the writings of Douglas Hofstadter (1979), Peter Russell (1983), Tom Atlee (1993), Pierre Lévy (1994), Howard Bloom (1995), Francis Heylighen (1995), Douglas Engelbart, Cliff Joslyn, Ron Dembo, Gottfried Mayer-Kress (2003) and other theorists. Collective intelligence is referred to as Symbiotic intelligence by Norman Lee Johnson.^[1] The concept is relevant in sociology, business, computer science and mass communications: it also appears in science fiction, frequently in the form of telepathically-linked species and cyborgs.

History

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). 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.^[2] Other antecedents are Vladimir Vernadsky's concept of "noosphere" and H.G. Wells's concept of "world brain" (see also the term "global brain"). Peter Russell, Elisabet Sahtouris, and Barbara Marx Hubbard (originator of the term "conscious evolution"^{[citation needed]}) are inspired by the visions of a noosphere — a transcendent, rapidly evolving collective intelligence — an informational cortex of the planet. The notion has more recently been examined by the philosopher Pierre Lévy.

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.^[3] 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.^[4]

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.^[4] 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^[5] 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."^[6] Atlee and Pór state that "collective intelligence also involves achieving a single focus of attention and standard of metrics which provide an appropriate threshold of action". Their approach is rooted in Scientific Community Metaphor.

Atlee and Pór suggest that the field of collective intelligence should primarily be seen as a human enterprise in which mind-sets, a willingness to share and an openness to the value of distributed intelligence for the common good are paramount, though group theory and artificial intelligence have something to offer. Individuals who respect collective intelligence are confident of their own abilities and recognize that the whole is indeed greater than the sum of any individual parts.^{[citation needed]} Maximizing collective intelligence relies on the ability of an organization to accept and develop "The Golden Suggestion", which is any potentially useful input from any member. Groupthink often hampers collective intelligence by limiting input to a select few individuals or filtering potential Golden Suggestions without fully developing them to implementation.

Robert David Steele Vivas in The New Craft of Intelligence portrayed all citizens as "intelligence minutemen," drawing only on legal and ethical sources of information, able to create a "public intelligence" that keeps public officials and corporate managers honest, turning the concept of "national intelligence" (previously concerned about spies and secrecy) on its head.

According to Don Tapscott and Anthony D. Williams, collective intelligence is mass collaboration. In order for this concept to happen, four principles need to exist;

Openness

Sharing ideas and intellectual property: though these resources provide the edge over competitors more benefits accrue from allowing others to share ideas and gain significant improvement and scrutiny through collaboration.

Peering

Horizontal organization as with the ‘opening up’ of the Linux program where users are free to modify and develop it provided that they make it available for others. Peering succeeds because it encourages self-organization – a style of production that works more effectively than hierarchical management for certain tasks.

Sharing

Companies have started to share some ideas while maintaining some degree of control over others, like potential and critical patent rights. Limiting all intellectual property shuts out opportunities, while sharing some expands markets and brings out products faster.

Acting Globally

The advancement in communication technology has prompted the rise of global companies at low overhead costs. The internet is widespread, therefore a globally integrated company has no geographical boundaries and may access new markets, ideas and technology.^[7]

Examples

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.

Military units, 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.

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.^[8][9][10] Learner generated contexts represent an ad hoc community that facilitates coordination of collective action in a network of trust. An example of Learner generated context is found on the Internet when collaborative users pool knowledge in a "shared intelligence space" such as Wikipedia. As the Internet has developed so has the concept of CI as a shared public forum. The global accessibility and availability of the Internet has allowed more people than ever to contribute and access ideas. (Flew 2008)

Improvisational actors also experience a type of collective intelligence which they term 'Group Mind'. A further example of collective intelligence is found in idea competitions.^[11]

Mathematical techniques

One measure sometimes applied, especially by more artificial intelligence focused theorists, is a "collective intelligence quotient" (or "cooperation quotient")—which presumably can be measured like the "individual" intelligence quotient (IQ)—thus making it possible to determine the marginal extra intelligence added by each new individual participating in the collective, thus using metrics to avoid the hazards of group think and stupidity.

In 2001, Tadeusz (Ted) Szuba from the AGH University in Poland proposed a formal model for the phenomenon of collective intelligence. It is assumed to be an unconscious, random, parallel, and distributed computational process, run in mathematical logic by the social structure.^[12]

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.^[12]

Digital media

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, Valerie Turchin, and Gottfried Mayer-Kress are among those who view collective intelligence through the lens of computer science and cybernetics. Collective intelligence can be defined as a form of networking enabled by the internet. 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.^[13] The driving force of this form of 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 (Flew 2008). Collective intelligence is not merely a quantitative contribution of information from all cultures, it is also qualitative.

Levy and de Kerckhove consider CI from a mass communications perspective, focusing on the ability of networked ICT’s to enhance the community knowledge pool. They suggest that these communications tools enable humans to interact and to share and collaborate with both ease and speed (Flew 2008). With the development of the Internet and its widespread use, the opportunity to contribute to community-based knowledge forums, such as Wikipedia, is greater than ever before. These computer networks give participating users the opportunity to store and to retrieve knowledge through the collective access to these databases and allow them to “harness the hive” (Raymond 1998; Herz 2005 in Flew 2008). Researchers^[14] 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 knowledge that is generally available to all members of a community while the latter is information known by all members of a community.^[15] 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.

It has been argued that media, particularly central media, cannot promote intelligence, due to the inherent inability of Central media to adequately deal with complex issues such as the Environmental Crisis. See The IRG Solution - hierarchical incompetence and how to overcome it1984, argued, that Central media and government type hierarchical organizations. The book argued that collective intelligence could only emerge from vast informal networks of human interaction, something which Media do not promote.

Growth of the Internet and mobile telecom has also produced "swarming" or "rendezvous" events that enable meetings or even dates on demand. The full impact has yet to be felt but the anti-globalization movement, for example, relies heavily on e-mail, cell phones, pagers, SMS and other means of organizing. Atlee discusses the connections between these events and the political views that drive them. The Indymedia organization does this in a more journalistic way. Such resources could combine into a form of collective intelligence accountable only to the current participants yet with some strong moral or linguistic guidance from generations of contributors - or even take on a more obviously democratic form to advance shared goals.

Social bookmarking

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".

Recent research using data from the social bookmarking website Del.icio.us, has shown that collaborative tagging systems exhibit a form of complex systems (or self-organizing) dynamics.^[16] 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.^[16] 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.^[17] Such vocabularies can be seen as a form of collective intelligence, emerging from the decentralised actions of a community of users.

Video games

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.^[13] 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,^[18] he refers to Pierre Levy’s concept of Collective Intelligence (Levy 1998) and argues this is active in videogames as clans or guilds in MMORPG constantly work to achieve goals. Henry Jenkins proposes that the participatory cultures emerging between games producers, media companies, and the end-users mark a fundamental shift in the nature of media production and consumption. Jenkins argues that this new participatory culture arises at the intersection of three broad new media trends.^[19] Firstly, the development of new media tools/technologies enabling the creation of content. Secondly, the rise of subcultures promoting such creations, and lastly, the growth of value adding media conglomerates, which foster image, idea and narrative flow. Cultural theorist and online community developer, John Banks considered the contribution of online fan communities in the creation of the Trainz product. He argued that its commercial success was fundamentally dependant 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^[20] and Bray and Konsynski,^[21] 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”^[22] as events that happen outside the game reality “reach out” into the player’s lives in order to bring them together. Solving the game requires “the collective and collaborative efforts of multiple players”; thus the issue of collective and collaborative team play is essential to ARG. Gosney argues that the Alternate Reality genre of gaming dictates an unprecedented level of collaboration and “collective intelligence” in order to solve the mystery of the game.

Stock market predictions

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 beapplied: 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^[23] - although the term collective intelligence is not used explicitly in his paper. Fama cites research conducted by Michael Jensen^[24] in which 89 out of 115 selected funds underperformed relative to the index during the period from 1955 to 1964. But after removing the loading charge (up-front fee) only 72 underperformed while after removing brokerage costs only 58 underperformed. On the basis of such evidence index funds became popular investment vehicles using the collective intelligence of the market, rather than the judgement of professional fund managers, as an investment strategy.

Views

Tom Atlee reflects that, although humans have an innate ability to gather and analyze data, they are affected by culture, education and social institutions. A single person tends to make decisions motivated by self-preservation. In addition, humans lack a way to make choices that balance innovation and reality. Therefore, without collective intelligence, humans may drive themselves into extinction based on their selfish needs.^[25]

Phillip Brown and Hugh Lauder quotes Bowles and Gintis (1976) that in order to truly define collective intelligence, it is crucial to separate ‘intelligence’ from IQism. They go on to argue that intelligence is an achievement and can only be developed if allowed to. For example, earlier on, groups from the lower levels of society are severely restricted from aggregating and pooling their intelligence. This is because the elites fear that the collective intelligence would convince the people to rebel. If there is no such capacity and relations, there would be no infrastructure on which collective intelligence is built (Brown & Lauder 2000, p. 230). This reflects how powerful collective intelligence can be if left to develop.

Research performed by Tapscott and Williams has provided a few examples of the benefits of collective intelligence to business:

Talent Utilization

At the rate technology is changing, no firm can fully keep up in the innovations needed to compete. Instead, smart firms are drawing on the power of mass collaboration to involve participation of the people they could not employ.

Demand Creation

Firms can create a new market for complementary goods by engaging in open source community.

Costs Reduction

Mass collaboration can help to reduce costs dramatically. Firms can release a specific software or product to be evaluated or debugged by online communities. The results will be more personal, robust and error-free products created in a short amount of time and costs.^[7]

Skeptics, especially those critical of artificial intelligence and more inclined to believe that risk of bodily harm and bodily action are the basis of all unity between people, are more likely to emphasize the capacity of a group to take action and withstand harm as one fluid mass mobilization, shrugging off harms the way a body shrugs off the loss of a few cells. This strain of thought is most obvious in the anti-globalization movement and characterized by the works of John Zerzan, Carol Moore, and Starhawk, who typically shun academics. These theorists are more likely to refer to ecological and collective wisdom and to the role of consensus process in making ontological distinctions than to any form of "intelligence" as such, which they often argue does not exist, or is mere "cleverness".

Harsh critics of artificial intelligence on ethical grounds are likely to promote collective wisdom-building methods, such as the new tribalists and the Gaians. Whether these can be said to be collective intelligence systems is an open question. Some, e.g. Bill Joy, simply wish to avoid any form of autonomous artificial intelligence and seem willing to work on rigorous collective intelligence in order to remove any possible niche for AI.

See also

Bees algorithm Cellular automaton Civic intelligence Collaborative filtering Collaborative human interpreter Collaborative innovation network Collaborative intelligence Collaborative software and Wikis Collective action Collective consciousness Collective decision-making Collective effervescence Connectivity Crowd psychology Crowdsourcing Customer engagement

Cybernetics Distributed cognition Enterprise bookmarking Global brain Global Consciousness Project Group behaviour Group mind (science fiction) Facilitation and Facilitator Human-based computation Hundredth monkey effect Information Routing Group Keeping up with the Joneses Knowledge ecosystem Meme Noosphere

Open-space meeting Open source intelligence Public Intelligence Prediction Markets Preference elicitation Recommendation system Smart mob Social commerce Social information processing Stigmergy Superorganism Systems intelligence Swarm Intelligence The Wisdom of Crowds Think tank

Notes

1. ^ Norman Lee Johnson, Collective Science site
2. ^ Émile Durkheim, The Elementary Forms of Religious Life, 1912.
3. ^ Howard Bloom, The Lucifer Principle: A Scientific Expedition Into the Forces of History, 1995
4. ^ ^{a b} Howard Bloom, Global Brain: The Evolution of Mass Mind from the Big Bang to the 21st Century, 2000
5. ^ Skrbina, D., 2001, Participation, Organization, and Mind: Toward a Participatory Worldview, ch. 8, Doctoral Thesis, Centre for Action Research in Professional Practice, School of Management, University of Bath: England
6. ^ George Pór, Blog of Collective Intelligence
7. ^ ^{a b} Tapscott, D., & Williams, A. D. (2008). Wikinomics: How Mass Collaboration Changes Everything, USA: Penguin Group
8. ^ Luckin, R., du Boulay, B., Smith, H., Underwood, J., Fitzpatrick, G., Holmberg, J., Kerawalla, L., Tunley, H., Brewster, D. and Pearce, D. (2005), 'Using Mobile Technology to Create Flexible Learning Contexts '. Journal of Interactive Media in Education, 22.
9. ^ Luckin, R. (2006), Understanding Learning Contexts as Ecologies of Resources: From the Zone of Proximal Development to Learner Generated Contexts. Paper presented at the Proceedings of World Conference on Elearning in Corporate, Government, Healthcare, and Higher Education 2006.
10. ^ Luckin, R., Shurville, S. and Browne, T. (2007), 'Initiating elearning by stealth, participation and consultation in a late majority institution'. Organisational Transformation and Social Change, 3, 4, 317–332.
11. ^ Jan Marco Leimeister, Michael Huber, Ulrich Bretschneider, Helmut Krcmar (2009): Leveraging Crowdsourcing: Activation-Supporting Components for IT-Based Ideas Competition. In: Journal of Management Information Systems (2009), Volume: 26, Issue: 1, Publisher: M.E. Sharpe Inc., Pages: 197-224, ISSN: 07421222, DOI: 10.2753/MIS0742-1222260108 [1], Winfried Ebner; Jan Marco Leimeister; Helmut Krcmar (2009): Community Engineering for Innovations -The Ideas Competition as a method to nurture a Virtual Community for Innovations. In: R&D Management, 39 (4),pp 342-356 DOI: 10.1111/j.1467-9310.2009.00564.x [2]
12. ^ ^{a b} Szuba T., Computational Collective Intelligence, 420 pages, Wiley NY, 2001
13. ^ ^{a b} Weiss, A. (2005). The Power of Collective Intelligence. Collective Intelligence, pp. 19-23
14. ^ http://cci.mit.edu/people/index.html
15. ^ Jenkins, H. 2006. Fans, Bloggers and Gamers: Exploring Participatory Culture. New York: New York University Press.
16. ^ ^{a b} Harry Halpin, Valentin Robu, Hana Shepherd The Complex Dynamics of Collaborative Tagging, Proceedings 6th International Conference on the World Wide Web (WWW'07), Banff, Canada, pp. 211-220, ACM Press, 2007.
17. ^ Valentin Robu, Harry Halpin, Hana Shepherd Emergence of consensus and shared vocabularies in collaborative tagging systems, ACM Transactions on the Web (TWEB), Vol. 3(4), article 14, ACM Press, September 2009.
18. ^ Flew, Terry and Humphreys, Sal (2005) “Games: Technology, Industry, Culture” in Terry Flew, New Media: An Introduction (2nd edn), Oxford University Press, South Melbourne 101-114.
19. ^ Henry Jenkins (2002) in Flew, Terry and Humphreys, Sal (2005) Games: Technology, Industry, Culture in Terry Flew, New Media: An Introduction (2nd edn), Oxford University Press, South Melbourne 101-114.
20. ^ L, Lessig,(2006)Code Version 2.0 (2nd ed.). New York: Basic Books.
21. ^ Bray, DA & Konsynski, BR, 2007, Virtual Worlds, Virtual Economies, Virtual Institutions, viewed 10th October 2008, p. 1-27 <http://ssrn.com/abstract=962501>
22. ^ Gosney, J.W, 2005, Beyond Reality: A Guide to Alternate Reality Gaming, Thomson Course Technology, Boston.
23. ^ Fama, E.F., (1970), Efficient Capital Markets: A Review of Theory and Empirical Work, Journal of Finance, Vol. 25 No. 2, pp. 383 – 417
24. ^ Jensen, M.C, (1967), The Performance of Mutual Funds in the Period 1945-1964, Journal of Finance, Vol. 23, No. 2, pp. 389-416, 1967
25. ^ Atlee, T. (2008). Reflections on the evolution of choice and collective intelligence, Retrieved August 26, 2008

References

• Hofstadter, Douglas (1979). Gödel, Escher, Bach: an Eternal Golden Braid. New York: Basic Books. 
• Brown, Philip; Lauder, Hugh (2000). "Collective intelligence". In S. Baron, J. Field & T Schuller. Social Capital: Critical Perspectives. New York: Oxford University Press. 
• Brown, Philip; Lauder, Hugh (2001). "Collective intelligence (chapter 13)". In Brown & Lauder. Capitalism and social progress: the future of society in a global economy. Palgrave. 
• CIA. (2008). The World Factbook. (accessed 3 September 2008)
• Flew, Terry (2008). New Media: an introduction. Melbourne: Oxford University Press. 
• Leiner, Barry, Cerf, Vinton, Clark, David, Kahn, Robert, Kleinrock, Leonard, Lynch, Daniel, Postel, Jon, Roberts, Larry and Wolff, Stephen. 2003. A Brief History of the Internet. Version 3.32 (accessed 3 September 2008)
• Rheingold, Howard (2002). Smart Mobs: The Next Social Revolution. Basic Books. 
• Ron, Sun (1979). Cognition and Multi-Agent Interaction. Cambridge University Press. 
• Riedl, Christoph; Blohm, Ivo; Leimeister, Jan Marco; Krcmar, Helmut (2010). RATING SCALES FOR COLLECTIVE INTELLIGENCE IN INNOVATION COMMUNITIES: WHY QUICK AND EASY DECISION MAKING DOES NOT GET IT RIGHT. http://home.in.tum.de/~riedlc/res/RiedlEtAl2010-ICIS.pdf. 
• Leimeister, Jan Marco (2010). Collective Intelligence. http://aisel.aisnet.org/bise/vol2/iss4/6/Collective. 
• Roy Chowdhury, Soudip; Rodriguez, Carlos; Daniel, Florian; Casati, Fabio (2010). Wisdom-aware computing: on the interactive recommendation of composition knowledge. http://dl.acm.org/citation.cfm?id=1987701. 
• Stephen R. Diasio, Nuria Agell, "The evolution of expertise in decision support technologies: A challenge for organizations," cscwd, pp.692-697, 2009 13th International Conference on Computer Supported Cooperative Work in Design, 2009. http://www.computer.org/portal/web/csdl/doi/10.1109/CSCWD.2009.4968139

Further reading

• Raven, Jean, ed (2008). Uses and Abuses of Intelligence. Unionville (NY): Royal Fireworks Press. ISBN 978-0-89824-356-7. Lay summary (6 July 2010). 
• Kaiser, C., Kröckel, J., Bodendorf, F. (2010). Swarm Intelligence for Analyzing Opinions in Online Communities. Proceedings of the 43rd Hawaii International Conference on System Sciences, pp. 1-9.

External links

• MIT Handbook of Collective Intelligence
• Cultivating Society's Civic Intelligence Doug 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