Individual differences |
Methods | Statistics | Clinical | Educational | Industrial | Professional items | World psychology |
Collective intelligence is a shared or group intelligence that emerges from the collaboration and competition of many individuals. Collective intelligence appears in a wide variety of forms of consensus decision making in bacteria, animals, humans, and computer networks. The study of collective intelligence may properly be considered a subfield of sociology, of business, of computer science, of mass communications and of mass behavior—a field that studies collective behavior from the level of quarks to the level of bacterial, plant, animal, and human societies. The concept also frequently appears in science fiction as telepathically linked species and cyborgs.
The above definition has 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. 
Some figures like Tom Atlee prefer to focus on collective intelligence primarily in humans and actively 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.”
Collective intelligence (CI) can also be defined as a form of networking enabled by the rise of communications technology, namely the Internet. Web 2.0 has enabled interactivity and thus, users are able to generate their own content. Collective Intelligence draws on this to enhance the social pool of existing knowledge. 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.
One CI pioneer, 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." Tom Atlee and George 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.
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 at the MIT Center for Collective Intelligence research and explore collective intelligence of groups of people and computers.
- 1 General concepts
- 2 History
- 3 Types of collective intelligence
- 4 Examples of collective intelligence
- 5 Mathematical techniques
- 6 Stock Market Predictions using Collective Intelligence
- 7 Collective Intelligence and the Media
- 8 Collective Intelligence and Social Bookmarking
- 9 Collective Intelligence in Videogames
- 10 Supporting views
- 11 Opposing views
- 12 Recent developments
- 13 See also
- 14 Notes
- 15 References
- 16 External links
General concepts[edit | edit source]
Howard Bloom traces the evolution of collective intelligence from the days of our bacterial ancestors 1 billion years ago to the present and demonstrates how a multi-species intelligence has worked since the beginning of life.
Tom Atlee and George Pór, on the other hand, feel that while group theory and artificial intelligence have something to offer, the field of collective intelligence should be seen by some as primarily 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. Individuals who respect collective intelligence, say Atlee and Pór, are confident of their own abilities and recognize that the whole is indeed greater than the sum of any individual parts. [How to reference and link to summary or text]
From Pór and Atlee's point of view, 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.
Knowledge focusing through various voting methods has the potential for many unique 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.
While these are the views of experts like Atlee and Pór, other founding fathers of collective intelligence see the field differently. Francis Heylighen, Valerie Turchin, and Gottfried Mayer-Kress view collective intelligence through the lens of computer science and cybernetics. Howard Bloom stresses the biological adaptations that have turned most of this earth's living beings into components of what he calls "a learning machine". And 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.
Perhaps we may draw parallels between this informational cortex and the Internet. Defined by the Internet Society in 1995 as ‘... the global information system that... provides, uses or makes accessible, either publicly or privately, high level services layered on... communications and related infrastructure...’ (Leiner et al. 2003) we can see how the Internet lends itself to becoming this ‘cortex’. Developing as far back as the late 1950’s, it wasn’t until 1991 that WWW (World Wide Web) was released. In 2005, there were as many as 1,018,057,389 Internet users worldwide (CIA 2008). So many users accessing the Internet can only mean one thing — a meeting of minds and collaboration of knowledge. The Internet is an information and communication tool, whether it be checking on the stock market or a celebrity gossip site, humans are primarily interested in the sharing of information, and the Internet serves this purpose.
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. These are openness, peering, sharing and acting globally.
- In the early stage of communications technology, people and companies are reluctant to share ideas, intellectual property and encourage self-motivation because these resources provide the edge over competitors. However, in time people and companies began to loosen hold over these resources as they reap more benefits in doing so. Allowing others to share ideas and bid for franchising will enable products to gain significant improvement and scrutiny through collaboration.
- This is a form of horizontal organization with the capacity to create information technology and physical products. One example is the ‘opening up’ of the Linux program where users are free to modify and develop it provided that they made it available for others. Participants in this form of collective intelligence have different motivations for contributing, but the results achieved are for the improvement of a product or service. As quoted, “Peering succeeds because it leverages self-organization – a style of production that works more effectively than hierarchical management for certain tasks.”
- This principle has been controversial with the question being “Should there be a law against the distribution of intellectual property?” Research has shown that more and more companies have started to share some, while maintaining some degree of control over others, like potential and critical patent rights. This is because companies have realized that by limiting all their intellectual property, they are shutting out all possible opportunities. Sharing some has allowed them to expand their market and bring out products faster.
- Acting Globally
- The advancements in communication technology has prompted the rise of global companies, or e-Commerce that has allowed individuals to set up businesses at low to almost no overhead costs. The influence of the Internet is widespread, therefore a globally integrated company would have no geographical boundaries but have global connections, allowing them to gain access to new markets, ideas and technology. Therefore it is important for firms to get updated and remain globally competitive or they will face a declining rate of clientèle.
History[edit | edit source]
An early precursor of the concept of collective intelligence was entomologist William Morton Wheeler's observation that seemingly independent individuals can cooperate so closely as to become indistinguishable from a single organism. In 1911 Wheeler saw this collaborative process at work in ants, who acted like the cells of a single beast with a collective mind. He called the larger creature that the colony seemed to form a "superorganism".
In 1912, Émile Durkheim identified society as the sole source of human logical thought. He argues in The Elementary Forms of Religious Life that society constitutes a higher intelligence because it transcends the individual over space and time.
Collective intelligence, which has antecedents in Vladimir Vernadsky's concept of "noosphere" as well as H.G. Wells's concept of "world brain," has more recently been examined in depth by Pierre Lévy in a book by the same name, by Howard Bloom in Global Brain (see also the term global brain), by Howard Rheingold in Smart Mobs (Rheingold 2002), and by Robert David Steele Vivas in The New Craft of Intelligence. The latter introduces the concept of all citizens as "intelligence minutemen," drawing only on legal and ethical sources of information, as able to create a "public intelligence" that keeps public officials and corporate managers honest, turning the concept of "national intelligence" on its head (previously concerned about spies and secrecy).
In 1986, Howard Bloom combined the concepts of apoptosis, parallel distributed processing, group selection, and the superorganism to produce a theory of how a collective intelligence works. Later, he went further and showed how collective intelligences like those of competing bacterial colonies and of competing human societies can be explained in terms of computer-generated "complex adaptive systems" and the "genetic algorithms", concepts pioneered by John Holland.
The developer of the World Wide Web, Tim Berners-Lee, has made it with the goal to promote sharing and publishing of information globally. Later, his employer opened up the WWW technology for free use. In the early ‘90s, the Internet’s potential was still untapped, until the mid ‘90s where ‘critical mass’, as termed by the head of the Advanced Research Project Agency (ARPA), Dr. J.C.R. Licklider demanded for more accessibility and utility of the Internet. Hence, it can be said that the driving force behind collective intelligence is the digitization of information and communication. This is because existence of hyperlink has made it easier to search and create websites and pages. Knowledge can be built in just a matter of minutes.
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 follows the development of the concept of a ‘group mind’ as articulated by Hobbes in relation to his Leviathan which functioned as a coherent entity and Fechner’s arguments for a collective consciousness of mankind. He cites Durkheim as the most notable advocate of a ‘collective consciousness” and Teilhard as the thinker who has developed the philosophical implications of the group mind more than any other.
Types of collective intelligence[edit | edit source]
Examples of collective intelligence[edit | edit source]
The best-known collective intelligence projects are political parties, which mobilize large numbers of people to form policy, select candidates and to finance and run election campaigns. Military units, trade unions, and corporations are focused on more narrow concerns but would satisfy some definitions of a genuine "C.I."—the most rigorous definition would require a capacity to respond to very arbitrary conditions without orders or guidance from "law" or "customers" that tightly constrain actions. Another example is in which online advertising companies like BootB and DesignBay are using collective intelligence in order to bypass traditional marketing and creative agencies.
Improvisational actors also experience a type of collective intelligence, which they term 'Group Mind'.
Another form of collective intelligence is the Learner generated context in which a group of users collaboratively marshall available 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. In this sense, the learner generated contexts represents an ad hoc community which facilitates the coordination of collective action in a network of trust.
The best example of Learner generated context is perhaps found on the Internet- a group of collaborative users pooling knowledge to result 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 their ideas and to access these collaborative intelligence spaces. (Flew 2008)
Ant societies exhibit more intelligence than any other animal except for humans, if we measure intelligence in terms of technology. Ant societies are able to do agriculture, in fact several different forms of agriculture. Some ant societies keep livestock of various forms, for example, some ants keep and care for aphids for "milking". Leaf cutters care for fungi and carry leaves to feed the fungi.
However, a majority will agree that the medium that displays collective intelligence in full is Wikipedia. It is an encyclopedia that can be altered by virtually anyone at any time. This concept is termed ‘wikinomics’ by Don Tapscott and Anthony D. Williams in their book similarly named, they quote Sunday Times, “’wikinomics’ is the new force that is bringing people together on the net to create a giant brain”. Through this application, the lines between a consumer and producer have been blurred, inventing the term ‘prod-user’ or ‘prosumer’.
More examples on collective intelligence can be seen in games. Games such as The Sims, Halo or Second Life are designed to be more non-linear and 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.
Mathematical techniques[edit | edit source]
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.
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 multithread 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.
Stock Market Predictions using Collective Intelligence[edit | edit source]
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 in long or even short term applications. Utilizing these attributes, websites have been created to aggregate stock market information that is as current as possible. Consequently, professional or amateur stock analysts can publish their viewpoints and participate in creating an aggregate opinion on specific stocks or the stock market in general. Although it has been commonly expected, at least within the investment community, for investment banks and brokerages to publish their ratings and reports on stocks, the Internet has enabled the amateur or less notorious investors to concurrently submit their financial opinions. As a result, the opinion of any investor can be weighted on par with any other. Thus, a pivotal premise of the effective application of collective intelligence can be more thoroughly applied: the masses, including a broad spectrum of stock market expertise, could be utilized to, in theory, more accurately predict the behavior of financial markets.
Collective intelligence underpins the Efficient Markets 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 it was found 89 out of 115 selected funds underperformed the index during the period from 1955 to 1964. After removing the loading charge (up-front fee), 72 underperformed and after removing brokerage costs 58 underperformed. It was on the basis of evidence such as this that index funds became popular investment vehicles - effectively using the collective intelligence of the market as an investment strategy, rather than the judgement of professional fund managers.
Collective Intelligence and the Media[edit | edit source]
New media is 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 it 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.
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, whilst the latter is information known by all members of a community.
On the other hand, it has been argued that Media, or in particular 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.
Collective Intelligence and Social Bookmarking[edit | edit source]
Another important example of emergence in web-based systems is social bookmarking (also called collaborative tagging). In collaborative tagging systems, 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 structures can be seen as reflecting the collective knowledge (or collective intelligence) of a community of users.
For example, 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.. 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.
Collective Intelligence in Videogames[edit | edit source]
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 Levy’s concept of Collective Intelligence (Levy 1998). He argues this concept is actively at play in videogames as clans or guilds in MMORG are constantly working together in order to achieve the goals/aims of the games. Henry Jenkins proposes that the participatory cultures emerging between games producers, media companies, and the end-users mark out 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. 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 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.
Supporting views[edit | edit source]
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 person, when analysed singularly tend to make decisions motivated by self-preservation. In addition, humans lack a way to make choices that has a balance between innovations and reality. Therefore, without collective intelligence, humans may just drive themselves into extinction based on their selfish needs.
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.
It is also critical to look at the benefits of collective intelligence for business. Research performed by Tapscott and Williams has provided a few examples:
- 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.
Opposing views[edit | edit source]
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.
Recent developments[edit | edit source]
Growth of the Internet and mobile telecom has also highlighted "swarming" or "rendezvous" technologies that enable meetings or even dates on demand. The full impact of such technology on collective intelligence and political effort has yet to be felt, but the anti-globalization movement relies heavily on e-mail, cell phones, pagers, SMS, and other means of organizing before, during, and after events. One theorist involved in both political and theoretical activity, Tom Atlee, quantifies on a disciplined basis the connections between these events and the political imperatives that drive them. The Indymedia organization does this in a more journalistic way, and there is some coverage of such current events even here at Wikipedia.
It seems likely that such resources could combine in the future into a form of collective intelligence accountable only to the current participants but with some strong moral or linguistic guidance from generations of contributors - or even take on a more obviously democratic form, to advance some shared goals.
See also[edit | edit source]
Notes[edit | edit source]
- Norman Lee Johnson, Collective Science site
- George Pór, Blog of Collective Intelligence
- Howard Bloom, Global Brain: The Evolution of Mass Mind from the Big Bang to the 21st Century, 2000
- Tapscott, D., & Williams, A. D. (2008). Wikinomics: How Mass Collaboration Changes Everything, USA: Penguin Group
- Émile Durkheim, The Elementary Forms of Religious Life, 1912.
- Howard Bloom, The Lucifer Principle: A Scientific Expedition Into the Forces of History, 1995
- Weiss, A. (2005). The Power of Collective Intelligence. Collective Intelligence, pp. 19-23
- 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
- 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.
- 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.
- 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.
- Fadul, Jose (2009). Collective Learning: Applying Distributed Cognition for Collective Intelligence. International Journal of Learning 16 (4): 211–220.
- Szuba T., Computational Collective Intelligence, 420 pages, Wiley NY, 2001
- Fama, E.F., (1970), Efficient Capital Markets: A Review of Theory and Empirical Work, Journal of Finance, Vol. 25 No. 2, pp. 383 – 417
- 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
- Jenkins, H. 2006. Fans, Bloggers and Gamers: Exploring Participatory Culture. New York: New York University Press.
- 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.
- 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.
- 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.
- 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.
- L, Lessig,(2006)Code Version 2.0 (2nd ed.). New York: Basic Books.
- Bray, DA & Konsynski, BR, 2007, Virtual Worlds, Virtual Economies, Virtual Institutions, viewed 10th October 2008, p. 1-27 <http://ssrn.com/abstract=962501>
- Gosney, J.W, 2005, Beyond Reality: A Guide to Alternate Reality Gaming, Thomson Course Technology, Boston.
- Atlee, T. (2008). Reflections on the evolution of choice and collective intelligence, Retrieved August 26, 2008
References[edit | edit source]
- Hofstadter, Douglas (1979). Gödel, Escher, Bach: an Eternal Golden Braid, New York: Basic Books.
- Brown, Philip (2000). "Collective intelligence" Social Capital: Critical Perspectives, New York: Oxford University Press.
- Brown, Philip (2001). "Collective intelligence (chapter 13)" 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.
[edit | edit source]
- Free Online Versions of COLLECTIVE INTELLIGENCE: Creating a Prosperous World at Peace edited by Mark Tovey
- MIT Handbook of Collective Intelligence
- The Global Brain is Coming! Nova Spivack talking at the GRID08 conference
- Kevin Kelly's TEDtalk on the ONE machine
- Managing Collective Intelligence, Toward a New Corporate Governance
- 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
- Collective Intelligence Monitor Panda's Collective Intelligence servers
|This page uses Creative Commons Licensed content from Wikipedia (view authors).|