Collaborative intelligence


Collaborative intelligence

Collaborative intelligence is a term used in several disciplines, and has several different meanings. In a business setting, it can describe the result of accessing a network of people. It is also used to denote non-anonymous heterogeneity in multi-agent problem-solving systems.

Contents

Overview

The term was used in 1999 in a business context to describe the behavior of an ecosystem of thought [1]. This defines Collaborative Intelligence, or CQ, as "the ability to build, contribute to and manage power found in networks of people."[2].

Collaborative intelligence was later defined as to require the investigation of aspects of collective intelligence, namely those that acknowledge identity, as in social networks, as the foundation for next generation problem-solving ecosystems, modeled on evolutionary adaptation in nature’s ecosystems.

The Internet, as a rich, but noisy, platform, enables collaborative intelligence ecosystems, such as Wikipedia, to emerge and evolve, as life itself may have emerged and evolved toward increasingly ordered complexity. Constraints to direct evolution toward increased functional effectiveness co-evolve with systems to tag, credit, time-stamp, and sort content.[3] Collaborative intelligence requires capacity for effective search, discovery, integration, visualization, and frameworks to support collaborative problem-solving.[4] Google+ is evolving a platform where next generation social networks can drive more effective search, a potential platform for collaborative intelligence. Crowdsourcing can move beyond menial pattern recognition tasks to harness collaborative intelligence, retaining the identity of individual contributors in the social network.

Distinguishing Collective from Collaborative Intelligence

In some disciplines, the definition of Collaborative Intelligence has much in common with Collective Intelligence, so it is useful to find distinguishing aspects.

The term collective intelligence originally encompassed both collective and collaborative intelligence, and many systems manifest attributes of both. Pierre Lévy coined the term “collective intelligence” in his book of that title, first published in French in 1994.[5] Lévy defined “collective intelligence” to encompass both collective and collaborative intelligence: “a form of universally distributed intelligence, constantly enhanced, coordinated in real time, and in the effective mobilization of skills…”[6] Following publication of Lévy’s book, computer scientists adopted the term collective intelligence to denote an application within the more general area to which this term now applies in computer science. Specifically, an application that processes input from a large number of discrete responders to specific, generally quantitative, questions (e.g. what will the price of DRAM be next year?) Algorithms homogenize input, maintaining the traditional anonymity of survey responders to generate better-than-average predictions. Note that collective intelligence in general does not require anonymity as a defining attribute, as shown by examples such as Wikipedia.

Collaborative intelligence relates to whether prediction is defined as active,[7] how heuristics are used,[8] and whether analogs to developmental processes for facilitated variation enable systems to evolve non-randomly toward increased functional effectiveness.[9] Recent dependency network studies suggest links between collective and collaborative intelligence. Partial correlation-based Dependency Networks, a new class of correlation-based networks have been shown to uncover hidden relationships between the nodes of the network. Research by Dror Y. Kennett and his Ph.D. supervisor Eshel Ben-Jacob uncovered hidden information about the underlying structure of the U.S. stock market that was not present in the standard correlation networks, and published their findings in 2011.[10]

Applications: Collective vs Collaborative Intelligence

In one embodiment, Collective intelligence operates via a multi-agent system. Individual differences contribute data to the prediction engine. Typical applications of this one embodiment focus on analysis and prediction of financial markets, such as

  • analysis of the macro-economic consequences of many individual decisions arising from the micro-behaviors of individual investors;
  • analysis and prediction of individual voter choices on election outcomes; and
  • prediction of financial values of commodities by gathering a large number of inputs from many sources, as in market forecasts.

Collaborative intelligence addresses problems where individual expertise, potentially conflicting priorities of stakeholders, and different interpretations of diverse experts are critical for problem-solving. Potential future applications include:

  • competitions, where submissions must be integrated to produce a synergistic outcome;
  • smart search, where social networks of searchers on related topics co-define search results;
  • professional groups, interest collectives, citizen science and other communities, where knowledge-sharing is a prerequisite for effective outcomes;
  • planning, development, and sustainable remediation project management;
  • smart systems to transform independent cities into collaborative, ecological urban networks;
  • global citizens facing climate change, asking: What can we do? Who’s doing what now?

Precursors in the Life Sciences

In the late 1980s, Eshel Ben-Jacob began to study bacterial self-organization, believing that bacteria hold the key to understanding larger biological systems. He developed new pattern-forming bacteria species, Paenibacillus vortex and Paenibacillus dendritiformis, and became a pioneer in the study of social behaviors of bacteria. P. dendritiformis manifests an intriguing collective faculty, which could be viewed as a precursor of collaborative intelligence, the ability to switch between different morphotypes[11][12][13] to better adapt with the environment. Primarily studied are the transitions between the branching (or tip-splitting) morphotype and the chiral morphotype, which is marked by curly branches with well defined handedness. Morphotype transitions can be viewed as identity switching[14][15] In order to make these switches colonies of bacteria must cooperatively make drastic alterations of their internal genomic state, effectively transforming themselves into cells that look and behave differently in order to generate colonies with an entirely different organization. Scientists have only recently begun to decode, how, using sophisticated chemical communication, bacteria can rapidly adapt to changes in the environment, distribute tasks, learn from experience, prepare for the future and make decisions.[16][17][18] Bacteria in a colony, numbering many times the population on Earth, exchange “chemical tweets” to synchronize their behavior.

Ants were first characterized by entomologist W. M. Wheeler as cells of a single “superorganism” where seemingly independent individuals can cooperate so closely as to become indistinguishable from a single organism.[19] Later research characterized some insect colonies as instances of collective intelligence. The concept of ant colony optimization algorithms, introduced by Marco Dorigo, became a dominant theory of evolutionary computation. Deborah M. Gordon shows that ant colonies operate without central control using algorithms based on a dynamical network of brief interactions. Colonies allocate workers to different tasks, and workers switch from one task to another in response to changing conditions.[20]

The mechanisms of evolution through which species adapt toward increased functional effectiveness in their ecosystems are the foundation for principles of collaborative intelligence. Topics such as stigmergy and evolutionary genetic algorithms are inspired by the life sciences.

Business Applications

CQ or C-IQ (Collaborative IQ or Collaborative Intelligence) measures the collaborative effectiveness of a group, which can be greater or less than the aggregate knowledge and capability possessed by individual group members.[21] Collaborative intelligence is a measure of capacity of a group, whether small and co-located or large and distributed, to innovate, solve problems, and achieve new discoveries. The classic work of Irving Janis on Groupthink[22] (how committees degenerate to the lowest common denominator) has more recently been countered by James Surowiecki in his study The Wisdom of Crowds.[23]

A new generation of tools to support collaborative intelligence is poised to evolve from crowdsourcing platforms, recommender systems, and evolutionary computation.[24] Existing tools to facilitate group problem-solving include collaborative groupware, such as Google+, Confluence, JIRA, Skype, NetMeeting, WebEx, and synchronous conferencing technologies such as instant messaging, online chat and shared white boards, which are complemented by asynchronous messaging like electronic mail, threaded, moderated discussion forums, web logs, and group Wikis. Managing the Intelligent Enterprise relies on these tools, as well as methods for group member interaction; promotion of creative thinking; group membership feedback; quality control and peer review; and a documented group memory or knowledge base.[25] As groups work together, they develop a shared memory, which is accessible through the collaborative artifacts created by the group, including meeting minutes, transcripts from threaded discussions, and drawings. The shared memory (group memory) is also accessible through the memories of group members; current interest focuses on how technology can support and augment the effectiveness of shared past memory and capacity for future problem-solving. Metaknowledge characterizes how knowledge content interacts with its knowledge context in cross-disciplinary, multi-institutional, or global distributed collaboration.[26]

See also

References

  1. ^ Isaacs, William (1999). Dialogue: The Art Of Thinking Together. Crown Business. ISBN 978-0385479998. 
  2. ^ Joyce, Stephen (2007). Teaching an Anthill to Fetch: Developing Collaborative Intelligence @ Work. Crown Business. ISBN 978-0978031206. 
  3. ^ Gill, Zann (2011) Algorithmic implications of evo-devo debates. GECCO 2011. International Conference on Genetic and Evolutionary Computation (combining the 20th International Conference on Genetic Algorithms ICGA and the 16th Annual Genetic Programming Conference. July 12 – 16. Dublin, Ireland.
  4. ^ Collaborative Intelligence Resources
  5. ^ Lévy P. (1994) L’Intelligence collective. Pour une anthropologie du cyberspace. Paris: La Découverte.
  6. ^ Lévy, P. (1997) Collective Intelligence: Mankind’s Emerging World in Cyberspace. New York: Plenum Press
  7. ^ Gill, Zann S (1986) The Paradox of Prediction. Daedalus: Journal of the American Academy of Arts and Sciences 115(3): 17 – 49
  8. ^ Gigerenzer, G., R. Hertwig, et al. (2010) Heuristics: the foundations of adaptive behavior. New York, NY: Oxford University Press
  9. ^ Kirschner, M. W. & Gerhart, J. C. (2005) The Plausibility of Life: Resolving Darwin's Dilemma. New Haven: Yale University Press
  10. ^ Kenett et al. (2010) PLoS ONE 5(12): e15032
  11. ^ Ben-Jacob E. Bacterial self-organization: co-enhancement of complexification and adaptability in a dynamic environment. Phil. Trans. R. Soc. Lond. A. 2003;361(1807):1283-1312.
  12. ^ Ben-Jacob E, Cohen I, Gutnick DL. Cooperative organization of bacterial colonies: from genotype to morphotype. Annu Rev Microbiol. 1998;52:779-806.
  13. ^ Ben-Jacob E, Cohen I. Cooperative formation of bacterial patterns. In: Shapiro JA, Dworkin M, eds. Bacteria as Multicellular Organisms New York: Oxford University Press; 1997:394-416.
  14. ^ Ben-Jacob E, Levine H. Self-engineering capabilities of bacteria. J R Soc Interface. 2005;3(6):197-214.
  15. ^ Ben-Jacob E, Cohen I, Golding I, et al. Bacterial cooperative organization under antibiotic stress. Physica A. 2000;282(1-2):247-282.
  16. ^ Ben-Jacob E. (2003) Bacterial self-organization: co-enhancement of complexification and adaptability in a dynamic environment. Phil. Trans. R. Soc. Lond. A. 361(1807):1283-1312.
  17. ^ Ben-Jacob E, Becker I, Shapira Y, Levine H. (2004) Bacterial linguistic communication and social intelligence. Trends Microbiol. Aug. 12(8):366-372.
  18. ^ Dwyer DJ, Kohanski MA, Collins JJ. Networking opportunities for bacteria. Cell. Dec 26 2008;135(7):1153-1156.
  19. ^ Wheeler, W. M. (1911) The Ant-Colony as an Organism. Journal of Morphology 22: 307-325.
  20. ^ Gordon, DM (2010) Ant Encounters: Interaction Networks and Colony Behavior, Princeton Univ Press. Colonies allocate workers to different tasks, and workers switch from one task to another, in response to changing conditions.
  21. ^ Veryard, Richard (2001). Component-Based Business. London: Springer. ISBN 1-852-33361-8. 
  22. ^ Janis, I. (1982) Groupthink: Psychological studies of policy decisions and fiascos. Boston, Houghton Mifflin.
  23. ^ Surowiecki, J. (2004). The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations. Boston, Little Brown.
  24. ^ Collaborative Intelligence Resources
  25. ^ Information and Collaboration Technologies (Chapter 5): Managing Collective Intelligence, Toward a New Corporate Governance
  26. ^ Evans, J.A. and Foster, J.G. (2011) Metaknowledge. Science. vol. 331. 11 February. p. 721-725.

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