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Collective Intelligence and its Implementation on the Web: Algorithms to Develop a Collective Mental Map

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  • Francis Heylighen

    (Free University of Brussels)

Abstract

Collective intelligence is defined as the ability of a group to solve more problems than its individual members. It is argued that the obstacles created by individual cognitive limits and the difficulty of coordination can be overcome by using a collective mental map (CMM). A CMM is defined as an external memory with shared read/write access, that represents problem states, actions and preferences for actions. It can be formalized as a weighted, directed graph. The creation of a network of pheromone trails by ant colonies points us to some basic mechanisms of CMM development: averaging of individual preferences, amplification of weak links by positive feedback, and integration of specialised subnetworks through division of labor. Similar mechanisms can be used to transform the World-Wide Web into a CMM, by supplementing it with weighted links. Two types of algorithms are explored: 1) the co-occurrence of links in web pages or user selections can be used to compute a matrix of link strengths, thus generalizing the technique of &201C;collaborative filtering&201D;; 2) learning web rules extract information from a user&2018;s sequential path through the web in order to change link strengths and create new links. The resulting weighted web can be used to facilitate problem-solving by suggesting related links to the user, or, more powerfully, by supporting a software agent that discovers relevant documents through spreading activation.

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  • Francis Heylighen, 1999. "Collective Intelligence and its Implementation on the Web: Algorithms to Develop a Collective Mental Map," Computational and Mathematical Organization Theory, Springer, vol. 5(3), pages 253-280, October.
    • Handle: RePEc:spr:comaot:v:5:y:1999:i:3:d:10.1023_a:1009690407292
    DOI: 10.1023/A:1009690407292
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    References listed on IDEAS

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    1. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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    Cited by:

    1. Heylighen, Francis, 2017. "Towards an intelligent network for matching offer and demand: From the sharing economy to the global brain," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 74-85.
    2. JinHyo Joseph Yun & EuiSeob Jeong & Xiaofei Zhao & Sung Deuk Hahm & KyungHun Kim, 2019. "Collective Intelligence: An Emerging World in Open Innovation," Sustainability, MDPI, vol. 11(16), pages 1-15, August.
    3. Yuan-Chu Hwang & Soe-Tsyr Yuan & Jung-Hui Weng, 2011. "A study of the impacts of positive/negative feedback on collective wisdom—case study on social bookmarking sites," Information Systems Frontiers, Springer, vol. 13(2), pages 265-279, April.
    4. Sven Dittes & Stefan Smolnik, 2019. "Towards a digital work environment: the influence of collaboration and networking on employee performance within an enterprise social media platform," Journal of Business Economics, Springer, vol. 89(8), pages 1215-1243, December.
    5. Enrico Imbimbo & Federica Stefanelli & Andrea Guazzini, 2020. "Adolescent’s Collective Intelligence: Empirical Evidence in Real and Online Classmates Groups," Future Internet, MDPI, vol. 12(5), pages 1-16, April.
    6. Jinhyo Joseph Yun & Zheng Liu & Euiseob Jeong & Sangwoo Kim & Kyunghun Kim, 2022. "The Difference in Open Innovation between Open Access and Closed Access, According to the Change of Collective Intelligence and Knowledge Amount," Sustainability, MDPI, vol. 14(5), pages 1-19, February.
    7. Kristian Stålne & Eja Pedersen, 2021. "Transdisciplinary Research on Indoor Environment and Health as a Social Process," IJERPH, MDPI, vol. 18(8), pages 1-18, April.
    8. Timoteo Carletti & Alessio Guarino & Andrea Guazzini & Federica Stefanelli, 2020. "Problem Solving: When Groups Perform Better Than Teammates," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(3), pages 1-4.
    9. Fan Yang & Wen Dong, 2020. "Integrating simulation and signal processing in tracking complex social systems," Computational and Mathematical Organization Theory, Springer, vol. 26(1), pages 1-22, March.
    10. Runsten, Philip, 2017. "TEAM INTELLIGENCE: THE FOUNDATIONS OF INTELLIGENT ORGANIZATIONS - A Literature Review," SSE Working Paper Series in Business Administration 2017:2, Stockholm School of Economics.
    11. Heylighen, Francis & Lenartowicz, Marta, 2017. "The Global Brain as a model of the future information society: An introduction to the special issue," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 1-6.

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