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Frameworks for Collective Intelligence: A Systematic Literature Review

Authors:

Vishwajeet Pattanaik,

Dirk DraheimAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 53, Issue 1

Article No.: 14, Pages 1 - 36

https://doi.org/10.1145/3368986

Published: 06 February 2020 Publication History

Abstract

Over the last few years, Collective Intelligence (CI) platforms have become a vital resource for learning, problem solving, decision-making, and predictions. This rising interest in the topic has to led to the development of several models and frameworks available in published literature. Unfortunately, most of these models are built around domain-specific requirements, i.e., they are often based on the intuitions of their domain experts and developers. This has created a gap in our knowledge in the theoretical foundations of CI systems and models, in general. In this article, we attempt to fill this gap by conducting a systematic review of CI models and frameworks, identified from a collection of 9,418 scholarly articles published since 2000. Eventually, we contribute by aggregating the available knowledge from 12 CI models into one novel framework and present a generic model that describes CI systems irrespective of their domains. We add to the previously available CI models by providing a more granular view of how different components of CI systems interact. We evaluate the proposed model by examining it with respect to six popular, ongoing CI initiatives available on the Web.

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Frameworks for Collective Intelligence: A Systematic Literature Review

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 53, Issue 1

January 2021

781 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3382040

Editor:
Albert Zomaya
University of Sydney, Australia

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Copyright © 2020 ACM.

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Publication History

Published: 06 February 2020

Accepted: 01 October 2019

Revised: 01 October 2019

Received: 01 March 2019

Published in CSUR Volume 53, Issue 1

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