research-article

A resource allocation framework for collective intelligence system engineering

Authors:

Dimitrios J. Vergados,

Ioanna Lykourentzou,

Epaminondas KapetaniosAuthors Info & Claims

MEDES '10: Proceedings of the International Conference on Management of Emergent Digital EcoSystems

Pages 182 - 188

https://doi.org/10.1145/1936254.1936285

Published: 26 October 2010 Publication History

Abstract

In this paper, we present a framework for engineering collective intelligence systems that will be used by web communities. The proposed framework enables the development of community-driven, self-regulating CI systems, which adapt their functionality to the activity and goals of the web community. The above engineering methodology is applied on the design of a popular web system, namely Wikipedia, to illustrate the way that the functionality of the latter could be improved, in terms of better and more prompt article quality production. The preliminary evaluation results of this application, obtained through simulation modeling are promising.

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Cited By

Hardisty AWorkman M(2024)A Collective Intelligence assembly approach to informing responsive net zero policy design: A greenhouse gas removal UK case studyCollective Intelligence10.1177/263391372412540993:2Online publication date: 27-May-2024
https://doi.org/10.1177/26339137241254099
Butt SSuran SPappel ISmærup MKrimmer RDraheim D(2023)A Digital Collaborative Platform for the Silver Economy: Functionalities Required by Stakeholders in a Multinational Baltic Sea Region ProjectDigital Government: Research and Practice10.1145/35926184:2(1-20)Online publication date: 14-Jun-2023
https://dl.acm.org/doi/10.1145/3592618
Sassi SIvanovic MChbeir RPrasath RManolopoulos Y(2022)Collective intelligence and knowledge exploration: an introductionInternational Journal of Data Science and Analytics10.1007/s41060-022-00338-914:2(99-111)Online publication date: 17-Jun-2022
https://doi.org/10.1007/s41060-022-00338-9
Show More Cited By

Index Terms

A resource allocation framework for collective intelligence system engineering
1. Computing methodologies
  1. Modeling and simulation
    1. Simulation theory
      1. Systems theory
2. Mathematics of computing
  1. Information theory

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cover image ACM Other conferences

MEDES '10: Proceedings of the International Conference on Management of Emergent Digital EcoSystems

October 2010

302 pages

ISBN:9781450300476

DOI:10.1145/1936254

Program Chair:
Richard Chbeir
Bourgogne University, France

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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NECTEC: National Electronics and Computer Technology Center
KU: Kasetsart University

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 October 2010

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MEDES '10

Sponsor:

NECTEC
KU

MEDES '10: The International Conference on Management of Emergent Digital EcoSystems 2010

October 26, 2010

Bangkok, Thailand

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MEDES '10 Paper Acceptance Rate 26 of 93 submissions, 28%;

Overall Acceptance Rate 267 of 682 submissions, 39%

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Cited By

Hardisty AWorkman M(2024)A Collective Intelligence assembly approach to informing responsive net zero policy design: A greenhouse gas removal UK case studyCollective Intelligence10.1177/263391372412540993:2Online publication date: 27-May-2024
https://doi.org/10.1177/26339137241254099
Butt SSuran SPappel ISmærup MKrimmer RDraheim D(2023)A Digital Collaborative Platform for the Silver Economy: Functionalities Required by Stakeholders in a Multinational Baltic Sea Region ProjectDigital Government: Research and Practice10.1145/35926184:2(1-20)Online publication date: 14-Jun-2023
https://dl.acm.org/doi/10.1145/3592618
Sassi SIvanovic MChbeir RPrasath RManolopoulos Y(2022)Collective intelligence and knowledge exploration: an introductionInternational Journal of Data Science and Analytics10.1007/s41060-022-00338-914:2(99-111)Online publication date: 17-Jun-2022
https://doi.org/10.1007/s41060-022-00338-9
Suran SPattanaik VDraheim D(2020)Frameworks for Collective IntelligenceACM Computing Surveys10.1145/336898653:1(1-36)Online publication date: 6-Feb-2020
https://dl.acm.org/doi/10.1145/3368986
Li KPerera D(2013)A Hardware Collective Intelligence AgentTransactions on Computational Collective Intelligence X10.1007/978-3-642-38496-7_4(45-59)Online publication date: 2013
https://doi.org/10.1007/978-3-642-38496-7_4

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