A Methodological Approach Towards Crisis Simulations: Qualifying CI-Enabled Information Systems

Chrysostomi Maria Diakou, Angelika I. Kokkinaki, Styliani Kleanthous

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    1 Citation (Scopus)

    Abstract

    Low probability high impact events (LoPHIEs) disrupt organizations’ processes severely. Existing methods used for the anticipation and management of such events, suffer from common limitations resulting in a huge impact to the quantification of probability, uncertainty and risk. Continues studies in the field of Crisis Informatics, present an opportunity for the development of a framework that fits the uncertainty related properties of LoPHIEs. The paper identifies the need for the development and conduction of a series of experiments, aiming to address the factors that qualify Collective Intelligence-enabled Information Systems with respect to their applicability towards support for LoPHIEs; and aims to propose an experiment framework as a methodology for scenario design in LoPHIEs settings.

    Original languageEnglish
    Title of host publicationComputational Collective Intelligence - 9th International Conference, ICCCI 2017, Proceedings
    PublisherSpringer Verlag
    Pages569-578
    Number of pages10
    Volume10448 LNAI
    ISBN (Print)9783319670737
    DOIs
    Publication statusPublished - 2017
    Event9th International Conference on Computational Collective Intelligence, ICCCI 2017 - Nicosia, Cyprus
    Duration: 27 Sep 201729 Sep 2017

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume10448 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other9th International Conference on Computational Collective Intelligence, ICCCI 2017
    Country/TerritoryCyprus
    CityNicosia
    Period27/09/1729/09/17

    Keywords

    • Collective Intelligence
    • Emergencies
    • High impact
    • Low probability

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