A Reductionist Approach to Hypothesis-Catching for the Analysis of Self-Organizing Decision-Making Systems

Lade...
Vorschaubild
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2013
Autor:innen
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
ArXiv-ID
Internationale Patentnummer
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published
Erschienen in
BILOF, Randall, ed.. 2013 IEEE 7th International Conference on Self-Adaptive and Self-Organizing Systems. Piscataway, NJ: IEEE, 2013, pp. 227-236. ISSN 1949-3673. eISSN 1949-3681. ISBN 978-0-7695-5129-6. Available under: doi: 10.1109/SASO.2013.10
Zusammenfassung

A difficulty in analyzing self-organizing decision-making systems is their high dimensionality which needs to be reduced to allow for deep insights. Following the hypothesis that such a dimensionality reduction can only be usefully determined in an act of a low-scale scientific discovery, a recipe for a data-driven, iterative process for determining, testing, and refining hypotheses about how the system operates is presented. This recipe relies on the definition of Markov chains and their analysis based on an urn model. Positive and negative feedback loops operating on global features of the system are detected by this analysis. The workflow of this analysis process is shown in two case studies investigating the BEECLUST algorithm and collective motion in locusts. The reported recipe has the potential to be generally applicable to self-organizing collective systems and is efficient due to an incremental approach.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
hypothesis formation, decision-making system, collective motion, swarm behavior
Konferenz
SASO 2013 : 7th International Conference on Self-Adaptive and Self-Organizing Systems, 9. Sept. 2013 - 13. Sept. 2013, Philadelphia, PA
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690HAMANN, Heiko, 2013. A Reductionist Approach to Hypothesis-Catching for the Analysis of Self-Organizing Decision-Making Systems. SASO 2013 : 7th International Conference on Self-Adaptive and Self-Organizing Systems. Philadelphia, PA, 9. Sept. 2013 - 13. Sept. 2013. In: BILOF, Randall, ed.. 2013 IEEE 7th International Conference on Self-Adaptive and Self-Organizing Systems. Piscataway, NJ: IEEE, 2013, pp. 227-236. ISSN 1949-3673. eISSN 1949-3681. ISBN 978-0-7695-5129-6. Available under: doi: 10.1109/SASO.2013.10
BibTex
@inproceedings{Hamann2013Reduc-59912,
  year={2013},
  doi={10.1109/SASO.2013.10},
  title={A Reductionist Approach to Hypothesis-Catching for the Analysis of Self-Organizing Decision-Making Systems},
  isbn={978-0-7695-5129-6},
  issn={1949-3673},
  publisher={IEEE},
  address={Piscataway, NJ},
  booktitle={2013 IEEE 7th International Conference on Self-Adaptive and Self-Organizing Systems},
  pages={227--236},
  editor={Bilof, Randall},
  author={Hamann, Heiko}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/59912">
    <dcterms:title>A Reductionist Approach to Hypothesis-Catching for the Analysis of Self-Organizing Decision-Making Systems</dcterms:title>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/59912"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2023-01-24T10:31:20Z</dcterms:available>
    <dc:language>eng</dc:language>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dcterms:abstract xml:lang="eng">A difficulty in analyzing self-organizing decision-making systems is their high dimensionality which needs to be reduced to allow for deep insights. Following the hypothesis that such a dimensionality reduction can only be usefully determined in an act of a low-scale scientific discovery, a recipe for a data-driven, iterative process for determining, testing, and refining hypotheses about how the system operates is presented. This recipe relies on the definition of Markov chains and their analysis based on an urn model. Positive and negative feedback loops operating on global features of the system are detected by this analysis. The workflow of this analysis process is shown in two case studies investigating the BEECLUST algorithm and collective motion in locusts. The reported recipe has the potential to be generally applicable to self-organizing collective systems and is efficient due to an incremental approach.</dcterms:abstract>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:issued>2013</dcterms:issued>
    <dc:contributor>Hamann, Heiko</dc:contributor>
    <dc:rights>terms-of-use</dc:rights>
    <dc:creator>Hamann, Heiko</dc:creator>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2023-01-24T10:31:20Z</dc:date>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Nein
Begutachtet
Diese Publikation teilen