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

No Thumbnail Available
##### Files
There are no files associated with this item.
2013
##### Publication type
Contribution to a conference collection
Published
##### Published in
2013 IEEE 7th International Conference on Self-Adaptive and Self-Organizing Systems / Bilof, Randall (ed.). - Piscataway, NJ : IEEE, 2013. - pp. 227-236. - ISSN 1949-3673. - eISSN 1949-3681. - ISBN 978-0-7695-5129-6
##### Abstract
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.
##### Subject (DDC)
004 Computer Science
##### Keywords
hypothesis formation, decision-making system, collective motion, swarm behavior
##### Conference
SASO 2013 : 7th International Conference on Self-Adaptive and Self-Organizing Systems, Sep 9, 2013 - Sep 13, 2013, Philadelphia, PA
##### Cite This
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, Sep 9, 2013 - Sep 13, 2013. In: BILOF, Randall, ed.. 2013 IEEE 7th International Conference on Self-Adaptive and Self-Organizing Systems. Piscataway, NJ:IEEE, 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},
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#" >
<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>

No