Please use this persistent identifier to cite or link to this item: doi:10.24405/471
DC FieldValueLanguage
dc.contributor.authorLang, Fabian-
dc.contributor.authorFink, Andreas-
dc.date.accessioned2017-10-24T14:12:11Z-
dc.date.available2017-10-24T14:12:11Z-
dc.date.issued2014-
dc.identifier.otherhttp://edoc.sub.uni-hamburg.de/hsu/volltexte/2014/3041/-
dc.identifier.urihttps://doi.org/10.24405/471-
dc.description.abstractDecision making in operational planning is increasingly affected by conflicting interests of different stakeholders such as subcontractors, customers, or strategic partners. Addressing this, automated negotiation is a well-suited mechanism to mediate between stakeholders and search for jointly beneficial agreements. However, the outcome of a negotiation is strongly dependent on the applied negotiation protocol defining the rules of encounter. Although protocol design is well discussed in literature, the question on which protocol should be selected for a given scenario is little regarded so far. Since negotiation problems and protocols are very diverse, the protocol choice itself is a challenging task. In this study, we propose a decision support system for negotiation protocol selection (DSS-NPS) that is based on a machine learning approach – an artificial neural network (ANN). Besides presenting and discussing the system, we, furthermore, evaluate the design artifact in elaborate computational experiments that take place in an intercompany machine scheduling environment. Our findings indicate that the proposed decision support system is able to improve the outcome of negotiations by finding adequate protocols dynamically on the basis of the underlying negotiation problem characteristics.-
dc.description.sponsorshipBWL, insb. Wirtschaftsinformatik-
dc.language.isoeng-
dc.relation.ispartofResearch paper / Institute of Computer Science-
dc.subjectEntscheidungsunterstützung-
dc.subjectMaschinelles Lernen-
dc.subjectPrognose-
dc.subject.ddc330 Wirtschaft-
dc.titleDecision support for negotiation protocol selection: a machine learning approach based on articial neural networks-
dc.typeWorking Paper-
dc.identifier.urnurn:nbn:de:gbv:705-opus-30412-
dcterms.bibliographicCitation.volume14-
dcterms.bibliographicCitation.issue02-
local.submission.typefull-text-
hsu.dnb.deeplinkhttps://d-nb.info/1107557550/-
item.grantfulltextopen-
item.languageiso639-1en-
item.fulltext_sWith Fulltext-
item.openairetypeWorking Paper-
item.fulltextWith Fulltext-
crisitem.author.deptBWL, insb. Wirtschaftsinformatik-
crisitem.author.parentorgFakultät für Wirtschafts- und Sozialwissenschaften-
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