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dc.contributor.authorSalge, Christoph
dc.contributor.authorPolani, D.
dc.date.accessioned2015-03-09T09:48:48Z
dc.date.available2015-03-09T09:48:48Z
dc.date.issued2014
dc.identifier.citationSalge , C & Polani , D 2014 , Don't Believe Everything You Hear : Preserving Relevant Information by Discarding Social Information . in ALIFE 14 : Procs of the 14th Int Conf on the Synthesis and Simulation of Living Systems . MIT Press , pp. 837-844 , ALIFE 2014 , New York , United States , 30/07/14 . https://doi.org/10.7551/978-0-262-32621-6-ch137
dc.identifier.citationconference
dc.identifier.otherBibtex: urn:4a7e71038f4de36d217885e3787a326c
dc.identifier.otherORCID: /0000-0002-3233-5847/work/86098049
dc.identifier.urihttp://hdl.handle.net/2299/15553
dc.description.abstractIntegrating information gained by observing others via Social Bayesian Learning can be beneficial for an agent’s performance, but can also enable population wide information cascades that perpetuate false beliefs through the agent population. We show how agents can influence the observation network by changing their probability of observing others, and demonstrate the existence of a population-wide equilibrium, where the advantages and disadvantages of the Social Bayesian update are balanced. We also use the formalism of relevant information to illustrate how negative information cascades are characterized by processing increasing amounts of non-relevant informationen
dc.format.extent8
dc.format.extent626135
dc.language.isoeng
dc.publisherMIT Press
dc.relation.ispartofALIFE 14
dc.titleDon't Believe Everything You Hear : Preserving Relevant Information by Discarding Social Informationen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionAdaptive Systems
rioxxterms.versionofrecord10.7551/978-0-262-32621-6-ch137
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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