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The analysis of distributed control and information processing in adaptive systems: a biologically motivated approach

dc.contributor.authorKampfner, Roberto R.en_US
dc.date.accessioned2006-04-10T15:24:23Z
dc.date.available2006-04-10T15:24:23Z
dc.date.issued1992en_US
dc.identifier.citationKampfner, Roberto R. (1992)."The analysis of distributed control and information processing in adaptive systems: a biologically motivated approach." Biosystems 26(3): 139-153. <http://hdl.handle.net/2027.42/30310>en_US
dc.identifier.urihttp://www.sciencedirect.com/science/article/B6T2K-49NY235-5S/2/f3824779587ee1bb5096aacd079eaa6ben_US
dc.identifier.urihttps://hdl.handle.net/2027.42/30310
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=1567993&dopt=citationen_US
dc.description.abstractBiological systems have evolved hierarchical, distributed control structures that greatly enhance their adaptability. Two important determinants of biological adaptability considered here are: (i) the pattern of distribution of self-control capabilities; (ii) the degree of programmability of information processing. In this paper we model organizations as goal-oriented, adaptive systems, possessing properties similar to those of biological systems. We use the notion of implicit control (defined as the capability of self-control that is embedded in a system's own dynamics) in the analysis of the impact of specific patterns of distribution of control and information processing on the adaptability of organizations. A principle of design of organizational information systems, that captures important aspects of adaptability-preserving strategies of information processing in biological systems, is stated in terms of the implicit control concept.en_US
dc.format.extent1305921 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherElsevieren_US
dc.titleThe analysis of distributed control and information processing in adaptive systems: a biologically motivated approachen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelNatural Resources and Environmenten_US
dc.subject.hlbsecondlevelMolecular, Cellular and Developmental Biologyen_US
dc.subject.hlbsecondlevelEcology and Evolutionary Biologyen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumComputer and Information Science, School of Engineering, The University of Michigan-Dearborn, Dearborn, MI 48128, USAen_US
dc.identifier.pmid1567993en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/30310/1/0000712.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1016/0303-2647(92)90073-8en_US
dc.identifier.sourceBiosystemsen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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