Publication: Computational modeling of organisational learning by self-modeling networks
dc.contributor.coauthor | Treur, Jan | |
dc.contributor.coauthor | Roelofsma, Peter H. M. P. | |
dc.contributor.department | Department of Computer Engineering | |
dc.contributor.kuauthor | Canbaloğlu, Gülay | |
dc.contributor.other | Department of Computer Engineering | |
dc.contributor.schoolcollegeinstitute | Graduate School of Sciences and Engineering | |
dc.date.accessioned | 2024-11-09T12:25:29Z | |
dc.date.issued | 2022 | |
dc.description.abstract | Within organisational learning literature, mental models are considered a vehicle for both individual learning and organizational learning. By learning individual mental models (and making them explicit), a basis for formation of shared mental models for the level of the organization is created, which after its formation can then be adopted by individuals. This provides mechanisms for organizational learning. These mechanisms have been used as a basis for an adaptive computational network model. The model is illustrated by a not too complex but realistic case study. | |
dc.description.fulltext | YES | |
dc.description.indexedby | WoS | |
dc.description.indexedby | Scopus | |
dc.description.openaccess | YES | |
dc.description.publisherscope | International | |
dc.description.sponsoredbyTubitakEu | N/A | |
dc.description.sponsorship | N/A | |
dc.description.version | Publisher version | |
dc.description.volume | 73 | |
dc.format | ||
dc.identifier.doi | 10.1016/j.cogsys.2021.12.003 | |
dc.identifier.eissn | 1389-0417 | |
dc.identifier.embargo | NO | |
dc.identifier.filenameinventoryno | IR03626 | |
dc.identifier.issn | 2214-4366 | |
dc.identifier.link | https://doi.org/10.1016/j.cogsys.2021.12.003 | |
dc.identifier.quartile | Q2 | |
dc.identifier.scopus | 2-s2.0-85124796318 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14288/1592 | |
dc.identifier.wos | 779488700006 | |
dc.keywords | Organisational learning | |
dc.keywords | Network model | |
dc.keywords | Mental model | |
dc.keywords | Second-order adaptive | |
dc.keywords | Control of adaptation | |
dc.language | English | |
dc.publisher | Elsevier | |
dc.relation.grantno | NA | |
dc.relation.uri | http://cdm21054.contentdm.oclc.org/cdm/ref/collection/IR/id/10485 | |
dc.source | Cognitive Systems Research | |
dc.subject | Computer science | |
dc.subject | Artificial intelligence | |
dc.subject | Neurosciences | |
dc.subject | Psychology, experimental | |
dc.title | Computational modeling of organisational learning by self-modeling networks | |
dc.type | Journal Article | |
dspace.entity.type | Publication | |
local.contributor.kuauthor | Canbaloğlu, Gülay | |
relation.isOrgUnitOfPublication | 89352e43-bf09-4ef4-82f6-6f9d0174ebae | |
relation.isOrgUnitOfPublication.latestForDiscovery | 89352e43-bf09-4ef4-82f6-6f9d0174ebae |
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