國立高雄大學圖資館 |

登入

回首頁

Computational modeling of multilevel organisational learning and its control using self-modeling network models

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computational modeling of multilevel organisational learning and its control using self-modeling network modelsedited by Gulay Canbaloglu, Jan Treur, Anna Wiewiora.
其他作者:	Canbaloglu, Gulay.
出版者:	Cham :Springer International Publishing :2023.
面頁冊數:	xi, 515 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Organizational learningMathematical models.
電子資源:	https://doi.org/10.1007/978-3-031-28735-0
ISBN:	9783031287350$q(electronic bk.)

Computational modeling of multilevel organisational learning and its control using self-modeling network models
Computational modeling of multilevel organisational learning and its control using self-modeling network models[electronic resource] /edited by Gulay Canbaloglu, Jan Treur, Anna Wiewiora. - Cham :Springer International Publishing :2023. - xi, 515 p. :ill. (chiefly col.), digital ;24 cm. - Studies in systems, decision and control,v. 4682198-4190 ;. - Studies in systems, decision and control ;v.3..

On Computational Analysis and Simulation for Multilevel Organizational Learning -- Multilevel Organisational Learning -- Modeling Dynamics, Adaptivity and Control by Self-Modeling Networks -- Modeling Mental Models: their Use, Adaptation and Control -- From Conceptual to Computational Mechanisms for Multilevel Organisational Learning -- Using Self-Modeling Networks to Model Organisational Learning.

Although there is much literature on organisational learning, mathematical formalisation and computational simulation, there is no literature that uses mathematical modelling and simulation to represent and explore different facets of multilevel learning. This book provides an overview of recent work on mathematical formalisation and computational simulation of multilevel organisational learning by exploiting the possibilities of self-modeling network models to address it. This is the first book addressing mathematical formalisation and computational modeling of multilevel organisational learning in a systematic, principled manner. A self-modeling network modeling approach from AI and Network Science is used where in a reflective manner some of the network nodes (called self-model nodes) represent parts of the network's own network structure characteristics. This is supported by a dedicated software environment allowing to design and implement (higher-order) adaptive network models by specifying them in a conceptual manner at a high level of abstraction in a standard table format, without any need of algorithmic specification or programming. This modeling approach allows to model the development of knowledge in an organisational setting in a neatly structured manner at three different levels for the usage, adaptation and control, respectively, of the underlying mental models. Several examples of realistic cases of multilevel organisational learning are used to illustrate the approach. Crucial concepts such as the aggregation of mental models to form shared mental models out of individual mental models are addressed extensively. It is shown how to model context-sensitive control of organisational learning taking into account a wide variety of context factors, for example relating to levels of expertise of individuals or to leadership styles of managers involved. Mathematical equilibrium analysis of models of organisational learning is also addressed, among others allowing verification of correctness of the implemental models in comparison to their conceptual design. Chapters in this book also contribute to the Management and Business Sciences research by demonstrating how computational modeling can be used to capture complex management phenomena such as multilevel organizational learning. This book has a potential implication for practice by demonstrating how computational modeling can be used to capture learning scenarios, which then provide a basis for more informed managerial decisions.

ISBN: 9783031287350$q(electronic bk.)

Standard No.: 10.1007/978-3-031-28735-0doiSubjects--Topical Terms:

944557
Organizational learning
--Mathematical models.

LC Class. No.: HD58.82

Dewey Class. No.: 658.3124

Computational modeling of multilevel organisational learning and its control using self-modeling network models
LDR:04104nmm a2200337 a 4500 001 637994
003 DE-He213
005 20230616105150.0
006 m d
007 cr nn 008maaau
008 230921s2023 sz s 0 eng d
020 $a 9783031287350$q(electronic bk.)
020 $a 9783031287343$q(paper)
024 7 $a 10.1007/978-3-031-28735-0 $2 doi
035 $a 978-3-031-28735-0
040 $a GP $c GP
041 0 $a eng
050 4 $a HD58.82
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 658.3124 $2 23
090 $a HD58.82 $b .C738 2023
245 0 0 $a Computational modeling of multilevel organisational learning and its control using self-modeling network models $h [electronic resource] / $c edited by Gulay Canbaloglu, Jan Treur, Anna Wiewiora.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xi, 515 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
490 1 $a Studies in systems, decision and control, $x 2198-4190 ; $v v. 468
505 0 $a On Computational Analysis and Simulation for Multilevel Organizational Learning -- Multilevel Organisational Learning -- Modeling Dynamics, Adaptivity and Control by Self-Modeling Networks -- Modeling Mental Models: their Use, Adaptation and Control -- From Conceptual to Computational Mechanisms for Multilevel Organisational Learning -- Using Self-Modeling Networks to Model Organisational Learning.
520 $a Although there is much literature on organisational learning, mathematical formalisation and computational simulation, there is no literature that uses mathematical modelling and simulation to represent and explore different facets of multilevel learning. This book provides an overview of recent work on mathematical formalisation and computational simulation of multilevel organisational learning by exploiting the possibilities of self-modeling network models to address it. This is the first book addressing mathematical formalisation and computational modeling of multilevel organisational learning in a systematic, principled manner. A self-modeling network modeling approach from AI and Network Science is used where in a reflective manner some of the network nodes (called self-model nodes) represent parts of the network's own network structure characteristics. This is supported by a dedicated software environment allowing to design and implement (higher-order) adaptive network models by specifying them in a conceptual manner at a high level of abstraction in a standard table format, without any need of algorithmic specification or programming. This modeling approach allows to model the development of knowledge in an organisational setting in a neatly structured manner at three different levels for the usage, adaptation and control, respectively, of the underlying mental models. Several examples of realistic cases of multilevel organisational learning are used to illustrate the approach. Crucial concepts such as the aggregation of mental models to form shared mental models out of individual mental models are addressed extensively. It is shown how to model context-sensitive control of organisational learning taking into account a wide variety of context factors, for example relating to levels of expertise of individuals or to leadership styles of managers involved. Mathematical equilibrium analysis of models of organisational learning is also addressed, among others allowing verification of correctness of the implemental models in comparison to their conceptual design. Chapters in this book also contribute to the Management and Business Sciences research by demonstrating how computational modeling can be used to capture complex management phenomena such as multilevel organizational learning. This book has a potential implication for practice by demonstrating how computational modeling can be used to capture learning scenarios, which then provide a basis for more informed managerial decisions.
650 0 $a Organizational learning $x Mathematical models. $3 944557
650 0 $a Organizational learning $x Computer simulation. $3 944558
650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Engineering Mathematics. $3 806481
650 2 4 $a Computer Modelling. $3 913151
700 1 $a Canbaloglu, Gulay. $3 944555
700 1 $a Treur, Jan. $3 761675
700 1 $a Wiewiora, Anna. $3 944556
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Studies in systems, decision and control ; $v v.3. $3 678532
856 4 0 $u https://doi.org/10.1007/978-3-031-28735-0
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)