Computational Modeling of Multilevel Organisational Learning: From Conceptual to Computational Mechanisms

Gülay Canbaloğlu, Jan Treur, Anna Wiewiora

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Within the literature, many specific conceptual learning mechanisms have been identified that play a role in organisational learning. Organisational learning emerges as an interplay of them. To be able to develop computational models for organisational learning, such conceptual mechanisms have to be formalised by computational mechanisms. Self-modeling networks provide powerful means to address the complexity of the interaction of different forms of learning, including the control over them. In this paper, recent developments are presented showing how this computational intelligence approach can be used to model the complex process of organisational learning and its underlying mechanisms covering both feed-forward learning to learn shared team or organisation mental models out of individually learned personal mental models and feedback learning to let individuals learn personal mental models from shared mental models. It is shown how by a self-modeling network, the different types of learning can be modeled by including a first-order self-model level and the control over them by including a second-order self-model level.
Original languageEnglish
Title of host publicationComputational Intelligence: Automate Your World. The Second International Conference on Information Technology, InCITe'22
PublisherSpringer Nature Switzerland AG
Publication statusAccepted/In press - 15 Sep 2021

Publication series

NameLecture Notes in Electrical Engineering
PublisherSpringer Nature

Fingerprint

Dive into the research topics of 'Computational Modeling of Multilevel Organisational Learning: From Conceptual to Computational Mechanisms'. Together they form a unique fingerprint.

Cite this