Computational Analysis and Simulation of Organisational Learning

Gülay Canbaloğlu, Jan Treur

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

Abstract

Joint Keynote Speech. Organisational learning emerges as a cyclic interplay of various mechanisms at different levels. To analyse and simulate organisational learning computationally, the self-modeling network modelling approach from AI provides a powerful means to address the complexity of the interaction of different mechanisms and the control over them. In this keynote speech, recent developments are presented showing how this approach can be used to analyse and simulate complex processes of organisational learning. This covers 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 using a first-order self-model for the learning and a second-order self-model level for the control over the learning. It will be discussed how this may be applied in the context of improving safety in health-related organisations such as hospitals.
Original languageEnglish
Title of host publicationProc. of the Fifth International Conference on Social Science, Public Health and Education, SSPHE'22
PublisherEDP Sciences
Publication statusAccepted/In press - 15 Nov 2021

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