Using Boolean Functions of Context Factors for Adaptive Mental Model Aggregation in Organisational Learning

Gülay Canbaloğlu, Jan Treur*

*Corresponding author for this work

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

Abstract

Aggregation of developed individual mental models to obtain shared mental models for the organization is a crucial process for organizational learning. This aggregation process usually does not only depend on the mental models used as input for it, but also on several context factors that may vary over circumstances and time. This means that for computational modeling of organizational learning the aggregation process better can be modeled as an adaptive dynamical process where adaptation is used to obtain a context-sensitive outcome of the aggregation. In this paper it is explored how Boolean functions of these context factors can be used to model this form of adaptation. Using self-modeling networks, mental model adaptation by learning, formation or aggregation can be modeled in an appropriate manner at a first-order self-model level, whereas the control over such processes can be modeled at a second-order self-model level. Therefore, for adaptation of aggregation of mental models in particular, a second-order adaptive self-modeling network model for organizational learning can be used. In this paper it is shown how in such a network model at the second-order self-model level, Boolean functions can be used to express logical combinations of context factors and based on this can exert context-sensitive control over the mental model aggregation process. Thus, a computational network model of organizational learning is presented in which the process of aggregation of individual mental models to form shared mental models takes place in an adaptive context-dependent manner based on any Boolean combinations of context factors.
Original languageEnglish
Title of host publicationBiologically Inspired Cognitive Architectures 2021 - Proceedings of the 12th Annual Meeting of the BICA Society
EditorsValentin V. Klimov, David J. Kelley
PublisherSpringer Science and Business Media Deutschland GmbH
Pages54-68
Number of pages15
Edition12th
ISBN (Print)9783030969929
DOIs
Publication statusPublished - 25 Mar 2022
Event12th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2021 - Virtual, Online
Duration: 12 Sep 202119 Sep 2021

Publication series

NameStudies in Computational Intelligence
Volume1032 SCI
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Conference

Conference12th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2021
CityVirtual, Online
Period12/09/2119/09/21

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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