Context-Sensitive Control of Adaptation: Self-Modeling Networks for Human Mental Processes Using Mental Models Applied to Model Organisational Learning

Gülay Canbaloğlu (Speaker), J. Treur (Speaker)

Activity: Talk or presentationTalk or presentation at a conference


Joint Keynote Speech. Within their mental and social processes, humans often learn, adapt, and apply specific mental models of processes in the world or other persons as a kind of blueprints. In this presentation, it is discussed how analysis of this provides useful inspiration for the development of new computational approaches from a Machine Learning and Network-Oriented Modeling perspective. Three main elements are: applying a mental model as a form of internal (mental) simulation, developing and revising a mental model by some form of adaptation, and exerting control over this adaptation in a context-sensitive manner. This concept of controlled adaptation relates to the Plasticity Versus Stability Conundrum from neuroscience. The presented analysis has led to a three-level computational architecture for controlled adaptation. It is discussed and illustrated by examples of applications how this three-level computational architecture can be specified based on a self-modeling network and used to model controlled learning and adaptation processes based on mental models in a context-sensitive manner. The abovementioned is joint work with Raj Bhalwankar and Laila van Ments; a Springer Nature book about it will be available in December 2021. Recently, as an important next step, it has been found out by Gülay Canbaloğlu how the very challenging topic of computational modeling of complex multilevel organisational learning can be addressed. After a number of published papers this year, another Springer Nature book focussing fully on the latter topic will appear in 2022. See also the playlist on Shared Mental Models and Organisational learning of the YouTube channel on self-modeling networks:
Period9 Nov 2021
Event titleThe 3rd International Conference on Machine Learning and Intelligent Systems
Event typeConference
LocationXiamen, China
Degree of RecognitionInternational