Computational Modeling Of Multilevel Organisational Learning And Its Control Using Self Modeling Network Models
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Computational Modeling of Multilevel Organisational Learning and Its Control Using Self-modeling Network Models
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.
Using Shared Mental Models and Organisational Learning to Support Safety and Security Through Cyberspace: A Computational Analysis Approach
Author: Peter H.M.P. Roelofsma
language: en
Publisher: Springer Nature
Release Date: 2025-01-02
Ensuring patient safety and security through cyberspace requires that all care professionals operate as a team and community . In order to be successful, it is of paramount importance that all members of the team have a shared understanding of the diagnosis, the condition of the patient, the secure use of medical devices and the plan of action. At present, to ensure that all mem bers have this 'shared mental model', members communicate and observe each other's actions. Based upon this information, members sho uld be able to confirm if indeed all have a shared mental model and speak up when deviations in one or more members and/or processes are suspected. From a group dynamical and information processing perspective, this verification process is known to be very vulnerable: how can red flags be detected in complicated surgery settings and do members feel psychologically safe enough to speak up when they have concerns about being on the same page as the rest of their team? This book presents a new approach for saf ety and security through cyberspace through introducing a concept of co designed clinical pathways supported by the AI coach. The AI coach will be an intervention for both improving hospital wide safety and security through cyberspace. The AI Coach will empower users by supporting and facilitating the development of a shared mental model for team and organisational learning. The AI coach will function as an information, communication, cooperation and decision support system. The book advises to incorporate issues or cybersecurity risk management into the total safety and security process, among others through co-creating security.
Complex Networks & Their Applications XII
This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the XII International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2023). The carefully selected papers cover a wide range of theoretical topics such as network embedding and network geometry; community structure, network dynamics; diffusion, epidemics and spreading processes; machine learning and graph neural networks as well as all the main network applications, including social and political networks; networks in finance and economics; biological networks and technological networks.