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.
Author(s): Gülay Canbalo˘glu; Jan Treur; Anna Wiewiora
Series: Studies in Systems, Decision and Control
Publisher: Springer
Year: 2023
Language: English
Pages: 512
Preface
Contents
Part I Introduction: Multilevel Organisational Learning and its Computational Analysis and Simulation
1 On Computational Analysis and Simulation for Multilevel Organisational Learning
1.1 Introduction
1.2 Types of Learning and Challenges for Computational Modeling
1.3 Overview of This Volume
1.3.1 Part II Background Knowledge
1.3.2 Part III Overall Computational Models of Multilevel Organisational Learning
1.3.3 Part IV Aggregation in the Formation of Shared Mental Models in Organisational Learning
1.3.4 Part V Computational Analysis of the Role of Leadership in Real-World Scenarios for Multilevel Organisational Learning
1.3.5 Part VI Computational Analysis of the Role of Organisational Culture for Multilevel Organisational Learning
1.3.6 Part VII Mathematical Analysis for Network Models and Organisation Learning
1.3.7 Part VIII Finalising
References
Part II Background Knowledge
2 Multilevel Organisational Learning
2.1 Introduction
2.2 The Nature of Organisational Learning
2.3 Multilevel Organisational Learning—Theoretical Underpinning
2.4 Mechanisms Facilitating Multilevel Learning Flows
2.4.1 Organisational Culture as a Mechanism Facilitating Multilevel Learning
2.4.2 Leader as a Mechanism Facilitating Multilevel Learning
2.4.3 Structure as a Mechanism Facilitating Multilevel Learning
2.4.4 Networks as a Mechanism Facilitating Multilevel Learning
2.5 Discontinuities of Learning Flows
2.6 Multilevel Learning Scenarios
2.6.1 Learning Scenario 1
2.6.2 Learning Scenario 2
2.6.3 Learning Scenario 3
2.6.4 Learning Scenario 4
2.7 Computational Modeling of Multilevel Learning
2.8 Benefits of Using Computational Modeling for Testing and Advancing Multilevel Learning Theory
2.9 Conclusion
References
3 Modeling Dynamics, Adaptivity and Control by Self-modeling Networks
3.1 Introduction
3.2 Modeling Adaptivity by Self-modeling Networks
3.2.1 Network-Oriented Modeling by Temporal-Causal Networks
3.2.2 Using Self-modeling Networks to Model Adaptive Networks
3.3 Modeling Adaptation Principles by Self-models
3.3.1 Modeling First-Order Adaptation Principles by First-Order Self-models
3.3.2 Modeling Second-Order Adaptation Principles for Control of Adaptation by Second-Order Self-models
3.4 Examples from the Organisational Learning Context
3.4.1 Examples for Individual Learning
3.4.2 Examples for Dyad or Group Learning
3.4.3 Examples for Feed Forward and Feedback Organisational Learning
3.5 Discussion
References
4 Modeling Mental Models: Their Use, Adaptation and Control
4.1 Introduction
4.2 A Three-Level Cognitive Architecture for Mental Models and Their Use, Adaptation and Control
4.3 Higher-Order Adaptive Network Models
4.4 Modeling Adaptation of a Mental Model and Its Metacognitive Control by Self-Modeling Networks
4.5 A Second-Order Adaptive Mental Network Model for Metacognitive Control of Adaptation of a Mental Model
4.6 Example Simulation Scenario
4.7 Discussion
4.8 Appendix: Full Specification by Role Matrices
4.8.1 Role Matrices for Connectivity Characteristics
4.8.2 Role Matrices for Aggregation Characteristics
4.8.3 Role Matrices for Timing Characteristics
References
Part III Overall Computational Network Models of Organisational Learning
5 From Conceptual to Computational Mechanisms for Multilevel Organisational Learning
5.1 Introduction
5.2 Overview: From Conceptual to Computational Mechanisms
5.3 The Self-Modeling Network Modeling Approach Used
5.4 Some Examples of Computational Mechanisms
5.5 Computational Models for Feed Forward and Feedback Learning
5.6 Discussion
References
6 Using Self-modeling Networks to Model Organisational Learning
6.1 Introduction
6.2 Background Literature
6.2.1 Mental Models
6.2.2 Shared Mental Models
6.2.3 Organisational Learning: From Individual to Shared Mental Models and Back
6.3 The Self-Modeling Network Modeling Approach Used
6.4 The Adaptive Network Model for Organisational Learning
6.5 Example Simulation Scenario
6.6 Mathematical Analysis of Equilibria of the Network Model
6.7 Discussion
6.8 Appendix: Full Specification by Role Matrices
6.8.1 Role Matrices for Connectivity Characteristics
6.8.2 Role Matrices for Aggregation Characteristics
6.8.3 Role Matrices for Timing Characteristics
References
7 A Controlled Adaptive Self-modeling Network Model of Multilevel Organisational Learning for Individuals, Teams or Projects, and Organisation
7.1 Introduction
7.2 Background Literature
7.3 The Self-modeling Network Modeling Approach
7.4 The Network Model for Multilevel Organisational Learning
7.5 Example Simulation Scenario
7.6 Discussion
7.7 Appendix: Role Matrices
References
8 Organisational Learning and Usage of Mental Models for a Team of Match Officials: A Second-Order Adaptive Network Model
8.1 Introduction
8.2 Background
8.3 The Modeling Approach Used
8.4 The Introduced Adaptive Network Model
8.5 Simulation of the Scenario Case
8.6 Mathematical Analysis of the Network Model
8.7 Discussion
8.8 Appendix: Overview of the States and Role Matrices
References
Part IV Approaches to Aggregation in the Formation of Shared Mental Models in Organisational Learning
9 Heuristic Context-Sensitive Control of Mental Model Aggregation for Multilevel Organisational Learning
9.1 Introduction
9.2 Background Literature
9.3 The Self-Modeling Network Modeling Approach Used
9.4 Adaptive Network Modeling for Organisational Learning with Controlled Mental Model Aggregation
9.5 Details of the Adaptive Network for Heuristic Control of Aggregation
9.6 Example Simulation for Heuristic Context-Sensitive Control of Aggregation
9.7 Discussion
9.8 Appendix Full Specifications by Role Matrices
References
10 Adaptive Mental Model Aggregation in Organisational Learning Using Boolean Propositions of Context Factors
10.1 Introduction
10.2 Mental Models and Organisational Learning
10.3 The Self-Modeling Network Modeling Approach Used
10.4 The Adaptive Network Model for Organisational Learning
10.5 States and Connections Used in the Model
10.6 Example Simulation Scenarios
10.7 Discussion
10.8 Appendix: Role Matrices
References
Part V Computational Analysis of the Role of Leadership in Real-World Scenarios for Multilevel Organisational Learning
11 Computational Analysis of the Role of Leadership Style for Its Context-Sensitive Control over Multilevel Organisational Learning
11.1 Introduction
11.2 Multilevel Organisational Learning and Leadership
11.2.1 Multilevel Organisational Learning
11.2.2 The Influential Role of Leaders in Facilitating Multilevel Learning
11.2.3 The Example Scenario Used as Illustration
11.3 The Self-modeling Network Modeling Approach Used
11.4 The Adaptive Computational Network Model Designed
11.4.1 Team Learning by Observation for Teams T1 and T2 and Feedback Learning from T2 to Individual A
11.4.2 Abstracted Overall View on the Process
11.4.3 Context-Sensitive Control of Institutionalisation of the Shared Mental Model by Managers D and E
11.5 Simulation Results
11.6 Discussion
11.7 Appendix: Role Matrices Specification
References
12 Computational Analysis of a Real-World Scenario of Organisational Learning for a Project Management Organisation
12.1 Introduction
12.2 Multilevel Organisational Learning in the Context of a Project-Based Organisation
12.2.1 Mental Models Activate Organisational Learning
12.2.2 Multilevel Learning in the Context of Project-Based Organisation—A Case for Computational Simulation
12.3 The Self-Modeling Network Modeling Approach Used
12.4 The Designed Controlled Adaptive Network Model
12.5 Simulation of the Scenario
12.6 Discussion
12.7 Appendix: Full Specification of the Network Model by Role Matrices
References
13 Computational Analysis of the Influence of Leadership and Communication on Learning Within an Organisation
13.1 Introduction
13.2 Background Knowledge
13.2.1 (Shared) Mental Models
13.2.2 Organisational Learning
13.2.3 Leadership
13.3 Real-World Scenario
13.4 The Self-modeling Network Modeling Approach Used
13.5 The Second-Order Adaptive Network Model
13.6 Simulation Results
13.6.1 Scenario 1: Inactive Team Leader and Low Natural Communication
13.6.2 Scenario 2: Inactive Team Leader and High Natural Communication
13.6.3 Scenario 3: An Active Team Leader and a Low Natural Communication
13.6.4 Scenario 4: An Active Team Leader and a High Natural Communication
13.7 Addressing Variations in Imperfect Communication
13.7.1 Scenarios 5 and 6
13.7.2 Simulation Results for Scenarios 5 and 6
13.8 Discussion
13.9 Limitations and Further Research
13.10 Appendix: Role Matrices
References
Part VI Computational Analysis of the Role of Organisational Culture for Multilevel Organisational Learning
14 Computational Simulation of the Effects of Different Culture Types and Leader Qualities on Mistake Handling and Organisational Learning
14.1 Introduction
14.2 Background Literature
14.2.1 Organisational Culture
14.2.2 Leadership Qualities
14.2.3 Organisational Learning
14.3 Methodology
14.3.1 The Self-modeling Network Modeling Approach
14.3.2 The Conceptual Model and Modeling Decisions
14.3.3 Illustrative Case Study
14.4 The Introduced Adaptive Self-modeling Network Model
14.5 Simulation Results
14.5.1 Comparison for Different Types of Culture and Leadership
14.5.2 Transition from One Type of Culture to Another One
14.6 Statistical Analysis
14.7 Computational Network Analysis
14.7.1 The Canonical Self-modeling Network Representation of an Adaptive Dynamical System
14.7.2 Analysis of Stationary Points and Equilibria
14.8 Discussion
14.9 Limitations and Future Research
14.10 Conclusion
14.11 Appendix: The Role Matrices Specification of the Model
References
15 Computational Analysis of Transformational Organisational Change with Focus on Organisational Culture and Organisational Learning: An Adaptive Dynamical Systems Modeling Approach
15.1 Introduction
15.2 Methodology
15.2.1 Research Logic and Philosophy
15.2.2 Research Basis (Information Collection and Analysis)
15.2.3 The Self-modeling Network Modeling Approach
15.3 Theory—Background Literature
15.3.1 General Concepts
15.3.2 Transforming Organisational Culture and Processes
15.3.3 Learning Mechanisms
15.4 Designing the Dynamical Systems Model
15.4.1 Research Focus
15.4.2 Description of a Case
15.4.3 The Designed Dynamical Systems Model
15.5 Simulation Results
15.5.1 Full Scenario
15.5.2 Learning from Mistakes
15.5.3 Daily Shift Reflections
15.5.4 Monthly Shift Reflections and Change of Teams
15.5.5 Scenario Variations
15.6 Discussion
15.6.1 Evaluation of the Computational Model for the Research Focus
15.6.2 Practical Implications
15.6.3 Theoretical Implications
15.6.4 Future Research and Limitations
15.7 Appendix: Role Matrices
References
Part VII Mathematical Analysis for Network Models and Organisation Learning
16 Modeling and Analysis of Adaptive Dynamical Systems via Their Canonical Self-modeling Network Representation
16.1 Introduction
16.2 Modeling Dynamics and Adaptation by Self-modeling Networks
16.3 Dynamical Systems and Their Canonical Network Representation
16.4 Adaptive Dynamical Systems and Their Canonical Self-modeling Network Representation
16.5 Basic Concepts for Equilibrium Analysis of Dynamic and Adaptive Networks
16.6 Equilibrium Analysis for Acyclic Networks
16.6.1 Stratification for Acyclic Networks
16.6.2 Using Stratification for Equilibrium Analysis of Acyclic Networks
16.7 Equilibrium Analysis for Any Network by Its Strongly Connected Components
16.7.1 Introducing Stratification for the Strongly Connected Components of a Network
16.7.2 Using the Stratification to Relate Equilibrium Values for Different Components
16.8 Discussion
References
17 Equilibrium Analysis for Multilevel Organisational Learning Models
17.1 Introduction
17.2 Modeling and Analysis of Dynamics and Adaptation for Networks
17.2.1 Modeling by Dynamic and Adaptive Networks
17.2.2 Basic Concepts for Equilibrium Analysis of Dynamic and Adaptive Networks
17.3 Equilibrium Analysis under Connectivity Conditions: Acyclic Networks
17.3.1 Stratification for Acyclic Graphs or Networks
17.3.2 Using Stratification for Equilibrium Analysis of Acyclic Networks
17.4 Equilibrium Analysis under Aggregation Conditions: Monotonicity and Comparison for Combination Functions
17.4.1 Equilibrium Analysis Using Monotonicity and Comparison Relations for Aggregation
17.4.2 Equilibrium Analysis Based on Monotonicity and Comparison for Specific Functions
17.5 Equilibrium Analysis Under Aggregation Conditions: Scalar-Freeness
17.5.1 Functions for Aggregation that Are Scalar-Free
17.5.2 Properties and Comparative Equilibrium Analysis for Scalar-Free Functions
17.6 Equilibrium Analysis under Aggregation Conditions: Using the Strongly Connected Components
17.6.1 Introducing Stratification for the Strongly Connected Components of a Network
17.6.2 Using the Stratification to Relate Equilibrium Values for Different Components
17.7 Application for Equilibrium Analysis of Multilevel Organisational Learning
17.7.1 Computational Modeling of Multilevel Organisational Learning
17.7.2 Applying Equilibrium Analysis Under Connectivity Conditions to a Network Model for Multilevel Organisational Learning
17.7.3 Application of Equilibrium Analysis for Comparison Relations
17.7.4 Application of Equilibrium Analysis Based on Strongly Connected Components
17.8 Discussion
References
Part VIII Finalising
18 Discussion: Perspectives on Computational Modeling of Multilevel Organisational Learning
18.1 Introduction
18.2 Self-Modeling Network Models
18.3 Computational Architecture for Use, Adaptation, and Control of Adaptation
18.4 What Has Been Addressed
18.5 Further Work Being Addressed
References
Index
