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Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models

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Abstract

This book addresses the challenging topic of modeling adaptive networks, which often have inherently complex behaviour. Networks by themselves usually can be modeled using a neat, declarative and conceptually transparent Network-Oriented Modeling approach. For adaptive networks changing the network’s structure, it is different; often separate procedural specifications are added for the adaptation process. This leaves you with a less transparent, hybrid specification, part of which often is more at a programming level than at a modeling level. This book presents an overall Network-Oriented Modeling approach by which designing adaptive network models becomes much easier, as also the adaptation process is modeled in a neat, declarative and conceptually transparent network-oriented manner, like the network itself. Due to this dedicated overall Network-Oriented Modeling approach, no procedural, algorithmic or programming skills are needed to design complex adaptive network models. A dedicated software environment is available to run these adaptive network models from their high-level specifications. Moreover, as adaptive networks are described in a network format as well, the approach can simply be applied iteratively, so that higher-order adaptive networks in which network adaptation itself is adaptive too, can be modeled just as easily; for example, this can be applied to model metaplasticity from Cognitive Neuroscience. The usefulness of this approach is illustrated in the book by many examples of complex (higher-order) adaptive network models for a wide variety of biological, mental and social processes. The book has been written with multidisciplinary Master and Ph.D. students in mind without assuming much prior knowledge, although also some elementary mathematical analysis is not completely avoided. The detailed presentation makes that it can be used as an introduction in Network-Oriented Modelling for adaptive networks. Sometimes overlap between chapters can be found in order to make it easier to read each chapter separately. In each of the chapters, in the Discussion section, specific publications and authors are indicated that relate to the material presented in the chapter. The specific mathematical details concerning difference and differential equations have been concentrated in Chapters 10 to 15 in Part IV and Part V, which easily can be skipped if desired. For a modeler who just wants to use this modeling approach, Chapters 1 to 9 provide a good introduction. The material in this book is being used in teaching undergraduate and graduate students with a multidisciplinary background or interest. Lecturers can contact me for additional material such as slides, assignments, and software. Videos of lectures for many of the chapters can be found at https://www.youtube.com/watch?v=8Nqp_dEIipU&list=PLF-Ldc28P1zUjk49iRnXYk4R-Jm4lkv2b.
Network-Oriented Modeling for Adaptive Networks:
Designing Higher-Order Adaptive Biological, Mental
and Social Network Models
Jan Treur
Social AI Group, Vrije Universiteit Amsterdam, The Netherlands
Email: j.treur@vu.nl
URL: https://www.researchgate.net/profile/Jan_Treur
Table of Contents
Note that the chapter titles have hyperlinks leading to their abstracts and (eventually) to Powerpoint
presentations.
Part I Introduction
1. On Adaptive Networks and Network Reification
1.1 Introduction
1.2 First and Second-order Adaptation
1.2.1 First-order Adaptation
1.2.2 Second-order Adaptation
1.3 Higher-Order adaptation
1.3.1 What Orders of Adaptation are Addressed?
1.3.2 Examples of Second and Third-order Adaptation from an Evolutionary Context
1.4 Using Network Reification to Model Adaptive Networks
1.4.1 The Hybrid Approach to Model Adaptive Networks
1.4.2 Modeling Adaptive Networks Based on Network Reification
1.5 Modeling Higher-Order Adaptive Networks by Multilevel Network Reification
1.6 Mathematical Analysis of Reified Networks
1.6.1 Mathematical Analysis of a Base Network
1.6.2 Mathematical Analysis Applied to Reification States
1.7 Discussion
2. Ins and Outs of Network-Oriented Modeling
2.1 Introduction
2.2 Network-Oriented Modeling: Conceptual Background
2.2.1 The Unifying Potential of Networks
2.2.2 On the Meaning of Basic Elements in a Network
2.2.3 Meaning as Defined by a Temporal-Causal Network
2.2.4 Biological, Mental and Social Domains Ask for Networks
2.3 Numerical Representation of a Temporal-Causal Network
2.3.1 Numerical-Mathematical Formalisation
2.3.2 Combination Functions as Building Block
2.4 Role Matrices to Specify a Network Model
2.4.1 Role Matrices as a Specification Format
2.4.2 From Network Structure to Network Behaviour: How Role Matrices Define
the Difference and Differential Equations
2.4.3 Simulations for the Example Social Network Model
2.5 Relating Emerging Network Behaviour to Network Structure
2.5.1 Network Emerging Behaviour and Network Structure
2.5.2 Network Structure Properties Relevant for Emerging Behaviour
2.5.3 Relating Network Structure Properties to Emerging Behaviour
2.6 The Wide Applicability of Network-Oriented Modeling
2.6.1 Network-Oriented Modeling Applies Beyond Perceived Networks
2.6.2 Network-Oriented Modeling Applies to Network Adaptation
2.7 Discussion
Part II Modeling Adaptive Networks by Network Reification
3. A Unified Approach to Represent Network Adaptation Principles by Network Reification
3.1 Introduction
3.2 Temporal-Causal Networks: Structure and Dynamics
3.2.1 Conceptual Representation of a Temporal-Causal Network Model
3.2.2 Numerical Representation of a Temporal-Causal Network Model
3.2.3 Basic Combination Functions, their Parameters, and Combining them
3.2.4 Normalisation, Stationary Points and Equilibria for Temporal-Causal Network Models
3.3 Modeling Adaptive Networks by Network Reification
3.4 Incorporating the Impact of Downward Causal Connections for Reification States
3.5 The Universal Combination Function and Difference Equation for Reified Networks
3.5.1 The Universal Combination Function for Combined Reification for all Network Structure
Characteristics
3.5.2 The Universal Difference Equation for Reified Networks and its Differential Equation
Variant
3.6 Using Network Reification for Unified Modeling of Network Adaptation Principles
3.6.1 Network Reification for Adaptation Principles for Hebbian Learning and Bonding by
Homophily
3.6.2 Network Reification for the Triadic Closure Adaptation Principle
3.6.3 Network Reification for the Preferential Attachment Adaptation Principle
3.6.4 Network Reification for the State-Connection Modulation Adaptation Principle
3.6.5 Network Reification for Response Speed Adaptation Principles
3.6.6 Network Reification for Aggregation Adaptation Principles
3.7 A Reified Network Model for Response Speed Adaptation and Aggregation Adaptation
3.7.1 Conceptual Graphical Representation of the Example Reified Network Model
3.7.2 Conceptual Role Matrices Representation of the Example Reified Network Model
3.7.3 Simulation outcomes for the Example Reified Network Model
3.7.4 Analysis of the Equilibria for the Example Reified Network Model
3.8 On the Added Complexity for Network Reification
3.9 Discussion
4. Modeling Higher-Order Network Adaptation by Multilevel Network Reification
4.1 Introduction
4.2 Structure and Dynamics of Temporal-Causal Networks
4.3 Addressing Network Adaptation by Network Reification
4.3.1 Extending the Network by Reification States
4.3.2 The Universal Combination function and Universal Difference Equation for
Reified Networks
4.4 Using Multilevel Network Reification for Higher-Order Adaptive Network Models
4.4.1 Using Multilevel Network Reification for Plasticity and Metaplasticity from
Cognitive Neuroscience
4.4.2 Role Matrices Covering Plasticity and Metaplasticity
4.5 Simulation for a Second-order Reified Network Model for Plasticity and Metaplasticity
4.6 On the Added Complexity for Higher-Order Network Reification
4.7 Discussion
Part III Applications of Higher-Order Adaptive Network Models
5. A Reified Network Model for Adaptive Decision Making Based on the Disconnect-Reconnect
Adaptation Principle
5.1 Introduction
5.2 Neurological Principles
5.3 The Reified Adaptive Network Model
5.3.1 The Base Network
5.3.2 Modeling First and Second-order Adaptation of the Connection Weights
by Reification States
5.4 Combination Functions and Role Matrices for the Reified Network Model
5.4.1 The Combination Functions Used
5.4.2 The Role Matrices
5.5 Example Simulation Scenarios
5.5.1 Scenario 1: First-order adaptation; no adaptive speed of connection weight change
5.5.2 Scenario 2: Second-order adaptation; adaptive speed of connection weight change
5.6 Verification of the Network Model by Mathematical Analysis
5.6.1 Solving the Linear Equilibrium Equations for the Base Network
5.6.2 Addressing the Nonlinear Equilibrium Equations for the Reification States
5.7 Discussion
6. Using Multilevel Network Reification to Model Second-Order Adaptive Bonding by Homophily
6.1 Introduction
6.2 Conceptual Representation of the Second-Order Adaptive Social Network Model
6.2.1 Reification states at the first reification level
6.2.2 Reification states at the second reification level
6.3 Combination Functions Used at the Three Levels
6.3.1 Base level and first reification level combination functions
6.3.2 Second reification level combination functions
6.4 Role Matrices for the Adaptive Social Network Model
6.5 Simulation of the Second-Order Adaptive Social Network Model
6.5.1 Scenario 1: Adaptive Connections for One Person: Homophily Modulation Factor 0.1
6.5.2 Scenario 2: Adaptive Connections for One Person: Homophily Modulation Factor 0.9
6.5.3 Scenario 3: Adaptive Connections for All Persons
6.6. Analysis of the Equilibria of the Reification States
6.7 Discussion
7 Modeling Higher-Order Adaptive Evolutionary Processes by Reified Adaptive Network Models
7.1 Introduction
7.2 Higher-Order Adaptation in Evolutionary Processes
7.3 A Reified Network Model for Fourth-order Adaptive Evolutionary Processes
7.3.1 Adaptive causal modeling of changing causal pathways in evolutionary processes
7.3.2 The reified adaptive network model for the described fourth-order adaptation case
7.4 Simulation Experiments
7.4.1 Simulation for Scenario 1: Occurrence of Pathogens and Defense System
7.4.2 Simulation for Scenario 2: Occurrence of Pregnancy
7.4.3 Simulation for Scenario 3: Occurrence of Disgust
7.5 Mathematical Analysis
7.5.1 General approach to the mathematical analysis of equilibria
7.5.2 Mathematical analysis of the different scenarios
7.5.3 Verification of the reified network model
7.6 Discussion
8 Reified Adaptive Network Models of Higher-Order Modeling a Strange Loop
8.1 Introduction
8.2 The notion of Strange Loop
8.2.1 Strange Loops in Music, Graphic Art and Paradoxes
8.2.2 Strange Loops in the Brain
8.3 A Twelve and Four Level Reified Adaptive Network Model Based on a Strange Loop
8.3.1 A 12 Level Reified Adaptive Network Model Forming a Cycle of Levels
8.3.2 A Simpler 4 Level Reified Example of an Adaptive Network Model for a Strange Loop
8.4 Simulation Example of the Four Level Strange Loop Reified Network Model
8.5 A Two Hands Reified Network Model for Adaptive Decision Making
8.6 An Example Simulation of the Two Hands Reified Network Model
8.7 Discussion
Part IV A Modeling Environment for Reified Networks
9. A Modeling Environment for Reified Temporal-Causal Network Models
9.1 Introduction
9.2 Role Matrices as Specification Format for Reified Network Models
9.3 The Combination Function Library
9.3.1 Different Groups of Combination Functions
9.3.2 The Standard Format for Combination Functions
9.4 The Computational Reified Network Engine
9.4.1 Splitting the Role Matrices and Copying them into Matlab
9.4.2 Retrieving Information from the Role Matrices
9.4.3 The Iteration Step from t to t+t
9.5 Discussion
10 On the Universal Combination Function and the Universal Difference Equation for Reified Temporal-
Causal Network Models
10.1 Introduction
10.2 A Short Route to the Universal Difference and Differential equation
10.3 Downward Causal Connections Defining the Special Effect of Reification States
10.3.1 The overall picture
10.3.2 Downward Causal Connections for role W for Connection Weight Reification
10.3.3 Downward Causal Connections for role H for Speed Factor Reification
10.3.4 Downward Causal Connections for roles C and P for Combination Function Weight
and Parameter Reification
10.4 Deriving the Universal Combination Function and Difference Equation for Reified Networks
10.4.1 Deriving the Universal Combination Function for Reified Networks
10.4.2 Deriving the Universal Difference Equation for Reified Networks
10.5 The Criterion for a Stationary Point for the Universal Difference Equation
10.6 Deriving the Universal Combination Function and Difference Equation from
the Role Matrices
10.7 Compilation of the Universal Difference Equation by Substitution
10.8 Discussion
Part V Mathematical Analysis of How Emerging Network Behaviour Relates to Base
Network Structure
11 Relating Network Emerging Behaviour to Network Structure
11.1 Introduction
11.2 Conceptual and Numerical Representation of a Network
11.3 Examples of a Network’s Emerging Behaviour
11.3.1 The Example Social Network
11.3.2 Three Simulations with Different Emerging Behaviour
11.4 Relevant Properties of Network Structure
11.4.1 Relevant Properties of Combination Functions
11.4.2 Relevant Properties of the Network’s Connectivity
11.5 Results Relating Emerging Behaviour to Network Structure
11.5.1 Basic Definitions and Results
11.5.2 Common Final Values for Acyclic and Strongly Connected Networks
11.5.3 The Effect of Independent States on a Network’s Emerging Behaviour
11.5.4 How the Speed Factors Affect a Common Equilibrium Value
11.5.5 Emerging Behaviour for Fully Connected Networks
11.6 Emerging Behaviour for Euclidean, Scaled Maximum and Scaled Minimum Combination
Functions
11.6.1 Emerging Behaviour for Euclidean Combination Functions of Different Orders
11.6.2 Comparing Equilibrium Values for Euclidean Combination Functions and Scaled
Maximum Combination Functions
11.7 Discussion
12 Analysis of a Network’s Emerging Behaviour via its Structure Involving its Strongly Connected
Components
12.1 Introduction
12.2 Temporal-Causal Networks
12.3 Emerging Behaviour of a Network
12.3.1 Basics on Stationary Points and Equilibria for Temporal-Causal Networks
12.3.2 An Example Network
12.3.3 Simulations for the Example Network
12.4 Network Connectivity and Strongly Connected Components
12.4.1 A Network’s Strongly Connected Components
12.4.2 The Stratified Condensation Graph of a Network
12.5 Relevant Properties of Combination Functions
12.6 Network Emerging Behaviour and Network Structure Characteristics
12.6.1 Network Emerging Behaviour for Special Cases
12.6.2 Network Emerging Behaviour for the General Case
12.7 Further Implications for Example Networks
12.7.1 Further Analysis of the Example Network from Section 8.3.2
12.7.2 Analysis of an Example Mental Network
12.8 Discussion
Part VI Mathematical Analysis of How Emerging Network Behaviour of Adaptive
Networks Relates to Reified Network Structure
13 Relating a Reified Adaptive Network’s Structure to its Emerging Behaviour for Bonding by
Homophily
13.1 Introduction
13.2 Network-Oriented Modeling by Temporal-Causal Networks
13.3 Reified Adaptive Networks for Bonding based on Homophily
13.3.1 Modeling the Bonding by Homophily Adaptation Principle by Reification
13.3.2 Various Examples of Homophily Combination Functions
13.4 Example Simulations for the Coevolution of Social Contagion and Bonding
13.4.1 Simulations for Varying Homophily Modulation Factor
13.4.2 Exploring the Birth of an Extra Cluster
13.5 Relevant Properties of Combination Functions for Bonding by Homophily and for Social
Contagion
13.5.1 Relevant Properties of Combination Functions for Bonding by Homophily
13.5.2 Relevant Properties of Combination Functions for Social Contagion
13.6 Relating Adaptive Network Structure to Emerging Bonding Behaviour
13.6.1 Relating Structure and Emerging Behaviour Independent of Social Contagion
Properties
13.6.2 Relating Structure and Emerging Behaviour for some Social Contagion Properties
13.7 Characterising Emerging Behaviour in Terms of Strongly Connected Components
13.8 Overview of Other Simulation Experiments
13.9 Discussion
14 Relating a Reified Adaptive Network’s Structure to its Emerging Behaviour for Hebbian learning
14.1 Introduction
14.2 Temporal-Causal Networks and Network Reification
14.3 Reified Adaptive Networks for Hebbian Learning
14.3.1 Reification States for Hebbian Learning and their Hebbian
Learning Combination Functions
14.3.2 An Example Reified Network Model with Multiple Hebbian
Learning Reification States
14.4 Relevant Properties of Hebbian Learning Functions and the Implied Emerging Behaviour
14.4.1 Relevant Properties of Hebbian Learning Combination Functions
14.4.2 Functional Relation for the Equilibrium Value of a Hebbian Learning Reification State
14.5 Variable Separation for Hebbian Learning Combination Functions and
the Implied Emerging Behaviour
14.5.1 Hebbian Learning Combination Functions with Variable Separation
14.5.2 Functional Relation for the Equilibrium Value of a Hebbian Learning Reification State:
the Variable Separation Case
14.6 Implications for Different Classes of Hebbian Learning Functions
14.6.1 Hebbian Learning Functions with Variable Separation and Linear Connection Factor
14.6.2 Hebbian Learning Functions with Variable Separation and Quadratic Connection
Factor
14.7 Discussion
Part VII Finalising
15 Mathematical Details of Specific Difference and Differential Equations and Mathematical Analysis of
Emerging Network Behaviour
15.1 Introduction
15.2 Two Different Formulations of Hebbian Learning are Equivalent
15.3 Numerical Representation for an Example Reified Network Model
15.4 The Difference Equations for Combined Hebbian Learning and State-Connection Modulation
15.5 Difference and Differential Equations for Connection Weight Reification States
15.6 Emerging Behaviour for Classes of Combination Functions and Types of Network
Connectivity
15.7 Using Strongly Connected Components to Explore Emerging Behaviour for a Class of
Combination Functions for any Type of Network Connectivity
15.8 Analysis of Emerging Behaviour for Classes of Homophily Functions
15.9 Analysis of Emerging Behaviour for Classes of Hebbian Learning Functions
16 Using Network Reification for Adaptive Networks: Discussion
16.1 Adaptive Networks and Network Reification
16.2 Conceptual Representations of Reified Networks: 3D Pictures and Role Matrices
16.3 The Universal Combination Function, and the Universal Difference and Differential
Equation
16.4 Analysis of How Emerging Reified Network Behaviour Relates to the Reified
Network’s Structure
16.5 Network-Oriented Modeling Based on Reified Networks
16.6 Relations to Longstanding Themes in AI and Beyond

Supplementary resource (1)

... Network-Oriented Modelling is a novel method of modelling introduced by Jan Treur [15], which applies itself well for interconnected and interactive processes, such as organizational learning. Given the direct relation between dynamics and causal relations, as highlighted in [15], this classical approach is extended to contain the notion of dynamics in a network-structure. ...
... Network-Oriented Modelling is a novel method of modelling introduced by Jan Treur [15], which applies itself well for interconnected and interactive processes, such as organizational learning. Given the direct relation between dynamics and causal relations, as highlighted in [15], this classical approach is extended to contain the notion of dynamics in a network-structure. ...
... More specifically, the nodes in a network are interpreted here as states (or state variables) that vary over time, and the connections are interpreted as causal relations that define how each state can affect other states over time. To acknowledge this perspective of dynamics and causality on networks, this type of network has been called a temporalcausal network [16]" [15]. ...
Conference Paper
Learning is an integral part of organizations. Therefore, as introduced by Kim [1], organizational learning has been coined as a key term, as it facilitates further development, both within individuals, amongst individuals, and as a result, within teams and organizations. Just safety culture, also known as safety culture or just culture, refers to a culture within an organization that promotes open communication, transparency, and trust amongst professionals in the field. [2] It is a tool to facilitate organizational learning. This research paper is part of the SAFEcoach Project, which aims to incorporate an AI Coach (AIC) into hospital organizations to assist the healthcare practitioners (HCPs) in their tasks and strive for a just safety culture. The task of the AI Coach includes reminding the HCP of forgotten steps, encouraging the HCP to take additional steps, and possibly even taking over the role of the HCP under certain circumstances to the AICs’ maximum capability. The increasing use of artificial intelligence (AI) to further the understanding of processes and learning capabilities within organizations, has proven vital. Hospitals are no exception to this. Hospitals are structured to have protocols for all aspects and actors within them. Protocols lay the foundation of all processes yet is predominantly focused on the essential aspects, such as medical processes, in, for example, childbirth. Childbirth is a sensitive and daily occurring natural phenomenon in hospitals. There are steps in place, for before, during and after, that healthcare professionals must follow to ensure a safe and healthy delivery of the child and for the mother. However, protocols are only in place for processes and deviations in processes that occur within the hospital. They fail to consider the possible adverse effects that may occur outside the hospital due to the actions of HCPs or lack thereof. Bedside manners are within the curriculum of medical studies; however, the importance of adequate patient communication is often underestimated and, therefore, a less valued step, and sometimes even a forgotten step, within the protocol. In the context of childbirth, a lack of communication with both the mother and partner can result in feelings of isolation. Moreover, the divide between parents and HCPs does not allow for open communication. This barrier is a factor in parents developing mental health problems, such as postpartum depression (PPD). Paternal postpartum depression (PPPD) commonly occurs, yet since fathers are not active actors in the birth-giving process, little attention is awarded to them during childbirth. PPPD is accompanied by adverse effects affecting both the mother and the child. This paper discusses methods that could facilitate better support for the father in order to decrease the frequency of fathers experiencing mental health issues. To incorporate an AIC into the field, as is the goal of SAFEcoach, the Coach needs perfect knowledge of the processes that occur for its actions to have merit. To do so, Network-Oriented Modelling is used, which encompasses creating conceptual and numerical representations of a healthcare practitioner’s mental model, as well as the protocols in place and incorporating that into the AIC. As a result, the AIC creates its own perfect mental model and has perfect knowledge about the protocols, making it a useful tool for HCPs. This paper presents models designed to encourage open and adequate communication both from the father’s side and the healthcare practitioner’s side. The states in these models encompass protocols and common knowledge for all actors involved, as well as their own individual knowledge. The aim is to expand the definition of safety culture, to involve not only the medical practitioners but also the patients and their families, and to encourage communication. In return, this implementation depicts the benefits of open communication and its subsequent effect of decreasing the risk of both the mother and father developing PPD.
... By graphically conceptualizing a complex process, the modeling approach acts as a basis of the conceptualization by representing the dynamic processes without separating or isolating different states. Temporal-causal networks specifically, incorporate the timing of dynamic or cyclical processes based on a continuoustime temporal dimension to time causal effects (Treur, 2020a(Treur, , 2020b(Treur, , 2020c. Such networks can be represented both conceptually and numerically. ...
... Such networks can be represented both conceptually and numerically. Moreover, this network-oriented modeling approach is an appropriate method for modeling a complex phenomenon such as handling mental models in mental processes (Treur and Van Ments, 2022) and organizational learning and the roles of different contextual factors; e.g., ( (Treur, 2020a(Treur, , 2020b to obtain an adaptive reified network, also referred to as a self-modeling network (Treur, 2020c). The name stems from the fact that network reification enables explicit representation of network characteristics by nodes in the network, which is also called self-modeling. ...
... The self-modeling construction can be iterated to create even higher levels of self-models (third-order, etc.). Although very few academic studies have used higher than second-order adaptive models (~22) (Treur, 2020b). In the current paper, a second-order adaptive selfmodeling network approach is used to conduct different simulation experiments (See section 3.4). ...
Conference Paper
This paper investigates computationally the following research hypotheses: (1) Higher flexibility and discretion in organizational culture results in better mistake management and thus better organizational learning, (2) Effective organizational learning requires a transformational leader to have both high social and formal status and consistency, and (3) Company culture and leader's behavior must align for the best learning effects. Computational simulations of the introduced adaptive network were analyzed in different contexts varying in organization culture and leader characteristics. Statistical analysis results proved to be significant and supported the research hypotheses. Ultimately, this paper provides insight into how organizations that foster a mistake-tolerant attitude in alignment with the leader, can result in significantly better organizational learning on a team and individual level.
... Only limited and recent work has begun investigating multilevel learning using computational analysis (Canbaloğlu, Treur, Roelofsma, 2022a;Canbaloğlu, Treur, Wiewiora, 2022b;Canbaloğlu, Treur, Wiewiora, 2022c). Building on previous work on self-modeling networks and mental models (Treur, 2020a(Treur, , 2020bTreur and Van Ments, 2022), recently an adaptive dynamical systems modeling approach has been developed that offers a platform for addressing multilevel organisational learning computationally (Canbaloğlu, Treur, Roelofsma, 2022a;Canbaloğlu, Treur, Wiewiora, 2022b;Canbaloğlu, Treur, Wiewiora, 2022c). In the current paper, this approach is applied to computationally analyse a real-world case for a large worldwide project-based organisation, as described in Wiewiora, Chang, Smidt (2020). ...
... Finally, yet another special feature is that detailed control and global control over the learning processes are distinguished from each other. To this end, adopting the selfmodeling network approach (Treur, 2020a(Treur, , 2020b, the following four different representational and computational levels are distinguished in the organisational learning processes: ...
... Realistic network models are usually adaptive: often not only their states but also some of their network characteristics change over time. By using a self-modeling network (also called a reified network), a networkoriented conceptualization can also be applied to adaptive networks to obtain a declarative description using mathematically defined functions and relations for them as well; see (Treur, 2020a;Treur, 2020b). This works through the addition of new states to the network (called self-model states) which represent (adaptive) network characteristics. ...
Conference Paper
This paper describes how the recently developed self-modeling network modeling approach for multilevel organisational learning has been tested on applicability for a real-world case of a project-based organisation. The modeling approach was able to successfully address this complex case by designing a third-order adaptive network model. Doing this, as a form of further innovation three new features have been added to the modeling approach: recombination of selected high-quality mental model parts, refinement of mental model parts, and distinction between context-sensitive detailed control and global control.
... The presented neural agent model is based on network-oriented modeling. Following (Treur, 2020a(Treur, , 2020b, a temporal-causal network model is characterized by (here X and Y denote nodes of the network, also called states): ...
... Realistic network models are usually adaptive: often not only their states but also some of their network characteristics change over time. By using a self-modeling network (also called a reified network), a similar network-oriented conceptualization can also be applied to adaptive networks to obtain a declarative description using mathematically defined functions and relations for them as well; see (Treur, 2020a;Treur, 2020b). This works through the addition of new states to the network (called self-model states) which represent (adaptive) network characteristics. ...
... We modeled adaptation and its control needed in the neural agent model using a 'selfmodeling network'; see Sect. 3 or (Treur, 2020a(Treur, , 2020b. Following what has been described in Sect. ...
Conference Paper
For a video presentation, see https://www.youtube.com/watch?v=PRUzrkf1mW4. When people interact, their behaviour tends to become synchronized, a mutual coordination process that fosters short-term adaptations, like increased affiliation, and long-term adaptations, like increased bonding. This paper addresses for the first time how such short-term and long-term adaptivity induced by synchronization can be modeled computationally by a second-order multi-adaptive neural agent model. It addresses movement, affect and verbal modalities and both intrapersonal synchrony and interpersonal synchrony. The behaviour of the introduced neural agent model was evaluated in a simulation paradigm with different stimuli and communication enabling conditions. The outcomes illustrate how synchrony leads to stronger short-term affiliation which in turn leads to more synchrony and stronger long-term bonding, and conversely.
... The presented controlled adaptive network model is based on network-oriented modeling. Following (Treur, 2020a(Treur, , 2020b, a temporal-causal network model is characterized by (here X and Y denote nodes of the network, also called states): ...
... Realistic network models are usually adaptive: often not only their states but also some of their network characteristics change over time. By using a self-modeling network (also called a reified network), a similar network-oriented conceptualization can also be applied to adaptive networks to obtain a declarative description using mathematically defined functions and relations for them as well; see (Treur, 2020a;Treur, 2020b). This works through the addition of new states to the network (called self-model states) which represent (adaptive) network characteristics. ...
Conference Paper
Although within therapeutical sessions, in an adaptive manner more interpersonal synchrony leads to a better alliance, in practice also ruptures often occur which break synchrony for some period of time. As in general synchrony favors alliance or liking, it might be expected that interruptions of synchrony negatively influence alliance or liking. However, it has been reported that such interruptions also favor alliance or liking to such an extent that time periods with synchrony interrupted by ruptures may favor alliance or liking even more than time periods with synchrony without ruptures. This paper introduces a controlled adaptive network model that addresses this effect.
... Realistic network models are usually adaptive: often not only their states but also some of their network characteristics change over time. By using a self-modeling network (also called a reified network), a similar network-oriented conceptualization can also be applied to adaptive networks to obtain a declarative description using mathematically defined functions and relations for them as well; see (Treur, 2020a(Treur, , 2020b. This works through the addition of new states to the network (called self-model states) which represent (adaptive) network characteristics. ...
Conference Paper
This research addresses the influence of leadership and communication on learning within an organisation by direct mutual interactions in dyads. This is done in combination with multilevel organizational learning as an alternative route, which includes feed forward and feedback learning. The results show that effective communication (triggered by the active team leader, and/or by natural, informal communication), leads to a faster learning process within an organization compared to the longer route via feed forward and feedback formal organisational learning. However, this more direct form of bilateral learning in general may take more of the employee's time, as a quadratic number of dyadic interactions in general is less efficient than a linear number of interactions needed for feed forward and feedback organisational learning.
... By using a self-modeling network (also called a reified network), a similar network-oriented conceptualization can also be applied to adaptive networks; see (Treur, 2020a;Treur, 2020b). This works through the addition of new states to the network (called self-model states) which represent (adaptive) network characteristics. ...
Conference Paper
Interpersonal synchrony usually induces behavioural adaptivity concerning the interaction between people. Such behavioural adaptivity is assumed to be driven by some form of subjective internal synchrony detection. In contrast to objective synchrony detection by an external (third-party) observer, such subjective synchrony detection can solely rely on subjective information available within the person by sensing. However, interaction between two persons involves time lags between the own actions and the sensing of actions of the other. In the computational agent model described in this paper, we explore the role of time lags in different types of subjective synchrony detection and its involvement in behavioural adaptivity. Multiple simulation experiments show expected types of patterns of subjective time-lagged synchrony detection and related behavioural adaptivity.
Chapter
This study introduces a second-level adaptive temporal-causal decision model for deciding whether to keep or not to keep a baby. In this model, different actors and factors that influence the decision-making process have been incorporated based on literature. Hereby, the actors are responsible for the Hebbian learning component and make the model adaptive. Complementary speed factors regulate when learning happens. Three scenarios for the different possible outcomes; keeping the baby, having an abortion and putting the baby up for adoption provide insights into the influences of the actors on making a decision.
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For a video presentation, see https://www.youtube.com/watch?v=PRUzrkf1mW4. When people interact, their behaviour tends to become synchronised, a mutual coordination process that fosters short-term adaptations, like increased affiliation, and long-term adaptations, like increased bonding. This paper addresses for the first time how such short-term and long-term adaptivity induced by synchronisation can be modeled computationally by a second-order multi-adaptive neural agent model. This neural agent model addresses movement, affect and verbal modalities and both intrapersonal synchrony and interpersonal synchrony. The behaviour of the introduced neural agent model was evaluated in a simulation paradigm with different stimuli and communication enabling conditions. The outcomes illustrate how synchrony leads to stronger short-term affiliation which in turn leads to more synchrony and stronger long-term bonding, and conversely.
Conference Paper
This paper introduces a novel controlled adaptive mental causal network model addressing how dreams overnight can influence creativity in waking life. The network model depicts in a causal, dynamic, and generic manner which adaptive mental processes underlie the connection between dreams and creativity and is shown to be validated with the existing cognitive neuroscience literature.
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