Network-Oriented Modeling:
Addressing Complexity in Cognitive, Affective,
and Social Interactions
Jan Treur
Table of Contents
Part I Network-Oriented Modeling: Introduction
1 Network-Oriented Modelling and Its Conceptual Foundations
An Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.1 Introduction . . . . . . . .. . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . .
1.2 Addressing Human Complexity by Separation Assumptions . . . . . . . .. . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . .
1.3 Addressing Complexity by Interaction in Networks Instead of by Separation . . . . . . . .. . . . . . . . . . . .
1.4 Network-Oriented Modelling . . . . . . . .. . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .. . . . . . . . . . .
1.5 The Dynamic Computational Modelling Perspective. . . . . . . .. . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . .
1.6 Network-Oriented Modelling Based on Temporal-Causal Networks . . . . . . . .. . . . . . . . . . . .
1.7 Scope of Applicability and Achievements. . . . . . . .. . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . .
1.8 Overview of the Book . . . . . . . .. . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . .
References. . . . . . . .. . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . .
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2 A Temporal-Causal Network Modeling Approach
With Biological, Neurological and Social Processes as Inspiration . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1 Introduction
2.2 Modeling Complex Processes by Temporal-Causal Networks . . . . . . . . . . .. . . . . . . . . . . .
2.3 Exploiting Knowledge About Physical and Biological Mechanisms in Modelling. . . . . . . . . .
2.3.1 Addressing Complexity by Higher Level Models Based on Knowledge from Computer Science . . . .
2.3.2 Addressing Complexity by Higher Level Models Based on Knowledge from Neuroscience. . . .
2.4 Conceptual Representation of a Temporal-Causal Network Model. . . . . . . . . . . . .
2.4.1 Conceptual Representations of a Temporal-Causal Network Model . . . . . . . . . . . . .
2.4.2 More Specific Examples of Conceptual Representations of Temporal-Causal Network Models. . .
2.5 Numerical Representation of a Temporal-Causal Network Model . . . . . .. . . . . . . . . .
2.5.1 The Systematic Transformation from Conceptual to Numerical Representation . . . . . .
2.5.2 Illustration of the Transformation for the Example of Fig. 2.10 . . . . . . .
2.5.3 Illustration of the Modelling Perspective for a Social Contagion Process . . . . . . . . . .
2.6 Standard Combination Functions . . . . . . . . . . . . . . . . . . . . . . .
2.6.1 Basic Standard Combination Functions . . . . . . . . . . .
2.6.2 Building More Complex Standard Combination Functions . . . . . . . . . . . .. . . . . . . . .
2.7 Properties for Combination Functions. . . . . . . . . . . . . . . . . . . .
2.8 Applying Computational Methods to Model Representations . . . . . . . . . . . . . ..
2.9 Applicability of the Modelling Perspective . . . . . . . . . . . . . . . .
2.9.1 The State-Determined System Assumption . . . . . . . .
2.9.2 State-Determined Systems and First-Order Differential Equations . . . . . . . . . .
2.9.3 State-Determined Systems and Modeling Based on Temporal-Causal Networks . . . .
2.10 Modelling Adaptive Processes by Adaptive Temporal-Causal Networks . . . . . . . . . . . . . . . . .
2.11 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Part II Emotions All the Way
3 How Emotions Come in Between Everything
Emotions Serving as Glue in All Mental and Social Processes . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Generating Emotional Responses and Feelings . . . . . . . . . . . . .
3.3 Emotion Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4 Interaction Between Cognitive and Affective States . . . . . . . . .
3.5 Emotion-Related Valuing in Decision-Making . . . . . . . . . . . . .
3.6 Emotions and Social Contagion . . . . . . . . . . . . . . . . . . . . . . . .
3.7 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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4 How Do You Feel Dreaming
Using Internal Simulation to Generate Emotional Dream Episodes. . . . . . . . . . . . . . . . . . . .
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Memory Elements, Emotions and Internal Simulation
in Dreaming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 A Temporal-Causal Network Model Generating
Dream Episodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4 Simulations of Example Dream Scenarios . . . . . . . . . . . . . . . .
4.5 Relations to Neurological Theories and Findings . . . . . . . . . . .
4.6 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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5 Dreaming Your Fear Away
Fear Extinction Learning During Dreaming . . . . . . . . . . . . . . . . . . . . .
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 An Adaptive Temporal-Causal Network Model for Fear Extinction Learning. . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2.1 Conceptual Representation of the Adaptive Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2.2 Numerical Representation of the Adaptive Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3 Simulations of Fear Extinction Learning in Dream Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.4 Relating the Adaptive Temporal-Causal Network
Model to Neurological Theories . . . . . . . . . . . . . . . . . . . . . . . .
5.5 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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6 Emotions as a Vehicle for Rationality in Decision Making
Experiencing Emotions for Decisions Based on Experience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2 The Adaptive Temporal-Causal Network Model for Decision Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.3 Simulation Results for a Deterministic World. . . . . . . . . . . . . .
6.4 Simulation Results for a Stochastic World . . . . . . . . . . . . . . . .
6.5 Simulation Results for a Changing Stochastic World . . . . . . . .
6.6 Evaluating the Adaptive Temporal-Causal Network Model on Rationality . . . . . . . . . . . . . . . . . .
6.7 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Part III Yourself and the Others
7 From Mirroring to the Emergence of Shared Understanding and Collective Power
Biological and Computational Perspectives on the Emergence of Social Phenomena. . . .. .
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2 Mirror Neuron Activation and Internal Simulation . . . . . . . . . .
7.2.1 The Discovery of Mirror Neurons . . . . . . . . . . . . . . .
7.2.2 Neurons for Control and Self-other Distinction . . . . .
7.2.3 Generating Emotions and Feelings by Internal Simulation: As-if Body Loops . . . . . . . . . . . . . . . . . .
7.2.4 Mirroring Process: Mirror Neuron Activation and Internal Simulation . . . . . . . . . . . . . . . . . . . . . . .
7.2.5 Development of the Discipline Social Neuroscience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.3 The Emergence of Shared Understanding . . . . . . . . . . . . . . . . .
7.3.1 The Emergence of Shared Understanding for External World States . . . . . . . . . . . . . . . . . . . . .
7.3.2 The Emergence of Shared Understanding for Internal Mental States . . . . . . . . . . . . . . . . . . . . .
7.4 The Emergence of Collective Power. . . . . . . . . . . . . . . . . . . . .
7.4.1 The Emergence of Collective Action Based on Mirroring . . . . . . . . . . . . . . . . . . . . . . . . . .
7.4.2 The Role of Feelings and Valuing in the Emergence of Collective Action . . . . . . . . . . . . . . . .
7.5 Integration of External Effects and Internal Processes. . . . . . . .
7.6 Abstraction of Complex Internal Temporal-Causal Network Models . . . . . . . . . . . . . . . . . . . . . . . . . .
7.7 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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8 Am I Going to Do This? Is It Me Who Did This?
Prior and Retrospective Ownership States for Actions . . . . . . . . . . . . .
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.2 Neurological Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.3 A Temporal-Causal Network Model for Ownership . . . . . . . . .
8.3.1 Conceptual Representation of the Temporal-Causal Network Model . . . . . . . . . .
8.3.2 Numerical Representation of the Temporal-Causal Network Model . . . . . . . . . .
8.4 Simulation of Example Scenarios . . . . . . . . . . . . . . . . . . . . . . .
8.4.1 Normal Execution and Attribution of an Action . . . .
8.4.2 Vetoing a Prepared Action Due to Unsatisfactory Predicted Effect . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.4.3 Effects of Poor Prediction; Schizophrenia Case . . . . .
8.4.4 Satisfactory Predicted Effects but Unsatisfactory Actual Effects . . . . . . . . . . . . . . . . . . . .
8.4.5 Mirroring Another Person . . . . . . . . . . . . . . . . . . . . .
8.5 Relations to Neurological Findings . . . . . . . . . . . . . . . . . . . . . .
8.6 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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9 How Empathic Are You
Displaying and Regulating Different Social Response Patterns . . . . . . . . . . . . . . . . . . . . . . .
9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.2 Neurological Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.2.1 Mirror Neurons . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.2.2 Control and Self-other Distinction . . . . . . . . . . . . . . .
9.2.3 Emotion Integration . . . . . . . . . . . . . . . . . . . . . . . . . .
9.2.4 Enhanced Sensory Processing Sensitivity and Emotion Regulation . . . . . . . . . . . . . . . . . . . . . .
9.2.5 Empathic Responses . . . . . . . . . . . . . . . . . . . . . . . . .
9.3 The Temporal-Causal Network Model . . . . . . . . . . . . . . . . . . .
9.3.1 Conceptual Representation of the Model . . . . . . . . . .
9.3.2 Numerical Representation of the Temporal-Causal Network Model . . . . . . . . . .
9.4 Types of Social Response Patterns Shown . . . . . . . . . . . . . . . .
9.4.1 Overview of Basic Patterns . . . . . . . . . . . . . . . . . . . .
9.4.2 Oscillatory Patterns: Limit Cycle Behaviour . . . . . . .
9.4.3 Comparison to Empirical Gaze Data . . . . . . . . . . . . .
9.4.4 Interaction of Two Persons Displaying Regulation of Enhanced Sensory Sensitivity . . . . . . .
9.5 Learning Social Responses by an Adaptive Temporal-Causal Network Model . . . . . . . . . . . . . . . . . . . . . . .
9.6 Example Simulations of Learning Processes . . . . . . . . . . . . . . .
9.7 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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10 Are You with Me? Am I with You?
Joint Decision Making Processes Involving Emotion-Related Valuing and
Mutual Empathic Understanding. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.2 Mirroring, Internal Simulation and Emotion-Related Valuing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.3 The Temporal-Causal Network Model . . . . . . . . . . . . . . . . . . .
10.3.1 Conceptual Representation of the Temporal-Causal Network Model . . . . . . . . . .
10.3.2 Numerical Representation of the Temporal-Causal Network Model . . . . . . . . . .
10.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.5 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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11 Changing Yourself, Changing the Other, or Changing Your Connection
Integrative Dynamics of States and Interactions in a Social Context . . . . . . . . . . . . . . . .
11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.2 Small World Networks and Random Networks. . . . . . . . . . . . .
11.2.1 Small World Networks . . . . . . . . . . . . . . . . . . . . . . .
11.2.2 Random Networks . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.3 Distribution of Node Degrees and Scale-Free Networks . . . . . .
11.3.1 Scale-Free Networks . . . . . . . . . . . . . . . . . . . . . . . . .
11.3.2 Identifying a Power Law . . . . . . . . . . . . . . . . . . . . . .
11.3.3 Clusters and Bridges . . . . . . . . . . . . . . . . . . . . . . . . .
11.4 Weak Ties, Strong Ties and Weighted Connections . . . . . . . . .
11.5 Different Types of Dynamics in Networks Based on Social Interaction. . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.6 Social Contagion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.7 Adaptive Network Dynamics and the Homophily Principle . . .
11.8 Adaptive Networks and the More Becomes More Principle . . .
11.9 Adaptive Networks and Actual Interaction Over Time . . . . . . .
11.10 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Part IV Analysis Methods for Temporal-Causal Network Models
12 Where Is This Going
Verification by Mathematical Analysis: Monotonicity, Equilibria, and Limit Cycles. . . ... . . . . . . . . .
12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12.2 Verifying a Temporal-Causal Network Model by Mathematical Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12.3 Mathematical Analysis for Equilibrium States: An Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12.4 Mathematical Analysis for Equilibrium States: Scaled Sum Combination Function . . . . . . . . . . . . . . . . . . . . . .
12.5 Mathematical Analysis for Equilibrium States: Hebbian Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12.5.1 Analysis of Increase, Decrease or Equilibrium for Hebbian Learning Without Extinction . . . . . . . . .
12.5.2 Analysis of Increase, Decrease or Equilibrium for Hebbian Learning with Extinction . . . . . . . . . . . .
12.5.3 How Much Activation of Is Needed to Let x Increase? . . . . . . . . . . . . . . . . . . . . . . . . . . .
12.6 Mathematical Analysis for Equilibrium States: Homophily Principle. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12.7 Mathematical Analysis for Behaviour Ending up in a Limit Cycle Pattern. . . . . . . . . . . . . . . . . . . . . . . . . . . .
12.8 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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13 What Is Happening
Identifying and Verifying Emergent Patterns. . . . . . . . . . . . . . . . . . . . .
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13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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13.2 Dynamic Properties and Temporal-Causal Network Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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13.2.1 A Temporal-Causal Network Model Describing Local Dynamics and Dynamic Properties
Describing Patterns Emerging in Overall Dynamics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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13.2.2 Identifying Emergent Dynamic Properties for a Given Model. . . . . . . . . . . . . . . . . . . . . . . . . . .
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13.2.3 Identifying Dynamic Properties Initially as Requirements for a Model. . . . . . . . . . . . . . . . . . .
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13.3 Dynamic Properties Versus Real World Dynamics Validation, Monitoring, and Analysis . . . . . . . . . . . . . . . . . . . .
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13.3.1 Validating Dynamic Properties Against Actual Real World Processes. . . . . . . . . . . . . . . . . . .
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13.3.2 Validating Dynamic Properties Against Patterns Reported in Literature . . . . . . . . . . . . . . . . . . . . . . . .
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13.3.3 Monitoring and Analysis of Real World Processes Using Dynamic Properties . . . . . . . . . . . . .
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13.4 Dynamic Properties Versus Model Dynamics: Verification and Personalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360
13.4.1 Testing, Focusing and Analysis of a Model by Verifying It Against Dynamic Properties . . . . . . .
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13.4.2 Personalizing Characteristics of a Model Based on Dynamic Properties . . . . . . . . . . . . . . . . . .
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13.4.3 Validation of a Model Based on Validated Dynamic Properties . . . . . . . . . . . . . . . . . . . . . . . . . .
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13.5 Conceptual Representations of Dynamic Properties . . . . . . . . .
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13.6 Numerical-Logical Representations of Dynamic Properties. . . .
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13.6.1 Numerical Representations of State Relations . . . . . .
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13.6.2 Using Numerical Representations Within a Dynamic Property Expression . . . . . . . . . . . . . . . . . .
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13.6.3 Numerical-Logical Representation of a Dynamic Property Expression. . . . . . . . . . . . . . . . . . . . . . . . . .
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13.7 Types of Dynamic Properties and Their Representations . . . . .
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13.7.1 Basic State Relation, Achievement, Grounding, Representation, Ordering and Monotonicity Properties. .
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13.7.2 Maintenance, Peak, Speed, Equilibrium and Limit Cycle Properties . . . . . . . . . . . . . . . . . . . .
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13.7.3 State Comparison, Trace Comparison and Trace Selection Properties. . . . . . . . . . . . . . . . . .
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13.8 Examples of Dynamic Properties in Some Case Studies. . . . . .
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13.9 Automatic Checking of Dynamic Properties . . . . . . . . . . . . . . .
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13.10 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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14 Who are You
Identifying Characteristics of Persons, Their Networks and Other Contextual Aspects by
Parameter Estimation and Validation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14.2 Determining Characteristics and the Use of Requirements . . . .
14.2.1 The Parameters in a Temporal-Causal Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14.2.2 Direct Measuring of Characteristics of a Situation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14.2.3 Using Requirements to Find Characteristics of a Situation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14.2.4 Using Error Measures for Requirements . . . . . . . . . .
14.3 Description of an Example Model . . . . . . . . . . . . . . . . . . . . . .
14.4 Parameter Tuning by Exhaustive Search. . . . . . . . . . . . . . . . . .
14.5 Parameter Estimation by Gradient Descent . . . . . . . . . . . . . . . .
14.6 Parameter Estimation by Random Gradient Descent . . . . . . . . .
14.7 Parameter Estimation by Simulated Annealing . . . . . . . . . . . . .
14.8 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Part V Philosophical, Societal and Educational Perspectives
15 We Don’t Believe in Ghosts, Do We?
What Is It that Drives Dynamics?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15.2 Is Motion of Nonliving Entities Driven by Ghosts? . . . . . . . . .
15.2.1 Zeno About Arrows that Are Moving and Unmoving. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15.2.2 Adding Anticipatory State Properties to Describe a State: Potentialities. . . . . . . . . . . . . . . .
15.3 Is Motion of Living Entities Driven by Ghosts? . . . . . . . . . . . .
15.3.1 Mental States Driving Motion . . . . . . . . . . . . . . . . . .
15.3.2 Can ‘Things of the Soul’ Move Objects? . . . . . . . . .
15.4 Explaining Changed States by Introducing Potentialities. . . . . .
15.4.1 Potentialities and Their Actualisation as a General Perspective on Dynamics . . . . . . . . . . .
15.4.2 Derivatives as Potentialities for Variables in Dynamical Systems . . . . . . . . . . . . . . . . . . . . . . . .
15.4.3 What Kind of State Properties Are Potentialities? . . .
15.4.4 Summary of Assumptions Underlying Potentialities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15.5 Potentialities in Physics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15.6 What Kind of Property Is a Potentiality: Getting Rid of Ghosts?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15.6.1 Why Velocities and Derivatives by Themselves Are not Genuine State Properties. . . . . . . . . . . . . . . .
15.6.2 Ghost-like Properties or Temporal Relations Involving Genuine Properties?. . . . . . . . . . . . . . . . . .
15.7 Potentialities for Causal Relations and Transition Systems . . . .
15.7.1 Transition Systems and Causal Relations. . . . . . . . . .
15.7.2 Potentialities for Transition Systems and Causal Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15.8 Realisers for Potentialities and the Role of Differential Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15.8.1 Realisers of Mental States in Philosophy of Mind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15.8.2 Realisers of Potentialities from a More General Perspective. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15.8.3 Realisers for Derivatives: First-Order Differential Equations . . . . . . . . . . . . . . . . . . . . . . . .
15.9 How to Explain Changed Potentialities. . . . . . . . . . . . . . . . . . .
15.9.1 Introducing Higher-Order Potentialities: Potentialities for Potentialities . . . . . . . . . . . . . . . . . .
15.9.2 Higher-Order Potentialities in Cognitive Models . . . .
15.9.3 Mathematical Formalisation of Higher-Order Potentialities in Calculus . . . . . . . . . . . . . . . . . . . . . .
15.9.4 How to Get Rid of an Infinite Chain of Higher Order Potentialities by Realisers . . . . . . . . . . . . . . . .
15.10 Changed Potentialities Due to Interaction . . . . . . . . . . . . . . . . .
15.10.1 Exchange of Potentialities by Interaction . . . . . . . . . .
15.10.2 The Role of Higher-Order Potentialities in the Exchange of Potentialities . . . . . . . . . . . . . . . .
15.10.3 Higher-Order Potentialities to Characterise Interaction in Physics. . . . . . . . . . .
15.11 Multiple Realisation of Potentialities. . . . . . . . . . . . . . . . . . . . .
15.12 State-Determined Systems and Potentialities . . . . . . . . . . . . . . .
15.13 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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16 Making Smart Applications Smarter
Societal Applicability of Computational Models . . . . . . . . . . . . . . . . . .
16.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
16.2 Multidisciplinarity: The Ingredients . . . . . . . . . . . . . . . . . . . . .
16.3 Combining the Ingredients . . . . . . . . . . . . . . . . . . . . . . . . . . . .
16.4 Coupled Reflective Systems . . . . . . . . . . . . . . . . . . . . . . . . . . .
16.5 Integrative Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
16.6 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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17 Multidisciplinary Education
Computational Modelling as the Core of a Multidisciplinary Curriculum . . . . . . .. . . . . . . . . . . . . .
17.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
17.2 Overall Structure of the Curriculum . . . . . . . . . . . . . . . . . . . . .
17.3 Computational Modelling Stream . . . . . . . . . . . . . . . . . . . . . . .
17.4 The Human Sciences and Exact Sciences Streams . . . . . . . . . .
17.5 Integration and Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
17.6 Evaluation and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Part VI Network-Oriented Modelling: Discussion
18 On the Use of Network-Oriented Modelling
A Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
18.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
18.2 Network-Oriented Modelling . . . . . . . . . . . . . . . . . . . . . . . . . .
18.3 Genericity of a Network-Oriented Modelling Approach . . . . . .
18.4 Applicability of Network-Oriented Modelling. . . . . . . . . . . . . .
18.5 Finally . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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