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E-raamat: Network-Oriented Modeling: Addressing Complexity of Cognitive, Affective and Social Interactions

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  • Sari: Understanding Complex Systems
  • Ilmumisaeg: 03-Oct-2016
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319452135
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Network-Oriented Modeling: Addressing Complexity of Cognitive, Affective and Social InteractionsSuurem pilt
  • Formaat: PDF+DRM
  • Sari: Understanding Complex Systems
  • Ilmumisaeg: 03-Oct-2016
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319452135
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This book presents a new approach that can be applied to complex, integrated individual and social human processes. It provides an alternative means of addressing complexity, better suited for its purpose than and effectively complementing traditional strategies involving isolation and separation assumptions.Network-oriented modeling allows high-level cognitive, affective and social models in the form of (cyclic) graphs to be constructed, which can be automatically transformed into executable simulation models. The modeling format used makes it easy to take into account theories and findings about complex cognitive and social processes, which often involve dynamics based on interrelating cycles. Accordingly, it makes it possible to address complex phenomena such as the integration of emotions within cognitive processes of all kinds, of internal simulations of the mental processes of others, and of social phenomena such as shared understandings and collective actions. A variety o

f sample models - including those for ownership of actions, fear and dreaming, the integration of emotions in joint decision-making based on empathic understanding, and evolving social networks - illustrate the potential of the approach. Dedicated software is available to support building models in a conceptual or graphical manner, transforming them into an executable format and performing simulation experiments. The majority of the material presented has been used and positively evaluated by undergraduate and graduate students and researchers in the cognitive, social and AI domains.Given its detailed coverage, the book is ideally suited as an introduction for graduate and undergraduate students in many different multidisciplinary fields involving cognitive, affective, social, biological, and neuroscience domains. This book presents a new approach that can be applied to complex, integrated individual and social human processes. It provides an alternative means of addressing com

plexity, better suited for its purpose than and effectively complementing traditional strategies involving isolation and separation assumptions.Network-oriented modeling allows high-level cognitive, affective and social models in the form of (cyclic) graphs to be constructed, which can be automatically transformed into executable simulation models. The modeling format used makes it easy to take into account theories and findings about complex cognitive and social processes, which often involve dynamics based on interrelating cycles. Accordingly, it makes it possible to address complex phenomena such as the integration of emotions within cognitive processes of all kinds, of internal simulations of the mental processes of others, and of social phenomena such as shared understandings and collective actions. A variety of sample models - including those for ownership of actions, fear and dreaming, the integration of emotions in joint decision-making based on empathic understanding, a

nd evolving social networks - illustrate the potential of the approach. Dedicated software is available to support building models in a conceptual or graphical manner, transforming them into an executable format and performing simulation experiments. The majority of the material presented has been used and positively evaluated by undergraduate and graduate students and researchers in the cognitive, social and AI domains.Given its detailed coverage, the book is ideally suited as an introduction for graduate and undergraduate students in many different multidisciplinary fields involving cognitive, affective, social, biological, and neuroscience domains.

Arvustused

This very interesting book considers a novel but important field of complex systems modeling involving human processes as a whole, and is of great use to researchers seeking to develop scientific models for human behavior, decision making, social interactions, including the complexities of human emotions, shared understanding, joint decision making and self-ownership. (Efstratios Rappos, zbMATH 1391.91005, 2018) The book is a collection of relatively independent chapters, each with its own bibliography. The volume will be of great interest to modeling practitioners and cognitive scientists, who will find great stimulation in the explanations it offers of various cognitive dynamics. (H. Van Dyke Parunak,Computing Reviews, July, 2018)

Part I Network-Oriented Modeling: Introduction
1 Network-Oriented Modeling and Its Conceptual Foundations
3(32)
An Introduction
3(1)
1.1 Introduction
3(1)
1.2 Addressing Human Complexity by Separation Assumptions
4(7)
1.3 Addressing Complexity by Interaction in Networks Instead of by Separation
11(3)
1.4 Network-Oriented Modeling
14(2)
1.5 The Dynamic Computational Modeling Perspective
16(2)
1.6 Network-Oriented Modeling Based on Temporal-Causal Networks
18(4)
1.7 Scope of Applicability and Achievements
22(1)
1.8 Overview of the Book
23(12)
References
29(6)
2 A Temporal-Causal Network Modeling Approach
35(70)
With Biological, Neurological and Social Processes as Inspiration
35(1)
2.1 Introduction
35(5)
2.2 Modeling Complex Processes by Temporal-Causal Networks
40(3)
2.3 Exploiting Knowledge About Physical and Biological Mechanisms in Modeling
43(2)
2.3.1 Addressing Complexity by Higher Level Models Based on Knowledge from Computer Science
43(1)
2.3.2 Addressing Complexity by Higher Level Models Based on Knowledge from Neuroscience
44(1)
2.4 Conceptual Representation of a Temporal-Causal Network Model
45(13)
2.4.1 Conceptual Representations of a Temporal-Causal Network Model
47(2)
2.4.2 More Specific Examples of Conceptual Representations of Temporal-Causal Network Models
49(9)
2.5 Numerical Representation of a Temporal-Causal Network Model
58(11)
2.5.1 The Systematic Transformation from Conceptual to Numerical Representation
59(5)
2.5.2 Illustration of the Transformation for the Example of Fig. 2.10
64(2)
2.5.3 Illustration of the Modeling Perspective for a Social Contagion Process
66(3)
2.6 Standard Combination Functions
69(8)
2.6.1 Basic Standard Combination Functions
69(3)
2.6.2 Building More Complex Standard Combination Functions
72(5)
2.7 Properties for Combination Functions
77(4)
2.8 Applying Computational Methods to Model Representations
81(4)
2.9 Applicability of the Modeling Perspective
85(7)
2.9.1 The State-Determined System Assumption
85(1)
2.9.2 State-Determined Systems and First-Order Differential Equations
86(2)
2.9.3 State-Determined Systems and Modeling Based on Temporal-Causal Networks
88(4)
2.10 Modeling Adaptive Processes by Adaptive Temporal-Causal Networks
92(7)
2.11 Discussion
99(6)
References
100(5)
Part II Emotions All the Way
3 How Emotions Come in Between Everything
105(20)
Emotions Serving as Glue in All Mental and Social Processes
105(1)
3.1 Introduction
105(2)
3.2 Generating Emotional Responses and Feelings
107(4)
3.3 Emotion Regulation
111(3)
3.4 Interaction Between Cognitive and Affective States
114(4)
3.5 Emotion-Related Valuing in Decision-Making
118(1)
3.6 Emotions and Social Contagion
119(1)
3.7 Discussion
120(5)
References
121(4)
4 How Do You Feel Dreaming
125(16)
Using Internal Simulation to Generate Emotional Dream Episodes
125(1)
4.1 Introduction
125(1)
4.2 Memory Elements, Emotions and Internal Simulation in Dreaming
126(2)
4.3 A Temporal-Causal Network Model Generating Dream Episodes
128(5)
4.4 Simulations of Example Dream Scenarios
133(3)
4.5 Relations to Neurological Theories and Findings
136(1)
4.6 Discussion
137(4)
References
138(3)
5 Dreaming Your Fear Away
141(16)
Fear Extinction Learning During Dreaming
141(1)
5.1 Introduction
141(1)
5.2 An Adaptive Temporal-Causal Network Model for Fear Extinction Learning
142(6)
5.2.1 Conceptual Representation of the Adaptive Network Model
142(4)
5.2.2 Numerical Representation of the Adaptive Network Model
146(2)
5.3 Simulations of Fear Extinction Learning in Dream Scenarios
148(4)
5.4 Relating the Adaptive Temporal-Causal Network Model to Neurological Theories
152(1)
5.5 Discussion
153(4)
References
154(3)
6 Emotions as a Vehicle for Rationality in Decision Making
157(26)
Experiencing Emotions for Decisions Based on Experience
157(1)
6.1 Introduction
157(2)
6.2 The Adaptive Temporal-Causal Network Model for Decision Making
159(9)
6.3 Simulation Results for a Deterministic World
168(3)
6.4 Simulation Results for a Stochastic World
171(1)
6.5 Simulation Results for a Changing Stochastic World
172(3)
6.6 Evaluating the Adaptive Temporal-Causal Network Model on Rationality
175(3)
6.7 Discussion
178(5)
References
179(4)
Part III Yourself and the Others
7 From Mirroring to the Emergence of Shared Understanding and Collective Power
183(26)
Biological and Computational Perspectives on the Emergence of Social Phenomena
183(1)
7.1 Introduction
183(2)
7.2 Mirror Neuron Activation and Internal Simulation
185(8)
7.2.1 The Discovery of Mirror Neurons
185(1)
7.2.2 Neurons for Control and Self-other Distinction
186(1)
7.2.3 Generating Emotions and Feelings by Internal Simulation: As-if Body Loops
187(1)
7.2.4 Mirroring Process: Mirror Neuron Activation and Internal Simulation
187(5)
7.2.5 Development of the Discipline Social Neuroscience
192(1)
7.3 The Emergence of Shared Understanding
193(4)
7.3.1 The Emergence of Shared Understanding for External World States
194(1)
7.3.2 The Emergence of Shared Understanding for Internal Mental States
195(2)
7.4 The Emergence of Collective Power
197(3)
7.4.1 The Emergence of Collective Action Based on Mirroring
197(2)
7.4.2 The Role of Feelings and Valuing in the Emergence of Collective Action
199(1)
7.5 Integration of External Effects and Internal Processes
200(2)
7.6 Abstraction of Complex Internal Temporal-Causal Network Models
202(1)
7.7 Discussion
203(6)
References
205(4)
8 Am I Going to Do This? Is It Me Who Did This?
209(26)
Prior and Retrospective Ownership States for Actions
209(1)
8.1 Introduction
209(2)
8.2 Neurological Background
211(2)
8.3 A Temporal-Causal Network Model for Ownership
213(7)
8.3.1 Conceptual Representation of the Temporal-Causal Network Model
213(2)
8.3.2 Numerical Representation of the Temporal-Causal Network Model
215(5)
8.4 Simulation of Example Scenarios
220(7)
8.4.1 Normal Execution and Attribution of an Action
221(1)
8.4.2 Vetoing a Prepared Action Due to Unsatisfactory Predicted Effect
222(2)
8.4.3 Effects of Poor Prediction; Schizophrenia Case
224(1)
8.4.4 Satisfactory Predicted Effects but Unsatisfactory Actual Effects
225(1)
8.4.5 Mirroring Another Person
226(1)
8.5 Relations to Neurological Findings
227(3)
8.6 Discussion
230(5)
References
231(4)
9 How Empathic Are You
235(34)
Displaying, Regulating, and Learning Adaptive Social Responses
235(1)
9.1 Introduction
235(2)
9.2 Neurological Background
237(6)
9.2.1 Mirror Neurons
237(1)
9.2.2 Control and Self-other Distinction
238(1)
9.2.3 Emotion Integration
239(1)
9.2.4 Enhanced Sensory Processing Sensitivity and Emotion Regulation
239(2)
9.2.5 Empathic Responses
241(2)
9.3 The Temporal-Causal Network Model
243(9)
9.3.1 Conceptual Representation of the Model
243(4)
9.3.2 Numerical Representation of the Temporal-Causal Network Model
247(5)
9.4 Types of Social Response Patterns Shown
252(7)
9.4.1 Overview of Basic Patterns
252(3)
9.4.2 Oscillatory Patterns: Limit Cycle Behaviour
255(1)
9.4.3 Comparison to Empirical Gaze Data
256(1)
9.4.4 Interaction of Two Persons Displaying Regulation of Enhanced Sensory Sensitivity
257(2)
9.5 Learning Social Responses by an Adaptive Temporal-Causal Network Model
259(1)
9.6 Example Simulations of Learning Processes
260(3)
9.7 Discussion
263(6)
References
265(4)
10 Are You with Me? Am I with You?
269(16)
Joint Decision Making Processes Involving Emotion-Related Valuing and Mutual Empathic Understanding
269(1)
10.1 Introduction
269(1)
10.2 Mirroring, Internal Simulation and Emotion-Related Valuing
270(2)
10.3 The Temporal-Causal Network Model
272(6)
10.3.1 Conceptual Representation of the Temporal-Causal Network Model
273(2)
10.3.2 Numerical Representation of the Temporal-Causal Network Model
275(3)
10.4 Simulation Results
278(3)
10.5 Discussion
281(4)
References
282(3)
11 Changing Yourself, Changing the Other, or Changing Your Connection
285(38)
Integrative Dynamics of States and Interactions in a Social Context
285(1)
11.1 Introduction
285(1)
11.2 Small World Networks and Random Networks
286(3)
11.2.1 Small World Networks
288(1)
11.2.2 Random Networks
288(1)
11.3 Distribution of Node Degrees and Scale-Free Networks
289(3)
11.3.1 Scale-Free Networks
289(1)
11.3.2 Identifying a Power Law
290(2)
11.3.3 Clusters and Bridges
292(1)
11.4 Weak Ties, Strong Ties and Weighted Connections
292(4)
11.5 Different Types of Dynamics in Networks Based on Social Interaction
296(3)
11.6 Social Contagion
299(5)
11.7 Adaptive Network Dynamics and the Homophily Principle
304(7)
11.8 Adaptive Networks and the More Becomes More Principle
311(2)
11.9 Adaptive Networks and Actual Interaction Over Time
313(4)
11.10 Discussion
317(6)
References
318(5)
Part IV Analysis Methods for Temporal-Causal Network Models 12 Where Is This Going
323(98)
Verification by Mathematical Analysis
323(1)
12.1 Introduction
323(1)
12.2 Verifying a Temporal-Causal Network Model by Mathematical Analysis
324(6)
12.3 Mathematical Analysis for Equilibrium States: An Example
330(3)
12.4 Mathematical Analysis for Equilibrium States: Scaled Sum Combination Function
333(3)
12.5 Mathematical Analysis for Equilibrium States: Hebbian Learning
336(5)
12.5.1 Analysis of Increase, Decrease or Equilibrium for Hebbian Learning Without Extinction
337(1)
12.5.2 Analysis of Increase, Decrease or Equilibrium for Hebbian Learning with Extinction
338(2)
12.5.3 How Much Activation Is Needed to Let co Increase?
340(1)
12.6 Mathematical Analysis for Equilibrium States: Homophily Principle
341(2)
12.7 Mathematical Analysis for Behaviour Ending up in a Limit Cycle Pattern
343(4)
12.8 Discussion
347(2)
References
348(1)
13 What Is Happening
349(44)
Identifying and Verifying Emergent Patterns
349(1)
13.1 Introduction
349(2)
13.2 Dynamic Properties and Temporal-Causal Network Models
351(3)
13.2.1 A Temporal-Causal Network Model Describing Local Dynamics and Dynamic Properties Describing Patterns Emerging in Overall Dynamics
351(1)
13.2.2 Identifying Emergent Dynamic Properties for a Given Model
352(1)
13.2.3 Identifying Dynamic Properties Initially as Requirements for a Model
353(1)
13.3 Dynamic Properties Versus Real World Dynamics: Validation, Monitoring, and Analysis
354(2)
13.3.1 Validating Dynamic Properties Against Actual Real World Processe
355(1)
13.3.2 Validating Dynamic Properties Against Patterns Reported in Literature
356(1)
13.3.3 Monitoring and Analysis of Real World Processes Using Dynamic Properties
356(1)
13.4 Dynamic Properties Versus Model Dynamics: Verification and Personalization
356(2)
13.4.1 Testing, Focusing and Analysis of a Model by Verifying It Against Dynamic Properties
357(1)
13.4.2 Personalizing Characteristics of a Model Based on Dynamic Properties
357(1)
13.4.3 Validation of a Model Based on Validated Dynamic Properties
358(1)
13.5 Conceptual Representations of Dynamic Properties
358(5)
13.6 Numerical-Logical Representations of Dynamic Properties
363(8)
13.6.1 Numerical Representations of State Relations
364(2)
13.6.2 Using Numerical Representations Within a Dynamic Property Expression
366(2)
13.6.3 Numerical-Logical Representation of a Dynamic Property Expression
368(3)
13.7 Types of Dynamic Properties and Their Representations
371(12)
13.7.1 Basic State Relation, Achievement, Grounding, Representation, Ordering and Monotonicity Properties
371(4)
13.7.2 Maintenance, Peak, Speed, Equilibrium and Limit Cycle Properties
375(5)
13.7.3 State Comparison, Trace Comparison and Trace Selection Properties
380(3)
13.8 Examples of Dynamic Properties in Some Case Studies
383(4)
13.9 Automatic Checking of Dynamic Properties
387(2)
13.10 Discussion
389(4)
References
390(3)
14 Who are You
393(28)
Identifying Characteristics of Persons, Their Networks and Other Contextual Aspects by Parameter Estimation and Validation
393(1)
14.1 Introduction
393(2)
14.2 Determining Characteristics and the Use of Requirements
395(5)
14.2.1 The Parameters in a Temporal-Causal Network Model
395(1)
14.2.2 Direct Measuring of Characteristics of a Situation
396(1)
14.2.3 Using Requirements to Find Characteristics of a Situation
397(1)
14.2.4 Using Error Measures for Requirements
398(2)
14.3 Description of an Example Model
400(3)
14.4 Parameter Tuning by Exhaustive Search
403(3)
14.5 Parameter Estimation by Gradient Descent
406(4)
14.6 Parameter Estimation by Random Gradient Descent
410(2)
14.7 Parameter Estimation by Simulated Annealing
412(5)
14.8 Discussion
417(4)
References
418(3)
Part V Philosophical, Societal and Educational Perspectives
15 We Don't Believe in Ghosts, Do We?
421(42)
What Is It that Drives Dynamics
421(1)
15.1 Introduction
421(3)
15.2 Is Motion of Nonliving Entities Driven by Ghosts?
424(4)
15.2.1 Zeno About Arrows that Are Moving and Unmoving
424(3)
15.2.2 Adding Anticipatory State Properties to Describe a State: Potentialities
427(1)
15.3 Is Motion of Living Entities Driven by Ghosts?
428(2)
15.3.1 Mental States Driving Motion
428(1)
15.3.2 Can `Things of the Soul' Move Objects?
429(1)
15.4 Explaining Changed States by Introducing Potentialities
430(3)
15.4.1 Potentialities and Their Actualisation as a General Perspective on Dynamics
430(1)
15.4.2 Derivatives as Potentialities for Variables in Dynamical Systems
431(1)
15.4.3 What Kind of State Properties Are Potentialities?
432(1)
15.4.4 Summary of Assumptions Underlying Potentialities
433(1)
15.5 Potentialities in Physics
433(2)
15.6 What Kind of Property Is a Potentiality: Getting Rid of Ghosts?
435(5)
15.6.1 Why Velocities and Derivatives by Themselves Are not Genuine State Properties
436(2)
15.6.2 Ghost-like Properties or Temporal Relations Involving Genuine Properties?
438(2)
15.7 Potentialities for Causal Relations and Transition Systems
440(2)
15.7.1 Transition Systems and Causal Relations
440(1)
15.7.2 Potentialities for Transition Systems and Causal Relations
441(1)
15.8 Realisers for Potentialities and the Role of Differential Equations
442(4)
15.8.1 Realisers of Mental States in Philosophy of Mind
442(1)
15.8.2 Realisers of Potentialities from a More General Perspective
443(1)
15.8.3 Realisers for Derivatives: First-Order Differential Equations
444(2)
15.9 How to Explain Changed Potentialities
446(4)
15.9.1 Introducing Higher-Order Potentialities: Potentialities for Potentialities
447(1)
15.9.2 Higher-Order Potentialities in Cognitive Models
448(1)
15.9.3 Mathematical Formalisation of Higher-Order Potentialities in Calculus
448(1)
15.9.4 How to Get Rid of an Infinite Chain of Higher Order Potentialities by Realisers
449(1)
15.10 Changed Potentialities Due to Interaction
450(5)
15.10.1 Exchange of Potentialities by Interaction
450(2)
15.10.2 The Role of Higher-Order Potentialities in the Exchange of Potentialities
452(1)
15.10.3 Higher-Order Potentialities to Characterise Interaction in Physics
453(2)
15.11 Multiple Realisation of Potentialities
455(2)
15.12 State-Determined Systems and Potentialities
457(2)
15.13 Discussion
459(4)
References
461(2)
16 Making Smart Applications Smarter
463(10)
Societal Applicability of Computational Models
463(1)
16.1 Introduction
463(2)
16.2 Multidisciplinarity: The Ingredients
465(1)
16.3 Combining the Ingredients
465(2)
16.4 Coupled Reflective Systems
467(1)
16.5 Integrative Modeling
468(2)
16.6 Discussion
470(3)
References
471(2)
17 Multidisciplinary Education
473(14)
Computational Modeling as the Core of a Multidisciplinary Curriculum
473(1)
17.1 Introduction
473(2)
17.2 Overall Structure of the Curriculum
475(2)
17.3 Computational Modeling Stream
477(2)
17.4 The Human Sciences and Exact Sciences Streams
479(1)
17.5 Integration and Projects
480(1)
17.6 Evaluation and Discussion
480(7)
References
483(4)
Part VI Network-Oriented Modeling: Discussion 18 On the Use of Network-Oriented Modeling
487(5)
A Discussion
487(1)
18.1 Introduction
487(1)
18.2 Network-Oriented Modeling
487(1)
18.3 Genericity of a Network-Oriented Modeling Approach
488(2)
18.4 Applicability of Network-Oriented Modeling
490(2)
18.5 Finally
492(1)
References 492(3)
Index 495
Prof. Jan Treur, Faculty of Sciences, the  Vrije Universiteit (VU) in Amsterdam, is the head of the Behavioural Informatics Group. This multidisciplinary group investigates methods and techniques for modelling and analysis of complex human and social processes. Such models are applied to design human- or socially aware applications that support humans or groups of humans in their daily functioning, for example, in the form of smartphone apps or specialized social media. Application areas include support for a healthy lifestyle, active lifestyle, ambient-assisted living, crowd behavior, virtual patients, chronic diseases, and mental disorders such as depression, post-traumatic stress disorders, or autism spectrum disorders.





Among Prof. Treurs research interests are Complex Adaptive Systems, Computational Modelling, Human-Aware and Socially Aware Computing, Ambient Intelligence, Social Networks, Socio-Technical Systems, Cognitive, Affective and Social Neuroscience, Biological or Biologically Inspired Modelling, Social and Organization Modelling, and Philosophy of Mind. He has taken part in various National and EU Projects, was and is involved in many conferences as Program Committee Member, Chair or Keynote Speaker, and has more than 600 papers to his name.  He has designed successful multidisciplinary curricula for Bachelor and Master programs in Lifestyle Informatics and Socially Aware Computing at his university. He has been and is teaching on modelling topics within these Bachelor and Master curricula and up to now has supervised well over 30 PhD theses within these areas.



For further information we refer to the Authors website.
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