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

501 Pages·2016·10.98 MB·English

by  Jan Treur (auth.)

Understanding Complex Systems
Jan Treur
Network-
Oriented
Modeling
Addressing Complexity of Cognitive,
Affective and Social Interactions
Springer Complexity
Springer Complexity is an interdisciplinary program publishing the best research and
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Editorial and Programme Advisory Board
HenryAbarbanel,InstituteforNonlinearScience,UniversityofCalifornia,SanDiego,USA
DanBraha,NewEnglandComplexSystemsInstituteandUniversityofMassachusettsDartmouth,USA
Péter Érdi, Center for Complex Systems Studies, Kalamazoo College, USA and Hungarian Academy of
Sciences,Budapest,Hungary
KarlFriston,InstituteofCognitiveNeuroscience,UniversityCollegeLondon,London,UK
HermannHaken,CenterofSynergetics,UniversityofStuttgart,Stuttgart,Germany
ViktorJirsa,CentreNationaldelaRechercheScientifique(CNRS),UniversitédelaMéditerranée,Marseille,
France
JanuszKacprzyk,SystemResearch,PolishAcademyofSciences,Warsaw,Poland
KunihikoKaneko,ResearchCenterforComplexSystemsBiology,TheUniversityofTokyo,Tokyo,Japan
ScottKelso,CenterforComplexSystemsandBrainSciences,FloridaAtlanticUniversity,BocaRaton,USA
MarkusKirkilionis,MathematicsInstituteandCentreforComplexSystems,UniversityofWarwick,Coventry,
UK
JürgenKurths,NonlinearDynamicsGroup,UniversityofPotsdam,Potsdam,Germany
RonaldoMenezes,DepartmentofComputerScience,FloridaInstituteofTechnology,Melbourne,FL,USA
AndrzejNowak,DepartmentofPsychology,WarsawUniversity,Poland
HassanQudrat-Ullah,SchoolofAdministrativeStudies,YorkUniversity,Toronto,ON,Canada
PeterSchuster,TheoreticalChemistryandStructuralBiology,UniversityofVienna,Vienna,Austria
FrankSchweitzer,SystemDesign,ETHZurich,Zurich,Switzerland
DidierSornette,EntrepreneurialRisk,ETHZurich,Zurich,Switzerland
StefanThurner,SectionforScienceofComplexSystems,MedicalUniversityofVienna,Vienna,Austria
Understanding Complex Systems
Founding Editor: S. Kelso
Future scientific and technological developments in many fields will necessarily
depend uponcomingtogripswithcomplexsystems.Such systems arecomplex in
both their composition – typically many different kinds of components interacting
simultaneouslyandnonlinearlywitheachotherandtheirenvironmentsonmultiple
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The Springer Series in Understanding Complex Systems series (UCS) promotes
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Jan Treur
Network-Oriented Modeling
Addressing Complexity of Cognitive,
Affective and Social Interactions
123
Jan Treur
Department ofComputer Science,
BehaviouralInformatics Group
VUUniversity Amsterdam
Amsterdam, Noord-Holland
TheNetherlands
ISSN 1860-0832 ISSN 1860-0840 (electronic)
Understanding ComplexSystems
ISBN978-3-319-45211-1 ISBN978-3-319-45213-5 (eBook)
DOI 10.1007/978-3-319-45213-5
LibraryofCongressControlNumber:2016948267
©SpringerInternationalPublishingSwitzerland2016
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Preface
During a sabbatical period in 2015 I decided to start working on a book on the
Network-OrientedModelingapproachdevelopedoverthepastyearsandwhichhas
turned out useful in modeling complex-integrated individual and social human
processesintheformofnetworks.Thedecisiontospendaconsiderableamountof
time on such an enterprise led to further reflection on the modeling approach, and
its presentation and positioning.
This book has been written with a multidisciplinary audience in mind without
assumingmuchpriorknowledge.Inprinciple,thedetailedexplanationsinthebook
allow it to be used as an introduction in Network-Oriented Modeling for multi-
disciplinary Master and Ph.D. students. In particular, this implies that, although
some more technical mathematical and formal logical aspects have also been
addressed, they have been kept to the minimum and are presented in a concise
mannerinPartIV.Theycanbeskippedifnotneeded.Muchofthematerialinthis
book has been and is being used in teaching multidisciplinary courses for under-
graduate and graduate students, and based on these experiences, the presentation
hasbeenadaptedtosuitrequirementsevenbetter.Sometimesthereissomeoverlap
between chapters, and this has been done on purpose in order to make it easier to
readeachchapterseparately.Lecturerscancontactmetoreceiveadditionalmaterial
such as slides, assignments and software.
Thecontentofthebookhasbenefitedmuchfromcooperationwithstudentsand
(past and current) members of the Behavioural Informatics Group (formerly the
Agent Systems Group) at the VU University in Amsterdam (Vrije Universiteit
Amsterdam).Inthediscussionsectionineachofthechapters,specificpublications
and authors related to the material presented in the chapter are mentioned.
Amsterdam, The Netherlands Jan Treur
June 2016
v
Contents
Part I Network-Oriented Modeling: Introduction
1 Network-Oriented Modeling and Its Conceptual Foundations. .... 3
An Introduction. .... .... ..... .... .... .... .... .... ..... .... 3
1.1 Introduction . .... ..... .... .... .... .... .... ..... .... 3
1.2 Addressing Human Complexity by Separation
Assumptions. .... ..... .... .... .... .... .... ..... .... 4
1.3 Addressing Complexity by Interaction in Networks
Instead of by Separation. .... .... .... .... .... ..... .... 11
1.4 Network-Oriented Modeling.. .... .... .... .... ..... .... 14
1.5 The Dynamic Computational Modeling Perspective ..... .... 16
1.6 Network-Oriented Modeling Based on Temporal-Causal
Networks ... .... ..... .... .... .... .... .... ..... .... 18
1.7 Scope of Applicability and Achievements.... .... ..... .... 22
1.8 Overview of the Book .. .... .... .... .... .... ..... .... 23
References. .... .... .... ..... .... .... .... .... .... ..... .... 29
2 A Temporal-Causal Network Modeling Approach. .... ..... .... 35
With Biological, Neurological and Social Processes as Inspiration.... 35
2.1 Introduction . .... ..... .... .... .... .... .... ..... .... 35
2.2 Modeling Complex Processes by Temporal-Causal
Networks ... .... ..... .... .... .... .... .... ..... .... 40
2.3 Exploiting Knowledge About Physical and Biological
Mechanisms in Modeling .... .... .... .... .... ..... .... 43
2.3.1 Addressing Complexity by Higher Level Models
Based on Knowledge from Computer Science.. .... 43
2.3.2 Addressing Complexity by Higher Level Models
Based on Knowledge from Neuroscience ..... .... 44
2.4 Conceptual Representation of a Temporal-Causal Network
Model.. .... .... ..... .... .... .... .... .... ..... .... 45
vii
viii Contents
2.4.1 Conceptual Representations of a Temporal-Causal
Network Model.... .... .... .... .... ..... .... 47
2.4.2 More Specific Examples of Conceptual
Representations of Temporal-Causal Network
Models.. ..... .... .... .... .... .... ..... .... 49
2.5 Numerical Representation of a Temporal-Causal Network
Model.. .... .... ..... .... .... .... .... .... ..... .... 58
2.5.1 The Systematic Transformation from Conceptual
to Numerical Representation .. .... .... ..... .... 59
2.5.2 Illustration of the Transformation for the
Example of Fig. 2.10.... .... .... .... ..... .... 64
2.5.3 Illustration of the Modeling Perspective
for a Social Contagion Process .... .... ..... .... 66
2.6 Standard Combination Functions .. .... .... .... ..... .... 69
2.6.1 Basic Standard Combination Functions.. ..... .... 69
2.6.2 Building More Complex Standard Combination
Functions..... .... .... .... .... .... ..... .... 72
2.7 Properties for Combination Functions... .... .... ..... .... 77
2.8 Applying Computational Methods to Model
Representations... ..... .... .... .... .... .... ..... .... 81
2.9 Applicability of the Modeling Perspective ... .... ..... .... 85
2.9.1 The State-Determined System Assumption .... .... 85
2.9.2 State-Determined Systems and First-Order
Differential Equations ... .... .... .... ..... .... 86
2.9.3 State-Determined Systems and Modeling
Based on Temporal-Causal Networks ... ..... .... 88
2.10 Modeling Adaptive Processes by Adaptive Temporal-Causal
Networks ... .... ..... .... .... .... .... .... ..... .... 92
2.11 Discussion... .... ..... .... .... .... .... .... ..... .... 99
References. .... .... .... ..... .... .... .... .... .... ..... .... 100
Part II Emotions All the Way
3 How Emotions Come in Between Everything . .... .... ..... .... 105
Emotions Serving as Glue in All Mental and Social Processes... .... 105
3.1 Introduction . .... ..... .... .... .... .... .... ..... .... 105
3.2 Generating Emotional Responses and Feelings.... ..... .... 107
3.3 Emotion Regulation .... .... .... .... .... .... ..... .... 111
3.4 Interaction Between Cognitive and Affective States ..... .... 114
3.5 Emotion-Related Valuing in Decision-Making .... ..... .... 118
3.6 Emotions and Social Contagion ... .... .... .... ..... .... 119
3.7 Discussion... .... ..... .... .... .... .... .... ..... .... 120
References. .... .... .... ..... .... .... .... .... .... ..... .... 121
Contents ix
4 How Do You Feel Dreaming... .... .... .... .... .... ..... .... 125
Using Internal Simulation to Generate Emotional Dream Episodes.... 125
4.1 Introduction . .... ..... .... .... .... .... .... ..... .... 125
4.2 Memory Elements, Emotions and Internal Simulation
in Dreaming . .... ..... .... .... .... .... .... ..... .... 126
4.3 A Temporal-Causal Network Model Generating
Dream Episodes .. ..... .... .... .... .... .... ..... .... 128
4.4 Simulations of Example Dream Scenarios ... .... ..... .... 133
4.5 Relations to Neurological Theories and Findings .. ..... .... 136
4.6 Discussion... .... ..... .... .... .... .... .... ..... .... 137
References. .... .... .... ..... .... .... .... .... .... ..... .... 138
5 Dreaming Your Fear Away ... .... .... .... .... .... ..... .... 141
Fear Extinction Learning During Dreaming .... .... .... ..... .... 141
5.1 Introduction . .... ..... .... .... .... .... .... ..... .... 141
5.2 An Adaptive Temporal-Causal Network Model
for Fear Extinction Learning.. .... .... .... .... ..... .... 142
5.2.1 Conceptual Representation of the Adaptive
Network Model.... .... .... .... .... ..... .... 142
5.2.2 Numerical Representation of the Adaptive
Network Model.... .... .... .... .... ..... .... 146
5.3 Simulations of Fear Extinction Learning in Dream
Scenarios ... .... ..... .... .... .... .... .... ..... .... 148
5.4 Relating the Adaptive Temporal-Causal Network Model
to Neurological Theories. .... .... .... .... .... ..... .... 152
5.5 Discussion... .... ..... .... .... .... .... .... ..... .... 153
References. .... .... .... ..... .... .... .... .... .... ..... .... 154
6 Emotions as a Vehicle for Rationality in Decision Making ... .... 157
Experiencing Emotions for Decisions Based on Experience ..... .... 157
6.1 Introduction . .... ..... .... .... .... .... .... ..... .... 157
6.2 The Adaptive Temporal-Causal Network Model
for Decision Making.... .... .... .... .... .... ..... .... 159
6.3 Simulation Results for a Deterministic World. .... ..... .... 168
6.4 Simulation Results for a Stochastic World ... .... ..... .... 171
6.5 Simulation Results for a Changing Stochastic World .... .... 172
6.6 Evaluating the Adaptive Temporal-Causal Network Model
on Rationality.... ..... .... .... .... .... .... ..... .... 175
6.7 Discussion... .... ..... .... .... .... .... .... ..... .... 178
References. .... .... .... ..... .... .... .... .... .... ..... .... 179
x Contents
Part III Yourself and the Others
7 From Mirroring to the Emergence of Shared Understanding
and Collective Power.... ..... .... .... .... .... .... ..... .... 183
Biological and Computational Perspectives on the Emergence
of Social Phenomena. .... ..... .... .... .... .... .... ..... .... 183
7.1 Introduction . .... ..... .... .... .... .... .... ..... .... 183
7.2 Mirror Neuron Activation and Internal Simulation . ..... .... 185
7.2.1 The Discovery of Mirror Neurons.. .... ..... .... 185
7.2.2 Neurons for Control and Self-other Distinction. .... 186
7.2.3 Generating Emotions and Feelings by Internal
Simulation: As-if Body Loops. .... .... ..... .... 187
7.2.4 Mirroring Process: Mirror Neuron Activation
and Internal Simulation .. .... .... .... ..... .... 187
7.2.5 Development of the Discipline Social
Neuroscience.. .... .... .... .... .... ..... .... 192
7.3 The Emergence of Shared Understanding.... .... ..... .... 193
7.3.1 The Emergence of Shared Understanding
for External World States .... .... .... ..... .... 194
7.3.2 The Emergence of Shared Understanding
for Internal Mental States .... .... .... ..... .... 195
7.4 The Emergence of Collective Power.... .... .... ..... .... 197
7.4.1 The Emergence of Collective Action Based
on Mirroring .. .... .... .... .... .... ..... .... 197
7.4.2 The Role of Feelings and Valuing in the
Emergence of Collective Action ... .... ..... .... 199
7.5 Integration of External Effects and Internal Processes.... .... 200
7.6 Abstraction of Complex Internal Temporal-Causal
Network Models.. ..... .... .... .... .... .... ..... .... 202
7.7 Discussion... .... ..... .... .... .... .... .... ..... .... 203
References. .... .... .... ..... .... .... .... .... .... ..... .... 205
8 Am I Going to Do This? Is It Me Who Did This? . .... ..... .... 209
Prior and Retrospective Ownership States for Actions .... ..... .... 209
8.1 Introduction . .... ..... .... .... .... .... .... ..... .... 209
8.2 Neurological Background.... .... .... .... .... ..... .... 211
8.3 A Temporal-Causal Network Model for Ownership ..... .... 213
8.3.1 ConceptualRepresentationoftheTemporal-Causal
Network Model.... .... .... .... .... ..... .... 213
8.3.2 Numerical Representation of the Temporal-Causal
Network Model.... .... .... .... .... ..... .... 215
8.4 Simulation of Example Scenarios.. .... .... .... ..... .... 220
8.4.1 Normal Execution and Attribution of an Action .... 221
8.4.2 Vetoing a Prepared Action Due to Unsatisfactory
Predicted Effect.... .... .... .... .... ..... .... 222