Download a Temporal-Causal Network Modelling Approach

ECAI'16 Tutorial
Network-Oriented Modelling:
a Temporal-Causal Network Modelling Approach
Jan Treur
This tutorial presents a dynamic Network-Oriented Modelling approach that
enables to design complex high level conceptual representations of models in the
form of temporal-causal networks, which can be automatically transformed into
executable numerical model representations. Dedicated software is available to
support designing models in a conceptual or graphical manner, and automatically
transforming them into an executable format and performing simulation
experiments. The temporal-causal network modelling format used makes it easy to
take into account theories and findings about complex brain processes known from
Cognitive, Affective and Social Neuroscience, which, for example, often involve
dynamics based on interrelating cycles. This enables to address complex
phenomena such as the integration of emotions within all kinds of mental and
social processes, and of internal simulation and mirroring of mental processes of
others. In this tutorial also the applicability is discussed in general terms, showing
for example that every process that can be modelled by first-order differential
equations, also can be modelled by the presented temporal-causal network
modelling approach. Usually dynamic properties of such dynamic models can be
analysed by conducting simulation experiments. But sometimes properties can also
be found by calculations in a mathematical manner, without performing
simulations. Examples of properties that can be explored in such a manner are.
Mathematical techniques addressing such questions have been developed. Such
types of properties found in an analytic mathematical manner can be used for
verification of the model by checking them for the values observed in simulation
experiments. If one of these properties is not fulfilled, then there will be some error
in the implementation of the model. Some methods to analyse such properties of
temporal-causal network models will be described and illustrated for some example
models, including a Hebbian learning model, and a model for dynamic connection
strengths in social networks. The properties analysed by the methods discussed
cover stationary points and equilibria, increasing or decreasing trends, and
recurring patterns: limit cycles.
Contents
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Addressing Complexity of Mental and Social Processes
o Separation assumptions and the Network-Oriented alternative
Network-Oriented Modeling and Dynamics
Temporal-causal network models
Temporal-Causal Network Models
o Conceptual and numerical representations
Examples of Temporal-Causal Network Models:
o Modeling integration of emotion in all mental and social processes
o Modeling integration of individual mental processes and social
interaction
Adaptive Temporal-Causal Network Models
o Modeling plasticity of mental processes
o Modeling evolving social interactions
Overview of Further Topics
o
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References
Most Detailed Reference:
Treur, J., Network-Oriented Modeling: Addressing Complexity of Cognitive,
Affective and Social Interactions. Springer Publishers, October 2016. Springer
References per part:

Addressing Complexity of Mental and Social Processes
o Separation assumptions and the Network-Oriented alternative



Network-Oriented Modeling and Dynamics
o Temporal-causal network models



Network-Oriented Modeling book, Chapter 1
Treur, J., Network-Oriented Modelling and its Conceptual Foundations.
In: Proc. of the 8th International Conference on Social Informatics,
SocInfo'16. Lecture Notes in AI, Springer Publishers, 2016, to appear. pdf file
Temporal-Causal Network Models
o Conceptual and numerical representations



Network-Oriented Modeling book, Chapter 1
Treur, J., Network-Oriented Modelling and its Conceptual Foundations.
In: Proc. of the 8th International Conference on Social Informatics,
SocInfo'16. Lecture Notes in AI, Springer Publishers, 2016, to appear. pdf file
Network-Oriented Modeling book, Chapter 2
Treur, J., Dynamic Modeling Based on a Temporal-Causal Network Modeling
Approach. Biologically Inspired Cognitive Architectures, 16, 131-168 (2016)
ResearchGate doi pdf file
Examples of Temporal-Causal Network Models:
o Modeling integration of emotion in all mental and social processes

Network-Oriented Modeling book, Chapter 3

o
Modeling integration of individual mental processes and social
interaction



Network-Oriented Modeling book, Chapter 7
Treur, J., Biological and Computational Perspectives on the Emergence of
Social Phenomena: Shared Understanding and Collective
Power. Transactions on Computational Collective Intelligence, 8, 168-191,
2012.
pdf file
Adaptive Temporal-Causal Network Models
o Modeling plasticity of mental processes


o
Network-Oriented Modeling book, Chapter 2
Treur, J., Dynamic Modeling Based on a Temporal-Causal Network Modeling
Approach. Biologically Inspired Cognitive Architectures, 16, 131-168 (2016)
pdf file doi
Modeling evolving social interactions




Treur, J., An Integrative Dynamical Systems Perspective on Emotions.
Biologically Inspired Cognitive Architectures Journal, 4, 27-40, 2013.
doi pdf file
Network-Oriented Modeling book, Chapter 11
Sharpanskykh, A., and Treur, J., Modelling and Analysis of Social Contagion
in Dynamic Networks. Neurocomputing Journal, 146, 140-150 (2014)
pdf file
Blankendaal, R., Parinussa, S., and Treur, J., A Temporal-Causal Modelling
Approach to Integrated Contagion and Network Change in Social Networks.
In: Proc. of the 22nd European Conference on Artificial Intelligence,
ECAI'16. IOS Press. pdf file
(Wednesday August 31, 15:30, Session on AI in Social Media, Europe 1&2)
Overview of Further Topics


Network-Oriented Modeling book, Chapter 2, 18
Treur, J., Dynamic Modeling Based on a Temporal-Causal Network Modeling
Approach. Biologically Inspired Cognitive Architectures, 16, 131-168 (2016)
pdf file doi