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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 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 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