Network-Oriented Modelling: A Temporal-Causal Network Modelling Approach to Complex Dynamical Systems (extended abstract)

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Abstract

This contribution presents a Network-Oriented Modelling approach based on temporal-causal networks. Causal modelling has a long tradition in the areas of AI, modelling and simulation. The temporal-causal modelling approach incorporates a dynamic perspective on causal relations. Basic elements are networks of nodes and connections with for each connection a connection weight for the strength of the impact of the connection, for each node a speed factor for the timing of the effect of the impact, and for each node the type of combination function used to aggregate multiple impacts on this node. The temporal-causal network modelling format used makes it easy to address complex phenomena such as the integration of emotions within all kinds of cognitive processes, of internal simulation and mirroring of mental processes of others, and of social networks. Also adaptive networks are covered in which connection weights of the network change over time, which, for example, can be used to model Hebbian learning in adaptive neuro-cognitive models or evolving social networks.

Original languageEnglish
Title of host publicationConference on Complex Systems, CCS'16
Number of pages1
Publication statusPublished - 2016

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title = "Network-Oriented Modelling: A Temporal-Causal Network Modelling Approach to Complex Dynamical Systems (extended abstract)",
abstract = "This contribution presents a Network-Oriented Modelling approach based on temporal-causal networks. Causal modelling has a long tradition in the areas of AI, modelling and simulation. The temporal-causal modelling approach incorporates a dynamic perspective on causal relations. Basic elements are networks of nodes and connections with for each connection a connection weight for the strength of the impact of the connection, for each node a speed factor for the timing of the effect of the impact, and for each node the type of combination function used to aggregate multiple impacts on this node. The temporal-causal network modelling format used makes it easy to address complex phenomena such as the integration of emotions within all kinds of cognitive processes, of internal simulation and mirroring of mental processes of others, and of social networks. Also adaptive networks are covered in which connection weights of the network change over time, which, for example, can be used to model Hebbian learning in adaptive neuro-cognitive models or evolving social networks.",
author = "J. Treur",
year = "2016",
language = "English",
booktitle = "Conference on Complex Systems, CCS'16",

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Network-Oriented Modelling: A Temporal-Causal Network Modelling Approach to Complex Dynamical Systems (extended abstract). / Treur, J.

Conference on Complex Systems, CCS'16. 2016.

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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N2 - This contribution presents a Network-Oriented Modelling approach based on temporal-causal networks. Causal modelling has a long tradition in the areas of AI, modelling and simulation. The temporal-causal modelling approach incorporates a dynamic perspective on causal relations. Basic elements are networks of nodes and connections with for each connection a connection weight for the strength of the impact of the connection, for each node a speed factor for the timing of the effect of the impact, and for each node the type of combination function used to aggregate multiple impacts on this node. The temporal-causal network modelling format used makes it easy to address complex phenomena such as the integration of emotions within all kinds of cognitive processes, of internal simulation and mirroring of mental processes of others, and of social networks. Also adaptive networks are covered in which connection weights of the network change over time, which, for example, can be used to model Hebbian learning in adaptive neuro-cognitive models or evolving social networks.

AB - This contribution presents a Network-Oriented Modelling approach based on temporal-causal networks. Causal modelling has a long tradition in the areas of AI, modelling and simulation. The temporal-causal modelling approach incorporates a dynamic perspective on causal relations. Basic elements are networks of nodes and connections with for each connection a connection weight for the strength of the impact of the connection, for each node a speed factor for the timing of the effect of the impact, and for each node the type of combination function used to aggregate multiple impacts on this node. The temporal-causal network modelling format used makes it easy to address complex phenomena such as the integration of emotions within all kinds of cognitive processes, of internal simulation and mirroring of mental processes of others, and of social networks. Also adaptive networks are covered in which connection weights of the network change over time, which, for example, can be used to model Hebbian learning in adaptive neuro-cognitive models or evolving social networks.

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