Dynamic Modeling Based on a Temporal-Causal Network Modeling Approach

TY - JOUR

T1 - Dynamic Modeling Based on a Temporal-Causal Network Modeling Approach

AU - Treur, J.

PY - 2016/4/1

Y1 - 2016/4/1

N2 - This paper presents a dynamic modelling approach that enables to design complex high level conceptual representations of models in the form of causal-temporal networks, which can be automatically transformed into executable numerical model representations. Dedicated software is available to support designing models in a 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 cognitive processes, and of internal simulation and mirroring of mental processes of others. In this paper also the applicability has been discussed in general terms, showing for example that every process that can be modelled by first-order differential equations, also can be modeled by the presented temporal-causal network modeling apporoach. A variety of example models that can be found in other papers illustrate the applicability of the approach in more detail.

AB - This paper presents a dynamic modelling approach that enables to design complex high level conceptual representations of models in the form of causal-temporal networks, which can be automatically transformed into executable numerical model representations. Dedicated software is available to support designing models in a 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 cognitive processes, and of internal simulation and mirroring of mental processes of others. In this paper also the applicability has been discussed in general terms, showing for example that every process that can be modelled by first-order differential equations, also can be modeled by the presented temporal-causal network modeling apporoach. A variety of example models that can be found in other papers illustrate the applicability of the approach in more detail.

KW - Causal

KW - Dynamic

KW - Modeling

KW - State-determined system

KW - Temporal

U2 - 10.1016/j.bica.2016.02.002

DO - 10.1016/j.bica.2016.02.002

M3 - Article

VL - 16

SP - 131

EP - 168

JO - Biologically Inspired Cognitive Architectures

JF - Biologically Inspired Cognitive Architectures

SN - 2212-683X

ER -