Network-Oriented Modeling for Simulation and Analysis of a Dynamic, Adaptive and Evolving World

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Networks have become a useful concept to model a wide variety of processes and phenomena in the world resulting in, for example, biological networks, neural networks, mental networks, economic networks, and social networks. Networks by themselves have a transparent type of structure based on nodes and connections between them which can be described in a transparent manner by declarative mathematical relations and functions, for example, a graph for follow-relationships in Social Media.

However, phenomena in the world usually are not static but dynamic, adaptive or evolving. Dynamics within a network can be described as nodes affecting each other through the connections, for example, social contagion in a social network, propagation of activation in a neural network, or mental causation in a mental network. Adaptive phenomena can be described by adaptation principles for changing network structure characteristics such as connection weights or excitability thresholds, for example, for plasticity of the brain, connections between mental states that become stronger due to learning, or connections in a social network that become stronger due to the extent of similarity of two persons.

This is not the end of the story. Such network adaptation principles themselves can be adaptive as well, which can be modeled by second-order adaptive networks, for example, in neural or mental networks metaplasticity describing under what circumstances plasticity should accelerate or decelerate or even completely be blocked, or in social networks criteria describing when similarity of two persons is strong enough to strengthen the connection. This may lead to multiple orders of adaptation.

Also here the story does not end. In evolutionary processes still more complex phenomena may take place. Given causal pathways developed at some stage of evolution, a next step in the evolution may be the addition of a causal pathway that modifies an earlier developed causal pathway in a dynamic manner. Also this phenomenon can be described by networks, where also the behavioural plasticity of individuals can be integrated. Such an integrated network model covers not only a biological network model but also a mental network model for the adaptive decision making about the behaviour (for example, the choice for a specific type of food), and learning of skills for such a choice, which in turn affect the physical evolutionary steps.

After the above sketched landscape one can easily not see the forest for the trees. Networks themselves may have transparent declarative descriptions as long as they are static, but as soon as dynamics, adaptation (of multiple orders) or evolution are involved, for modeling usually all kinds of procedural algorithmic programming-like descriptions are added so that a less transparent and less declarative hybrid form of model occurs.

In the current paper, an alternative way of network modeling is described which makes that such procedural specifications are not needed for dynamic, adaptive or evolving networks, but instead also more transparent declarative descriptions can be used for them, based on mathematical functions and relations, in conjunction with a transparent notion of architecture for which a dedicated modeling environment has been implemented.
Original languageEnglish
Title of host publicationProceedings of the 2020 International Symposium on Computational Intelligence, ISOCI'20
Publication statusAccepted/In press - 16 Jan 2020


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