Simulation and Modelling Complex Adaptive Systems (SiMCAS)

Dear Colleagues,

Nowadays, the ICT ecosystem has achieved a huge complexity thanks to the emergent scenarios driven by new technologies like sensor networks, 5G, traffic management, Internet of Things (IoT), machine learning, digital and green economy, smart cities, intelligent transportation systems, crowdsourcing and cybersecurity, among others. The huge amount of entities interacting in such systems, the relative unpredictability of their dynamic organization, and the emergence of new technological opportunities make it really difficult to predict future economic and technologic scenarios in order to optimize human activities for the common good.

A complex adaptive system (CAS) is complex in the sense that contains multiple and diverse interconnected elements with a high degree of adaptive capacity, which makes the system resilient against disturbances. CAS systems usually show characteristic properties, such as self-similarity, emergence and self-organisation. Recently, the research community has accomplish a strong effort to study natural and artificial CAS, and presently we are beginning to understand the emergent patterns of global organisation, and finding that apparently very dissimilar systems share fundamental commonalities.

Trying to model a large number of entities, with non-linear interactions in a continuously changing environment, by means of classical mathematical tools has become a very hard task. Within this context, computer simulations emerged as the natural tool for performing such kind of analysis and evaluation. The CAS system resulting from the interactions among the agents is usually modelled as an Agent-based System (ABS), or a Multi-agent System (MAS), and such models have been strongly used in the last two decades to describe and simulate CAS by means of Agent-based Simulations (ABS). The aim of these agent-based models and simulations is to better understand real CAS scenarios, analysing their properties, strengths, weaknesses and limitations; but also to consider alternative scenarios, in order to explore new possible solutions to the present problems, and to discard inappropriate paradigms, assumptions or models.

In the past decades, the research community has made a huge effort to understand and simulate CAS, but we are still far away from delivering well-defined frameworks to support their design, engineering, management, and evaluation. However, before we can do that, we will still have to develop new theories that shed more light on CAS definition, representation, dynamics, sensitivity analysis, verification, and validation.

These are the topics addressed in this Special Issue titled “Simulation and Modelling for Complex Adaptive Systems”. Authors are invited to submit a paper on one or more of the non-exhaustive list of potential topics of interest, including the following:

• Agent-based modelling and simulation
• Hierarchical- and coalition-based complex adaptive systems
• Cellular automaton modelling and simulation
• Complex networks, social networks, and social media analysis
• Self-organization and emergence
• Evolutionary game theory for simulating complex adaptive systems
• Artificial societies and social simulation
• Bioinformatics and bio-inspired paradigms
• Computational Intelligence: deep learning and machine learning
• Computational game-theoretic analysis of financial scenarios: markets, digital economy, blockchain, etc.
• Artificial intelligence techniques for financial systems simulation
• Clean energy and green power systems
• Smart cities and smart homes
• Intelligent transport systems and automotive industry
• Artificial intelligence and crowdsourcing
• Artificial intelligence and cybersecurity
• Artificial Intelligence techniques for communication networks: sensor networks, 5G, Internet of Things (IoT)
• Surveys on the above topics.

Prof. Dr. Juan C. Burguillo
Guest Editor

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