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Systems Computational Modelling
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Systems Computational Modelling (SCM) is a vast field that encompasses a variety of topics and approaches, depending on the nature of the system to be modelled and the objective of the research work.
Computational Modelling of Economic and Social Systems: Computational modelling of social systems is an interdisciplinary approach that uses computational techniques to simulate and analyse behaviours, interactions, and phenomena within societies and human groups. This approach allows researchers to better understand how social patterns emerge from individual interactions, predict the impact of policies, and explore future scenarios. It allows us to understand Social Complexity by simulating the dynamics of social interactions to understand how complex behaviours emerge from simple rules. These models make it possible to assess the potential impact of public policies on various aspects of society, including the economy, health, education and environment. They also enable the projection of future scenarios based on current trends, aiding strategic decision-making and long-term planning, and to investigate the spread of information, opinions and behaviors within social networks, including the formation of consensus and the dissemination of innovations.
Artificial Intelligence and Machine Learning for Systems Modelling: The application of Artificial Intelligence (AI) and Machine Learning to systems modelling represents a significant advance in the way we understand and simulate complexity in diverse domains, from natural to man-made systems. These technologies offer powerful methods for analysing large volumes of data, identifying patterns, making predictions and optimising processes. Applying AI techniques to improve the modelling of complex systems, including deep learning for prediction and analysis of simulation data, is one of the most advanced research proposals today. The use of Machine Learning to predict future occurrences based on historical data, essential in economics, meteorology, and resource planning; the use of AI algorithms to find optimal or near-optimal solutions to complex problems, such as in logistics, network design, and energy management; the modelling of ecological systems, traffic networks, or social systems, where AI and Machine Learning can help understand emergent behaviours and system dynamics; the identification of unusual patterns in data, which can indicate fraud, system failures, or rare events in environmental monitoring, are all techniques where AI and Machine Learning can contribute.
Agent-based modelling: Agent-Based Modelling (ABM) is a class of computer simulations that mimics the behaviour of complex systems from the interactions of autonomous agents within a defined environment. This approach offers a powerful tool for understanding the emerging dynamics of systems in various fields, such as economics, ecology, sociology, political science, engineering and public health. One of the key concepts of agent-based modelling is emergence, where complex patterns and systemic phenomena arise from local interactions between agents and between agents and the environment. This allows for the analysis of how simple behaviours at the individual level can lead to complex and often unexpected results at the system level.
Complex Network Modelling: Complex network modelling is an interdisciplinary field of study dedicated to understanding, describing and predicting the behaviour and characteristics of networks that exhibit complex structures and patterns. These networks can be found in a wide range of systems, including social networks, biological networks (such as neural networks and networks of interactions between proteins), technological networks (such as the internet and energy distribution networks), and much more.
