Professor Rui Perdigão’s course on Complex System Dynamics, headquartered at the Meteoceanics Institute for Complex System Science, is now also available as a semester doctoral course at the Interuniversity Institute for Intelligence, Complexity and Security, offering 6 ECTS for students enrolled in programs from partner universities.

The course is specially tailored to a wide-spectrum interdisciplinary audience spanning across natural, social, technical and exact sciences, and has already attracted bright doctoral and post-doctoral collaborators to Rui Perdigão’s interuniversity chair and institutes.

Objectives

• Acquisition of fundamental competences in complexity sciences, their relevance and implementation in the conceptualization, systematization, modeling and formal analysis of the complex dynamics underlying climate change;
• Learning fundamental principles that allow to formulate the dynamics of complex systems, including emergence of extreme phenomena, in an elegantly simple and effective way without loss of rigor nor generality;
• Deepening scientific research, development and communication at the interface between natural and social frontier sciences.

Program

• Fundamental notions on the dynamics of complex systems, principles and underlying mechanisms in dynamic systems theory and physical information;
• Methods of systematization of dynamic systems: simple conceptual structures representing complex natural, technical and social phenomena;
• Fundamentals of the dynamics of the Earth system and the emergence of regimes, critical transitions and extreme events in the context of complexity sciences;
• Coevolutionary models of climate change in a holistic perspective involving dynamics of the oceans, atmosphere, geosphere, biosphere and society;
• Dynamic methods of extraction and analysis of information related to the dynamics of complex systems, from empirical and computational records;
• Detection of patterns of spatial and temporal climatic variability from data of the dynamics of the Earth system and attribution to underlying mechanisms;
• Methods of evaluating uncertainty and predictability in complex system dynamics, for representative model optimization and decision support.