A complex system can be viewed as a system composed of many interacting parts, with the ability to generate a new collective behaviour through self-organisation, for example, though the spontaneous formation of temporal, spatial or functional structures. Complex systems are therefore adaptive as they evolve and may contain self-driving feedback loops. Most real-world systems such as global climate, an ecosystem, a city, the human brain, and the entire universe, are complex systems. Therefore, complex systems are much more than a sum of their parts.The general characteristics of the structure and dynamics of complex systems have been characterised, including path dependence, positive feedback loops, self-organisation, and emergence. Complex system types include nonlinear systems, chaotic systems, and complex adaptive systems.
Traditional approaches focus on the individual system components and define a system as the sum of its parts. Whereas the modern approach relies on complexity theory and complex adaptive systems, to emphasise the linkages between system components in order to understand complex systems as a whole. Agent-based models, for example, have been highly recommended for studying complex adaptive spatial systems because they support the explicit representation of situation-dependent information for decision making within dynamic spatial environments.