Deep uncertainties veil our view of the future of our urban planet after 2050. Within this timeframe, the planet will face changes in migration, climate change, disruptions in financial systems, shifting energy supplies, and pandemics – these and more are complex enough on their own, let alone together.
The cities of the future will be huge, dense and the statistics paint a bleak future.
The urban sphere is central to building sustainability for people, planet, and prosperity. Because of its importance, we need expanded and flexible scientific research and knowledge generation about urban systems. Creating this new urban knowledge requires interdisciplinary efforts for greater understanding of complex urbanization processes and their multi-scale interactions and feedbacks with the Earth system.
Cities are highly complex, socio-ecological and technical in nature and are difficult to analyze. By definition complex adaptive system are systems in which a perfect understanding of the individual parts does not automatically convey a perfect understanding of the whole system’s behavior. The study of complex adaptive systems, a subset of nonlinear dynamical systems, is highly interdisciplinary and blends insights from the natural and social sciences to develop system-level models and insights that allow for heterogeneous agents, phase transition, and emergent behavior.
We were largely unable to view this complex system holistically. Existing modeling and design tools invoke static ‘playbook’ concepts that don’t adequately represent the complexity with its constantly changing variables. Simple measures have proven to not work terribly well, the influences across the system are more powerful.
The understanding of adaptive and evolving complex systems and the ability to process relevant (!) amounts of data supported by artificial intelligence, with its ability to analyze scores of information from varied sources, can tease out the interactions between agents and let us understand the levers across the system we need to activate to enact change.
These programs will leverage rapid improvements in computing and neural networks in the coming decades. The challenges to design an intelligent analytic, predictive and prescriptive model to support decision making are certainly formidable. A model that will employ a number of purpose-built AI programs and machine-learning algorithms to process and monitor the vast amounts of incoming sensory data.
To keep such a system real time, sensors — whether cameras, acoustic networks, swarm drones and other wireless systems — will communicate information about the health and status of the city and its infrastructure. Geostationary and other satellites and orbital platforms will monitor the city’s atmosphere, pollution levels, weather systems, and local environment. Could the increasingly complex systems needed to manage the next generation of megacities become our first true artificial intelligence? The challenge of city intelligence is, to employ a biological analogy, more like genetic engineering than mechanical engineering, and part of the solution will require rewriting a city’s DNA
Dynamic systems such as these with novel AI and mathematical techniques generate capabilities to exploit and provide a deeper understanding of urban system component interactions and a unified view of the urban system behaviors. In fact it could be the first truly human-scale AI system capable of reactive and independent cognition.