By definition, complex systems have many agents, but what makes it different from simply a large or complicated system is that the behavior of the system is not solely determined by the behavior of the agents, but also of the relationships between the agents. It has suites of interactivity, feedback loops and tipping points and emergent properties. These interdependencies increase the overall value (emergent value), may decrease the required investments. Complexity is the result of the interactions among the various agents, it can not be simplified without losing its essence.
As a complex system the city emerges out of the interactional activities of its agents, but once it emerges, it affects the behavior of its agents and so on in circular causality. The city in this respect is a complex evolving system. The city is a dual complex system as each of its agents is also a complex system. The implication is that we have to include the cognitive capability of the urban agents in the dynamics of cities. The city is an hybrid, co-evolving system.
Understanding these complex hybrid co-evolving systems takes intricate study, testing agents and their relation to each other in nuanced detail. Cities are comprehensive complex systems and many models have been developed that mimic its behavior. We can quite accurately replicate past behavior, but there are many aspects, feedback systems, emergence in urban systems that we don’t understand.
As post-industrial societies change into information and knowledge societies, gradually arizes the digital city of ubiquitous computers that are so prevalent that they are invisible, effectively melding into the background while having a profound effect in our everyday lives. Although the digital city is often referred to as a smart city – a label widely adopted by marketing departments of corporations and cities alike – its scope is much greater. Beyond a reductionist view of discrete solutions centered on digital technologies aiming at improving urban efficiencies, the digital city encompasses a deeper evolution: transforming them into systems capable of dynamically mediating the interactions between humans and their environments.
Improvements and convergences in machine learning and neurosciences combined with the availability of massive datasets and the ubiquity of high-performance scalable computing are propelling us into a new age of Artificial Intelligence (AI). The ability of advanced machine-learning algorithms to mine the growing stocks and flows of data related to the planning and operations of complex systems at the micro or macro levels is likely to trigger a wave of optimization across domains.
The impact that artificial intelligence (AI) has and will continue to have on our cities and the way we live and work in them over the next couple of decades, can be tremendous. AI presents a complex set of considerations for cities. As with any new technology, the possibilities are exciting but also raise deep policy (and philosophical) questions about their impact across several areas of urban life. But can artificial intelligence also support us to observe, understand, measure the interaction between agents, view this complex evolving urban system holistically, encourage anticipation of surprises and thresholds, encourage reflection and adaptive management?
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. AI can provide an virtual nervous model that employs a number of purpose-built AI programs and machine-learning algorithms to process the vast amounts of incoming data. A model that reflects the real social-physical-ecological city committed to support a new form of urban modelling that is capable of self-correction, predictive analytics and decision support.
Promising?