By maart 20, 2020 Algemeen


In policy-and decision-making multifaceted phenomena have to be tackled; conflicts of interests, migration, climate change, the automation of professions, economic and financial aspects (crises), healthcare, education, international trade, social integration, disruptive technologies, etc. The degree of uncertainty present is enormous and increases with rising interconnectedness. Denser interaction which complexifies causality chains. Efforts in research and practice have led to approaches and processes to analyze these phenomena and make decisions under uncertainty.

International commitment to the appropriately ambitious Paris climate agreement and the United Nations Sustainable Development Goals in 2015 has pulled into the limelight the urgent need for major scientific progress in understanding and modelling the Anthropocene, the tightly intertwined social-environmental planetary system that humanity now inhabits. By pushing Earth’s climate and biosphere out of the dynamics of the Holocene humanity is at risk of moving our planet outside a safe operating space for humanity by altering important feedback loops, potentially producing abrupt and irreversible systemic changes with impacts on current and future generations.


Cities are one of the most distinctive features of the Anthropocene, yet one of the least understood Earth systems. The future trajectory of our cities is equally governed by two kinds of internal processes; those operating in the physical, chemical, and biological systems and those occurring in its human societies, their cultures and economies. The history change is the history of the increasing (planetary-scale) entanglement of these two domains and its social-ecological feedbacks.

As major drivers of global change, cities have a prominent role in enabling the Earth’s transition to sustainability. Urban regions operate as hubs of global and regional flows of people, capital, services, and information that drive the global economy. Despite this increasing global interdependence and integration, today’s urbanizing regions remain highly diverse with regard to physical structures, social organizations, biophysical environments, and political contexts. While there are important commonalities across many metropolitan regions, there is also great diversity across regions and across cities of differing sizes. Convergence and interactions of functional, structural, and social changes result in challenges of unprecedented complexity for city governments.

Cities are characterized by complex interactions among multiple heterogeneous agents and components across multiple scales. Cities are nonequilibrium systems; random events produce system shifts, discontinuities, and bifurcations, and patterns emerge from complex interactions that take place at the local and global scale. Understanding these changes and their implications requires a fundamental knowledge and understanding of integrated systems. Linking urban changes to social-ecological-technical change is critical to gaining new insights for the future of ecological and human well-being.

In cities and urbanizing regions, agents ( households, individual businesses, real estate developers, local and regional governments, nonprofit organizations, and academic institutions that make a variety of decisions) interact dynamically within communities and through social networks, economic markets, and many public institutions (including governmental and other nonprofit and nongovernmental organizations), giving rise to emergent properties. It is through these multiple interactions across time and space that urban agents generate observable emergent physical, behavioral, social, economic, ecological, and environmental patterns.

In this context we must view cities not simply as just places on earth but as systems of networks and flows. To understand this we must understand flows, and to understand flows, we must understand networks—the relations between objects that compose the system of the city. In a rapidly changing world in which smart cities are desired and urban megacities are a reality, we need to explore new knowledge and new approaches. Current descriptive and disparate approaches to the review, analysis and design of our cities need to be challenged.


The profession and politics of the built environment continue to operate within discipline silos. Planning, architecture, engineering, transport, water, power, commercial and retail development, urban design, community services and more are all dealt with in relative isolation. The links between them are only examined as necessary, or as legislatively required.

As a result, our cities are a legacy of incremental solutions, fragmented decision-making and competing urban priorities.


Our approach allows decision-makers, designers and the community to understand the complex nature of humans, technology and their environments. It is possible to create cities that cope with complexity rather than collapse under the weight of it.

Complexity science expands on the reductionistic framework by not only understanding the parts that contribute to the whole but by understanding how each part interacts with all the other parts and emerges into a new entity, thus having a more comprehensive and complete understanding of the whole. Individual causal research in complex systems is near futile; a comprehensive approach is required to account for the unpredictability found in complex systems. To better understand such systems, complexity science offers complex adaptive systems as a framework for understanding these systems.

Understanding complex systems and technologies that can be used to collect and process increasing amounts of data offer us an opportunity to better observe and understand complex systems, natural or man-made. We can increasingly measure the activity of the variables that constitute these systems. This provides insight in the quality, quantity and connectivity of most variables that control a city as a adaptive complex system. When all these variables work together, they make up a system that appears to us as one unit that is alive.

The process of modelling aims to capture the essence of the complexity, abstracting the real system into a manageable size that is cognitively, mathematically and theoretically explainable. Models that simulate real-world complex systems are built to capture the dynamics and architecture of a system to predict the system’s future behavior and to explain its past behavior. Such models help us to better understand and potentially fix system failures.

For dynamical models to be realistic, they need to have accurate initial conditions, exact causality between systems variables and defined kinetics. Models that capture complex systems’ structure and dynamics lead to an ability to better create, control, predict and fix the complex systems around us, including ourselves and our society, and our natural, economic and technological environments.

This kind of thinking / modelling shift its focus from:

  • the reductionist three-tiered approach to sustainable development to one recognizing inseparable social-ecological systems;
  • local interventions aimed at the present to including global multi-scalar interventions operating across geographies and time scales in a dynamic cross-scale context;
  • discard assumptions of linearity, predictability, and incrementality for an approach embracing complexity and the possibility of non-linear systemic tipping points;
  • not be solely dependent on the state as its principal actor/agent and primary legitimizing source of authority;
  • adapting to focusing on transforming for change, including pathways to create a new system when ecological, economic, or social structures make the existing system untenable.

A Decision Support Model will be vital to fully understand the urban web and maximize social-economic and ecological assets and values and catalyze a myriad of innovations. A real-time digital ‘dashboard’ for urban system management that would enable the monitoring, modelling probabilistic programming and an array of statistical machine-learning techniques.

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