By februari 5, 2020 Algemeen

Science and technology allow us to understand our environment as well as manipulate it and create new environments and new systems. This led humans to emerge out of nature, and recently to create new complex worlds that highly resemble natural systems. Human-made systems often follow the same design principles governing natural systems. The most important of these design principles is evolution by natural selection. However, human-made systems are not exactly the same as those created by nature. We are gaining an increasing ability to create new complex environments and new technologies that perform as well as, or even better than, natural organisms.

Complex systems have emerged through natural or manmade evolution. This has produced parallels between natural and technological systems despite their differences. While natural evolution has been evolving for billions of years, man-made technological, social and economic evolution has made a significant impact on the Earth only in the past few thousands of years. Hence, evolutionary rates are much different when comparing the two types of complex systems: man-made versus natural. Natural evolution needs to wait for random favorable mutations in the DNA of an organism to occur over many generations, whereas in technological evolution new ideas can become new products overnight.

Our social-economic-technological evolution is constantly accelerating; it is moving at various rates across the planet, but overall, since the industrial revolution, the rate of complexity of man-made systems seems to be generally accelerating. Technological evolution is moving at much faster rates in major cities or on the Web, where interactions between people and the demand for new products are greater than in less habitable regions on the globe. However, there are forces that balance these trends. Geographical diffusion of innovations and the spread of complexity make technological and natural complexity spread to remote places on Earth.


Without a tiring list of, well-known, interrelated environmental and social-economic problems, one of the most important and difficult tasks for the coming decades is to manage cities towards (re)development that fits within natural systems’ boundaries, in that way accounting for all forms of life on earth. Parallel to the city as source of our current problems, we see cities as the solution-space: cities are our greatest opportunity, as they are the places where innovation happens, where solutions that improve lives are born, and where wealth generation is accelerated.

Together with social innovation and our creative capabilities, we believe that cities are the place where promising solutions (will) come forward. Therefore it is essential to understand what a city is, of what it consists, and especially to understand ‘how a city works’. Understanding how cities work is all about feedback loops, sensitive dependence on initial conditions and emergent phenomena.


We rank our cities to be the most sustainable, smartest, the most bike friendliest or as the top shopping city, but that does not define a city per se. An urban environment may be termed a city by looking at the density of its population, commuting patterns, or by administrative borders. The problem with these definitions is that they are a very narrow representations of what is in fact a complex system. The city is best addressed as a complex adaptive system. A systems approach offers a better understanding of the social, economic and political interconnections inherent in city systems. Our society has become highly dynamic, volatile and a-linear, which makes a city system complex and unpredictable.


Man-made complex systems, like cities as multi-user networks and intelligent technologies that can be used to collect and process increasing amounts of information offer us an opportunity to better observe and understand these urban complex systems. The physical-morphological elements of the city system only represent a small part of the complex system. It is crucial to consider the bigger spectrum of elements that make up the city system: social-cultural relations and transformations, economic dynamics, technical innovations, and ecological changes and boundaries.

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 in a city, neighborhood or region.


Complex urban systems are generally diverse and made up of multiple interconnected elements. They are adaptive in that they have the capacity to change and learn from events. Building on complexity theory, complex adaptive systems (CAS) contain adaptive components and capacities. Adaptive components allow systems to change and evolve over time in response to feedbacks and changes in the systems context. The principles that underly a complex adaptive system -a city- : it is relational, adaptive, autopoietic, non-linear, dynamic, open, contextually determined and emerges through complex causality.

These patterns are the essential elements for understanding and successful interventions for the transformation in our cities. Cities, regions that can function, compete, adapt and evolve towards increased fitness and overall growth.


Until recently, as we transformed our society, we have unintentionally changed our environments resulting in drastic changes in natural systems and patterns. It cannot be stressed enough that we are at a critical point in time. The next decade will determine if we are able to sustain a future that is worth remembering; one that represents quality of life. This calls for an intentional transformation of our urban areas. We have created our past, our present, and so can we create our future. The techniques are there and our creative energy and industry offer hope. Then what is holding us back to create something truly transformational?

As we have come in a time where we are more than ever aware of our impact, we argue for an intentional transformation of our cities into more sustainable living environments, by using an integrated and holistic perspective. We propose -and are working on- a ‘decision support model’ we develop intelligent technologies and the connection of cyberphysical systems as opportunity to develop a better insight in the complex systems that our cities are.


The objective is to define more precisely the properties towards a greater understanding of the workings of a city (neighborhood, region) as a whole, beyond the understanding of one specific domain, or one specific design concept.

The process of urban modelling aims to capture the essence of the complexity, abstracting the real system into a manageable structure that is cognitively, mathematically and theoretically explainable. Urban dynamic models built to capture the dynamics and architecture of a system to predict the system’s future behavior and to explain its present behavior. Such models help us to better understand and potentially fix system failures, anticipate internal and external disturbances and ‘run’ predictive analyses. For dynamical models to be realistic, they need to have accurate initial conditions, exact causality between systems variables and defined kinetics. Because of the complex relationships between the variables in complex systems, the dynamics of the system quickly become nonlinear and complex.  The essence of dynamical models of complex systems is to determine its nonlinearity characteristics.

A decision support model assisted by AI provides a real-time digital model which is able to simulate future urban scenarios by including all different assets of a city. It is well-known that advances in modeling and computing technology have led to a deeper understanding of complex systems in many areas. Real time dynamics and scenario-driven simulation of the multiple interaction, interdependencies, cascade-effects are measured and visualized. Visible through interactive dashboards and 3- 4D simulation enables decision-makers to explore every possible future scenario. Supported by detailed analysis, predictive multiple results of scenarios, results can be ordered and probability can be assigned as a support for making decisions. The future can be explored by visualizing cascade-effects of causal relationships.

With these realistic insights possible and desired futures can be explored and decisions and policy can validated, motivated and managed supported by scientific models and critical data. Once customized and purposefully built, the decision support model provides a useful policy simulator to explore the strength and weakness of alternative policy options. It may reveal system-level risks and inefficiencies which are usually caused by the lack of coordination in policy making and provide a consistent evidence base across disciplines to facilitate policy debates and collaboration among urban professionals.


We, as a team of motivated people -professionals, researchers, from different knowledge institutions and tech-driven companies- are developing and constantly working on the modelling because  its strength lies in the fact that the underlying model is researched, used and tested over the last years. In this testing, qualitative expert modelling and data were leading, and till this day, the amount of information and our knowledge has advanced, and experiences broadened. The motivation to upgrade our tested model towards a digital decision support model stems from the issue that different aspects of city life change at different rates. Our build environment is designed for 40-50 years, our daily life changes at a pace of 2-5 years, our cities are influenced by external drivers and our daily technological worlds and high tech advances change as we speak.

As a team we want to be able to continuously envision, design, realize, and evaluate our insights and decisions. A digital model allows this continuity and is better able to give insight in cities complexity in a constant flux . In collaboration with different knowledge institutions the remaining knowledge gaps were filled in and further studies into the next step in our human Society 5.0 have been performed. The beginning of a digital infrastructure is being ‘under construction’ as ‘neural network architecture’ that contain many layers.

As a society we have arrived at a point where our increase in cultural fitness, achieved by means of technological developments, have a mismatch with biological fitness. Resulting in an accumulation of considerable sustainability debt. It cannot be stressed enough that we are at a critical point in time. The next decade will determine if we are able to sustain a future life that is worth remembering; one that represents quality of life. We argued for an intentional transformation of our urban areas supported by intelligent decision support systems.

We as a team are ready for it (and to support you in the transformation).

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