Cybernetic Cities: Designing and controlling adaptive and robust urban systems

📝 Abstract

Cities are changing constantly. All urban systems face different conditions from day to day. Even when averaged regularities can be found, urban systems will be more efficient if they can adapt to changes at the same temporal scales at which these occur. Still, the functionality of urban systems must be robust to changes, either caused by adaptation or by other factors. Technology can assist humans in designing and regulating this adaptation and robustness. To achieve this, we propose a description of cities as cybernetic systems. We identify three main components: information, algorithms, and agents, which we illustrate with current and future examples. The implications of cybernetic cities are manifold, with direct impacts on mobility, sustainability, resilience, governance, and society. Still, the potential of a cybernetic perspective on cities will not depend so much on technology as on how we use it.

💡 Analysis

Cities are changing constantly. All urban systems face different conditions from day to day. Even when averaged regularities can be found, urban systems will be more efficient if they can adapt to changes at the same temporal scales at which these occur. Still, the functionality of urban systems must be robust to changes, either caused by adaptation or by other factors. Technology can assist humans in designing and regulating this adaptation and robustness. To achieve this, we propose a description of cities as cybernetic systems. We identify three main components: information, algorithms, and agents, which we illustrate with current and future examples. The implications of cybernetic cities are manifold, with direct impacts on mobility, sustainability, resilience, governance, and society. Still, the potential of a cybernetic perspective on cities will not depend so much on technology as on how we use it.

📄 Content

Cybernetic Cities:
Designing and controlling adaptive and robust urban systems

Carlos Gershenson1,2,3,4,5,6,*, Paolo Santi3,7, and Carlo Ratti3

1 Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México. 2 Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México. 3 SENSEable City Lab, Massachusetts Institute of Technology, USA. 4 MoBS Lab, Network Science Institute, Northeastern University, USA. 5 ITMO University, Russian Federation. 6 Lakeside Labs GmbH, Klagenfurt am Wörthersee, Austria. 7 Istituto di Informatica e Telematica, CNR, Italy.

• Corresponding author: IIMAS, UNAM, A.P. 20-126, 01000, México, CDMX, México. Email: cgg@unam.mx
  Abstract Cities are changing constantly. All urban systems face different conditions from day to day. Even when averaged regularities can be found, urban systems will be more efficient if they can adapt to changes at the same temporal scales at which these occur. Still, the functionality of urban systems must be robust to changes, either caused by adaptation or by other factors. Technology can assist humans in designing and regulating this adaptation and robustness. For this purpose, we propose a description of cities as cybernetic systems. We identify three main components: information, algorithms, and agents, which we illustrate with current and future examples. The implications of cybernetic cities are manifold, with direct impacts on mobility, sustainability, resilience, governance, and society. Still, the potential of a cybernetic perspective on cities will not depend so much on technology as on how we use it. Keywords big data; cities; complexity sciences; self-organization

1. Introduction Cities have become central to our species, with an increasing majority of people living in them (Cohen 2003; Butler 2010) and producing most of the wealth of our globalized society (Sassen 2011; Dobbs et al. 2011). They serve as magnets for migration as they offer several advantages and opportunities over rural areas (Glaeser 2011; Bettencourt et al. 2007; Bettencourt and West 2010). Densification of population is desirable for a sustainable urban development. However, a high population density also generates several problems which we must face, better sooner than later. We can identify urban problems related to mobility (Heimer 1999), pollution (Bulkeley and Betsill 2003), sanitation (Jacobi et al. 2010), segregation (Musterd and Ostendorf 2013), marginalization (Adler de Lomnitz 1975), and crime (Glaeser and Sacerdote 1996), just to name a few. Even when we are increasingly dependent on urban systems, they are becoming unmanageable with traditional techniques. This is because of the inherent complexity of cities. The term complexity comes from the Latin plexus which means intertwined. A complex system is such that its elements are difficult to separate. As elements are interdependent, their future depends not only on initial and boundary conditions, but on the interactions that take place in time and space, generating novel information (Gershenson 2013b). This information generated by interactions limits predictability. Since traditional techniques (such as optimization) rely on predictability, they cannot cope with the increasing complexity of our urban systems. Complexity is increasing because interactions and interdependencies are increasing. A more connected system can have advantages, as information, energy, and matter can spreads faster through it, it can respond faster to changes (Khanna 2016). However, an increased connectivity also has its drawbacks: having many components affecting each other can potentially increase the fragility of a system (Taleb 2012; Helbing 2013). Given the complex nature of urban systems, they change constantly (Batty 1971), and thus problems change as well, i.e. they are non-stationary (Gershenson 2007). Moreover, we humans are complex and changing on our own and part of cities, making them hybrid complex systems (Portugali 2011; 2016). In other words, cities can be seen as cyber-physical and cyber-social systems (Gershenson 2020). This implies that trying to find optimized solutions will be inefficient, as the optimal solution changes with the problem. If traditional techniques cannot cope with the complexity and dynamics of urban systems, how can we regulate them? Adaptation is required to let urban systems to change their behavior according to their current situation (Gershenson 2013a; Rauws and De Roo 2016). We have plenty of examples of adaptation in living systems, which can serve as an inspiration for urban solutions (Alexander 2003-2004; Gershenson 2013c).
2. Cybernetics The term comes from the Ancient Greek kybernḗtēs, which means steersman or governor. Plato actually used it to refer to the self- governance of city states. Ampère described la cybernétique as the science of governance. Its modern usage as “the

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