Background
We have entered a full speed era of technology mediated cities, with ubiquitous digital tools constantly guiding us - not only in the form of computational, virtual networks, but through corporeal cities utilizing myriads of sensors, sondes and probes (Batty 2016, Ridell 2019). The digital applications, tools, and apparatus allegedly make our cities “smarted”, that is, more ecologically and economically sustainable, efficient, and more convenient in many senses for us humans. Great expectations lay in architecture and planning praxis which are supposed to create smart buildings and smart urban environment capable of ultimate optimization of the performance of cities, and the lives of the upper, urban middle class. The emphasis is on sheer efficiency and machine-like behavior – often ignoring needs of digitally excluded and vulnerable ones (Carli et al. 2018, Krishnan & Balasubramanian 2016, Tanwar et al 2021).
For architecture, urban planning and design practices computation has been well-established after the digital twist in the 1990s, forming today an integral part of these professions (Carpo 2012). Computation affects not only the means to design but the very content, enabling previously impossible solutions both regarding the form and cognitive content (Botazzi 2018). Digital twist in design is not methodological but a qualitative cultural transition. Moreover, many endeavors in computational design strive towards generative approaches that would bridge scales, and more importantly, embrace better understanding of cities and their complexity and embed it into the design worlds (Aguiar & Cardoso 2017, He et al. 2012).
According to current understanding, cities are complex adaptive systems that self-organize from their internal premises and evolve through revolutionary transitions (Portugali 1999, Batty 2007, Mitchell 2009). Consequently, cities resist any attempts to be tamed using even the most sophisticated, digitalized planning tools if they are built on the premises of the rational comprehensive model (Batty & Mashall 2009, Allmendinger 2017). This is often the case of smart city development, which still have not adopted the bottom up perspective and relies largely on neoliberal project-based, top-down thinking (Cardullo & Kitchin 2019).
The urban paradigm embracing complexity enables fast progress, crossbreeding ideas from computation and theories of self-organization in biological systems and cities; urban evolution; and typomorhpological tradition. This enables intellectual progress in methods development, applying new concepts and stances from these disciplines. Here I suggest that first, self-organization forms an underlying common mechanism for almost-universal order formation in all (including non-living and artificial) complex systems, and hence could help us to build understanding and tools, complementing city planning praxis, design and implementation. Secondly, I consider that thorough understanding of nature of self-organization would assist building more considerate planning approaches for complex urbanity.