Abstract

Digitalizing cities has become increasingly complex and difficult to control despite advanced computational tools. The comprehension of emergent, dynamic agent–pattern interaction is limited. Studies show that the implementation of large-scale plans occasionally fails to meet expectations due to uncertainty in urban actor processes and institutions. Theories of complexity and resilience reflecting urban unpredictability and non-equilibrium enable understanding and planning methods for guiding actors. We explored empirically via close reading and spatial analyses the ability of the traditional master planning instrument to steer the actor allocation in Tampere, Finland. The plan apparently failed to appropriately guide the actors, who formed self-organizing patterns colliding with the planning aims enabled by deviations and lower-level planning instruments. The planning mode was either enabling or reactive. We recognized three types of self-organization: single-point attraction, emergent type, and location-based self-organization. Self-organization was the major force behind urban transition. Only certain large-scale projects in the city center somewhat complied with the planning aims, however through negotiations. We proposed planning solutions encouraging and guiding self-organizing patterns by recognizing complexity in strategies, and with loose plans, constant monitoring, correcting, and experimenting in planning. The results participate in building more general knowledge of planning considering self-organizing urban dynamics and provide applications for urban planning.

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