The new emergent paradigm of urban development theory that is based on complexity sciences allows us to understand and analyse cities in a new way. Theoretically, complexity sciences enable us to depict the fundamental characteristics of urban development, including nonlinearity, self-organization, and emergence. Empirically, the agent-based modelling (ABM) approach can help us to conduct simulations of complex systems, including cities, in an effective way. In the present paper, we demonstrate a computer simulation of urban growth based on the spatial garbage can model represented in an ABM framework. In the simulation, we treated the city as an open system in that the fundamental elements of the system flow in and out of the system over time. We then computed over time the levels of entropy as a measurement of the degree of structural order of the systems, namely, decision and spatial structures. The results showed that these entropies decreased over time, indicating that the city self-organizes itself reminiscent of a dissipative structure.
