Self-organizing city: experiments using multi-agent model and space syntax

This paper describes a process of using local interactions to generate intricate global patterns and emergent urban forms. Starting with network topology optimization, agent-based model (ABM) is used to construct the micro-level complexity within a simulated environment. The authors focus on how agent-driven emergent patterns can evolve during the simulation in response to the "hidden hand" of macro-level goals. The research extends to the agents' interactions driven by a set of rules and external forces. An evaluation method is investigated by combining network optimization with space syntax. The multi-phase approach starts with defining the self-organizing system, which is created by optimizing its topology with ABM. A macro-level "attraction map" is generated based on space syntax analysis. Then the map is used to control various construction operations of an adaptive urban model.