Complexity theory offers a new way of understanding spatial patterns as self-organising morphologies. This provides a promising paradigm for exploring spatial organizations as the emergent outcome of dynamic relations between simple elements bounded together by multiple feedback loops. Self-organising spatial morphologies can be defined as a part of a process, usually a simple one, and modelled employing iterative algorithms.
This paper reports on how various versions of the canonical flocking [1] algorithm can be utilized to interactively evolve emergent spatial patterns. The reason for selecting flocks as a study area is the fascinating asymmetry between the simplicity of the rules and the spatial complexity of the outcomes, when observed from a synoptic viewpoint. The flocks are modelled as Agent Based Systems using Netlogo [2] language. Together with traditional behaviours (separate, align, and cohere) the models employ up to five additional rules and a variety of parameters. The focus of the models range from obstacle avoidance, to learning and evolutionary flocking. The aim of the research is to investigate how complex architectural possibilities can be generated bottom-up, using distributed representation.
Theoretically the research is related to the work of Paul Coates [3], Sebastian von Mammen, Aaron Westre, James Macgill, and many others.
