Informal: An investigative study into complex behavioral modelling.

The following research explores the computational modelling of informal settlement growth and characteristics, and represents the outputs using a visual, representational, format.

To achieve this, a rule-based algorithm within a digital environment was developed, with the introduction of complexity through incremental steps justified in response to comparing the resemblance of simulation to real-life observations, statistical data, and relevant cases. Furthermore, the performance of the developed self-iterative model as an analytical tool was analyzed in terms of its effectiveness in responding to the detriments of informal settlements, by identifying areas of improvement.

The outcome of this experiment revealed that whilst singular actions can be defined by basic rulesets, the accuracy of behavioral modelling requires more complexity in the simulation, with constant adaptability, and multiple responses triggered by various situations when implemented in certain environments. The performance of a Cellular Automaton as an analytical tool appeared to provide appropriate suggestions but is, however, limited by the complexity of the initial model with regards to comprehensive validity.

Overall, the work suggests that modelling by rulesets defined by observations and statistics provides an alternative approach that analyses a settlement and its buildings and spaces by reference to the relevant data.

This research emphasizes the capabilities of behavioral simulations to inform understanding of settlements, settlement layouts, and the challenges in the imitation of complex human nature and actions in defined algorithmic rulesets.