Schelling's Bounded Neighbourhood Model: A systematic investigation

This thesis explores the role of modelling and computational simulation, in relation to social systems, with specific focus on Schelling's Bounded Neighbourhood Model. It discusses the role of computational modelling and some techniques that can be used in the Social sciences. Simulation of social interaction consistently creates debate in the Social sciences. However, most models are dismissed as either too simplistic or unrealistic. In an attempt to counter these criticisms, more complex models have been developed. However, by increasing the complexity of the model, the underlying dynamics can be lost. Schelling's models of segregation are a classic example, with much of the work building on his simple segregation model. The complexity of the models being developed are such that, real world implications are being inferred from the results. The Complex Systems Modelling and Simulation (CoSMoS) process has a proven track record in developing simulations of complex models. In a novel application, the CoSMoS process is applied to Schelling's Bounded Neighbourhood Model. The process formalises Schelling's Bounded Neighbourhood Model and develops a simulation. The simulation is validated against the results from Schelling's model and then used to question the model. The questioning of the model is an attempt to examine the underlying dynamics of the segregation model. In this respect, two measures, static and dynamic, are used in the analysis of the results. Initally, the effect of ordered movement was tested by changing the movement, from ordered to random. A second experiment examined agents' perfect knowledge of the system. By introducing a sample, the agents' knowledge of the system is reduced. The third experiment introduced a friction parameter, to examine the effect of ease of movement into and out of the neighbourhood. In the final experiment, Schelling's model is recast as a network model. Although the recasting of the model is slightly unorthodox, it opens the model up to network analysis. This analysis allows the easy definition of a `social network' that is overlaid on Schelling's `neighbourhood network'. Two different networks are applied, Random and Small World. The results of the experiments showed, that Schelling's model is remarkably robust. Whilst the adjustments to the model all contributed to changes in the output, the only significant difference occurred when the social network was added.