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Emergence and Self-organisation of Residential Segregation

The notion of emergence has several meanings (see Dessalles & Phan, 2005, for example). It is also possible to discern different kinds of emergence (e.g. weak or strong). In this thesis, emergence is seen as a coherent developing behaviour, pattern, or structure that manifests at the macro‐level as a result of interaction between the parts at the micro‐level. The properties of these macro‐level behaviours, patterns, and structures are not necessary contained in the property of the parts.
In this sense, although knowledge of individual attributes and interaction rules is not usually sufficient to predict the emerging behaviour of the whole system, a dynamic model will permit a better understanding of the properties of interaction structures, and therefore will make it easier to gain a better understanding of the emergence process. From this viewpoint, designating the residential segregation manifestation as an emergent phenomenon does not mean that it would be impossible to explain or model it (Dessalles & Phan, 2005).

Self‐Organisation

In choosing residential location, individuals (households) do not have a global notion of segregationist structures. In other words, they do not choose to live or not to live in a segregated urban area (this is sometime regarded as ‘weak emergence’ condition). They are however simply conditioned by different optional (e.g. distance to work, proximity to family member) and no‐optional (e.g. race/ ethnicity) factors in selecting their residential location. The dynamic of these local interactions; that is, these individual micro choices, each shaped by different (and often insubstantial) conditions and criteria for choosing a neighbourhood, will eventually induce a ‘chain reaction’ (Schelling, 1987, p. 150) that engenders the formation of spatial homogeneous patterns.
In other words, this ‘unravelling process’ occurs by micro/ individual behaviours which are organised autonomously (or at least semi‐autonomously). The notion of self‐organisation is naturally present in a system where individuals make decision and act autonomously. That is presumably how ‘self‐forming neighbourhoods’ (Schelling, 1987, p. 147) occur. In a purely lateral meaning of the term, self‐organisation can be described as a system that can organise itself without any external control or manipulation. It is logical to think that a sophisticated residential segregation model should normally allow decision‐maker entities (e.g. agents) to be totally autonomous, learn, and adapt themselves to their environment (which is also constantly changing). In this case, it would be more appropriate to describe such a model as a ‘complex adaptive system’ (CAS).
However, like many other concepts (e.g. emergence, complexity science, and the like), there is a degree of confusion (or at least lack of consensus) about the meaning of self‐organisation. For many, the above lateral description is unsatisfactory. For nstance, Prigogine’s theory of self‐organisation emphasises the process of dissipation (and self‐organisation of dissipative structures) whilst Portugali (2000, p. 51) emphasises the ‘creative’ nature of such a system, as well as its interconnection between parts and components in a ‘nonlinear’ fashion.
This thesis takes a computer science view of self‐organisation by considering the primary areas of its application, which are ‘learning’ and ‘adaptation’ (Wolf & Holvoet, 1998). In the final version of the model presented in this thesis, agents do not have an aptitude for learning and adaptability (in its pure meaning). In this sense, they are considered ‘proto‐agents’ (Howe et al., 2006). Therefore, although in the loose and lateral meaning of the terminology, the behaviours of agents (in the model presented in the thesis) can be described as autonomous (i.e. self‐organized). But, because of lack of learning and adaptive capability, this thesis does not claim that this model (in its current version) should be recognized as a system that exhibits (complete) selforganisational behaviour.

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Explanatory Factors

Figure 2.1 also shows examples of the factors that potentially play a role in the dynamics of residential location choice and hence in the occurrence of segregation. Some of these factors are at an individual (micro) level, including demographic or socioeconomic factors such ethnicity, education, and income. Ethnicity, for example, is a factor that cannot normally be changed, unlike the family‐ties factor which is an individual preference. On the other hand, ethnic (and cultural) background can potentially play an influential role in deciding how important it would be for an individual to live or not live near his or her relatives. In Li’s (2006, p. 11) view, these may be seen as “internal pull factors”, which can also include ‘ethnic solidarity’ and ‘mutual interest’.
On the other side of the spectrum, macro and communal forces such as government (or municipal) policies or different forms of institutional discrimination play an important role in residential segregation formation. From Li’s (2006, p. 11) viewpoint, these may be seen as “external push factors by the host society” (e.g. discrimination). In fact, the labour market (e.g. type and level of skills demanded) and access to social benefit systems (e.g. housing subsidies or social housing programs) , both of which are influenced by government policies, impact significantly on the position of people in the social ladder (i.e. their socio‐economic position) and hence on where they can afford to live. Choices may also be influenced by attributes (such as distance) related to schools or places of work.

1. Introduction
1.1 Overview
1.1 Research Components and Their Relationships
1.2 Motivations
1.3 Research Hypotheses
1.4 Research Aims and Objectives
1.5 Research Approach
1.6 Specific Research Questions
1.7 Organisation of the Thesis
2. Background
2.1 Introduction
2.2 Residential Segregation
2.3 Modelling Residential Segregation
2.4 Conclusion
3. Research Design
3.1 Introduction
3.2 Hybrid Approach
3.3 Units of Analysis
3.4 Conceptual Framework
3.5 Research Model
3.6 Time, Equilibrium and Convergence
3.7 Discussion and Conclusion
4. Design of the Simulation Model
4.1 Introduction
4.2 HAAMoS
4.3 Discussion and Conclusion
5. Theoretical Experiments
6. Empirical Case Study: Auckland 1991‐2006
7. Simulation of Scenarios
8. Conclusions and Future Work
Bibliography
Appendices