Schelling’s social segregation model has been extensively used for human behavior analysis and studied over the years. A major implication of the model is that individual preferences of similarity lead to a collective segregation behavior. Schelling used Agent-Based Modeling (ABM) with uni-dimensional agents. In reality, people are multidimensional. This raises the question of whether multi-dimensionality can boost stability or reduce segregation in society.
In this paper, we first adopt ABM to reconstruct Schelling’s original model and discuss its convergence behaviors under different threshold levels. Then, we extend Schelling’s model with multidimensional agents and investigate convergence behaviors of the model conducting a human behavior analysis. Results suggest that if agents have high levels of demand for identical neighbors, the society might become less stable or even chaotic. Also, several experiments suggest that multidimensional agents are able to form a stable society that is not segregated if agents prefer to stay adjacent to not only "identical" but also "similar" neighbors.
Schelling’s original model is an oversimplified version of relocation behaviors in society, where two groups of people decides where to live solely on the neighborhood. However, in reality, different situations might lead to violations of these assumptions. For example, some people may have a different preference level than others regarding identical neighbors; some may not consider neighborhood to be the only deciding factor for relocation and most people in society have multi-dimensional attributes. These unaddressed issues of the original model offer opportunities to extensions that capture more realistic features of how communities are formed during relocation. In this paper, we extend the original model so that it is capable of incorporating these new features.