I was following up on a conjecture I heard regarding *immigration* (!) -- that there is a "tipping point for tolerance" to immigrants which is reached at approx. 15% of the population. I thought that this was quite strange -- that there might be a mathematical model in which such a statement could have a numerical answer. The answer appears to be yes! It turns out that these "Social Dynamics" kinds of theories started with this seminal paper: SCHELLING, T. 1971. "Dynamic Models of Segregation". Journal of Mathematical Sociology 1: 143-186. https://www.stat.berkeley.edu/~aldous/157/Papers/Schelling_Seg_Models.pdf There are also continuous (analytic) models (Pollicott, Weiss. "The Dynamics of Schelling-Type Segregation Models and a Nonlinear Graph Laplacian Variational Problem", Adv. in Appl. Math. 27, 1 (July 2001), 17-40.) This paper is a kind of overview of the "Social Dynamics" field: http://jasss.soc.surrey.ac.uk/4/4/6.htmlhttp://jasss.soc.surrey.ac.uk/4/4/6.... Do Irregular Grids make a Difference? Relaxing the Spatial Regularity Assumption in Cellular Models of Social Dynamics Journal of Artificial Societies and Social Simulation vol. 4, no. 4 <http://jasss.soc.surrey.ac.uk/4/4/6.html> Three decades of CA-modelling in the social sciences have shown that the cellular automata framework is a useful tool to explore the relationship between micro assumptions and macro outcomes in social dynamics. However, virtually all CA-applications in the social sciences rely on a potentially highly restrictive assumption, a rectangular grid structure. In this paper, we relax this assumption and introduce irregular grids with variation in the structure and size of neighbourhoods between locations in the grid. We test robustness of two applications from our previous work that are representative for two broad classes of CA models, migration dynamics and influence dynamics. We tentatively conclude that both influence dynamics and migration dynamics have important general properties that are robust to variation in the grid structure. In the wake of Schelling's (1971) and Sakoda's (1971) pioneering contributions[1], cellular automata (CA) models are increasingly used to study a variety of social dynamics (for some background cf. Hegselmann 1996, Hegselmann and Flache 1998). The most prominent reason is that CAs can be seen as multi-agent systems based on locality with overlapping interaction structures. In this perspective, CAs are attractive as a modelling framework that may provide a better understanding of micro/macro relations. However, many social scientists reject the CA approach on the grounds that the CA framework is far too simple, or -to put it mildly- far too idealised, to be an appropriate tool for modelling social processes. They argue that standard CA assumptions like discrete time, regular grid structures, finite sets of states etc. may make the approach so overly simplistic that it is questionable whether its results can be generalised beyond the particular framework. While we do not necess arily share this concern, we feel that systematic analysis of the robustness of CA modelling is necessary.