Schelling's segregation model can be said to be the first sociology Agent Based Model (ABM). It took a complex social behaviour of racial segregation and asked if it could be captured by some simple rules. What makes the model exceptional are the range and types of behaviours that this simple model exhibits. These types of behaviours are known as emergent and this is one of the first models of its type to exhibit this. Many sociological ABMs can trace their lineage back to this model. If you are interested in ABMs for simulating non-communicable disease (NCD) is well worth your while getting to know Schelling's segregation model.

Like Schelling, Axelrod is a pioneer in the use of agent based models in social science. Like the Schelling segregation model the Axelrod model looks at the influence on the behaviour of an individual agent ( person ) by a social network of it’s immediate neighbours. In the Axelrod model it is not its geographic position that changes but that agent ideas or culture. Like the Shelling model the Axelrod model is a theoretical model but one that is equal powerful as the Shelling model in illuminating the emergent complex behaviours of social networks.

One of the other modelling methods in complex systems science is network models. Two of the pioneers of modern network modelling were Duncan Watts and Steven Strogatz. Among their contributions to modelling was the small world network. This model gives one possible explanation for the famous "Six degrees of separation". In the 1960’ the social psychologist Stanley Milgram tried to get a number of letters sent to random people in the USA by people passing on the letter to people they personally knew who may be closer to the target then they were. The results showed that on average it took just six intermediate people to pass on the letter. If our connections to other people were just random then the number of intermediates required for people to pass on the letter would be much higher. Watts and Strogatz small world model posited that if connections are locally made but with the occasional link to someone more distant, this would account for the behaviour seen by Milgram.The connections between agents (people ) are important to understanding emergent social behaviour. We have all recently experienced this with the spread of COVID19. In this demonstration we use a simple agent based model (AMB) combined with the small world model to show the effect of long distance links to the spread of a disease.

This is an excellent example of using modelling to ask “what if” question of aspects of individual behaviour that would be very difficult to replicate with a real population. In this case the positioning of food outlets. This is a model of the inequalities in diet in the context of urban residential segregation. It has been used by Auchincloss and Garcia as an example of how to create an Agent Based Model (ABM) to address complex Non Communicable Disease(NDC) problems. The model is used as an introductory guide to the philosophy and practices of ABMs and is used to illustrate why ABMs are a good fit for NDC investigation. The paper describes very clearly how to take descriptions of individual behaviour and incorporate them into an ABM framework and run “what if “ experiments with it.

This is a good model to explore and understand network theory. One of the most important features of an ABM is the way in which the agents interact with each other. In this model social networks within a population are created by forming links between agents. The agent-to-agent links represent connections between a person and their friends. In this model this friendship network is used to simulate the effect that a person's friends have on their body weight. This abstract model shows that the structure of this friendship network is very important in determining population level patterns of behaviour.

This is a particularly good example of using an abstract, spatially discrete ABM (Agent Based Model) to investigate the fundamental drivers of behavior. The model looks at the effect that other people’s drinking behavior has on an individual's drinking behavior and places this in a quite simple spatial context. It finds that the spatial aspects of social interactions are an important driver in determining population level drinking behavior. This model is also a much-cited precursor to more applied ABMs used for investigating drinking behavior such as SimDrink.