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Social Networks and Spatial Distribution

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Simulating Social Complexity

Part of the book series: Understanding Complex Systems ((UCS))

Abstract

In most agent-based social simulation models, the issue of the organisation of the agents’ population matters. The topology, in which agents interact, be it spatially structured or a social network, can have important impacts on the obtained results in social simulation. Unfortunately, the necessary data about the target system is often lacking; therefore, you have to use models in order to reproduce realistic spatial distributions of the population and/or realistic social networks among the agents. In this chapter, we identify the main issues concerning this point and describe several models of social networks or of spatial distribution that can be integrated in agent-based simulation to go a step forwards from the use of a purely random model. In each case, we identify several output measures that allow quantifying their impacts.

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Notes

  1. 1.

    The diameter of a graph is defined as the length of the longest-shortest path in the graph.

  2. 2.

    Short path being defined by Watts and Strogatz as comparable to those found in random graphs of the same size and average degree.

  3. 3.

    The preferential attachment mechanism has appeared in several different fields under different names. In information science, it is known as cumulative advantage (de Solla Price 1976), in sociology as the Matthew effect (Merton 1968) and in economics as the Gibrat principle (Simon 1955).

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Author information

Authors and Affiliations

  1. Institut de Recherche en Informatique de Toulouse (IRIT), Université Toulouse 1 Capitole, Toulouse, France

    Frédéric Amblard

  2. IMT School for Advanced Studies, Lucca, Italy

    Walter Quattrociocchi

Authors
  1. Frédéric Amblard
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  2. Walter Quattrociocchi
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Corresponding author

Correspondence to Frédéric Amblard .

Editor information

Editors and Affiliations

  1. Centre for Policy Modelling, Manchester Metropolitan University, Business School, Manchester, United Kingdom

    Bruce Edmonds

  2. Centre for Policy Modelling, Manchester Metropolitan University, Business School, Manchester, United Kingdom

    Ruth Meyer

Further Reading

Further Reading

The literature on dynamic aspects of social networks is rapidly developing. For social network models and their analysis, we currently recommend Newman et al. (2006). For spatial aspects, we recommend Diggle (1983). For more details concerning random graphs models, we refer the interested reader to Bollobas (2001).

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Amblard, F., Quattrociocchi, W. (2017). Social Networks and Spatial Distribution. In: Edmonds, B., Meyer, R. (eds) Simulating Social Complexity. Understanding Complex Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-66948-9_19

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  • DOI: https://doi.org/10.1007/978-3-319-66948-9_19

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