SMADSC: Introduction

Complex systems are the core topic of  Social Modelling, Agent-Based Simulation, and Complexity. Complex systems usually emerge as an artefact of interaction. The output of a complex system follows the Power Law and may have a regime or phase changes, known as tipping points. Emergent properties and scale-free organisation are a typical feature of complex systems. It would it be possible to analyse top-down, but is best studied bottom-up.

In general, a social system is analysed by creating a mental model of it, deriving hypotheses regarding endogenous and exogenous forces that drive it and finally instantiating an agend-based model (ABM) in code that is simulated in silicio.

Recommended reading for the week is Chapter 9 in Complex adaptive
systems: An introduction to computational models of social life (Miller & Page, 2009) and Chapter 8 in Introduction to computational
social science: principles and applications (Cioffi-Revilla, 2013).

Agent-based Models (ABM)

Usually, an object-oriented software system that instantiates a model of living systems of social entities. Agent-based models go beyond numerical analysis, rather they observe emergent behaviour. Broad paradigms that influence ABMs are cellular automata, big data, social networks, and generative models. Concepts will be emergence, bottom-up computation micro-level rules lead to macro-level behaviours. There are two main dominant characteristics of ABMs:

  1.  A positive representation attempts to closely recreate or capture the abstract or detailed essence  of a prototype system.
  2. A normative representation provides input control for exogenous steering of internal feedback loops.

Generative ABMs are useful in three general cases:

  1. Modelling historical systems, that cannot be revisited
  2. Long-lived systems, that span a longer time than can be observed
  3. Unethical, illegal, unsafe or unlikely environmental  settings or exogenous  stimuli to the system

The Game of Life (Conway, 1970)

A game with two states {dead, alive} and the rules:

Each cell checks the Life State of itself and those of the cells in its local neighbourhood at a Moore distance of 1. If alive then display a pixel if dead do not. If this cell has less than two neighbours alive or more than three neighbours alive then, set this cell dead. If there are exactly three alive neighbours, set Life State alive. Randomized activation of cells continues “forever.”

It uses the concepts of cellular automata and either Moore or von Neumann distance as well as distance-neighbourhoods.

Other famous ABMs are Flocking (Reynolds, 1987), Swarming (Bonabeau & Meyer, 2001), Residential Segregation (Schelling, 1969), Residential Segregation using vector-based GIS (Crooks, 2010)


Bonabeau, E., & Meyer, C. (2001). Swarm intelligence. Harvard Business Review, 79(5), 106–114.
Cioffi-Revilla, C. (2013). Introduction to computational social science: principles and applications. Springer Science & Business Media.
Conway, J. (1970). The game of life. Scientific American, 223(4), 4.
Crooks, A. T. (2010). Constructing and implementing an agent-based model of residential segregation through vector GIS. International Journal of Geographical Information Science, 24(5), 661–675.
Miller, J. H., & Page, S. E. (2009). Complex adaptive systems: An introduction to computational models of social life. Princeton University Press.
Reynolds, C. W. (1987). Flocks, herds and schools: A distributed behavioral model. ACM SIGGRAPH Computer Graphics, 21(4), 25–34.
Schelling, T. C. (1969). Models of segregation. The American Economic Review, 59(2), 488–493.