Although there are no fixed rules as to what constitutes an agent, they can usefully be defined in terms of their primary characteristics, discussed in subsection 8.2.3.2: autonomy; heterogeneity; pro-active or reactive behaviour; bounded rationality; communications capabilities; mobility; and learning capabilities.

The term agent-based modelling (ABM) refers to the use of computational methods to investigate processes and problems viewed as dynamic systems of interacting agents. An example might be attempting to model crowd behaviour in a football stadium using computational agents to represent individuals in the crowd. Agent-based models seek macro-level understanding based on micro-level processes, i.e. they involve bottom-up rather than top-down modelling. In many respects ABM and CA are very closely linked ― indeed, as noted earlier, it is often possible to use ABM toolsets to model a variety of CA and in principle the reverse is possible, although this may be awkward to achieve.

The subsections that follow draw on the recent publications and research of a number of authors. Readers interested in more details should refer to these books and papers, and to the documentation associated with the toolkits that are now widely available. These publications include: Axelrod (2006); Axelrod and Tesfatsion (2006); Axtell (2000); Bonabeau (2002); Brown (2006); Casti (1997); Couclelis (2002); Epstein (1999); Epstein and Axtell (1996); Gilbert and Troitzsch (2005); Macal and North (2005); O'Sullivan (2004); Parker et al. (2003); Torrens (2004); Wooldridge and Jennings (1995); and Franklin and Graesser (1996).