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Agent-Based Models (ABM) can be seen as the natural extension of the Ising model (Ising 1925) or Cellular Automata-like models (Wolfram 1994), which have been very successful in the past decades in shedding light on various physical phenomena.

One important characteristic of ABMs, which distinguishes them from Cellular Automata, is the potential asynchrony of the interactions among agents and between agents and their environments. In ABM agents typically do not simultaneously perform actions at constant time-steps, as in CAs or boolean networks. Rather, their actions follow discrete-event cues or a sequential schedule of interactions. The discrete-event setup allows for the cohabitation of agents with different environmental experiences. Also ABMs are not necessarily grid-based nor do agents" tile" the environment.