Within that framework, Swarm makes no assumptions about the particular sort of model being implemented. There are no domain specific requirements such as particular spatial environments, physical phenomena, agent representations, or interaction patterns. Swarm simulations have been written for such diverse areas as chemistry, economics, physics, anthropology, ecology, and political science. The basic unit of a Swarm simulation is the agent. An agent is any actor in a system, any entity that can generate events that affect itself and other agents.
Simulations consist of groups of many interacting agents.
For example, an ecosystem simulation could consist of agents representing coyotes, rabbits, and carrots. In an economic simulation, agents could be companies, stockbrokers, shareholders, and a central bank. Simulation of discrete interactions between agents stands in contrast to continuous system simulations, where simulated phenomena are quantities in a system of coupled equations. Agents define the basic objects in the Swarm system, the simulated components. A schedule of discrete events on these objects defines a process occurring over time.