The modeling formalism that Swarm adopts is a collection of independent agents interacting via discrete events. 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. In Swarm, individual actions take place at some specific time; time advances only by events scheduled at successive times. A schedule is a data structure that combines actions in the specific order in which they should execute. For example, the coyote/rabbit simulation could have three actions: ``rabbits eat carrots,'' ``rabbits hide from coyotes,'' and ``coyotes eat rabbits''. Each action is one discrete event: the schedule combines the three in a specific order, e.g. ``each day, have the rabbits eat carrots, then they hide from the coyotes, then the coyotes try to eat the rabbits''. The passage of time is modeled by the execution of the events in some sequence.
