Scientific applications

Agent-based modeling seeks to replicate these complexities and adaptations in computational environments where these interactive emergent behaviors can be analyzed multi-dimensionally. By defining and assigning agencies reflective of prescribed behaviors, known or estimated, to active software agents in a computer simulation, scientists can approximate experimental results not possible in natural temporal frameworks.

Swarm and other agent-based modeling platforms afford scientists the opportunity to conduct and visualize experiments in these synthetic macro and microenvironments for testing scientific theories, natural data sets, and other analyses while free of pressing constraints like time, volume, hazards, or many other parameters.

Commercial applications

Agent-based models have been used since the mid-1990s to solve a variety of business and technology problems. Examples of applications include:

• supply chain optimization and logistics;
• modeling of consumer behavior, including word of mouth and social network effects;
• distributed computing;
• workforce management;
• traffic management; and
• portfolio management.

In these and other applications, the system of interest is simulated by capturing the behavior of individual agents and their interconnections. Agent-based modeling tools can be used to test how changes in individual behaviors will affect the overall, emergent system behavior.