What is Agent-Based Modeling (ABM)

Agent-based modeling (ABM) is a powerful tool for simulating complex systems and understanding the behavior of their components. In essence, it is a computer-based model of a system composed of autonomous agents that interact with each other and with their environment. ABM is used in a wide variety of disciplines, including economics, sociology, ecology, and engineering.

ABM has many advantages over traditional modeling techniques. For one, it allows for the simulation of complex systems with many interacting parts. This makes it ideal for studying phenomena that require the modeling of multiple agents, such as in social science or economics. Additionally, ABM can be used to test hypotheses, such as those concerning the effects of certain policies on the behavior of different populations. 

One of the most important benefits of ABM is that it can be used to explore the behavior of a system over time. This helps to gain an understanding of the factors that drive the behavior of the agents, as well as how their behavior changes in response to external factors. Furthermore, ABM can help to identify emergent behaviors that may not be obvious from traditional analysis.

ABM can also be used to assess the impact of policy decisions on a system. This can be done by running simulations with different policy scenarios to identify the one that yields the most desirable outcomes. Additionally, ABM can be used to forecast future trends in the system by simulating different scenarios and observing the results.

Overall, ABM is a powerful tool for modeling and understanding complex systems. It can be used to test hypotheses, study the behavior of agents over time, and assess the impact of policy decisions. As such, it is an invaluable tool for researchers in a wide variety of disciplines.

Agent-based models often have one or more of the following characteristics:

• An agent-based model is made up of a set of autonomous agents. Agent-based modeling is inherently object-oriented.
• Agents behave according to some set of rules or decision-making goals, usually defined from the agent's perspective.
• Agents are aware of the surrounding environment.
• Agent-based modeling can involve a very large number of agents.

The video below shows a very simple example. Three machine operators move to a 10x10 meter area and randomly walk and wait inside the area.