Agent-Based Model (ABM)

An agent-based model (ABM) is a representation of the relationship(s) between the components or actors in a system or domain. This modeling technique is used to understand the system's functioning and predict its behavior in response to drivers in its environment - triggers, actions, interactions, and outcomes.

Agent-Based Models are used for simulations in engineering and business to understand, for example, how organizations or groups work, with each person in the group being an autonomous agent who acts and interacts with others to determine the outcome of an environmental stimulus.

In agent-based modeling (ABM), a system is modeled as a collection of autonomous decision-making entities called agents. Each agent individually assesses their situation and makes decisions based on rules. Agents may execute various behaviors appropriate for the system they represent—for example, producing, consuming, or selling. Repetitive competitive interactions between agents are a feature of agent-based modeling, which relies on the power of computers to explore dynamics out of the reach of pure mathematical methods. At the simplest level, an agent-based model consists of a system of agents and the relationships between them. Even a simple agent-based model can exhibit complex behavior patterns and provide valuable information about the dynamics of the real-world system it emulates. In addition, agents may be capable of evolving, allowing unanticipated behaviors to emerge. Sophisticated ABM sometimes incorporates neural networks, evolutionary algorithms, or other techniques to allow realistic learning and adaptation. [1]

An agent-based model (ABM) is a computational model for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) to understand the behavior of a system and what governs its outcomes. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo methods are used to understand the stochasticity of these models. Particularly within ecology, ABMs are also called individual-based models (IBMs). A review of recent literature on individual-based models, agent-based models, and multiagent systems shows that ABMs are used in many scientific domains, including biology, ecology and social science.[2] Agent-based modeling is related to, but distinct from, the concept of multi-agent systems or multi-agent simulation in that the goal of ABM is to search for explanatory insight into the collective behavior of agents obeying simple rules, typically in natural systems rather than in designing agents or solving specific practical or engineering problems. [2]

See Also

  1. Agent: An agent in an ABM represents an individual entity or actor within the modeled system. Agents can be autonomous and have their behaviors, decision-making processes, and interaction rules. Depending on the model context, agents can be humans, organizations, animals, or any other relevant entities.
  2. System dynamics: System dynamics refers to the study of how the elements within a system interact and evolve. ABMs capture the dynamics of complex systems by modeling the interactions and behaviors of individual agents, which collectively shape the overall behavior and patterns observed in the system.
  3. Emergent behavior: Emergent behavior refers to the collective behavior that emerges from the interactions and behaviors of individual agents within a system. ABMs are particularly useful for studying emergent phenomena, as they allow for the exploration of how simple local rules or behaviors can give rise to complex global patterns or outcomes.
  4. Simulation: Simulation involves creating a computer-based model that replicates the behavior of a real-world system or process. ABMs are simulation models that simulate the behavior and interactions of individual agents within a larger system. By running simulations, researchers can observe and analyze the outcomes and emergent behavior that arise from agent interactions.
  5. Complexity Theory: Complexity science is an interdisciplinary field that studies complex systems and their behavior. ABMs are rooted in complexity science, as they provide a means to understand and analyze complex systems by capturing the interactions, feedback loops, and adaptive behaviors of individual agents within the system.


  1. PNAS Definition of ABM
  2. Wikipedia Definition of ABM