What is ABM?
Agent-Based Models replicate the behavior of complex, real-world systems by simulating the actions and interactions of agents. The agents can represent individuals, households, organizations, companies, or nations, depending on the application. Agents are defined by attributes like income, geographical location, population segment, knowledge, or preferences and rules like decision-making algorithms or behavioral probabilities. The overall behavior of the system emerges from the interactions of individual agents over a simulated time period based on the attributes and rules of individual agents. ABM provides a low-risk way to test the impact of different strategies on a complex system while gaining practical insight into which factors have the highest degree of influence on the outcome.
When is ABM most useful?
Agent-based models are particularly useful when:
- Individual behavior is nonlinear and can be characterized by thresholds, if-then rules, or nonlinear coupling. Describing discontinuity in individual behavior is difficult with differential equations.
- Individual behavior exhibits memory, path-dependence, temporal correlations, learning and adaptation.
- Network effects are present in the system. Aggregate flow equations usually assume global homogeneous mixing, but the topology of the interaction network can lead to significant deviations from predicted aggregate behavior.
- Averages will not work. Aggregate differential equations tend to smooth out fluctuations – ABM does not, which is important because under certain conditions, fluctuations can be amplified: the system is linearly stable but unstable to larger perturbations.