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Agent-based modeling provides a bottom-up approach for studying complex systems, whereas analytical models have a top-down one (Bonabeau, 2002). The two approaches are complementary and they both can benefit from insights that the other approach contributes. Analytical models make many simplifying assumptions about the systems they study. This results in systems that are tractable and lead to clear conclusions. Agent-based models, on the other hand, are more difficult to make sense of because they relax many assumptions of equation-based approaches. This is at the same time an advantage of agent-based models because it allows observing the effect of agent and environment heterogeneity and stochasticity, which can change a model’s behavior (Farmer & Foley, 2009). Agent-based modeling is a particularly important tool for studying complex systems where a system’s behavior cannot be predicted and has to be explored.

Agents.jl: agent-based modeling framework in Julia