Understanding and Applications of Agent-based Model

Agent-based modeling (ABM) is an interdisciplinary approach to understand macroscopic patterns of a large system, based on massive computation of its interacting constituents (i.e., agents). We explain when this approach is especially useful, with providing two game-theoretic examples: The first example is an analytically intractable model system, although the agents’ decision rules are easily programmable, for which ABM is the only feasible methodology. The second example argues that the payoff structure among agents can also be calculated from their microscopic interactions. These examples show that ABM is a powerful tool with a high degree of flexibility, but also that one has to carefully choose the level of complexity in a model because this choice directly affects the computational burden as well as the applicability of the model.