Agent-based modeling (ABM) has been widely utilized in social science as a powerful modeling tool in the past few years. However, it still remains difficult to conclude an accurate definition of ABM. When it comes to the study of human systems, the situation becomes more complicated due to the uncertainties in human society.
In this paper, Bonabeau conclude that ABM provides three important benefits for simulating human systems. First, it presents a natural description of the study target. Second, it is quite flexible and can be easily adapted for different dimension with the ability to learn and evolve by itself. Finally, ABM captures emergent, which is quite challenging and important in study of human systems. All these features have conceptualized ABM as a necessary tool to handle the complexity in human system studies.
I tend to define ABM as a collection of modeling methodologies, rather than a group of theories. ABM contains a wide body of disciplines, and it seems the number of disciplines will continue increasing. It relies on the methodologies and techniques of statistics, pattern recognition, reinforcement learning, control theories, system identification, game theories, and so on. However, ABM distinguishes itself from the mentioned disciplines by more emphasis on practical usage, not on the solid theory background. For example, ABM can model auction activities without the proof of Nash Equation’s existence, which is not acceptable in game theory studies. Moreover, ABM may utilize reinforcement learning as a tool for self-learning, without any theoretical proof of whether it can converge to the satisfying result or not. Therefore, I think ABM as a collection of practical modeling methodologies, and it will change its definition with different applications.
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