Agent-based modeling (ABM, or individual based modeling in ecology) is on the rise. ABM is an important and powerful quantitative scientific method. ABM differs from other simulation approaches in that an agent-based model allows the scientist to specify the behavior of individual agents and allow their interactions to create an emergent outcome. Computational scientists then study the way in which particular behaviors lead to various outcomes, in a rigorous fashion, to draw conclusions from their models. Mature agent-based models incorporate real world data for greater veracity and precision. Agent based modeling is a useful tool for anyone who studies a complex system, especially those who study a complex system with:
- autonomous agents
- heterogeneous agents
- agents with bounded rationality
- local interactions
- explicit space
For a very nice explanation of the unique properties of agent-based modeling, read Josh Epstein’s 1999 paper in Complexity (I linked to the longer version of that paper, which appears in his book on generative social science). The point today is that agent-based modeling is on the rise! Papers published on agent based models are growing rapidly, at somewhere between 35 and 50 additional papers per year. That’s pretty fast for what was recently considered a niche methodology. More impressive, is the growth of citations within the field of agent-based modeling. The number of citations to ABM research is doubling roughly every three years. That’s fast.
And that’s a nice lead-in for my 2014 graduate course on agent-based modeling.