They can be translated into the following design tasks in actual coding using a programming language like Python: In the meantime, items 2, 3, 4, and 5 in Macal and North’s list above are more focused on the technical aspects of modeling. My point is that we shouldn’t misinterpret outcomes obtained from such exploratory ABMs as a validated prediction of reality. Of course, a free exploration of various collective dynamics by testing hypothetical agent behaviors to generate hypothetical outcomes is quite fun and educational, with lots of intellectual benefits of its own. Otherwise, the simulation results would have no implications for the real-world system being modeled. These two approaches are different in terms of the scales of the known and the unknown (A uses micro-known to produce macro-unknown, while B uses micro-unknown to reproduce macro-known), but the important thing is that one of those scales should be grounded on well-established empirical knowledge. The former is to use ABMs to make predictions using validated theories of agent behaviors, while the latter is to explore and develop new explanations of empirically observed phenomena. Build an ABM using hypothetical model assumptions, and then reproduce empirically observed collective phenomena by simulation.Build an ABM using model assumptions that are derived from empirically observed phenomena, and then produce previously unknown collective behaviors by simulation.In order for an ABM to be scientifically meaningful, it has to be built and used in either of the following two complementary approaches: It is important to keep in mind that just building an arbitrary ABM and obtaining results by simulation wouldn’t produce any scientifically meaningful conclusion. Design of an environment and the way agents interact with itĪmong those points, 1, 6, and 7 are about fundamental scientific methodologies.Design of agents and their static/dynamic attributes. ![]() Specific problem to be solved by the ABM.Macal and North suggest considering the following aspects when you design an agent-based model: ![]() In fact, there are many great tutorials already out there about how to build an ABM, especially those by Charles Macal and Michael North, renowned agent-based modelers at Argonne National Laboratory. Let’s get started with agent-based modeling.
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