I am excited to share my insights about a compelling article titled 'Distributed, Automated Calibration of Agent-based Model Parameters and Agent Behaviors,' authored by Matteo D'Auria, Eric O. Scott, Rajdeep Singh Lather, Javier Hilty, and Sean Luke in 2020. Despite its brevity, I found this article about ABM (Agent-based Models) highly captivating. The piece delves into the challenges of calibrating ABMs, particularly dealing with the high parameter count, using a Refugee Model to analyze migration patterns in the Syrian refugee crisis. Additionally, the authors explore the calibration of agent behaviors through the Serengeti Model, where 'lion' agents aim to capture 'gazelles' in a toroidal environment.
As a solution, the authors developed a distributed optimization model calibration technique, which they implemented as a versatile open-source tool. This tool proves immensely valuable for researchers in computational biology, social sciences, multi-agent systems, and robotics. It combines the MASON agent-based modeling toolkit and the ECJ evolutionary optimization library, utilizing a parallel algorithm with multiple processors. Notably, the optimization of agent behaviors employs a Koza style genetic programming approach, wherein behaviors are represented as executable Lisp parse trees.
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