Abstract
Agent-based simulation models with large experiments for a precise and robust result over a vast parameter space are becoming a common practice, where enormous runs intrinsically require highly intensive computational resources. This paper proposes a grid based simulation environment, named Social Macro Scope (SOMAS) to support parallel exploration on agent-based models with vast parameter space. We focus on three types of simulation methods for agent-based models with various objectives: (1) forward simulation to conduct experiments in a straightforward way by simply operating sets of parameter values to obtain sets of results; (2) inverse simulation to search for solutions that reduce the error between simulated results and actual data by means of solving “inverse problem”, which executes the simulation steps in a reverse order and employs optimization algorithms to fit the simulation results to the desired objectives; and (3) model selection to find optimal model structure with subset of parameters and procedures, which conducts two-layer optimization to obtain a simple and more accurate simulation result. We have confirmed the practical scalability and efficiency of SOMAS by a case study in history simulation domain.
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Yang, C., Ono, I., Kurahashi, S., Jiang, B., Terano, T. (2015). A Grid Based Simulation Environment for Parallel Exploring Agent-Based Models with Vast Parameter Space. In: Zu, Q., Hu, B., Gu, N., Seng, S. (eds) Human Centered Computing. HCC 2014. Lecture Notes in Computer Science(), vol 8944. Springer, Cham. https://doi.org/10.1007/978-3-319-15554-8_44
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DOI: https://doi.org/10.1007/978-3-319-15554-8_44
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