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A grid based simulation environment for agent-based models with vast parameter spaces

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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 perform sensitivity analysis; (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 an 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 one case study in history simulation domain.

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Authors and Affiliations

  1. Business School, Hunan University, Changsha, China

    Chao Yang

  2. College of Computer Science and Electronic Engineering, Hunan University, Changsha, China

    Bin Jiang & Isao Ono

  3. Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Tokyo, Japan

    Chao Yang, Bin Jiang & Takao Terano

  4. Graduate School of Business Sciences, University of Tsukuba, Tsukuba, Japan

    Setsuya Kurahashi

Authors
  1. Chao Yang
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  2. Bin Jiang
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  3. Isao Ono
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  4. Setsuya Kurahashi
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  5. Takao Terano
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Correspondence to Bin Jiang.

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Yang, C., Jiang, B., Ono, I. et al. A grid based simulation environment for agent-based models with vast parameter spaces. Cluster Comput 19, 183–195 (2016). https://doi.org/10.1007/s10586-015-0500-6

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  • DOI: https://doi.org/10.1007/s10586-015-0500-6

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