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

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• 作者：Chao Yang ; Bin Jiang ; Isao Ono ; Setsuya Kurahashi ; Takao Terano
• 关键词：Agent ; based simulation ; Grid computing ; Forward simulation ; Inverse simulation ; Model selection
• 刊名：Cluster Computing
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：19
• 期：1
• 页码：183-195
• 全文大小：1,838 KB
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• 作者单位：Chao Yang (1) (3)
  Bin Jiang (2) (3)
  Isao Ono (2)
  Setsuya Kurahashi (4)
  Takao Terano (3)
  
  1. Business School, Hunan University, Changsha, China
  3. Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Tokyo, Japan
  2. College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
  4. Graduate School of Business Sciences, University of Tsukuba, Tsukuba, Japan
  
• 刊物类别：Computer Science
• 刊物主题：Processor Architectures
  Operating Systems
  Computer Communication Networks
  
• 出版者：Springer Netherlands
• ISSN：1573-7543

文摘

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.