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P-SAM: A Post-Simulation Analysis Module for Agent-Based Models

S. M. Niaz Arifin, Ryan C. Kennedy, Kelly E. Lane, Gregory R. Madey, Agustin Fuentes and Hope Hollocher

The 2010 Summer Computer Simulation Conference (SCSC 10)
Ottawa, Canada, July 11-14, 2010


Agent-Based Models (ABMs) can produce large volumes of textual output, often in the range of hundreds of gigabytes. To understand the simulation results and patterns exhibited by agents, it is crucial to be able to effectively analyze this voluminous textual output, and to produce the desired visualization. Appropriate analysis and visualization also play important roles in verification and validation of ABMs. In this paper, we describe a software module, called P-SAM (Post-Simulation Analysis Module), developed for simulation output analysis and visualization. As a case study, we describe the application of P-SAM to a biological simulation model named LiNK that analyzes the spread of pathogens among long-tailed macaques, and produces voluminous textual output. We show how P-SAM can analyze and visualize logical structures inherent in LiNK output. Results indicate the importance of using P-SAM to perform verification and validation of the LiNK model. The P-SAM architecture, its major strengths, and computational resource requirements are also discussed. The paper concludes with some future plans to improve P-SAM performance and enlarge its horizon by including other types of simulation models from versatile domains.

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