Agent Based Simulation Output Analysis
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In most realistic simulations there are multiple outputs of interest and the overall performance of the sys-tem can only be estimated in terms of these multiple outputs. We propose a method that uses agent-based modeling to determine a truncation point to remove significant initialization bias. Mapping the output of multiple replications into agent paths that traverse the sample space helps determine when a near steady state has been reached. By viewing these paths in reversed time, qualitative and quantitative methods can be used to determine when the multivariate output is leaving its near-steady state regime as the paths coa-lesce back towards their common initialization state. The methodology is more efficient and general than typical approaches for finding a truncation point for scalar outputs of individual replicates. Artificial bootstrap-like re-sampling of simulation runs is proposed for expensive simulations to estimate system performance sensitivity.
Refereed Conference PaperProceedings of the 2011 Winter Simulation Conference S. Jain, R.R. Creasey, J. Himmelspach, K.P. White, and M. Fu, eds.
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