Abstract
As increasingly large-scale multiagent simulations are being implemented, new methods are becoming necessary to make sense of the results of these simulations. Even concisely summarizing the results of a given simulation run is a challenge. Here we pose this as the problem of simulation summarization: how to extract the causally-relevant descriptions of the trajectories of the agents in the simulation. We present a simple algorithm to compress agent trajectories through state space by identifying the state transitions which are relevant to determining the distribution of outcomes at the end of the simulation. We present a toy-example to illustrate the working of the algorithm, and then apply it to a complex simulation of a major disaster in an urban area.
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Acknowledgments
We thank our external collaborators and members of the Network Dynamics and Simulation Science Lab (NDSSL) for their suggestions and comments. This work has been supported in part by DTRA CNIMS Contract HDTRA1-11-D-0016-0001, DTRA Grant HDTRA1-11-1-0016, NIH MIDAS Grant 5U01GM070694-11, NIH Grant 1R01GM109718, NSF NetSE Grant CNS-1011769, and NSF SDCI Grant OCI-1032677.
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Parikh, N., Marathe, M., Swarup, S. (2016). Summarizing Simulation Results Using Causally-Relevant States. In: Osman, N., Sierra, C. (eds) Autonomous Agents and Multiagent Systems. AAMAS 2016. Lecture Notes in Computer Science(), vol 10003. Springer, Cham. https://doi.org/10.1007/978-3-319-46840-2_6
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DOI: https://doi.org/10.1007/978-3-319-46840-2_6
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