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Contextualized Behavior Recommendation from Complex Agent-Based Simulations of Disasters

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Journal of the Indian Institute of Science Aims and scope

Abstract

We present an approach for generating contextualized behavior recommendations from a large, data-driven, complex agent-based simulation. We extend a previous method for generating a summary description by decomposing the output of a simulation into a tree of causally-relevant states, and show how behavior recommendations can be generated by ranking these causally relevant states in terms of their impact on an outcome of interest. An end-user can provide a query specifying a partial state description, which is used to retrieve the appropriate set of states from the summary description. The structure of the tree is used to generate the contexts that differentiate the behavior recommendations. We apply our method to a very complex simulation of a disaster in a major urban area and present results for multiple queries.

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Notes

  1. It may seem that similar to the CCSR algorithm9, we could potentially merge all clusters that lead to the same probability distribution over the final outcomes at each time step t. However, as shown by a toy example in Appendix C of11, merging clusters this way could potentially lead to suboptimal causal state sequences in the following time steps. Also, merging clusters would lead to nodes with multiple parents (i.e., simulation would be decompose into a graph that is not a tree) and the behavior recommendation algorithm described in Sect. 3.3, requires a tree structure to extract ancestor information (or historical context) for the clusters matching the query.

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Acknowledgements

We thank our colleagues in the Network Systems Science and Advanced Computing Division at the Biocomplexity Institute and Initiative at the University of Virginia for many interesting discussions.

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Authors and Affiliations

  1. Information Systems and Modeling Group (A-1), Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, NM, USA

    Nidhi Parikh

  2. Biocomplexity Institute and Initiative, and Department of Computer Science, University of Virginia, Charlottesville, VA, USA

    Madhav V. Marathe

  3. Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA

    Samarth Swarup

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  1. Nidhi Parikh
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  2. Madhav V. Marathe
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Correspondence to Nidhi Parikh.

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This work was partially supported by University of Virginia Strategic Investment Fund award number SIF160, and Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007.

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Parikh, N., Marathe, M.V. & Swarup, S. Contextualized Behavior Recommendation from Complex Agent-Based Simulations of Disasters. J Indian Inst Sci 101, 403–417 (2021). https://doi.org/10.1007/s41745-021-00256-y

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