Contextualized Behavior Recommendation from Complex Agent‑Based Simulations of Disasters

Abstract

We present an approach for generating contextualized
behavior recommendations from a large, data-driven, complex agentbased
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.