title: Summarizing Simulation Results using Causally-relevant States.
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.
authors.affiliation: Network Dynamics and Simulation Science Lab, Biocomplexity Institute of Virginia Tech, Virginia Tech, Blacksburg, VA, USA.
authors.forename: Nidhi
authors.initials: N
authors.lastname: Parikh
authors.affiliation: Network Dynamics and Simulation Science Lab, Biocomplexity Institute of Virginia Tech, Virginia Tech, Blacksburg, VA, USA.
authors.forename: Madhav
authors.initials: M
authors.lastname: Marathe
authors.affiliation: Network Dynamics and Simulation Science Lab, Biocomplexity Institute of Virginia Tech, Virginia Tech, Blacksburg, VA, USA.
authors.forename: Samarth
authors.initials: S
authors.lastname: Swarup
keywords: causal states; simulation summarization