Interactive visual exploration of simulator accuracy: A case study for stochastic simulation algorithms

Visual Analytics offers various interesting methods to explore high dimensional data interactively. In this paper we investigate how it can be applied to support experimenters and developers of simulation software conducting simulation studies. In particular the usage and development of approximate simulation algorithms poses several practical problems, e.g., estimating the impact of algorithm parameters on accuracy or detecting faulty implementations. To address some of those problems, we present an approach that allows to relate configurations and accuracy visually and exploratory. The approach is evaluated by a brief case study, focusing on the accuracy of Stochastic Simulation Algorithms.