Researchers in AI and Operations Research employ the framework of Markov Decision Processes (MDPs) to formalize problems of sequential decision making under uncertainty. A common approach is to implement a simulator of the stochastic dynamics of the MDP and a Monte Carlo optimization algorithm that invokes this simulator to solve the MDP. The resulting software system is often realized by integrating several systems and functions that are collectively subject to failures of specification, implementation, integration, and optimization. We present these failures as queries for a computational steering visual analytic system (MDPVIS). MDPVIS addresses three visualization research gaps. First, the data acquisition gap is addressed through a general simulator-visualization interface. Second, the data analysis gap is addressed through a generalized MDP information visualization. Finally, the cognition gap is addressed by exposing model components to the user. MDPVIS generalizes a visualization for wildfire management. We use that problem to illustrate MDPVIS.