This paper describes initial research to define and demonstrate an integrated set of algorithms for conducting high-level Operational Simulations. In practice, an Operational Simulation would be used during an ongoing military mission to monitor operations, update state information, compare actual versus planned states, and suggest revised alternative Courses of Action. Significant technical challenges to this realization result from the size and complexity of the problem domain, the inherent uncertainty of situation assessments, and the need for immediate answers. Taking a top-down approach, we initially define the problem with respect to high-level military planning. By narrowing the state space we are better able to focus on model, data, and algorithm integration issues without getting sidetracked by issues specific to any single application or implementation. We propose three main functions in the planning cycle: situation assessment, parameter update, and plan assessment and prediction. Situation assessment uses hierarchical Bayes Networks to estimate initial state probabilities. A parameter update function based on Hidden Markov Models then produces revised state probabilities and state transition probabilities - model identification. Finally, the plan assessment and prediction function uses these revised estimates for simulation-based prediction as well as for determining optimal policies via Markov Decision Processes and simulation-optimization heuristics.