Gaming simulations with environmental trajectories that maximize information gain

Gaming simulations put real actors in simulated environments. Example applications are training and scenario analysis in transport operations and disaster management. Running a single gaming simulation is an expensive endeavor and therefore must be led through interesting scenario configurations to maximize the learning or research outcomes. This article presents an approach to automatically control the simulated environment in account for the real players´ behavior such that a maximum usability of the session is ensured. The approach accesses elements from discrete choice theory and provides the game designer with different options to tailor the type of learning. An archetypical application demonstrates the usefulness of the approach.