  Understanding decision-making in dynamic and complex settings is a challenge
yet essential for preventing, mitigating, and responding to adverse events
(e.g., disasters, financial crises). Simulation games have shown promise to
advance our understanding of decision-making in such settings. However, an open
question remains on how we extract useful information from these games. We
contribute an approach to model human-simulation interaction by leveraging
existing methods to characterize: (1) system states of dynamic simulation
environments (with Principal Component Analysis), (2) behavioral responses from
human interaction with simulation (with Hidden Markov Models), and (3)
behavioral responses across system states (with Sequence Analysis). We
demonstrate this approach with our game simulating drug shortages in a supply
chain context. Results from our experimental study with 135 participants show
different player types (hoarders, reactors, followers), how behavior changes in
different system states, and how sharing information impacts behavior. We
discuss how our findings challenge existing literature.
