To Trust or to Stockpile: Modeling Human-Simulation Interaction in Supply Chain Shortages
This work addresses the challenge of analyzing human decision-making in complex simulations, which is incremental as it applies existing methods to a new domain.
The paper tackled the problem of extracting useful information from simulation games to understand decision-making in dynamic settings like supply chain shortages, by modeling human-simulation interaction using methods like PCA and HMMs, and found different player types and behavioral changes in a study with 135 participants.
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.