Restoration and maintenance decisions in critical infrastructure sectors are mainly driven by individual municipalities. Numerous studies have studied optimal restoration activities to improve the overall infrastructure network performance. However, not all dynamic interactions and human factors such as managers’ knowledge of interdependencies and decision-making skills can be modelled via mathematical models. Therefore, we designed a simulation-based game platform that allows players evaluate their strategies through a feedback learning environment. Players interact with the simulation model using a game engine. The agent-based simulation serves as the backbone of logical behavior among players and infrastructure networks. We implemented the proposed learning game for a partition of water distribution and road networks in Tampa, FL.
Game Link: Go-Repair