Temporal Evolution of Risk Behavior in a Disease Spread Simulation

Human behavior is a dynamic process that evolves with experience. Understanding the evolution of individual's risk propensity is critical to design public health interventions to propitiate the adoption of better biosecurity protocols and thus, prevent the transmission of an infectious disease. Using an experimental game that simulates the spread of a disease in a network of porcine farms, we measure how learning from experience affects the risk aversion of over $1000$ players. We used a fully automated approach to segment the players into 4 categories based on the temporal trends of their game plays and compare the outcomes of their overall game performance. We found that the risk tolerant group is $50\%$ more likely to incur an infection than the risk averse one. We also find that while all individuals decrease the amount of time it takes to make decisions as they become more experienced at the game, we find a group of players with constant decision strategies who rapidly decrease their time to make a decision and a second context-aware decision group that contemplates longer before decisions while presumably performing a real-time risk assessment. The behavioral strategies employed by players in this simulated setting could be used in the future as an early warning signal to identify undesirable biosecurity-related risk aversion preferences, or changes in behavior, which may allow for targeted interventions to help mitigate them.