  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.
