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.

翻译：人类行为是随经验演化的动态过程。理解个体风险倾向的演变对于制定公共卫生干预措施、促进更优生物安全协议的采纳并预防传染病传播至关重要。我们通过一项模拟猪场网络中疾病传播的实验游戏，测量了来自经验的学习如何影响超过1000名玩家的风险规避倾向。采用全自动方法根据玩家游戏行为的时序趋势将其划分为四类，并比较其整体游戏表现结果。研究发现，风险容忍组发生感染的概率比风险规避组高50%。同时发现，尽管所有玩家随游戏经验增加决策时间均有所缩短，但存在两类不同群体：一类采用恒定决策策略，其决策时间快速下降；另一类情境感知决策组在决策前思考时间更长，推测其同时进行实时风险评估。本模拟场景中玩家采用的行为策略未来可作为早期预警信号，用于识别不良生物安全相关的风险规避偏好或行为变化，从而通过针对性干预措施加以缓解。