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：人类行为是一个随经验动态演变的过程。理解个体风险倾向的演变对于设计促进更优生物安全规程采纳的公共卫生干预措施、进而预防传染病传播至关重要。我们通过一项模拟猪场网络内疾病传播的实验游戏，测量了经验学习如何影响超过1000名玩家的风险规避程度。我们采用全自动化方法，基于玩家游戏行为的时序趋势将其划分为4个类别，并比较其整体游戏表现的差异。研究发现，风险容忍组发生感染的概率比风险规避组高出50%。同时发现，尽管所有个体随着对游戏更熟悉而缩短决策时间，但存在两类决策群体：一类采用恒定决策策略，快速缩短决策时间；另一类采用情境感知决策策略，在决策前进行更长时间的思考，推测其正在进行实时风险评估。本模拟场景中玩家采取的行为策略，未来可作为早期预警信号，用于识别不理想的生物安全相关风险规避偏好或行为变化，从而便于实施针对性干预以缓解这些问题。