Understanding decision-making in dynamic and complex settings is a challenge yet essential for preventing, mitigating, and responding to adverse events (e.g., disasters, financial crises). Simulation games have shown promise to advance our understanding of decision-making in such settings. However, an open question remains on how we extract useful information from these games. We contribute an approach to model human-simulation interaction by leveraging existing methods to characterize: (1) system states of dynamic simulation environments (with Principal Component Analysis), (2) behavioral responses from human interaction with simulation (with Hidden Markov Models), and (3) behavioral responses across system states (with Sequence Analysis). We demonstrate this approach with our game simulating drug shortages in a supply chain context. Results from our experimental study with 135 participants show different player types (hoarders, reactors, followers), how behavior changes in different system states, and how sharing information impacts behavior. We discuss how our findings challenge existing literature.
翻译:了解动态和复杂环境中的决策是一项挑战,但对于预防、减轻和应对不利事件(例如灾害、金融危机)来说,这是一个至关重要的挑战。模拟游戏显示有希望增进我们对此类环境中决策的理解。然而,一个未决问题仍然是我们如何从这些游戏中获取有用信息。我们通过利用现有方法促进模拟人类模拟互动的方法,其特征为:(1) 动态模拟环境的系统状态(与主构件分析一起)、(2) 人类互动与模拟(与隐藏马尔科夫模型一起)的行为反应,以及(3) 跨系统国家的行为反应(与序列分析一起) 。我们展示了这种方法,在供应链中模拟了我们的游戏的药物短缺。我们与135名参与者进行的实验研究的结果显示了不同的玩家类型(爱好者、反应堆、追随者 ), 不同系统的行为变化如何表现,以及如何分享信息行为。我们讨论了我们的调查结果如何挑战现有文献。