We model the behavioral biases of human decision-making in securing interdependent systems and show that such behavioral decision-making leads to a suboptimal pattern of resource allocation compared to non-behavioral (rational) decision-making. We provide empirical evidence for the existence of such behavioral bias model through a controlled subject study with 145 participants. We then propose three learning techniques for enhancing decision-making in multi-round setups. We illustrate the benefits of our decision-making model through multiple interdependent real-world systems and quantify the level of gain compared to the case in which the defenders are behavioral. We also show the benefit of our learning techniques against different attack models. We identify the effects of different system parameters on the degree of suboptimality of security outcomes due to behavioral decision-making.
翻译:我们以人类决策的行为偏差为模型,确保相互依存的系统,并表明此类行为决策导致资源分配模式与非行为(理性)决策相比不尽人意;我们通过有145名参与者参与的受控主题研究,为是否存在此类行为偏差模式提供经验证据;然后我们提出三种学习技巧,以加强多层次结构的决策;我们通过多重相互依存的现实世界系统说明我们的决策模式的好处,并量化与维权者行为性案例相比的收益水平;我们还展示了我们学习技术对不同攻击模式的好处;我们确定了不同系统参数对行为决策导致安全成果不优劣程度的影响。