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.

本文分析和证明了人类的行为偏见在安全系统的决策制定中会导致资源配置的次优模式，并提出了三种学习技术以增强多轮交互的决策制定过程，完成了多个实际系统的演示并阐明了决策模型的益处。

Morshed: 引导行为决策者更好地在相互依赖系统中进行安全投资

Morshed: Guiding Behavioral Decision-Makers towards Better Security  Investment in Interdependent Systems

Generative neural networks have been shown effective on query suggestion.
Commonly posed as a conditional generation problem, the task aims to leverage
earlier inputs from users in a search session to predict queries that they will
likely issue at a later time. User inputs come in various forms such as
querying and clicking, each of which can imply different semantic signals
channeled through the corresponding behavioral patterns. This paper induces
these behavioral biases as hypotheses for query generation, where a generic
encoder-decoder Transformer framework is presented to aggregate arbitrary
hypotheses of choice. Our experimental results show that the proposed approach
leads to significant improvements on top-$k$ word error rate and Bert F1 Score
compared to a recent BART model.

通过提出行为偏差的假设，本文提出了一种通用的编码器 - 解码器变压器框架，以聚合任意假设，进而在检索任务中实现了较大的性能提升，包括 top-$k$ 词语错误率和 Bert F1 得分。