Morshed: Guiding Behavioral Decision-Makers towards Better Security Investment in Interdependent Systems
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingSimilar Papers 제목 키워드 기반
IBCB: Efficient Inverse Batched Contextual Bandit for Behavioral Evolution History
Traditional imitation learning focuses on modeling the behavioral mechanisms of experts, which requires a large amount of interaction history generated by some fixed expert. However, in many streaming applications, such …
Decision MakingImitation LearningOut-of-Distribution GeneralizationRecommendation SystemsABI Approach: Automatic Bias Identification in Decision-Making Under Risk based in an Ontology of Behavioral Economics
Organizational decision-making is crucial for success, yet cognitive biases can significantly affect risk preferences, leading to suboptimal outcomes. Risk seeking preferences for losses, driven by biases such as loss av…
Decision MakingSystematic Literature ReviewBehavioral Causal Inference
When inferring the causal effect of one variable on another from correlational data, a common practice by professional researchers as well as lay decision makers is to control for some set of exogenous confounding variab…
Causal InferenceIntegrating Response Time and Attention Duration in Bayesian Preference Learning for Multiple Criteria Decision Aiding
We introduce a multiple criteria Bayesian preference learning framework incorporating behavioral cues for decision aiding. The framework integrates pairwise comparisons, response time, and attention duration to deepen in…
Towards the Design of Prospect-Theory based Human Decision Rules for Hypothesis Testing
Detection rules have traditionally been designed for rational agents that minimize the Bayes risk (average decision cost). With the advent of crowd-sensing systems, there is a need to redesign binary hypothesis testing r…
Two-sample testing