Conventional risk assessment frameworks usually define risk as a function of vulnerabilities and threats, but they frequently lack a single quantitative model that incorporates the unique features of each element. In order to close this gap, this paper creates a flexible, open, and theoretically...

Handling uncertainty is a key aspect of production management. This paper employs a Bayesian network (BN) approach to improve uncertainty analysis in production decision-making by identifying common causal factors that may affect one or more components of an integrated supply chain problem. A...

Economic agents often infer latent structures - such as preference types - from data, without exogenously specified priors. We model such agents as empirical Bayesians. They estimate both the prior over types and the meanings of types via maximum likelihood. We show this estimation is equivalent...

In risk assessment, numerous subfactors influence the probabilities of the main factors. These main factors reflect adverse outcomes, which are essential in risk assessment. A Bayesian network can model the entire set of subfactors and their interconnections. To assess the probabilities of all...

Economic agents often infer latent structures - such as preference types - from data, without exogenously specified priors. We model such agents as empirical Bayesians. They estimate both the prior over types and the meanings of types via maximum likelihood. We show this estimation is equivalent...