Robust Bayesian Variable Selection for Gene-Environment Interactions

Gene-environment (G×E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G×E studies have been commonly encountered, leading to the development of a broad spectrum of robust penalization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a robust Bayesian variable selection method for G×E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects. An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++

A Model of Sequential Crisis Management

We propose a model of how multiple societies respond to a common crisis. A government faces a damned-either-way policymaking dilemma: aggressive intervention contains the crisis, but the resulting good outcome makes people skeptical about the costly response; light intervention worsens the crisis and causes the government to be faulted for not doing enough. When multiple societies encounter the crisis sequentially, due to this policymaking dilemma, late societies may underperform despite having more information, while early societies can benefit from a dynamic counterfactual effect

A Model of Crisis Management

We propose a model of how multiple societies respond to a common crisis. A government faces a “damned-either-way” policy-making dilemma: aggressive intervention contains the crisis, but the resulting good outcome makes people skeptical of the costly response; light intervention worsens the crisis and causes the government to be faulted for not doing enough. This dilemma can be mitigated for the society that encounters the crisis first if another society faces the same crisis afterward. Our model predicts that the later society does not necessarily perform better despite having more information, while the earlier society might benefit from a dynamic counterfactual effect