Posterior Regret Γ-Minimax Estimation and Prediction with Applications on k-Records Data Under Entropy Loss Function

Robust Bayesian analysis is connected with the effect of changing a prior within a class Γ instead of being specified exactly. The multiplicity of prior leads to a collection or a range of Bayes actions. It is interesting not only to investigate the range of estimators but also to recommend the optimal procedures. In this article, we deal with posterior regret Γ-minimax (PRGM) estimation and prediction of an unknown parameter θ and a value of a random variable Y under entropy loss function. Applications for k-records such as estimation and prediction problems are discussed.     

Adaptive Heuristics

We exhibit a large class of simple rules of behavior, which we call adaptive heuristics, and show that they generate rational behavior in the long run. These adaptive heuristics are based on natural regret measures, and may be viewed as a bridge between rational and behavioral viewpoints. Taken together, the results presented here establish a solid connection between the dynamic approach of adaptive heuristics and the static approach of correlated equilibria.     

(Mis)Understanding Abortion Regret

The debate about abortion regret rests on competing assumptions about women's attachment to pregnancy. Antiabortion claimants argue women always attach to pregnancy (inevitably regretting abortion), while abortion rights supporters counter that women do not attach to pregnancies they choose to terminate (feeling relief instead). Neither assumption explains women's experience; research shows that attachment is discursively produced. Using interview data from 21 women, this study moves past these political claims to empirically identify three sources of women's emotional difficulty around abortion: social disapproval, romantic relationship loss, and head versus heart conflict. Findings point to the importance of attention to women's lived experience and space for complex feelings around abortion.