The previous exercise is from the book 'The Bayesian Choice', page 87.
What does the author mean by uniformly optimal stat. procedure?
This exercise refers to a Decision theory chapter, in a section where the only result related to some kind of 'uniformity' is one which states that to minimize the integrated risk is equivalent to minimize the expected posterior loss.
Are we supposed to prove that given $\pi(\theta|x)$, there's no $d(x)$ which minimizes $\int_{\mathbb{R}}L(\theta,d)\pi(\theta|x) \ d\theta$? If so, how do we do it?