Before revision:  We propose a theoretically-motivated class of default priors on a non-parametric nuisance parameter. These priors imply computationally tractable Bayes decision rules in the limit problem, while leaving the prior on the structural parameter free to be selected by the researcher .
After revision:  We propose a theoretically-motivated class of priors which give rise to quasi-Bayes decision rules as a limiting case. Together with results in the previous literature, this establishes desirable properties for the quasi-Bayes approach regardless of model identification status .