In Bayesian priors, why do we use soft rather than hard constraints?

Luiz Max Carvalho has a question about the prior distributions for hyperparameters in our paper, Bayesian analysis of tests with unknown specificity and sensitivity:

My reply:

1. We recommend soft rather than hard constraints when we have soft rather than hard knowledge. In this case, we don’t absolutely know that spec and sens are greater than 50%. There could be tests that are worse than that. Conversely, to the extent that we believe spec and sens to be greater than 50% we don’t think they’re 51% either.

2. I typically use normal rather than beta because normal is easier to work with, and it plays well with hierarchical models.