Page "Loss function" Paragraph 25

One of the consequences of Bayesian inference is that in addition to experimental data, the loss function does not in itself wholly determine a decision.

What is important is the relationship between the loss function and the prior probability.

So it is possible to have two different loss functions which lead to the same decision when the prior probability distributions associated with each compensate for the details of each loss function.