In this paper, we aim to design a monetary policy for the euro area that is robust to the high degree of model uncertainty at the start of monetary union and allows for learning about model probabilities. To this end, we compare and ultimately combine Bayesian and worst-case analysis using four reference models estimated with pre-EMU synthetic data. We start by computing the cost of insurance against model uncertainty, that is, the relative performance of worst-case or minimax policy versus Bayesian policy. While maximum insurance comes at moderate costs, we highlight three shortcomings of this worst-case insurance policy: (i) prior beliefs that would rationalize it from a Bayesian perspective indicate that such insurance is strongly oriented toward the model with highest baseline losses; (ii) the minimax policy is not as tolerant of small perturbations of policy parameters as the Bayesian policy; and (iii) the minimax policy offers no avenue for incorporating posterior model probabilities derived from data available since monetary union. Thus, we propose preferences for robust policy design that reflect a mixture of the Bayesian and minimax approaches. We show how the incoming EMU data may then be used to update model probabilities, and investigate the implications for policy.