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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 using the data that has become available since then. To this end, we compare and ultimately combine Bayesian and worst-case analysis using four reference models developed at the ECB and 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. We find that maximum insurance across this range of models can be obtained at moderate costs. Costs are measured in terms of lower expected performance relative to a Bayesian policy with flat priors. Further scrutiny, however, highlights three shortcomings of this worst-case insurance policy: (i) prior beliefs that would rationalize the Minimax policy from a Bayesian perspective indicate that such insurance is strongly oriented towards the model with highest baseline losses; (ii) the Minimax policy is not as tolerant towards 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. These preferences, referred to as intermediate ambiguity aversion, incorporate model probabilities while still giving extra weight to the worst uncertain outcomes. We show how the incoming EMU data may then be used to update model probabilities and investigate the implications for policy design.