Modeling language is a fundamental step for understanding human communication and improving human-computer interaction. The Rational Speech Act (RSA) model provides a flexible framework to pursue this objective by catching pragmatic reasoning. However, state-of-the-art models still have limitations in dealing with context. We present a new RSA framework for metaphor understanding that accounts explicitly for the role of context by emphasizing the mutual shared information between the speaker and the listener in the estimation of the communicative goal. The model is tested extensively against 24 metaphors (with either intrinsic or emergent properties) and its predictions are compared to human data.