Both mathematical modelling and simulation methods in general have contributed greatly to understanding, insight and forecasting in many fields including macroeconomics. Nevertheless, we must remain careful to distinguish model-land and model-land quantities from the real world. Decisions taken in the real world are more robust when informed by estimation of real-world quantities with transparent uncertainty quantification, than when based on 'optimal' model-land quantities obtained from simulations of imperfect models optimized, perhaps optimal, in model-land. The authors present a short guide to some of the temptations and pitfalls of model-land, some directions towards the exit, and two ways to escape. Their aim is to improve decision support by providing relevant, adequate information regarding the real-world target of interest, or making it clear why today's model models are not up to that task for the particular target of interest.
uncertainty Modelling and simulation decision-making model evaluation uncertainty structural model error dynamical systems radical