Abstract
Abstract 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.
Highlights
Computational simulations and associated graphical visualisations have become much more sophisticated in recent decades due to the availability of ever-greater computational resources
The qualitative visual appeal of these simulations has led to an explosion of simulationbased, often probabilistic forecasting in support of decision-making in everything from the UK’s GDP and unemployment to weather forecasting and American Football, to nuclear stewardship and the Earth’s future climate
We argue that the utility and decision-relevance of model simulations must be judged based on consistency with the past, and where possible on out-of-sample predictive performance, and on expert judgement; never based solely on the plausibility of their underlying principles or on the visual “realism” of outputs
Summary
Computational simulations and associated graphical visualisations have become much more sophisticated in recent decades due to the availability of ever-greater computational resources. Decision-support in modelland implies taking the output of model simulations at face value (perhaps using some form of statistical processing to account for blatant inconsistencies), and interpreting frequencies in model-land to represent probabilities in the real-world. Our aim is a decision-making process that remains acceptable to all involved regardless of the outcome; ideally a process retained without modification and used again under similar conditions in the future regardless of the outcome, unless a deeper understanding of the system has been obtained.
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