Policy making using computer simulators for complex physical systems;
Bayesian decision support for the development of adaptive strategies

Abstract

Policy makers increasingly rely on computer models to aid policy judgements for complex systems. The climate system, for example, is extremely complicated and its reaction to changes in radiative forcing through CO2 emissions can only be explored using models. Bayesian methods for making inferences about physical systems that combine information from computer simulators and system observations have become increasingly well studied. We apply some of these methods to the policy problem where the decisions to be made are inputs to the computer model. Particular features of our methodologies include: the provision of Bayesian decision support for the policy problem when it is known that policy may be adapted in reaction to future observations of the complex system; and careful integration of the knowledge that our computer simulators will evolve and improve over time, which may affect downstream strategies and, hence, current policy.
Our methods also allow research investment questions to be explored in the context of the wider policy problem. For example, the question of whether or not an improved version of a computer simulator should be built and how much it should be run can be addressed as part of the policy problem.