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Governments around the world aspire to ground decision-making on evidence.Many of the foundations of policy making - e.g. sensing patterns that relate tosocietal needs, developing evidence-based programs, forecasting potentialoutcomes of policy changes, and monitoring effectiveness of policy programs -have the potential to benefit from the use of large-scale datasets orsimulations together with intelligent algorithms. These could, if designed anddeployed in a way that is well grounded on scientific evidence, enable a morecomprehensive, faster, and rigorous approach to policy making. IntegratedAssessment Models (IAM) is a broad umbrella covering scientific models thatattempt to link main features of society and economy with the biosphere intoone modelling framework. At present, these systems are probed by policy makersand advisory groups in a hypothesis-driven manner. In this paper, weempirically demonstrate that modern Reinforcement Learning can be used to probeIAMs and explore the space of solutions in a more principled manner. While theimplication of our results are modest since the environment is simplistic, webelieve that this is a stepping stone towards more ambitious use cases, whichcould allow for effective exploration of policies and understanding of theirconsequences and limitations.

Can Reinforcement Learning support policy makers? A preliminary study with Integrated Assessment Models