The paper proposes a novel way to handle the relation between decision theory and uncertainty in the context of policy design. Present risk governance is based primarily on two institutions - insurance markets and public risk governance - supported by a powerful theory: the expected utility approach to risk. New systemic risks like those of nuclear war, pandemics, climate change and global financial breakdowns call for further progress. Such progress is feasible because recent research has developed ways to address the basic difficulties of expected utility without loosing its valuable insights. They involve three major advances. First, to introduce a risk function that generalizes expected utility so as to overcome well-known difficulties like the Allais paradox. Second, to embed expected utility in a framework of iterated network games so as to take into account the social learning processes that are essential for real world risk governance. And third, to accommodate the logic of complementary descriptions called for by the new systemic risks. The coming breakthrough in risk research may best be achieved by bringing these advances to bear on practical efforts aiming at integrated risk governance.