ABSTRACT
This chapter considers the concepts and tools that could provide a useful practical contribution to implementing adaptive governance by improving the ability to experiment, learn and adapt when formulating the delivering policy. The chapter focuses upon exploring the extent to which Bayesian inference might provide a practical basis for delivering the required decision-support information. The remainder of this chapter takes a closer look at how these approaches to learning and adaptation might be configured so that they can be used as a more general approach to implementing adaptive governance in so doing better equipping governments to cope with substantive uncertainties and quantifiable risks. This chapter has considered the advantages of approaching governance and public policy as a Bayesian process of learning and adaptation. It has highlighted the ways in which using the analytical methods used in signal processing and machine learning provide a more easily grasped version of Bayesian methods.
