ABSTRACT
This chapter draws upon the preceding arguments to develop a methodological manifesto for delivering the transition to ways of doing public policy that are better equipped to cope with uncertainties and risks. It discusses how signal processing methods could be adapted to facilitate learning and adaptation in public policy using structured hypotheses testing techniques and stresses a range of advantages to be exploited if such a transition were to be made. In OECD countries, the formal monitoring and evaluation frameworks that are, in theory, a key driver of policy learning can be excessively administrative' and focused on compliance with funding contracts and/or standards and guidelines rather than on maximising opportunities to learn-by-doing in policy implementation. As stressed earlier, information theory can be applied to missed opportunities to learn in public policy. In 2013 the UK National Audit Office (NAO) reported on a major assessment of the adequacy of evaluations of UK government programmes.
