How better methods for coping with uncertainty and ambiguity can strengthen government-civil society collaboration

This chapter considers the implications for collaboration with government that stem from broader efforts to improve how governments cope with uncertainty, ambiguity and risk.1 The issue of how well governments handle uncertainty, ambiguity and risk is important because it impacts upon the cost efficiency and the effectiveness with which public services are delivered. When public administration principles and systems are focused on coping with uncertainty, ambiguity and risk there is a reduced likelihood that taxpayers’ funds will be wasted. This is because attempts to govern as if uncertainty, ambiguity and risk can, both in principle and in practice, be avoided tends to result in a range of inter-connected problems associated with attempts to achieve a level of precision and ‘the right solutions’ that are, in many cases, impossible to achieve simply because the world does not work in the sort of way that current public management principles would like it to. In contrast, if policy and its delivery are approached as processes of learningby-doing in an ambiguous and uncertain world then effective policy will inevitably require adjustment and effective responses to unanticipated events. This approach is referred to here as intelligence-based policymaking. If those adjustments and responses are made more difficult by prevailing norms, procedures and guidelines that neglect the importance of intelligence-based policymaking, then public expenditure can be wasted. These issues are discussed in detail in Matthews (2016) and include a discussion of how governing effectively and efficiently can require governments espousing ‘evidence-based policymaking’ to open up the black box of the technical issues faced in determining what evidence is, how it should be assessed, analysed – and used in practice. The book pays particular attention to the potential usefulness of Bayesian inference as a technique for implementing intelligence-based policymaking by helping governments cope with uncertainty, ambiguity and risk – particularly when standardised Bayesian approaches are used that, by design, align with the different stages in familiar policy formulation, delivery, and monitoring and evaluation cycles.