Application of Complexity Theory to the Public Policy Implementation Gap

Abstract

An ‘Implementation Gap’ refers to the challenge of translating evidence and/or policy into effective outcomes. Its persistence in UK public policy implementation has been extensively documented across multiple fields, particularly within policy, public management, and healthcare. Traditional theoretical approaches to the implementation gap have typically relied on linear models, in which centrally designed policies produce predictable outcomes in sequential stages. However, such models inadequately reflect the complex, adaptive, and evolving environments in which contemporary policy implementation takes place. Increasingly, implementation is understood to occur within complex systems, where outcomes emerge from dynamic interactions between actors rather than from linear cause-and-effect relationships. This has led to growing interest in complexity theory as a more appropriate explanatory framework.

Despite this shift, there remains a lack of complexity-informed explanatory theory accounting for how and why implementation gaps persist across public policy domains. Lack of suitable explanatory theory may explain why decades of efforts to improve implementation have not delivered results. This research addresses this explanatory gap by developing a complexity-informed explanatory theory of the public policy implementation gap, refined through empirical application.

The research adopts pandemic preparedness as a critical empirical case through which to develop and test this theory. In pandemic preparedness, the implementation gap was acutely exposed when the UK government failed to act on lessons from prior pandemic planning exercises before the onset of COVID-19. Six years on, the gap endures: despite a strengthened evidence base, implementation of tangible preparedness improvements remains limited, presenting an ongoing risk to global health security. The pandemic preparedness implementation environment demonstrates defining characteristics of a complex system, including layered multi-actor coordination, non-linear trajectories, and emergent risks. While complexity theory has been applied to related policy fields, it has not been systematically applied to pandemic preparedness.

The research adopts a complexity-informed, multi-method design using realist evaluation to develop and empirically test a programme theory, which will be operationalised in the pandemic preparedness use case to examine how implementation outcomes are generated. In doing so, the research addresses the central question: What causes public policy implementation gaps in complex systems, and how can actors effectively influence them?