Transparency in policy making: A complexity view

Abstract The literature on transparency in participatory policy making is flourishing. With the increased digitization of our world, recent work suggests that the digitally-enabled relationships of how policy makers and citizens observe each other may transform policy making in a fundamental way. In this paper, we use complexity theory to examine how digitally-enabled transparency affects the effectiveness of policy making in aligning citizens with the policy goal to improve collective human welfare. We map Kauffman's NKC fitness landscape model, a generalizable theory of co-evolutionary complexity, to the phenomenon of transparent policy making in order to explain how transparency as an enabling generative mechanism encourages citizens to align with the policy goal without exercising central control. In our framework, citizens are agents who co-evolve by adapting to information available in their citizen landscapes. These landscapes represent the citizens' decision context, which policy makers observe and modify throughout an iterative policy cycle. In our study we identify three types of transparencies that relate to three properties of the citizens' decision context: (1) individual decision interdependence; (2) decision bias; and (3) collective decision interdependence. Using conceptual modeling, a form of inquiry combining formal representation with empirical sense making in three policy domains (e-health, smart transportation, and smart energy), we articulate and empirically validate two generative mechanisms that explain transparency effects for each of the three transparencies: (1) orchestration via iterative landscape “tuning” to reduce ambiguity and simplify citizens' alignment with the policy goal; and (2) social learning via information sharing, a co-evolutionary social “nudge” that encourages citizens to be more open to behavioral changes. Our findings have implications for the literature on transparency in participatory policy making as well as the literature on complexity in public policy and public administration.