Beliefs Updating Through Information Processing

Policy learning is a key element of policymaking processes, and there is a wide array of approaches to understand policy learning and its effects on public policy. This chapter explores nascent policy learning through the information processing model. The model combines belief systems and policy-oriented learning from the Advocacy Coalition Framework with insights on information processing from Punctuated Equilibrium Theory. Additionally, the model utilizes a Bayesian approach where policy learning is understood as belief updating following the processing of information. In the model, beliefs are understood as distributions with a central tendency, the belief, and variance, or uncertainty about the belief. Information is processed relative to prior beliefs, and the possible outcomes of information processing include no learning, belief decay (an increase in uncertainty), belief reinforcement (a decrease in uncertainty), and belief change (a significant shift in the central tendency of the belief). The chapter discusses the model, its applications, and areas for future research.