Daniel Berliner
Participatory institutions often aim to yield information useful to policymakers, whether about public preferences, problems, or solutions. But how can large numbers of public contributions be processed into interpretable and actionable information outputs? As theorists and practitioners increasingly call for participatory institutions to operate at larger scales, often enabled by new technologies, this challenge only becomes more important. This article reviews recent work on participatory institutions in order to develop several insights: ( a ) that there are different types of information that policymakers may aim to learn and that are relevant to different policy stages; ( b ) that information must be effectively processed in order to be interpretable and actionable for policymakers; ( c ) that there are different types of information processing, depending on the specificity and novelty of the information outputs that policymakers aim to learn; and ( d ) that there are different ways in which this processing can be delegated, whether to experts, ordinary people, or automated algorithms. Better recognizing these differences will help both researchers and practitioners better understand the potential and the limitations of participatory institutions in different settings and with different goals.