Despite decades of pledges to prevent genocide and other mass atrocities, identity-based violence persists worldwide. This introductory article to the Journal of Comparative Policy Analysis special issue, “Comparative Analysis of Policy and Practice for Atrocity Prevention”, argues that comparative policy analysis is underutilized yet can be essential for understanding and mitigating atrocity risk. By examining policies across diverse contexts and actors – state and non-state – the special issue illuminates how prevention approaches can mitigate atrocities before, during, and after conflict. The collection advances theory, empirical evidence, and policy recommendations to strengthen the practice of atrocity prevention globally.

This study combines bibliometric meta-analysis with secondary literature and expert interviews to unveil a five-stage narrative of the development of policy sciences: (1) It has grown and globalised from a North American orientation towards European scholarship; (2) Lasswell’s classical subfield provided an epistemic foundation up to the 1980s; (3) Three further subfields – change and power, institutions, and post-positivist epistemology – spearheaded diversification from the 1980s to the 2000s; (4) Since then, policy sciences has Europeanised and complexified; (5) Despite some fragmentation in the 1990s, policy sciences in the 2020s remains coherent and institutionalised. It may today be considered an “interdisciplinary discipline”.

This article investigates the diachronic evolution of institutional approaches to research and innovation policies (R&I) in France and the UK, with a focus on how institutional arrangements and coordination practices influence the temporal evolution and layering of policy design choices. Designing R&I policies involves the intersection of diverse policy areas often overlapping with, or extending beyond, traditional ministerial responsibilities. These policies require integrating diverse instruments across knowledge production and technological development, often leading to institutional coordination challenges, which may affect policy design choices. Using historical data on R&I funding instruments, the article examines the organizational challenges faced by public institutions and explores how alternative institutional and organizational setups shape policy instrument evolution.

How do we develop trust in artificial intelligence (AI), which can threaten our fundamental rights or might cause physical harm? This article proposes a governance-centred framework and argues that trust in high-risk AI systems is a function not only of the features of the technology and personal traits but is also a function of trust in institutions under which they operate. The article tests and compares the validity of the framework using 3,600 online survey responses in Seoul, Singapore, and Tokyo. The results show the significant differences in patterns of institutional trust that directly affect the degree of trust in high-risk AI in the three cities.

Price-based environmental policies aim to incentivize individuals to reduce consumption behaviors that negatively impact the environment. However, cognitive constraints often limit individuals’ ability to associate waste disposal with the costs imposed by such pricing mechanisms. This study compares South Korea’s shift from volume-based to weight-based food waste pricing policies, which makes disposal costs more salient to households by directly linking them to food waste amounts. Panel analyses show that weight-based food waste pricing policies are effective in reducing food waste, suggesting that enhanced price salience can drive behavioral change among policy targets, although this effect diminishes over time.

This study addresses the challenge of missing data exceeding 50 per cent in international comparative research, where traditional imputation methods often introduce bias. Machine learning-based imputation captures complex variable relationships, enhancing dataset accuracy and integrity. The paper compares machine learning with traditional methods, validating its superiority through simulations. Findings show that machine learning improves consistency in time trends, uncovers dynamic relationships, and provides reliable tools for policymakers and researchers. Additionally, the study explores the impact of digital technologies on public services across national datasets, demonstrating how machine learning improves data quality and offers valuable insights for decision-making.

This editorial marks the relaunch of the Policy Innovation section of this journal, responding to the urgent need for innovative public policy amid global challenges such as climate change, economic inequality, and technological disruption. It reflects on the journal’s pivotal contributions to the subject and underscores the importance of comparative analysis in driving policy innovation. The section invites submissions that push theoretical and methodological boundaries, expand research into underexplored geographies and domains, and tackle the implications of policy innovation for equity, fairness, and justice. By broadening the scope of policy innovation, the journal aims to inspire cutting-edge, rigorous, and actionable research.