Alexander Wuttke

The deployment of Artificial Intelligence (AI) in public administration is accelerating (Margetts et al., 2024). Its ability to automate routine and rule-based tasks promises significant efficiency...

The increasing popularity of strongman rule in democratic societies underscores the need to explore how authoritarian regime preferences might spread socially. We assess the role of social influence on support for leaders with authoritarian inclinations through preregistered field and survey experiments in the Norwegian Armed Forces. The field experiment randomly assigned soldiers to different rooms during boot camp, so soldiers lived among peers with varying levels of openness to authoritarian rule. We found that many individuals adjusted their privately reported support for authoritarian rule to align more closely with their peers. Further survey‐experimental evidence among soldiers and the general Norwegian population confirms that learning about others’ level of support for authoritarian rule changes both perceptions and own support. Our results suggest that support for authoritarian rule can have a social basis and could potentially spread through social contagion in established democracies.

We advance the understanding of so-called “cancel culture” at the university by presenting the results of three survey experiments among university students. Designed in an “adversarial collaboration” among researchers with competing perspectives, these experiments disentangle whether students’ preferences for curtailing academic freedom are based on viewpoint discrimination, professional academic standards, or prosocial concerns. Our findings show that a substantive share of university students support viewpoint-based restrictions on academic discourse. While they also apply academic and prosocial criteria, they apply them more strongly to conservative viewpoints. The results further show that conservative statements are perceived as causing more social harm. However, prosocial concerns do not fully explain the higher demand for ideological viewpoint discrimination. These results are important because they can inform the debate about universities as ideological spaces—a view often invoked in recent government-led attacks on academic freedom.

The rise of many-analysts studies has underscored substantial variability in research outcomes when different teams independently analyze identical data sets and hypotheses. This paper demonstrates how the many-analysts framework can be effectively integrated into research methods education. We propose a pedagogical approach in which multiple students independently analyze the same research question using identical datasets in their term papers, followed by a meta-analysis of their collective results. This instructional method highlights critical methodological concepts, such as researcher degrees of freedom and epistemic humility. Moreover, this study applies the many-analysts approach specifically to causal inference using observational data with difference-in-differences analyses. The analyses conducted by students revealed a broad range of effect sizes and led to divergent conclusions, thus emphasizing the educational and methodological value of robustness checks involving multiple independent analysts. These findings illustrate how integrating a many-analysts framework in teaching can enhance students' methodological rigor and appreciation of empirical uncertainty.

This report documents our replication of Braley et al.’s (2023) study, which examined whether voters become less willing to subvert democratic norms upon learning that their political opponents are more committed to democracy than previously assumed. The original study found that correcting voters' misperceptions about their opponents' democratic commitments effectively reduced their own willingness to undermine democratic norms. We replicate this finding within the German multiparty context, performing a direct replication with minimal modifications to the original design. Necessary adaptations address the multiparty structure in Germany, where identifying an out-party is less straightforward than in a two-party system. Additionally, we refined some survey items to enhance clarity. Consistent with the original findings, our replication study shows that correcting voters' misperceptions about the democratic commitment of out-partisans reduces their own willingness to subvert democratic norms, from 0.25 in the control group to 0.19 in the treatment group (on a 0-1 scale). In standardized terms, the observed treatment effect size (Cohen’s d = 0.4) closely matches the original effect, exceeding it by 8%.

The use of Artificial Intelligence (AI) in public administration is expanding rapidly, moving from automating routine tasks to deploying generative and agentic systems that autonomously act on goals. While AI promises greater efficiency and responsiveness, its integration into government functions raises concerns about fairness, transparency, and accountability. This article applies principal-agent theory (PAT) to conceptualize AI adoption as a special case of delegation, highlighting three core tensions: assessability (can decisions be understood?), dependency (can the delegation be reversed?), and contestability (can decisions be challenged?). These structural challenges may lead to a "failure-by-success" dynamic, where early functional gains obscure long-term risks to democratic legitimacy. To test this framework, we conducted a pre-registered factorial survey experiment across tax, welfare, and law enforcement domains. Our findings show that although efficiency gains initially bolster trust, they simultaneously reduce citizens' perceived control. When the structural risks come to the foreground, institutional trust and perceived control both drop sharply, suggesting that hidden costs of AI adoption significantly shape public attitudes. The study demonstrates that PAT offers a powerful lens for understanding the institutional and political implications of AI in government, emphasizing the need for policymakers to address delegation risks transparently to maintain public trust.

Ordinary citizens can serve as a critical defence against democratic backsliding. But beneath the surface, citizens' commitment to democracy is sometimes fragile, with crises exacerbating existing anxieties. We introduce ‘democratic persuasion’ as an actionable intervention to foster the resilience of citizens' commitment to liberal democracy. ‘Democratic persuasion’ seizes the opportunity of communicating with wavering democrats. ‘Democratic persuasion’ entails actively making the case for democracy and discussing democracy's inherent trade-offs while engaging existing doubts and misperceptions. Amid the COVID-19 pandemic, which stirred frustrations with democracy and highlighted democratic trade-offs, we invited citizens via Facebook to participate in one of sixteen Zoom town halls to engage in discussions on pandemic politics with members of German state and federal parliaments. Each representative hosted two town halls, with random assignment to a condition of ‘democratic persuasion’ in one of the two town hall meetings. The field experiment yielded mixed results, demonstrating significant effects on some indicators of democratic commitment but not on others. This study contributes to the nascent body of research aimed at reinforcing the societal pillars of liberal democracies.

This study investigates researcher variability in computational reproduction, an activity for which it is least expected. Eighty-five independent teams attempted numerical replication of results from an original study of policy preferences and immigration. Reproduction teams were randomly grouped into a ‘transparent group’ receiving original study and code or ‘opaque group’ receiving only a method and results description and no code. The transparent group mostly verified original results (95.7% same sign and p -value cutoff), while the opaque group had less success (89.3%). Second-decimal place exact numerical reproductions were less common (76.9 and 48.1%). Qualitative investigation of the workflows revealed many causes of error, including mistakes and procedural variations. When curating mistakes, we still find that only the transparent group was reliably successful. Our findings imply a need for transparency, but also more. Institutional checks and less subjective difficulty for researchers ‘doing reproduction’ would help, implying a need for better training. We also urge increased awareness of complexity in the research process and in ‘push button’ replications.