When aiming to change behavior, policymakers confront the challenge of implementing behavioral interventions across contexts. However, the effectiveness of behavioral solutions often hinges on context, posing a significant hurdle to scaling interventions. This study explores the application of a behavioral pattern language approach as a means to enhance intervention efficacy and support policymakers and practitioners who seek to solve problems at scales that cross diverse contexts. The study demonstrates how a pattern language can inform contextually aware solutions, fostering collaboration and knowledge sharing among stakeholders. Additionally, the research finds practitioners deploy multiple solutions within complex systems to achieve more difficult behavioral change goals. Despite challenges related to replicability and evolving methodologies, the findings suggest that pattern languages offer a promising avenue for systematically generating and disseminating behavioral insights. This research contributes to advancing applied behavioral science by providing a structured approach for collaborative policymaking and research endeavors that are contextually relevant and effective.

Considering the alarming energy demand for cooling and seeking sustainable cooling alternatives to over-reliance on air conditioning, our pre-registered study is the first attempt to apply social norm nudges on two cooling behaviors – lighter clothing and the use of personal cooling devices (PCDs). To examine and compare the effectiveness of a descriptive norm message, an injunctive norm signal from leadership, and the two norms combined, we conducted an online randomized controlled survey experiment among financial employees (n = 743) in Guangdong, China. We measured their behavioral intentions before and after the intervention, and their level of commitment to these behaviors as an alternative outcome. We found that while single-norm conditions did not lead to desirable increases in lighter clothing, the both-norm condition nudged participants toward selecting lighter work clothes and boosted commitment to lighter clothing. Outcomes related to the use of PCDs were not affected by any of the norm conditions. These mixed findings present a cautionary tale for designing social norm interventions in office spaces and highlight the boundaries of their effectiveness in energy-saving behaviors.

This study tests whether citizens’ evaluations of the performance of artificial intelligence (AI) in public policies are subject to motivated reasoning. Specifically, we test whether respondents’ preferences for AI regulation or their subjective attitudes toward AI are sources of motivated reasoning across varying use cases, differing in nature, complexity, safety-criticality and normative considerations: AI in municipal services, self-driving cars and recidivism prediction. Experimental results from two preregistered studies conducted among German citizens reveal that subjective attitudes toward AI cause substantial and robust motivated reasoning across all three policy domains. Regulatory preferences are only a selective source for motivated reasoning about AI in public policy. Overall, the results point to the cognitive limitations of strategies that attempt to objectify the benefits of AI without considering the context of the application domain. Politicians and policymakers need to consider these limitations in their attempts to increase citizens’ appreciation of AI in public policy.

Mobile-based trading apps have made investing easier than ever before, but this includes enabling access to risky investments that many investors may not be able to trade safely. The UK financial regulator thereby requires Contract for Difference (CFD) trading apps to make disclosures such as, ‘89% of retail investor accounts lose money when trading CFDs with this provider’. However, these disclosures might be counteracted by either their suboptimal implementation, or by other aspects of these apps’ deceptive choice architecture. Therefore, the present study audited choice architecture characteristics of demo-modes of the 14 most-popular CFD trading apps in the UK. A content analysis found for example that 31.6% of risk warnings did not comply with the regulator’s standards, and that only 35.7%% of apps contained risk warnings within the app’s main tabs. A thematic analysis suggested that apps’ educational resources could instil users with the hope of winning, by emphasising practice, strategies and psychological mindset – instead of acknowledging luck as the predominant factor underlying CFD trading profitability. Overall, this study added to previous research highlighting the similarities between certain high-risk investments and gambling, and added to the behavioural public policy literature on deceptive choice architecture.

Littering involves throwing away waste in places not designated for it, harming the environment. This study introduces a strategy to encourage more use of Littergram, an app designed to fight littering. Littergram allows users to photograph litter and share these images publicly, alerting local councils to clean up the waste. The study aimed to increase user engagement with Littergram by implementing an intervention designed using the Behaviour Change Wheel (BCW), a comprehensive framework for developing behaviour change interventions. We developed and applied a digital campaign through emails, following BCW guidelines. The process began with identifying the behaviour we wanted to change. We conducted a survey to understand what influences this behaviour and selected techniques for changing behaviour based on these insights. The campaign involved sending targeted emails to Littergram users. There was an increase in the use of Littergram during the campaign, and growth returned to initial levels after the campaign ended. This outcome shows that the approach can effectively increase engagement temporarily. Future research should focus on challenges in maintaining long-term behaviour change.

Possibly the most important argument for the permissibility of choice architecture is the inevitability argument (IA), which states that if choice contexts are to inevitably influence individuals in some way, they should be arranged to best promote their welfare. Opponents point out a moral difference between influence from unmodified environments (including environments designed without any thought given to behavioral influence) and environments modified specifically to produce behavioral effects. Only the latter are said to subvert the will of those affected, and thereby raise concerns pertaining to manipulation, mistreatment of rational agency, etc. We argue, however, that if choice architects can reliably predict the behavioral effects of both the unmodified environment and available alternatives, the moral difference between these environments seems insignificant. In such cases, one version of the IA persists. This version establishes the permissibility of choice architecture in circumstances of reliable prediction, but is itself neutral toward available normative directions.

Nudging is often proposed as a promising policy approach for improving healthy choices among lower socioeconomic position (SEP) populations. However, nudges may not suit lower SEP contexts and empirical studies are scarce. We examined the efficacy of a repositioning nudge at a supermarket in a deprived neighborhood in improving healthier snack purchases and described customers’ healthy grocery shopping needs. Daily sales were collected during 4-week control and intervention periods, where healthier snacks replaced unhealthy snacks at the checkout. Besides, questionnaires were administered. More healthier snacks were sold per day during the intervention (Median = 2.50, Q1 = 2.00, Q3 = 3.50) than control period (Median = 0, Q1 = 0, Q3 = 1.00), B = 1.77, z = 5.50, p < .001, incidence rate ratio = 5.87 (95%CI [3.52, 10.17]). Despite statistical significance, the absolute number of healthier snacks sold remains negligible. Customers report to find it important to eat healthily and to think carefully about their groceries, but to hardly buy snacks at the checkout and desire a healthy and affordable food offer. Repositioning nudges may not suit the circumstances and needs of lower SEP populations. Interventions must be tailored to lower SEP groups and integrated with system-level policies.

We report the results of a field experiment designed to increase honest disclosure of claims at a U.S. state unemployment agency. Individuals filing claims were randomized to a message (‘nudge’) intervention, while an off-the-shelf machine learning algorithm calculated claimants’ risk for committing fraud (underreporting earnings). We study the causal effects of algorithmic targeting on the effectiveness of nudge messages: Without algorithmic targeting, the average treatment effect of the messages was insignificant; in contrast, the use of algorithmic targeting revealed significant heterogeneous treatment effects across claimants. Claimants predicted to behave unethically by the algorithm were more likely to disclose earnings when receiving a message relative to a control condition, with claimants predicted to most likely behave unethically being almost twice as likely to disclose earnings when shown a message. In addition to providing a potential blueprint for targeting more costly interventions, our study offers a novel perspective for the use and efficiency of data science in the public sector without violating citizens’ agency. However, we caution that, while algorithms can enable tailored policy, their ethical use must be ensured at all times.

This paper critically assesses Rizzo and Whitman’s theory of inclusive rationality in light of the ongoing cross-disciplinary debate about rationality, welfare analyses and policy evaluation. The paper aims to provide three main contributions to this debate. First, it explicates the relation between the consistency conditions presupposed by standard axiomatic conceptions of rationality and the standards of rationality presupposed by Rizzo and Whitman’s theory of inclusive rationality. Second, it provides a qualified defence of the consistency conditions presupposed by standard axiomatic conceptions of rationality against the main criticisms put forward by Rizzo and Whitman. And third, it identifies and discusses specific strengths and weaknesses of Rizzo and Whitman’s theory of inclusive rationality in the context of welfare analyses and policy evaluation.

Dark patterns that manipulate consumer behaviour are now a pervasive feature of digital markets. Depending on the choice architecture utilised, they can affect the perception, behaviour and purchasing patterns of online consumers. Using a novel empirical design, we find strong evidence that individuals across all groups are susceptible to dark patterns, and only weak evidence that user susceptibility is materially affected by commonly used general proxies for consumer vulnerability (such as income, educational attainment or age). Our conclusions provide empirical support for broad restrictions on the use of dark patterns, such as those contained in the EU’s Digital Services Act, that protect all consumer groups. Our study also finds that added friction, in the form of required payment action following successful deployment of dark patterns, reduces their effectiveness. This insight highlights the instances in which dark patterns would be most effective – when no further action is required by the user. Consumer vulnerability is therefore more pronounced when dealing with online providers who store users’ payment details and can rely on a ‘single click’ to complete the purchase.

This essay is a write-up of my Professorial Inaugural Lecture, delivered at the London School of Economics on 9 December 2024. Herein, I describe how I became involved and have helped develop the field of behavioural public policy (BPP). I detail how the intellectual architecture of BPP – its journal, Annual International Conference and Association – came into existence, and allude to my hopes for how the field might develop as we go forward.

This essay is in celebration of the contributions of Mario Rizzo. I argue that among contemporary economists in the Austrian School of Economics tradition it is Rizzo that advanced, more than his contemporaries, the scientific research program of rational choice as if the choosers were human beings; the dynamic subjectivism and the agony of choice and social interaction; the causal processes and the institutional dynamics that make up a complex social order; and the role of law, politics and civil society in shaping commercial life. In making this argument, I attempt to arbitrage the contributions of two essays of Rizzo’s: ‘Law Amid Flux’ and ‘The Genetic-Causal Tradition and Modern Economic Theory’.

Safety villages are interventions that aim to boost children's knowledge and behaviour regarding risk-taking behaviours and their consequences via an experiential learning approach. In safety villages, children experience scenarios involving risks that resemble real-life situations. We investigated the extent to which desirable learning outcomes from a single-session safety village visit are visible outside the safety village context. In a well-powered quasi-experimental preregistered field study, we compared students (aged 11–13) who received experiential safety education to a control group of students who had not yet received the education on three important learning outcomes: Knowledge-application, risk-taking behaviour and general risk-taking tendencies. Data were collected outside of the safety village environment, before or after the visit, and without explicit reminders of the visit. Results show students who received experiential safety education outperformed those who did not yet receive experiential education on knowledge-application and reduced risk-taking behaviours. We found no differences on general risk-taking tendencies. These results show a single visit to a safety village visit can reduce risk-taking of risks that were experienced in the village, but not general risk-taking tendencies. Theoretical and policy implications are discussed.

Viewed from the perspective of public policy, behavioural public policy (BPP) faces challenges in four main areas: Systems, Impatience, Nudging, and Scaling. To address these challenges, several suggestions are proposed. First, understanding how BPP interventions unfold in complex systems requires better diagnostics and the development of predictive and generative models of human behaviour. Second, the rapid pace of policy processes necessitates a shift towards generating timely and fit-for-purpose evidence. Third, maximising the opportunities presented by BPP, beyond merely ‘nudging’, demands the early and proactive application of behavioural science in the policy cycle. Fourth, achieving widespread impact in BPP initiatives means considering scale-up from the start. Lastly, the consistent and comprehensive integration of behavioural science into standard policymaking practices would support sustainable progress in addressing these challenges.

This special issue explores foundational questions in behavioral economics and behavioral public policy, drawing on the work of Mario Rizzo, a critical voice in the debate on behavioral paternalism. Behavioral economics has offered significant insights into decision-making, often challenging traditional economic models. However, it has also introduced normative frameworks into policy analysis, such as preference purification, that critics argue oversimplify human decision-making and risk imposing external values. Contributions to this issue examine themes including the tension between standard rationality and inclusive rationality, the epistemological limitations of paternalistic interventions, and the role of tacit knowledge and dynamic learning in policymaking. By engaging perspectives from economics, psychology, philosophy, and law, the issue discusses process-based approaches to policy analysis that respect individual agency and accommodate uncertainty. It also highlights the political economy dimensions of behavioral public policy, emphasizing the need for institutional reforms that enable learning and systematic change rather than narrowly focusing on individual cognitive biases. This issue serves as both a tribute to Mario Rizzo’s intellectual contributions and a call for a deeper reflection on the methodological and normative foundations of behavioral public policy.

Pro-environmental behavior, including waste sorting and recycling, involves a combination of future-oriented (futureness) and other-oriented (otherness) attributes. Inspired by the perspective of intergenerational choice, this work explores whether eliciting concerns for future others could increase public support for recycling policy and recycling behavior. Study 1 consisted of an online random controlled trial and a laboratory experiment. In Study 1A (N = 400), future other-concern was primed using a static text manipulation, whereas in Study 1B (N = 192), a dynamic virtual manipulation was employed. The results showed that people in the conditions that elicited future other-concern reported greater household recycling intentions and more actual recycling behavior than those in the control conditions. Study 2A (N = 467) and Study 2B (N = 600) generalized this effect on the acceptance of the ‘Certain Time Certain Place’ waste sorting policy in China. Consistent with the intergenerational choice model, envisioning the future of subsequent generations is more impactful in gaining policy approval than merely envisioning a future time. These findings provide a new method for promoting public policy approval and recycling behavior, suggesting that people could become environmentally friendly when they are guided to consider future others.

Indoor air pollution is one of the leading causes of morbidity and mortality worldwide, but its sources and impacts are largely misunderstood by the public. In a randomised controlled trial including 281 households in France, we test two interventions aimed at changing indoor polluting behaviour by raising households’ awareness of health risks associated with indoor air pollution. While both generic and personalised information increased knowledge, only personalised information including social comparison feedback changed behaviour, leading to a reduction of indoor PM2.5 (particulate matter with an aerodynamic diameter ≤2.5 µm) emissions by 20% on average. Heterogeneous treatment effects show that this effect is concentrated on the most polluted households at baseline, for whom the reduction reaches 40%.

Developing countries, with limited monitoring and auditing capabilities, face significant tax evasion issues. This study examines the impact of various text message combinations on promoting tax compliance, particularly in encouraging service providers to submit monthly sales tax returns in Khyber Pakhtunkhwa, Pakistan. A randomised controlled trial involved 18,087 service providers and tested three types of SMS reminders. These included a basic reminder for the due date, a reciprocity message emphasising social responsibility, and a loss aversion (LA) message highlighting financial penalties and deactivation. Subsequently, service providers who didn’t file on time received one of three warning messages. These warnings included a basic alert about potential legal action, financial penalties, and deactivation, as well as a message framing continued non-compliance as an active choice (AC). Overall, the interventions did not significantly influence tax filing behaviour beyond basic reminders and warnings. However, compliance improved for early registrants with the LA reminder and AC warning, and these results were robust to multiple hypothesis testing corrections. Compliance worsened for recent registrants in all combinations except the LA reminder and AC warning. These findings suggest that targeted low-cost messages that convey vague threats can improve tax compliance among certain taxpayer groups.

Despite a long history of research on democratic backsliding, the process itself − in which the executive branch amasses power and undermines democratic processes and institutions − remains poorly understood. We seek to shed light on the underlying mechanisms by studying democratic near misses: cases in which a period of autocratic governance is quickly reversed or full backsliding is prevented at the last minute. Building on the literature on near misses in sociotechnical systems such as nuclear power plants, we adapt the drift-to-danger model to the study of democratic systems. Two key findings emerge: First, democratic backsliding is often triggered by political elites pushing the boundaries of their power by violating norms, which are crucial yet vulnerable safeguards for democracy. Second, democratic backsliding is unpredictable and non-linear, being driven by the interaction between political elites and the public. Norm-violating elites may feel legitimized by a supportive public that sees norm violations as justified. At the same time, political elites may signal that norm-violating behaviour is acceptable, potentially leading the public to adopt anti-democratic beliefs and behaviours. We identify risk factors that make norm violations more likely and outline behavioural sciences-based interventions to address these violations.

In this paper, we revisit the Knowledge Problem addressed by Hayek eight decades ago and emphasised more recently by Rizzo and Whitman in their critique of the new paternalist approach of mainstream behavioural economics promoted by Sunstein and Thaler. We do this in light of the work of Michael Polanyi. Polanyi developed a theory of knowledge which has some commonalities with Hayek’s but also departs from it by emphasising the tacit, personal and perceptual dimensions of any process of knowing, thus radically renouncing any attempt of a knowledge typology separating different types of tacit knowledge (TK) and even denying that general knowledge could exist independently of TK.