Promoting agency – people's ability to form intentions and to act on them freely – must become a primary objective for Behavioural Public Policy (BPP). Contemporary BPPs do not directly pursue this objective, which is problematic for many reasons. From an ethical perspective, goals like personal autonomy and individual freedom cannot be realised without nurturing citizens’ agency. From an efficacy standpoint, BPPs that override agency – for example, by activating automatic psychological processes – leave citizens ‘in the dark’, incapable of internalising and owning the process of behaviour change. This may contribute to non-persistent treatment effects, compensatory negative spillovers or psychological reactance and backfiring effects. In this paper, we argue agency-enhancing BPPs can alleviate these ethical and efficacy limitations to longer-lasting and meaningful behaviour change. We set out philosophical arguments to help us understand and conceptualise agency. Then, we review three alternative agency-enhancing behavioural frameworks: (1) boosts to enhance people's competences to make better decisions; (2) debiasing to encourage people to reduce the tendency for automatic, impulsive responses; and (3) nudge+ to enable citizens to think alongside nudges and evaluate them transparently. Using a multi-dimensional framework, we highlight differences in their workings, which offer comparative insights and complementarities in their use. We discuss limitations of agency-enhancing BPPs and map out future research directions.

Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.

In 2022, Nick Chater and George Loewenstein published a pre-print called ‘The i-frame and the s-frame: How focusing on individual-level solutions has led behavioral public policy astray’. The paper argues that “behavioral scientists“ have focused too much on policy ideas that aim to shape individual behavior (the “i-frame“), rather than the systems in which people behave (the “s-frame“) and therefore they “may have unwittingly promoted the interests of the opponents of systemic change”. I greatly respect the authors of this article and agree with their ultimate goal of applying behavioral science to public policy more effectively. However, I find this paper to be deeply flawed and ultimately self-defeating. My concerns come in three categories.

Introduction: As emotions are strong predictors of climate policy support, we examined multiple discrete emotions that people experience in reaction to various types of information about climate change: its causes, the scientific consensus, its impacts, and solutions. Specifically, we assessed the relationships between four types of messages and five discrete emotions (guilt, anger, hope, fear, and sadness), testing whether these emotions mediate the impacts of information on support for climate policy. Methods: An online experiment exposed participants (N = 3,023) to one of four informational messages, assessing participants' emotional reactions to the message and their support for climate change mitigation policies as compared to a no-message control group. Results: Each message, except the consensus message, enhanced the feeling of one or more emotions, and all of the emotions, except guilt, were positively associated with policy support. Two of the messages had positive indirect effects on policy support: the impacts message increased sadness, which in turn increased policy support, and the solutions message increased hope, which increased policy support. However, the solutions message also reduced every emotion except hope, while the impacts, causes, and consensus messages each suppressed hope. Discussion: These findings indicate that climate information influences multiple emotions simultaneously and that the aroused emotions may conflict with one another in terms of fostering support for climate change mitigation policies. To avoid simultaneously arousing a positive motivator while depressing another, message designers should focus on developing content that engages audiences across multiple emotional fronts.

The use of a “choice triad” model that encompasses choice posture, choice architecture, and choice infrastructure can help bridge disciplinary gaps. • The triad can be employed throughout the design process, supporting diagnostic, generative, and evaluative design activities. • Together these lenses can shift policymakers’ attention beyond public health outputs alone toward designing and maintaining conditions that allow solutions to flourish (condition design).

When it comes to reducing obesity, evidence shows that changing food environments is more effective than measures that try to educate or change the behaviour of individuals. The interventions that participants consider to be most acceptable are the same as those that they perceived to be most effective at tackling obesity. However, the interventions that were reported to be least effective and least acceptable — such as reducing portion sizes and the taxation of unhealthy foods — may actually have the greatest potential for promoting healthy eating at the population level. While we must be cautious with how we interpret correlations like this, it suggests that addressing the disconnect between the evidence base and public understanding may be a viable way of influencing public acceptability

We present four strategies, informed by behavioural science, that government and researchers can deploy to improve the use of evidence in policy decisions.

for urban planning - improving the quality of public space, countering crime and anti-social behaviour, or improving traffic safety

nudges appear to have the greatest impact on choice when people have less developed preferences because they are ambivalent or in doubt about their choice.

A new meta-analysis published in PNAS by Stephanie Mertens, Mario Herberz, Ulf J. J. Hahnel, and Tobias Brosch provides more evidence about the effectiveness of nudges. Here’s a summary:

The presentarticle reviews the debate and research on nudges byfocusing on three main dimensions: (1) the exact defi-nition of nudges; (2) the justification of nudge policies,with a focus on “libertarian paternalism”; and (3) theeffectiveness of nudges, both over time and in compari-son with standard policies.

In 2010, Colombia's defense minister contacted an ad agency to create an idea to demobilize FARC members, the oldest guerrilla army in Latin America. The agency, after spending over a year talking to nearly 100 of its members, learned two main things (1). -First, guerrilla members are ordinary men and women and not only guerrillas, a fact which is often forgotten after 60 years at war. -Secondly, they are more likely to demobilize during Christmas as it is a sensitive and emotional period. Based on these insights, they had a clever idea to put a Christmas tree in strategic walking paths in the middle of the jungle that would light up when someone passed by with a message promoting demobilization. The results? Three hundred thirty-one people who demobilized named this idea as one of the reasons to do so. Over the years, several campaigns from the same agency were quite successful, and overall, they were named in over 800 demobilizations. Causality, of course, cannot be established. Nevertheless, any measurable, non-violent efforts like this one are praised. Next time you think you have a difficult-to-reach customer, maybe think again!

Controlling for expected value, we found that a policy combining a high probability of inspection with a low severity of fines (HILS) was more effective than an economically equivalent policy that combined a low probability of inspection with a high severity of fines (LIHS). The advantage of prioritizing inspection frequency over punishment severity (HILS over LIHS) was greater for participants who, in the absence of enforcement, started out with a higher violation rate. Consistent with studies of decisions from experience, frequent enforcement with small fines was more effective than rare severe fines even when we announced the severity of the fine in advance to boost deterrence.

Much of the discussion of behaviourally informed approaches has focused on ‘nudges’; that is, non-fiscal and non-regulatory interventions that steer (nudge) people in a specific direction while preserving choice. Less attention has been paid to boosts, an alternative evidence-based class of non-fiscal and non-regulatory intervention. The goal of boosts is to make it easier for people to exercise their own agency in making choices. For instance, when people are at risk of making poor health, medical or financial choices, the policy-maker – rather than steering behaviour through nudging – can take action to foster or boost individuals’ own decision-making competences.

This chapter goes beyond classic nudges in introducing public policy practitioners and researchers worldwide to a wide range of behavioural change interventions like boosts, thinks, and nudge pluses. These policy tools, much like their classic nudge counterpart, are libertarian, internality targeting and behaviourally informed policies that lie at the origin of the behavioural policy cube as originally conceived by Oliver. This chapter undertakes a review of these instruments, in systematically and holistically comparing them. Nudge pluses are truly hybrid nudge-think strategies, in that they combine the best features of the reflexive nudges and the more deliberative boosts (or, think) strategies. Going forward, the chapter prescribes the consideration of a wider policy toolkit in directing interventions to tackle societal problems and hopes to break the false synonymity of behavioural based policies with nudge-type interventions only

Ultimately, the greatest threat to controlling the pandemic is the failure of people to get tested as soon as they have symptoms, and to provide their contacts and self-isolate. Providing adequate support for isolation is critical to all of these. And so, by deprioritising the case for support, blaming the public fuels the pandemic. The government’s psychological assumptions have, in fact, squandered the greatest asset we have for dealing with a crisis: a community that is mobilised and unified in mutual aid. When an inquiry is eventually held about the UK’s response to COVID-19, it is essential that we give full attention to the psychological and behavioural dimensions of failure as much as the decisions and policies implemented. Only by exposing the way in which the government came to accept and rely upon the wrong model of human behaviour can we begin to build policies that work.

Key Terms in this Chapter Behavioural Policy Cube: The policy cube encapsulates three core features of the ‘libertarian paternalism’ framework; namely if an intervention or policy tool is informed by the standard axiomatic assumptions of rational man theory or by insights from behavioural theories, if it is internality or externality targeting, and if it is regulatory or libertarian in nature (Oliver, 2017b). Nudge: A nudge is any aspect of the choice architecture that alters people's behavior in a predictable way without forbidding any options or significantly changing their economic incentives (Thaler & Sunstein, 2009). Boost: A boost improves the competency of a decision-maker by enriching his or her repertoire of skills and decision tools and/or by restructuring the environment such that existing skills and tools can be more effectively applied (Grüne-Yanoff & Hertwig, 2016). Think: A think is a schooling strategy that involves large-scale deliberations to enable citizens to own the process of behavioural reforms. These often include citizen forums and large-scale behavioural therapies. Nudge Plus: Nudge plus refers to an intervention that has a reflective strategy embedded into the design of the nudge. It can be delivered either as a one-part device in which the classic nudge and the reflective plus are intrinsically combined, or as a two-part device whereby the classic nudge is extrinsically combined with a deliberative instrument that prompts individual reflection on the nudge. (Banerjee & John, 2020).

The evidence shows that this kind of behaviour change needs to happen collectively, not just individually. So we need joined-up governance at local, national and international levels. Food systems also contribute significantly to greenhouse gas emissions. This can be addressed by reducing waste or directing it back into the supply chain. A mix of different measures will be most effective. The evidence shows that taxation is one of the most effective ways to modify behaviour. Accreditation and labelling schemes can also have an impact.

Explore your policy problem from a behavioural perspective

behavioral contagion

the small tax on bags was the actual driver for change, but people thought ecological factors, not the tax, had convinced them. The BeSci lessons here are first, that you can use tiny levers to effect significant change and secondly, that we don't always know, or want to admit, why we take certain decisions.

Want to learn more about applying behavioural insights to public policy? Take our free online course—Behavioural insights for public policy. There’s six learning modules, each with a quiz, to measure learning and understanding. It should help you understand the basics of BI, the mission and work of BETA, as well as the ethical application of the field. It takes about two hours – but you can save your progress and do it at your own pace.

This is the website for a PhD-level mini-course in behavioral public economics developed by Hunt Allcott and Dmitry Taubinsky. Through the lens of neoclassical economics, the role of government is to provide public goods, correct externalities, provide information, and address other market failures. In practice, however, some public policies are motivated by the concern that people do not act in their own best interest. For example, many countries ban drugs, tax cigarettes, alcohol, and sugary drinks, or subsidize retirement savings and energy-efficient appliances, all largely on the grounds that consumers would be better off consuming more or less than they do. Standard approaches to policy analysis rely on revealed preference assumptions to measure an agent’s welfare. Under these assumptions, the direct effect of any policy that changes choices is to reduce consumer welfare. However, empirical evidence from behavioral economics in a variety of domains suggests that people sometimes do make systematic mistakes. The field of behavioral public economics extends the theoretical and empirical tools of public economics to incorporate the possibility of consumer mistakes into questions about policy evaluation and design. This is a PhD-level mini-course in behavioral public economics. In this course, we’ll consider questions like the following: How can we do welfare analysis if choice does not necessarily identify utility? How do we empirically measure consumer biases? How do we set socially optimal policies in settings when consumers may not act in their own best interest? Nudges change behavior at low cost. Does that mean they are a good idea? What are the costs and benefits of tax complexity?

Great examples of how behavioral insights have been applied to behavior change in India

case studies

Nearly every major challenge the United States faces—from alleviating unemployment to protecting itself from terrorism—requires understanding the causes and consequences of people’s behavior. Even societal challenges that at first glance appear to be issues only of medicine or engineering or computer science have social and behavioral components. Having a fundamental understanding of how people and societies behave, why they respond the way they do, what they find important, what they believe or value, and what and how they think about others is critical for the country’s well-being in today’s shrinking global world. The diverse disciplines of the social, behavioral, and economic (SBE) sciences ―anthropology, archaeology, demography, economics, geography, linguistics, neuroscience, political science, psychology, sociology, and statistics―all produce fundamental knowledge, methods, and tools that provide a greater understanding of people and how they live.

Behavioral Design Teams: A Model for Integrating Behavioral Design in City Government - open source playbook