The COVID-19 pandemic has forced governments around the world into drastic measures without the normal evidence base or analyses of consequences. We present a quantitative model that can be used to rapidly assess the introduction and interaction of nonpharmaceutical infection prevention measures (NPI) both in rapid a priori predictions and in real-world a posteriori evaluations. Two of the most popular NPIs are imposing minimum physical interpersonal distancing and the use of face coverings. The success of both measures is highly dependent on the behavior of the public. However, there is very little published information about the interactions between distance, mask wearing, and the behavioral adaptations that they are likely to generate. We explore the relation between these two fundamental NPIs and the behavioral responses that they may induce, considering both risk compensation and social norms enhancement. At present, we do not have the necessary information to parameterize our model to a sufficient degree to generate quantitative, immediately applicable, advice, but we explore a vast parameter space and illustrate how the consequences of such measures can range from highly beneficial to paradoxically harmful in plausible real situations.

Evidence on the effectiveness of health promotion interventions is mixed, especially in terms of the magnitude of their impact and long-term adherence. This paper proposes a comprehensive approach informed by behavioural economics of developing behavioural change programmes, which is designed to educate, activate, engage and empower people by taking into consideration individual and social mechanisms. Three applied pilots and their results are presented in order to illustrate the approach using cognitive and social mechanisms to lead to better health outcomes, individually and community-wide. More research is needed to explore levers and barriers for the systemic adoption of this framework in implementing health promotion interventions.

Surveys based on self-reported hygiene-relevant routine behaviors have played a crucial role in policy responses to the COVID-19 pandemic. In this article, using anchoring to test validity in a randomized controlled survey experiment during the COVID-19 pandemic, we demonstrate that asking people to self-report on the frequency of routine behaviors are prone to significant measurement error and systematic bias. Specifically, we find that participants across age, gender, and political allegiance report higher (lower) frequencies of COVID-19-relevant behaviors when provided with a higher (lower) anchor. The results confirm that such self-reports should not be regarded as behavioral data and should primarily be used to inform policy decisions if better alternatives are not available. To this end, we discuss the use of anchoring as a validity test relative to self-reported behaviors as well as viable alternatives to self-reports when seeking to behaviorally inform policy decisions.

Nudging has become a well-known policy practice. Recently, ‘boosting’ has been suggested as an alternative to nudging. In contrast to nudges, boosts aim to empower individuals to exert their own agency to make decisions. This article is one of the first to compare a nudging and a boosting intervention, and it does so in a critical field setting: hand hygiene compliance of hospital nurses. During a 4-week quasi-experiment, we tested the effect of a reframing nudge and a risk literacy boost on hand hygiene compliance in three hospital wards. The results show that nudging and boosting were both effective interventions to improve hand hygiene compliance. A tentative finding is that, while the nudge had a stronger immediate effect, the boost effect remained stable for a week, even after the removal of the intervention. We conclude that, besides nudging, researchers and policymakers may consider boosting when they seek to implement or test behavioral interventions in domains such as healthcare.

Insights from the behavioural sciences are increasingly used by governments and other organizations worldwide to ‘nudge’ people to make better decisions. Furthermore, a large philosophical literature has emerged on the ethical considerations on nudging human behaviour that has presented key challenges for the area, but is regularly omitted from discussion of policy design and administration. We present and discuss FORGOOD, an ethics framework that synthesizes the debate on the ethics of nudging in a memorable mnemonic. It suggests that nudgers should consider seven core ethical dimensions: Fairness, Openness, Respect, Goals, Opinions, Options and Delegation. The framework is designed to capture the key considerations in the philosophical debate about nudging human behaviour, while also being accessible for use in a range of public policy settings, as well as training.

Nudges are increasingly being proposed and used as a policy tool around the world. The success of nudges depends on public acceptance. However, several questions about what makes a nudge acceptable remain unanswered. In this paper, we examine whether policy alternatives to nudges influence the public's acceptance of these nudges: Do attitudes change when the nudge is presented alongside either a more paternalistic policy alternative (legislation) or a less paternalistic alternative (no behavioral intervention)? In two separate samples drawn from the Swedish general public, we find a very small effect of alternatives on the acceptability of various default nudges overall. Surprisingly, we find that when the alternative to the nudge is legislation, acceptance decreases and perceived intrusiveness increases (relative to conditions where the alternative is no regulation). An implication of this finding is that acceptance of nudges may not always automatically increase when nudges are explicitly compared to more paternalistic alternatives.

This article argues that nudges can often be turned into self-nudges: empowering interventions that enable people to design and structure their own decision environments – that is, to act as citizen choice architects. Self-nudging applies insights from behavioral science in a way that is practicable and cost-effective, but that sidesteps concerns about paternalism or manipulation. It has the potential to expand the scope of application of behavioral insights from the public to the personal sphere (e.g., homes, offices, families). It is a tool for reducing failures of self-control and enhancing personal autonomy; specifically, self-nudging can mean designing one's proximate choice architecture to alleviate the effects of self-control problems, engaging in education to understand the nature and causes of self-control problems and employing simple educational nudges to improve goal attainment in various domains. It can even mean self-paternalistic interventions such as winnowing down one's choice set by, for instance, removing options. Policy-makers could promote self-nudging by sharing knowledge about nudges and how they work. The ultimate goal of the self-nudging approach is to enable citizen choice architects’ efficient self-governance, where reasonable, and the self-determined arbitration of conflicts between their mutually exclusive goals and preferences.

A criticism of behavioural nudges is that they lack precision, sometimes nudging people who – had their personal circumstances been known – would have benefitted from being nudged differently. This problem may be solved through a programme of personalized nudging. This paper proposes a two-component framework for personalization that suggests choice architects can personalize both the choices being nudged towards (choice personalization) and the method of nudging itself (delivery personalization). To do so, choice architects will require access to heterogeneous data. This paper argues that such data need not take the form of big data, but agrees with previous authors that the opportunities to personalize nudges increase as data become more accessible. Finally, this paper considers two challenges that a personalized nudging programme must consider, namely the risk personalization poses to the universality of laws, regulation and social experiences, and the data access challenges policy-makers may encounter.

Behavioural public policy analysts have examined cases of individuals’ failures of reason or judgement to attain their ends and have used these to justify ‘means’ paternalism: a form of government intervention that tries to save individuals from the consequences of those reasoning failures and to enable them better to achieve those ends. This has been challenged on a number of grounds, including too great a focus on choice-preserving interventions such as nudges, the privileging of future preferences over current ones and the possibility of state failures as damaging to individual well-being as the original reasoning failure. This paper summarizes the principal arguments in favour of means paternalism and then addresses these challenges.