Faculty Bibliography

It is rational to care about proximity; closer is often better. Proximity bias can be found when people overweight proximity and are willing (for example) to suffer serious welfare losses in terms of health or wealth in return for modest welfare gains as a result of proximity. In extreme cases, proximity bias leads people to stay where they are, at significant cost to their own welfare. Proximity bias is paralleled by proximity neglect, which can be found when people underweight the welfare benefits of proximity. Proximity bias can be seen as a product of present bias, though it often has additional or distinctive characteristics (including overestimation of the welfare costs of getting from one place to another). Extreme forms of proximity bias can be counted as pathological (“hodophobia”). There is clear evidence of the importance of proximity, and suggestive evidence of proximity bias, in diverse contexts, including medical care; vaccination; eviction; voting; and public assistance. Proximity bias has significant implications for policy and law. It suggests that there may be large effects from increasing (or reducing) proximity or making proximity less (or more) salient, perhaps through the use of debiasing, online alternatives, or various forms of choice architecture.

Scientific evidence regularly guides policy decisions, with behavioural science increasingly part of this process. In April 2020, an influential paper 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.

This paper results from the Arthur M. Sackler Colloquium of the National Academy of Sciences, “The Science of Deep Learning,” held March 13–14, 2019, at the National Academy of Sciences in Washington, DC. Preventing discrimination requires that we have means of detecting it, and this can be enormously difficult when human beings are making the underlying decisions. As applied today, algorithms can increase the risk of discrimination. But as we argue here, algorithms by their nature require a far greater level of specificity than is usually possible with human decision making, and this specificity makes it possible to probe aspects of the decision in additional ways. With the right changes to legal and regulatory systems, algorithms can thus potentially make it easier to detect—and hence to help prevent—discrimination.

Governments around the world have implemented measures to manage the transmission of coronavirus disease 2019 (COVID-19). While the majority of these measures are proving effective, they have a high social and economic cost, and response strategies are being adjusted. The World Health Organization (WHO) recommends that communities should have a voice, be informed and engaged, and participate in this transition phase. We propose ten considerations to support this principle: (1) implement a phased approach to a ‘new normal’; (2) balance individual rights with the social good; (3) prioritise people at highest risk of negative consequences; (4) provide special support for healthcare workers and care staff; (5) build, strengthen and maintain trust; (6) enlist existing social norms and foster healthy new norms; (7) increase resilience and self-efficacy; (8) use clear and positive language; (9) anticipate and manage misinformation; and (10) engage with media outlets. The transition phase should also be informed by real-time data according to which governmental responses should be updated.

Understanding factors that influence public support for “nudging” policies, like pictorial cigarette pack warnings, may offer insight about how to increase such support. We sought to examine factors that influence smokers’ support for requiring pictorial warnings on cigarette packs. In 2014 and 2015, we randomly assigned 2149 adult US smokers to receive either pictorial warnings or text-only warnings on their cigarette packs for 4 weeks. The outcome examined in the current study was support for a policy requiring pictorial warnings on cigarette packs in the US. Support for pictorial warnings was high at baseline (mean: 3.2 out of 4). Exposure to pictorial warnings increased policy support at week 4 (β = .05, p = .03). This effect was explained by increases in perceived message effectiveness (p < .001) and reported conversations about policy support (p < .001). Message reactance (i.e., an oppositional reaction to the warning) partially diminished the impact of pictorial warnings on policy support (p < .001). Exposing people to a new policy through implementation could increase public support for that policy by increasing perceived effectiveness and by prompting conversations about the policy. Reactance may partially weaken the effect of policy exposure on public support.