Why policy-makers should care about behavioural science: Supriya Syal

As someone who likes ‘nudge’ and related behavioural approaches, liked this article for the examples it cited, both outside and inside Canada:

Classic examples of behavioural interventions for the public sector include measures that help people save more for retirement, consume less electricity, be more likely to pay taxes, sign up for organ donations and get jobs. But the application of behavioural science to policy-making is becoming increasingly diversified around the world. For instance, the Copenhagen Airport in Denmark, in partnership with iNudgeyou (a Denmark-based social purpose company), combatted the problem of people smoking just outside terminal entrances by using stickers that guide them to a designated smoking area a few metres away. Instead of telling people what not to do (no-smoking zones near doors), providing guidance on what behaviour is desirable (smoking zones away from doors) proved more effective. In South Africa, the government of the Western Cape, in partnership with Ideas42 (a US-based nonprofit behavioural design and consulting firm) and the University of Cape Town, used a computer-based “HIV risk game” to educate at-risk youth about HIV. Instead of the typical one-off information brochure, a gamification approach that got youth to make repeated decisions about HIV risk and provided immediate feedback about their choices was a more powerful aid for increasing young people’s understanding of such risks. In Australia, Alfred Health (a hospital), working with Deakin University, Monash University, VicHealth and the Behavioural Insights Team (UK), increased healthier food choices in its cafés by using a traffic-light colour system to classify nutritional value and portion sizes of beverages. Instead of telling people about the health costs of sugary beverages, the team marked drinks as red (most unhealthy), amber or green (most healthy), and “red” drinks were simply removed from the displays and self-service refrigerators and placed under the counters — still available for sale, just less obviously so. The total number of beverages that were sold didn’t change, but the sale of unhealthy sugary beverages went down by 28 to 71 percent in a range of trials.

Meanwhile, in Canada, the Canada Revenue Agency is successfully using behavioural nudges to get people to pay outstanding taxes and to file their taxes online. The Privy Council Office Impact and Innovation Unit (IIU) is using behavioural insights to help Statistics Canada increase its survey response rates, and to help the Department of National Defence recruit more women into the armed forces. The Innovation Lab at Employment and Social Development Canada has used behavioural insights to help people find jobs and has partnered with the IIU to increase uptake of the Canada Learning Bond among low-income families. And the foremost public sector behavioural insights group in Canada, the Ontario Behavioural Insights Unit, has succeeded in increasing organ donation rates and online licence-plate-sticker renewals using behavioural insights.

Despite these initiatives and widespread academic expertise, support and talent, the public sector application of behavioural science in Canada lags behind that of other comparable nations, and the opportunity to create better outcomes for Canadians through behavioural evidence-based policy is immense. Moreover, the bulk of behavioural-insights policy work in Canada amounts to what one might call repair jobs — it is as if we were retrofitting old buildings to make them work in the modern-day context — and behavioural science interventions are used mainly to improve implementation or compliance with a pre-existing policy. Which is important, no doubt, but we could also be building new structures that benefit from modern advancements and would be much better suited to their users’ needs. Indeed, behavioural science provides powerful tools that could help us get it right at the policy design and formation stages. And, in addition to using behavioural science to design better for the public, we could also be using these insights to design better for public servants. But there are few, if any, applications of behavioural science interventions to organizational decision-making within the Canadian government or the bodies that it regulates, so this is a significant area for exploration.

Some of our greatest challenges today are large-scale, complex problems of public policy, public perception and public action: climate change, vaccinations against infectious diseases, diversity and inclusion of historically marginalized groups, humanitarian crises, to name a few. Behavioural science promises empirically validated solutions that are derived from an understanding of the human beings that make up that “public.” And it rests its propositions on evidence in favour of what works to help people themselves make decisions that are better for them, so they can lead better lives. A pretty irresistible call to arms, wouldn’t you say?

via Why policy-makers should care about behavioural science

Daniel Kahneman Testimonials

For policy wonks and “nudge nerds”, a good collection of testimonials to the impact of Daniel Kahneman’s work, summarized in his best-selling book, Thinking Fast and Slow. One example from Richard Thaler and Sendhil Mullainathan:

Kahneman and Tversky’s work did not just attack rationality, it offered a constructive alternative: a better description of how humans think. People, they argued, often use simple rules of thumb to make judgments, which incidentally is a pretty smart thing to do. But this is not the insight that left us one step from doing behavioral economics. The breakthrough idea was that these rules of thumb could be catalogued. And once understood they can be used to predict where people will make systematic errors. Those two words are what made behavioral economics possible.

Consider their famous representativeness heuristic, the tendency to judge probabilities by similarity. Use of this heuristic can lead people to make forecasts that are too extreme, often based on sample sizes that are too small to offer reliable predictions. As a result, we can expect forecasters to be predictably surprised when they draw on small samples. When they are very optimistic, the outcomes will tend to be worse than they thought, and unduly pessimistic forecasts will lead to pleasant but unexpected surprises. To the great surprise to economists who had put great faith in the efficiency of markets, this simple idea led to the discovery of large mispricing in domains that vary from stock markets to the selection of players in the National Football League.

ON KAHNEMAN | Edge.org.