Behavioural science has been defined as the branch of science that deals primarily with human action and often seeks to generalise about human behaviour in society. Matteo M Galizzi (Assistant Professor of Behavioural Science at the LSE) defines behavioural science as a cross-disciplinary, open-minded science of understanding how people behave. He adds that it cross-fertilises and brings closer together insights and methods from a variety of fields and disciplines, from experimental and behavioural economics to social and cognitive psychology, from judgement and decision-making to marketing and consumer behaviour, from health and biology to neuroscience, from philosophy to happiness and wellbeing research.

The skill to understand, predict, and change human behaviour is now considered an essential ingredient for one’s professional success. Given its broad appeal and great potential, behavioural science has been attracting increasing attention from a number of industries as an alternative and complementary approach for accomplishing business objectives. As a result, organisations both in the private and public sector have turned to behavioural science insights to increase the effectiveness of their practices. In addition, behavioural teams are now a prominent unit in governments, businesses, and other organisations around the world. The financial industry is no exception and behavioural science principles, when integrated into financial products and design, have been shown to make a lasting impact on consumers’ financial actions.

Recently the Behavioural Insights Team (the first organisation in the world to apply behavioural science to policy) has published a 10 point plan to upgrade economic policy with a deep understanding of human behaviour at its core (Link). The BI Team notes that recognising the true nature of people’s incentives, motivations and behaviours will improve the design of traditional policy levers and open up entirely new categories of policy tools. This will help governments and regulators design more effective policy, improve the way our economy works, and address issues of low productivity, exclusion and unfairness, benefiting citizens and businesses right across our society. The recommendations are grouped into three areas of economic policy: micro, meso (ie market), and macro-level.  

The 10 recommendations are:

  1. Saving: Help people save for the future through restructuring tax incentives to build on the dramatic shift in personal savings that has occurred during the pandemic.
  2. Jobs: Use behavioural and data science to open up new job opportunities and equip jobseekers with the support they need, focusing on job goals rather than compliance with benefit criteria.
  3. Market Data: Measure whether markets are delivering for consumers and small businesses, collecting data on how complex it is to change suppliers and get the best deal.
  4. Market transparency: Make it easier to see the best performers in professional services such as accountancy and law as well as with all government suppliers. Make it easier to see the best employers for job quality.
  5. Switching costs: Design and test Smart Data initiatives to attack switching costs and help consumers compare and change energy, telecoms and financial providers.
  6. Disruption: Kick-start market disruptors using challenge funds and shared facilities and equipment that reduce the cost of investing in innovation.
  7. Plan ahead: Prepare for the next economic shock by building data, infrastructure and know-how on how to get stimulus spending to where it’s most needed – and fast.
  8. Promote investment: Restructure business tax reliefs to target key moments when business leaders are making crucial investment decisions.
  9. Social trust: Measure it and make it an integral part of economic policy making.
  10. Challenge: Test, measure and learn by using pre-mortems and encouraging oppositional thinking to established norms (so called ‘red teams’) to minimise biases that result in bad decisions such as optimism bias, confirmation bias and group reinforcement. Then reinforce this approach with rapid online testing before rolling out new policies.