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Interview with Oliver Hauser

Behavioural Science is a rapidly expanding field and everyday new research is being developed in academia, tested and implemented by practitioners in financial organisations, development agencies, government ‘nudge’ units and more. This interview is part of a series interviewing prominent people in the field. And in today's interview the answers are provided by Oliver Hauser. Oliver is Professor of Economics and interim Co-Director of the Institute for Data Science and Artificial Intelligence at the University of Exeter and Faculty Affiliate at Harvard University's Sustainability, Transparency and Accountability Research Initiative. In addition to advisory roles to the Behavioural Insights Team (the “Nudge Unit"), behavioural consultancy MoreThanNow and blind recruiting platform MeVitae, he currently also serves as Vice-Chair of the BBC Children in Need’s South West Advisory Committee. Oliver previously taught and researched at the Harvard Business School, Harvard Kennedy School of Government and Harvard Extension School, and has held fellowships at the Alan Turing Institute, the Harvard Behavioral Insights Group and Harvard Women and Public Policy Program. Oliver received his Ph.D. in Biology from Harvard University in 2016 and his B.Sc. in Physics from the University of Innsbruck in 2010.


Who or what got you into behavioural science?

My career didn’t start out in behavioural science at all. In fact, I initially went into physics: after completing an undergraduate degree at the University of Innsbruck, I continued physics for my PhD at Harvard University. However, after a year of grad school, I realized that I couldn’t see myself as a physics professor in the long run.

Fortunately, Harvard gave me the opportunity to explore other research areas. I went around campus, sat in on many different lectures, and met with many fascinating professors. One day, I sat in a lecture by Martin Nowak, a professor of mathematics and biology at Harvard, and was absolutely fascinated by his research on the evolution of cooperation. One thing led to the next and I had the opportunity to join his lab (and in doing so, my PhD technically switched to biology). I ended doing a mix of evolutionary theory and experimental economics research, graduating with a PhD in 2016.

During my PhD, I read a lot about behavioural science/economics and embraced some exciting opportunities that came along, including working with the UK Behavioural Insights Team (back then still known as the “Nudge Unit” at No 10) and collaborating with faculty at the Harvard Business School. After my PhD, I was fortunate to get the opportunity to do postdoc fellowships with Mike Norton at Harvard Business School and with Iris Bohnet at Harvard Kennedy School. During this time, my research became increasingly more applied, running field experiments in collaboration with large organizations and government agencies. I even did a part-time postdoc in data science at Ultimately, with a portfolio of applied behavioural research and game theory papers, I found a wonderful, supportive home as a faculty member in the Department of Economics at the University of Exeter, where I have been for the past five years.

Clearly, this wasn’t an obvious path into behavioural science and I owe so much to all my mentors and advisors—formal and informal—during my PhD and postdoc, all of whom have supported me on this journey from physics to biology to behavioural economics.


What are you proudest of having achieved as a behavioural scientist?

Every day, I’m proud to be doing science—contributing to a public good for society. Of course, some moments are particularly memorable. For example, early in my career during my PhD, I was fortunate to have a paper published in Nature (Cooperating with the future, 2014). It was a lot of work at the time, but it has paid off in the long term in many ways, including leading to more research by me and others on this topic and having sparked the interest of policy-makers and decision-makers.

But there is another aspect of this paper I’m proud of: when large-scale replications first began to be conducted by international teams, there was a big effort that set out to replicate all papers published in Nature and Science between a certain time period. “Cooperating with the future” was one of those papers: and I’m proud that our original effect replicated, even matching the same effect size of our treatment. Furthermore, when other researchers were asked to predict which studies would replicate, our study was predicted as the most likely to be replicable.

Aside from the science itself, there are also some more intangible achievements I’m proud of. Often in our work as behavioural scientists, we focus on informing other people’s decision-making; but I’m also trying to improve my own by learning from our collective body of research. For instance, my team and I study how to make workplaces more inclusive and equitable through large-scale randomized controlled trials, which we hope will inspire companies to take up these initiatives if they work. I also try to implement such initiatives and behaviours in my team – and I’m proud (and touched) when I hear that my team members feel included, supported and, as a result, they thrive. And when my team members go off on their diverse paths to become inspiring behavioural scientists who do good in the world, I’m proud that I had a (small) part to play in their development.

… And what do you still want to achieve?

This is a timely question for me personally, as I have just recently been promoted to full professor (a wonderful milestone in my career), which means I now have the benefit of a secure job and many years to fill between now and retirement: so how can I make this time worthwhile, not just for myself but mostly for others as well?

One thing I keep coming back to is that I’d like to do more to have a real-world impact with our research findings and help to translate those findings to make them relevant and actionable for decision-makers in governments, charities, and businesses. I think it is imperative for all of us to contribute to this effort: lots of research is publicly funded (especially in the UK and the rest of Europe) and I think we need to work on building trust in research and science. Of course, not everything we do as academics needs to be “useful” in practical ways (e.g., lots of important research in the humanities and the social sciences is important for other reasons, such as expanding our thinking and opening our minds to new ideas). But I think there is a lot of good that academics do for society – but it is not often recognized and we don’t spend much time on translating research into practice (since the incentives in the field are to focus on publishing more).

Therefore, in addition to my ongoing work with field partners, I’d like to put considerable effort into contributing to this public good more broadly – that is, demonstrate that research produces a lot of valuable insights to governments, organisations and society as a whole and help decision-makers implement research findings in their context. This might include leading conversations with decision-makers and findings ways that we can bring behavioural science and the role of experimentation closer to executives, managers, and policy-makers and show them the value of behavioural science first hand – be it through talks, workshops, education, writing, or other initiatives.


How do you see behavioural science develop say in the next 10 years?

I think that it is likely that we will see more interdisciplinarity and, in particular, I think the field will interact with, learn from, and build on the data science/AI revolution. I hope this will be a fruitful endeavour, but it’s yet to be seen how much we will learn from it.

For instance, with the ever-increasing amount of data about human behaviour available to us—and the computing power to match—what can we learn about decision-making that was inaccessible to us before? I think research in this area will not just bring new answers but also lead to us to confront new challenging questions around how we conduct research – the ethics of using AI in carrying out research and writing up papers, the implications of data and privacy for our participants, and so forth.

But AI and data science will also enable some exciting new opportunities to expand on our existing toolkit, such as predicting for whom a treatment might work particularly well – that is, rather than thinking about just the average treatment effect, it is possible that our field will start to talk more about the distributional effects of treatments, and how treatments can be personalized to individuals through AI and data science. There are many important applications of this principle in medicine, education, public policy, and others.

Do you foresee a challenge actually getting there?

I think that the AI/data science revolution is moving fast—sometimes a bit too fast—for our own good. I work with a lot of companies and, with the advent of generative AI, many of them are quick to say “we want to use this new generative AI technology to become more productive and efficient, let's use it in this context right now” – but my reaction as a researcher is push back and ask: “how do you know that this actually works in this context?” People are quick to jump to the conclusion that there will only be upsides to this new technology rather than think about the distributional effects.

So I think behavioural science can add value in overcoming this challenge: rather than just adopting AI and then hope that it will just work out, I think we should investigate and study it like we would study any treatment – with experimentation and randomized controlled trials. And I would go as far as saying that we should adopt a systematic, scientific approach to the development of (generative) AI in a similar way: rather than deploying the next iteration of Frontier AI immediately, we should test it safely and carefully before releasing it onto the world. Behavioural scientists have a role to play in designing effective systems, in which we can develop and foster the strengths of new technology while minimizing its risks.


What would you recommend for someone looking to get into behavioural science?

I think there has never been a better time to become part of the behavioural science world. The field is quite established now across a wide range of fields, both in academia and outside it. So, if you want to address any real-world important problem—whether it's sustainability and climate change or health or household finance—there's a good chance behavioural science is going to be part of that.

The field has also become more sophisticated in the methods it uses. One key “method” is having and cultivating an analytical skill set (including maths for those who are so inclined). More generally, given that the problems we want to solve are complex, being able to think logically and systematically come up with ideas to study them will help us to make advances. I would recommend to cultivate this scientific mindset in behavioural science, whether you are interested in working inside or outside academia.

In addition, I think those interested in entering behavioural science might want to brush up on their data and statistics skills: there is more and more data available and it’s likely that data analysis (whether in the form of “standard” statistics or AI/data science) will be ever more important, so understanding the basic statistics behind those methods and learning a programming language like Python or R will be useful. This may not (yet) be a pre-requisite for PhD or Master’s programmes, but I think it’s a skill that will only become more useful and important (in behavioural science and beyond).

What do you think you would be or what would have happened to your life if you hadn't really found behavioural science?

I have been lucky that my career has ended up leading me down this path, as I love contributing to research, education, and public policy through behavioural science. But there have been many turning points in my life where things could have turned out differently – as my career path shows, I started somewhere entirely differently than where I ended up. Luck has definitely played a role, but I also believe that it’s important to be optimistic and inquisitive to learn more about opportunities when they come along.

So, if I hadn’t found my way into behavioural science in academia, I think it would have ended up pursuing my passion for contributing to the public good in some other way – it might have been in science or research, but I could also see myself having gone into civil service/government. In any of this, it’s hard to imagine that I wouldn’t have come across behavioural science at some point. But, ultimately, I’m sure I would have done my best in any job—with or without behavioural science at its core—to contribute to making the world a bit of a better place than we inherited it.


Thank you so much for taking the time to answer my questions Oliver!

As I said before, this interview is part of a larger series which can also be found here on the blog. Make sure you don't miss any of those, nor any of the upcoming interviews!

Keep your eye on Money on the Mind!

Behavioural Science

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