Interview with Rich Lewis & Henry Stott

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 both Henry Stott and Rich Lewis!

Dr Henry Stott has nearly thirty years of commercial experience at Dectech and Oliver, Wyman, a strategy consultancy where he was the Director who co-created their widely respected Risk Practice.  At Dectech his clients include Tesco, British Gas and Sky.  He spent ten years working for Spurs, inventing the expected goal metric used by their scouts, and co-authoring the Fink Tank football column in the Times.  Henry now spends most of his week managing Dectech’s equity portfolio. 

Dr Rich Lewis is a founding director of Dectech, one of the longest-established Behavioural Science consultancies. He has more than two decades of experience advising private and public sector organisations across the world. Dectech has pioneered the use of immersive online experiments and their award-winning Behaviourlab approach has helped leading companies to understand and influence customer behaviour in financial services, telecoms, energy, and retail. 

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How did you actually get into behavioural science?  

Henry: My introduction to behavioural science was so called liquidity premiums. I had spent a lot of time working on credit risk and trying to understand the premium above the risk-free rate that was demanded by lenders in order to account for the probability of default of their counterparty. But when you then looked at the yields on the bonds of these different counterparties, they did something else entirely. They were not entirely related to the default risk that was being carried. And when you said that to the traders, they’d go: ‘well, that's the liquidity premium’. And what they meant by that was that they were putting extra yield on top of the economically rational yield in some way because of the fear that one day they'd get stuck with this thing in a changing market. The more I worked on that floor and the more I heard answers like that, the more I thought to myself: this price is a function of the psychology of the traders and their career prospects and their chances of getting fired. This price is not a function of economics; it's a function of the behaviour of the humans that trade it. And so, then I was like, if I'm going to ever understand this liquidity premium, then I need to go back to university to study human decision making, which is where I met Rich.    Rich: So, I stumbled into behavioural science entirely by accident. My undergraduate degree was actually in engineering - a joint honours in engineering and economics and management. I was doing the usual milk round interviews when I graduated, primarily aiming for management consulting roles. I had applied to Oliver Wyman, that Henry at the time was a director of. So, I first met him when he interviewed me. I didn’t get the job, but later Henry reached back out to me asking if I was still looking for a job, as he was starting this company [Decision Technology] doing behavioural science. And I thought, why not? Sounds like fun. I know nothing about this, but I'll give it a go. And 21 and a bit years later, I'm still here! 

Throughout the building of this company, being in this company, and the contributions that you've made to behavioural science, what would either of you say, were your biggest challenges and your greatest achievements?  

Rich: The challenge has evolved over time. As Henry said, when Dectech started it was pre the Kahneman and Tversky Nobel Prize. It was pre the BIT. It wasn't called behavioural science. It was called cognitive psychology, or judgment and decision making. And then the economists tried to rebrand it as behavioural economics. But we would be walking into clients and say: ‘we do behavioural economics; we look at customer decision making’, and they would look at us blankly. Fast forward 20 years and now we walk into clients and they're like: ‘yeah, we've got our own behavioural science team, how are you different?’ So, the challenges have definitely shifted over time, from getting people to understand what we do to getting them to understand what we do differently from other behavioural scientists. 

What we've done from fairly early on has been a focus on online experiments: hypothetical decision making, but in realistic contexts. What we've subsequently branded as Behaviourlab, because you've got to have a nice brand name for things! I’m proud of having been one of the first to do that. And as a result, we approach our work with a very open mind. We don't know how customers are making a decision in a specific context any more than our clients do. But we’ll try to recreate that context and go from there. 

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What is it that you still want to achieve in the future? What is the next step for both yourselves as individual behavioural scientists, as well as Dectech?    Henry: I think one of the challenges that we faced - going back to the earlier question and bringing it into this question - is that it's quite a handcrafted thing to design experiments and build them and have the ideas. It's quite expensive relative to other more industrialized mechanized forms of market testing or A/B testing on your existing systems. There's various tools out there that have made that extremely cheap to do. But from our perspective, they are prone to errors. Because they don't replicate the actual decision environment, and the decision environment is nearly everything. So, there's a role for the kind of work that we do, but it's to some level constrained by the slightly cottage industry nature of the way it gets delivered. And so, I think going forward from here, the main challenge is to try and industrialize that more and make it cheaper and make it more mechanized, particularly in the environment now. Especially given the large language models that are changing the face of experimentation.   Rich: I agree with everything Henry said there. I don't know what's coming next, but I feel like AI is going to be important. When we first started Dectech online research was very new; we were quite early adopters of that and that was quite transformative in what we were able to do in terms of the scale and cost of doing research. I have an intuition that AI is going to have a similar transformative effect on the work we do, but I think it's too early days to tell what it is. 

So where do you see behavioural science develop? Where do you think this field is likely to be at? And is that a good thing?    Rich: That's a good question. At the moment, behavioural science is in an odd place where no one really knows what it is. It’s all things to all men, and there's lots of people out there saying they're doing behavioural science and you could get them all in a room and every single one of them is doing something completely different. I suspect it's going to start to fragment into more defined disciplines. 

I also think the replication crisis is still somewhat playing out, particularly on the academic side. I think that whole model of journal publishing and the overall incentive structure for academics doing primary research is clearly flawed, but I think everyone's seen the problem, but not found the solution yet. I suspect there's big changes to come on that front as well.   Henry: I think behavioural science more generally, from this point out will probably bifurcate, as you say. Because I think there's been two important domains. One of which is the public policy type domains, e.g. government type applications; and the other, which is the more commercial applications.  On the former, we may well have hit ‘peak nudge’ because at some level, in line with Nick’s paper (i-frame vs s-frame) the government needs to put rules in place rather than simply play around the edges by encouraging individuals to do the right thing or to make better decisions because, that has been attempted and found quite wanting. So, one suspects that there will be a counter swing against nudge in government. But on the other hand, when it comes to corporate activity, the outperformance of one company against another, the economics of a company is really built around half percents. If I can grind out an extra percent of revenue, 10 different ways, I'm a genius. This is why data science is going to only get stronger and stronger in commercial environments, because those models are helping grind out additional optimization, additional efficiencies, which are basically more revenue, more margin in little tiny bites across an organization. That process is not stopping. And that process is in part a data science problem. So therefore, at some level, not a behavioural science problem, but of course, those models are endeavouring to model the behaviour of the agents in whatever system you're in. Behavioural data science as a field is going to be a big growth area.  

Do you foresee any challenges in the future that you've just outlined? 

Henry:  It’s true that where the data environment is growing exponentially, data science can be seen as having the upper hand, but I think there's various reasons why the behavioural scientists are still needed in that context. Above and beyond Rich's point about having ideas about what to test. Look at interpretability for instance. The ‘expert versus algorithm’ battle that was started by Meehl in 1954, that’s been going for 70 years, and I still go into plenty of meetings where the experts are convinced their expertise can't be beaten. I was once in a meeting where someone asked me which football team my computer had played for when I was saying that we could out forecast player performance relative to a coach - which indeed I think is still true - but you could see how people bridled at that. So, I think the more that you are able to deconstruct a forecasting problem or a data science problem into something that resonates with your client, the more traction you're going to get in actual implementation of those models too. So not only will the models work better out of the domain, but also they will be easier to implement because they will chime with the people that are operating and using those models. 

Rich: As Henry was saying - cycling back to earlier - I think that there has definitely been pushback on the public sector side: government have other tools that they can use and other levers that are more effective. That will continue to play out. But in the commercial domain that simply isn't the case. Maybe there's a risk right now that behavioural science starts to become seen as a bit of a fad. There’s a cycle of adoption of these things and once you get past the hype stage, you need to be able to establish whether we are really gaining any value from our investment in this topic. What is the ROI of our building a behavioural science team? I suspect that there will be some degree of backlash in some organizations, where they probably have over-invested in behavioural science, or they've done it wrong.  

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So when you’re looking to hire people, given the future of behavioural science that you've outlined, and the challenges that you foresee, what is the skill set that you're really looking for? 

Rich: That has changed quite a lot over the history of our company. When we started, behavioural science as a field didn't exist. We hired whoever was out there and was willing to work for us for the pittance that we could afford to pay them! There are certain kind of characteristics that we would look for that still apply today, but there's more around the personality of the person in terms of intellectual curiosity, integrity, and conscientiousness. Now there's this conveyor belt of courses spewing out behavioural scientists and data scientists, whereas when we started, we'd have to upskill and do a lot of the training ourselves. Now you can kind of get someone at least ‘half finished, off the shelf’ as it were, in terms of sort of the skill sets that they bring. 

What we're increasingly hiring for now is more the data science side of things than, the behavioural science side. I think the former is harder to train and takes longer. If you get someone who's got three or four years of training in data science, you can teach them the relevant bits of behavioural science more easily than, than kind of trying to do it the other way around. 

Henry: the other thing that's interesting about the new labour market compared to the labour market 20 years ago was that 20 years ago, we were very much hiring UK based; Russell group graduates who as to Rich's point, had a degree in something vaguely related, because there was no masters in behavioural science or cognitive decision making because they didn't exist. Whereas now it's a much more global workforce. Which also means that we’re no longer recruiting on University brand name, but we also have various tests that give us a sense of people's skills, which has become increasingly important.  Rich: And there’s even further to go in terms of skills for the current labour market. Work will become more and more virtual and remote as it has for us. And that requires a different skill set from the people that work in an office environment. It's very different going into an office five days a week and learning from your peers, just absorbing what's going on around you, to sitting on your own at home and having to proactively go out and seek that knowledge and learn those skills. 

Looking at your journeys into behavioural science. I don't think nowadays that would be particularly replicable, given how much has changed. So, what would you recommend someone, take a more junior perspective, to actually get into behavioural science?  Rich:  First, what we do is not the whole of behavioural science, right? We are a niche of behavioural science. We are one part of a much bigger world. And many of those other companies/opportunities don't require the same technical skills. Second, I think the behavioural science degree courses are increasingly recognizing that reality. Those behavioural science graduates are coming out with strong skills in R and Python and data analysis. If you are looking at those courses, make sure you pick one that has those rigorous statistical analysis elements to them.  

Henry: I think that's right. I'm raising children and campaigning hard for them all to study maths, given that it's such a useful passport into modelling things. Turning the world into numbers and forecasts. Which is in some ways what we do. 

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Do you have like a personal frustration with the field? 

Henry: Slow adoption. It's a pretty brave endeavour to model the human mind, in effect. But it's interesting that we're still using Tversky and Kahneman's model. Prospect Theory is still going. Original Prospect Theory is from 1979. That's a long time for something to be a leading theory. I'm a bit frustrated by the fact that it wasn't wiped out 20 years ago by something better. But I think that gives you some sense as to the nature of the problem and the scale of the problem and perhaps also the availability of the necessary tools to understand how people are making decisions.    Rich: Mine's probably a little bit narrower. We've done a lot of work over the years, and I know this is an area that you personally are interested in [directed at Merle] around how people manage money and finances. About 15 years ago we were working with a bank initially in South Africa, looking at redesigning banking around how people actually think about money and helping them to make those decisions. And we've worked on that same problem with multiple institutions over the years. And we keep coming back to the same solution. The solution is fairly easy, but actually no one's implemented it and still no one's really done it. Especially thanks to the technology, this solution doesn't feel like it should be hard to do.  And yet the products that are out there are still as clunky and as hard to use as ever. And there's billions of people struggling every day to manage finances. So, my greatest regret is that we've never managed to get somebody across the line to actually implement a product that would really transform how people manage their finances and make their life easier. The nearest thing probably is some of the things that Monzo [UK fintech] are doing now, but it really is just wallets and sweeps. It's not really moved on in the way one would hope. 

Do you apply behavioural science to your personal life, if so how? 

Rich: Of course. Once you understand some of the basic principles of how and why we make decisions then it would be crazy not to try and apply those ideas to improving your own wellbeing. Now I’m a parent of two children, those ideas are even more useful in my day-to-day life. When my kids were small it was about nudging them to make better decisions – eat more vegetables, be nice to their sibling, and so on. Now they are older I’m trying to teach them some of the underlying ideas to apply for themselves. How to reframe a situation to see it from a more positive perspective. How to recognise the tricks used by advertisers to constantly tempt you into ultimately unsatisfying purchases. How to recognise and manage emotions, and to be more empathetic and so on. 

Henry:  Great question that you should probably ask my wife.  I’m not sure how much I explicitly implement those insights into my own decision-making rather than it simply seeps in because it’s part of the way I’m now wired.  So the perspective of an independent third party who has a better sense of what’s different about how I behave might give a clearer answer.  But, I certainly make every choice in life by mulling over the probability of different outcomes and how I’d feel about them.  And I guess, as part of that, I try to avoid the various biases which we know creep into those judgements. For example, I’ve grown into a much more aggressive financial risk-taker having studied the Easterlin paradox and well-being more generally.    

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If you wouldn’t have become a behavioural scientist, what do you think would have become?  for Henry: what if you never realised the psychology of the liquidity premium?  Well, as an undergraduate, I studied all the Astrophysics options and was a keen amateur Astronomer, so it’s possible I’d have started out there.  But I can’t help suspect that, going down that path would have led me towards Astrostatistics, given all the signal extraction involved in modern Astronomy, and from there back towards Data Science.  I’m really only truly happy when I’m modelling numbers and making predictions. 

for Rich: what if you never met Henry/he never reached back out again?  Ha! The “Sliding Doors” question... I really don’t know how to answer that, as so many important things in my life today were a result of that chance job offer. For example, if I hadn’t moved to the Warwick campus to work for Dectech, then I’d never have met my wonderful German wife who was there on an academic exchange year. If I hadn’t joined Dectech then I’d like to think I’d be doing something equally entrepreneurial and intellectually stimulating, but who knows! 

Who has inspired you as a behavioural scientist? 

Henry: I think Danny Finkelstein would make a great guest.  He’s a British politician who has worked at the heart of government for several decades.  As such, he’s been in the room when history was discussed and determined.  But, more relevantly, he’s also a highly regarded journalist and author who, in my experience, uniquely understands behavioural science and what it means for policy-makers and geo-politics.  So he’s very much a kindred spirit in that sense.  Working with him for twenty years on the Fink Tank football column was a winning combination of fascinating and fun.  We both remain staggered by how people can't accept that unlikely things do happen, such as the statistical inevitability of Giant Killers in the FA Cup every year. 

Rich: My single biggest inspiration as a behavioural scientist is Nick Chater, who I’ve been privileged to work with as a colleague since he co-founded Dectech with Henry. He’s a true polymath, who’s made contributions to so many areas of behavioural science, both academic and applied, as well as being a wonderfully kind and generous soul. But I know you've interviewed him already, so – at the risk of sounding sycophantic – I'd like to nominate you for the next interview! Your blog and these interviews are a fantastic resource and inspiration to so many people, especially those looking to take their first steps in a behavioural science career. As you were open to the innovation of a double interview, perhaps you’d also be open to sitting on the other side of the table and sharing your own experiences? 

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Thank you so much for taking the time to answer my questions both Henry and Rich!

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!