Editor’s note: Ben Kozary, who you may already know from his articulate and thought-provoking post on why he has decided to leave academia, has become the latest recruit into the InDecision team, giving us a view of decision sciences from the other side of the world in Australia. We asked Ben to report on the first International Behavioural Insights Conference in Sydney from the perspective of someone moving from academia into industry – here he reflects on his first experience of the world of behavioural insights Out There.
A few days ago, I read an interesting piece on the differences between behavioural economics and psychology. Truth be told, even after attending the inaugural Behavioural Exchange conference in Sydney on June 2 and 3, I’m still not sure what exactly the difference is. But does the distinction even matter? The academic in me screams, “YES!” but, outside of the ivory tower, it seems that most people aren’t concerned. Behavioural Exchange (hereafter referred to as bx2014) was, after all, a public policy conference – and as such, the emphasis of the conference was on actionable and applicable insights from the behavioural sciences.
It was the promise of these insights that drew me to bx2014. As a final year PhD candidate researching Consumer Psychology, I was looking to ground the heavily theoretical work I do in some concrete practical applications. My hope was that, in doing so, my work would take on greater meaning, even if only to me – not to mention that I’d be able to pad out the practical implications component of my dissertation. Additionally, as someone transitioning from academia into industry, I was curious as to what the transition was going to entail. Who are the sorts of people I’m going to be dealing with in industry, and how are they similar/different to academics? What value is placed on the skills and knowledge I have, and how might I be best able to apply myself? And how well received are the ideas that I’ve been exposed to throughout my time in academia? In this post, I hint at the answers to these questions but, as you may have guessed, they’re by no means definitive.
bx2014: an overview
Speakers at the event included academics, the majority of whom were from Harvard University; as well as members of government, primarily from Australia, but also from the US, UK, and Singapore; and businesspeople from around the world, including CEOs, consultants, and designers. For me, the first day seemed to focus more on government, whilst the second day was primarily about business – but regardless of the relevance (or not) to me, I found all of the sessions fascinating. Individual presenters and panels alike dealt with such issues as:
- The importance and benefits of nudging
- How governments can embrace, and have already undertaken, nudging
- Opportunities, risks, and common challenges of nudging
- The fundamentals of nudging, including data, design, and delivery
- How the integration of findings from academia and experiences in business and government can make nudges more effective
- Reflections and insights from business and academia on the application of nudges in the corporate world
- The future of nudging and behavioural science
Nudging: is it just a fad?
At this point, you may have noticed that the term “nudging” seems to have been applied as a catchcry for any behavioural intervention. Overgeneralisation may be a common sin of consumer psychology researchers, but our thinking appears more nuanced than that of our industry counterparts. For instance, many of the initiatives suggested or discussed at bx2014 were based upon research describing numerous cognitive biases, including the sunk-cost fallacy, present bias, hindsight bias, confirmation bias, anchoring, and framing effects – but there was almost no mention of the intricacies of these biases, nor any real emphasis placed on the conditions specific to particular case studies upon which they had been used as part of a successful behavioural intervention. Given that I was asked on more than ten separate occasions during the two days whether I’d read Kahneman’s Thinking, Fast and Slow (I haven’t, which apparently put me firmly in the minority of conference attendees), my concern here is that many of the attendees were searching for quick, easily applied solutions to issues affecting their stakeholders.
This overgeneralisation is dangerous ground to walk, because it flirts with the prospect of nudging becoming yet another apparent management or political panacea, when it’s anything but. Instead, we need to heed what Professor Cass Sunstein said in the first presentation of the conference; that effective nudging is about recognising individual differences. “Nudges are like GPS units: they tell you the most efficient, or ‘best’, route, but you don’t have to take it; you can go your own way and choose the scenic route, if you like.” In other words, nudges should preserve individual choice by not being overly paternalistic; this is what separates them from mandates. In that sense, I feel that Professor Sunstein was nudging us (if you will) to not overgeneralise.
Experimentation: “test, learn, adapt”
If you’re thinking executing Professor Sunstein’s advice is easier said than done, you’re right – but that was also addressed at bx2014. One of the recurring points of the conference was the need for experimentation, despite how challenging it may be. Dr David Halpern, Chief Executive of the UK Behavioural Insights Team, told us to live by one simple principle: “Test, learn, adapt.” Another presenter suggested, “It’s better to say, ‘I don’t know,’ and then test something, than to skip trials and push ahead to a full roll-out on a hunch.”
We were also reminded that the most successful companies, especially in the technology sphere – Google, Facebook, Amazon, etc. – perpetually experiment. Randomised controlled trials (RCTs) are the gold standard of experimentation, but they’re not always viable. When that’s the case, we were advised to “do whatever experiments or tests you can, provided the costs don’t outweigh the potential benefits – but always strive to get the best data available.” And therein lay two of the foremost challenges of behavioural interventions in industry: funding, and time. Fortunately for me, my time in academia has me well versed in both of these issues…
Replication: it’s essential in industry, too
The issue of replication is one that we should all be familiar with by now – and it didn’t go unmentioned at bx2014, either. For instance, Professor Richard Thaler, from the University of Chicago, told us that managers and policy makers generally think they’re right, and they don’t like taking risks; however, they are often too impatient to run experiments, and don’t see the point of replication, with their philosophy being, “It worked already, so why do we need to spend more time and money to test it again?” This problem is an obvious one, but it can be overcome with education and training.
A more serious problem with replication was highlighted during the Design breakout session I attended on the afternoon of the first day, where one of the presenters said, “Relative to hard sciences, social science is difficult, because the results will not always replicate. You can implement a nudge or a system of some sort as an effective intervention, but in 6-12 months’ time (or maybe more), people might have adapted and changed their behaviours such that it no longer works – and therefore won’t replicate in any RCTs or experiments you run.” From an academic standpoint, I find this idea intriguing, because it’s something that we rarely consider; we tend to take the more general view that, if an effect is real, it will replicate. I’m yet to hear people’s adaptability offered as a reason for some of the recent failures for studies to replicate – and I’m not saying that it’s necessarily a legitimate reason, but it does highlight something that we as researchers risk forgetting: that there are real people behind our statistics, and they can be unpredictable and subject to change.
Big data: how do we use it?
On the second day, I attended the Data breakout session, during which several interesting points were made. The focus of the session was on big data, which Dr James Guszcza, of the Deloitte Analytics Institute in Singapore, told us referred to predictive analytics and modelling. These models, he said, can point us in the right direction, and tell us who to target our interventions to, but they don’t tell us how to prompt the desired behaviour change. For that, behavioural insights are required; thus, he recommended that behavioural insights and predictive modelling be infused, because – to echo Professor Sunstein – solutions will often need to be nuanced and individually focused. Dr Guszcza also advised that we be flexible with our data, and be open to the possibility that it can be useful in ways that you wouldn’t previously have imagined. “Old” datasets are particularly useful in this regard, he said, so you should also be mindful of “digital exhaustion” (that is, the deletion of older data). “With today’s storage capabilities, you shouldn’t need to delete anything simply because it’s old.”
Collaboration in nudging: how academia and industry can work together
Strangely, the importance of collaboration between academia and industry wasn’t strongly highlighted at the conference; however, one presenter did note its significance. For collaboration to be effective, he said, it’s a matter of recognising each other’s needs. That means that academics should look at questions important to organisations, and organisations should allow academics to publish – especially given the type of rich data they have access to. Furthermore, collaboration could help solve what was highlighted as a critical issue affecting research into behavioural insights. That is, Professor Max Bazerman, of Harvard University, described how the judgement and decision-making field originated decades ago under the notion that, “If we understand what’s wrong with the human mind, we can fix it – but this approach is flawed. Instead, we should focus on understanding and accepting that this is the way the world works, and therefore we can learn to adapt and be effective.”
At the conclusion of the conference, we were asked to fill out a short questionnaire. One of the questions asked us to describe bx2014 in one sentence; I wrote: “Nudging nudgers’ nudges”, because I felt that neatly summed up the notion that most people were there to learn how to implement more effective behavioural interventions (plus, my brain was fried after an intense two days, as well as having being punished by my downing more than a few drinks at the reception the night before…). But, the truth is, this conference can’t be compressed into a sentence; the ideas are just too big. So, with that in mind, I’d like now to share with you a few thought provoking ideas that I jotted down over the two days:
If I were young and wanted to start a business, I would start a choice engine, because they will do for other industries what travel websites did for that industry. The amount of data emerging and becoming available is monumental. -Professor Richard Thaler, University of Chicago
Nudges lead people to engage in behaviour – but, as a psychologist, I’m interested in the outcome beyond that behaviour; for example, are people happy or unhappy? -Professor Mike Norton, Harvard University
When thinking about nudges, consider this piece from the late author, David Foster Wallace: There are these two young fish swimming along, and they happen to meet an older fish swimming the other way, who nods at them and says, “Morning, boys, how’s the water?” And the two young fish swim on for a bit, and then eventually one of them looks over at the other and goes, “What the hell is water?” Remember: there will always be water – and there will always be nudges, even if we don’t realise they’re there. We must open our eyes to the extensive possibilities of nudging. -Professor Cass Sunstein, Harvard University
Done is better than perfect. -Mia Garlick, Head of Policy Australia and New Zealand, Facebook
So, in the interests of applying something I learned at bx2014, I’m calling this post done. It’s not perfect – but as several speakers remarked at the conference, satisficing is better than optimising. And, at the end of the day, I think that’s a pretty good example of behavioural economics in action.