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Thank you for inviting me to give this lecture. It’s a great honour and a privilege to be invited to speak in such an illustrious setting. My title for this evening is, perhaps, a bit unusual.  Why did I choose it?

Well, I thought I had better come clean from the start. I’m a zoologist by training and therefore you might well ask whether I have any qualifications to speak to you about financial services. 

It’s a bit like the story about Einstein’s chauffeur. In 1921 Einstein toured the UK, lecturing on relativity and seeking to raise money for the proposed Hebrew University of Jerusalem.

After the first few lectures, Einstein’s chauffeur, who happened to have a passing resemblance to the great man, commented that he had heard the same lecture so many times that he could give it himself.

So, Einstein said: “Why don’t you have a go”, and so at the next destination they swapped places: the chauffeur gave the lecture and Einstein sat at the back in the chauffeur’s uniform.

The lecture was excellent.  Everything went perfectly until question time, when a physicist in the audience asked the speaker to explain a detail of non-Euclidean Geometry. 

The chauffeur hesitated for a moment before pointing to Einstein at the back of the room, saying “that question’s so easy that even my chauffeur could answer it!” I will be very pleased to hand on the “easy” questions to my colleagues who are in the audience.

My talk will be in three parts. First, I am going to talk about the biology of risk and decision making. Then I will talk about the role of regulation versus informed choice, drawing on my experience from the Food Standards Agency. Third, I will discuss how we might use insights from behavioural science to help consumers of financial products to make better choices.  My key point in this final section will be that behavioural science has not displaced classical economic models, but has the potential to enrich our understanding of human decision-making.

From zoology to risk

How did I travel from zoology to financial services? Humans are not the only organisms that have to deal with risk. My colleagues, students, and I have spent many years investigating, both in theory and in experiments, how birds, bees and plants respond to risk and uncertainty. About 20 years ago we were asked by an oil company to apply our biological perspective to decision-making about exploration decisions.

We set up a company, Oxford Risk, which has also worked on risk and rail safety, helping to answer the question “how safe is safe enough?” and on trying to develop better ways of detecting terrorist attacks. Now we work on the application of behavioural science in financial services.

So what do biologists have to say about risk?

Biology is one of a number of disciplines to study decision making under risk and uncertainty. Economists have traditionally formulated normative models of risk, based on maximising utility. Psychologists and behavioural economists claim to have shown that normative models don’t predict what people actually do.  They have replaced them with descriptive models such as prospect theory and with a list of “irrational biases” such as confirmation bias, risk aversion over losses, and mental accounting. 

This year’s Nobel Prize winner, Richard Thaler, is a leading exponent of this field and in particular the concept of choice architecture or nudging to influence people’s choices.  Daniel Kahnemann, who shared the Nobel Prize in Economics 15 years ago, developed prospect theory as well as the idea of the two cognitive systems, “fast” and “slow” that we all deploy in decision-making.

The strength of this approach is that it based on what people actually do. Its weakness is the absence of a unifying normative theory. Biologists, like classical economists, have developed normative models of risk and decision making. At the same time they also model the mechanisms of decision making.

Biological models derive optimality functions from Darwinian fitness, or a proxy for fitness such as food intake, growth rate, or reproductive success.  It can be argued that normative economic models, because they have no external reference point equivalent to Darwinian fitness, have an element of circularity: utility is that which is maximised.

Here’s an example of a biological model to put a bit of flesh on the bones.  For the vegetarians in the room, it doesn’t involve animal but rather plant flesh. One of the standard biological models of risk, first formalised by my graduate student Dave Stephens 35 years ago, is called the ‘expected energy budget rule’. Imagine a choice between two food sources, a ‘safe’ option and a ‘risky’ option.  Which one is it better for the organism to choose? The theoretical answer depends on the organism’s internal state.

If the safe option provides, say 10 units of food and the organism needs only nine units to stay alive, while the risky option yields either 20 or 0 with equal probability, the fitness-maximiser should choose the safe option. 

However, if the organism needs 11 units to survive choosing the safe option means certain death, while the risky one gives a 50% chance of staying alive and therefore risk proneness is the predicted outcome. My colleague Alex Kacelnik has tested the predictions of this model using pea seedlings.

You may not think of peas as being very clever, but they have been equipped by natural selection with mechanisms for detecting nutrient concentration in the soil, a valuable survival device. When a seedling germinates, the young roots grow towards parts of the soil that are rich in nutrients.  This is, perhaps, not too surprising.

More interesting is what happens if the roots are divided between two pots with different nutrient profiles, one constant and the other fluctuating: where does the pea put its roots?  In other words, how does it respond to risk?

Pea roots behave as predicted by the expected energy budget rule.  If one pot contains a constant but low level supply of nutrients, the peas roots grow largely into the variable environment, whereas if the constant pot contains a higher level of nutrients, they avoid growing into the variable nutrient pot.

I want to make two general comments about this experiment.

First, the fact that pea plants follow the predictions of a normative evolutionary model of risk underscores the point that a brain, or conscious thought, isn’t needed to make the right decisions. Instead, the species we study rely on rules of thumb that have evolved because they yield the right answer, or at least an approximation to it. 

Second, understanding these rules of thumb not only helps to gain insight into differences between the optimal solution and observed behaviour, but could also provide a general theory of decision making that complements and enriches the normative optimality models.

There’s a parallel here with the difference between normative economic models and the rules that people actually use to make decisions (what Gerd Gigerenzer calls ‘fast and frugal heuristics’).  

This is all very well, but you may ask “so what?” Does the study of biological models and mechanisms have any relevance to how we think about financial decisions and risk? This could keep us here for the rest of the evening, but I want to give you just one example, an experiment which shows that the way in which choices are presented has a dramatic effect on response to risk.

In this experiment animals were offered choice either between two options, one safe and one risky, or between three options, two safe and one risky. With a particular set of payoffs, the animals showed a partial preference for the risky option in the binary choice and for the safe option in the trinary choice.

It was as though they had assigned value not just to ‘risky’ versus ‘safe’ but to each option, so that the sum of the values of the two safe options exceeded the value of the single risky option when all three were offered at once. It’s easy to see how this finding, if applicable to humans, could be used to manipulate people’s choice of investment portfolios.

This is one of a number well-documented examples from studies of human and non-human risky decision making where preference reversals can be induced by the way the choice is framed, results that are at odds with the principle of invariance that is fundamental to theories of rational choice.

As the psychologist Paul Slovic put it: “...economists should not resist these developments but, instead, examine them for insights into how people make decisions and the ways that the practice of decision-making can be improved”.

The role of the regulator

This leads me to my second topic, the role of the regulator. 

If the construction of options for investment can be used to steer people’s decisions, should they be regulated, or is it a case of caveat emptor?

I’m not going to try to answer this question directly, but I’ll share some thoughts from my experience at the Food Standards Agency.

When I was head of the Food Standards Agency, people were always happy to offer me advice.  One common piece of advice among the North Oxford dinner party set was “why don’t you ban it?”.

“It” was most commonly McDonalds, but could have been anything from GM foods, to colourants such as tartrazine in soft drinks, to palm oil.

The conversation would usually go like this:

Me: “Why would you want me to ban McDonalds?”

Dinner guest: “Because MacDonalds burgers are full of unhealthy saturated fat and salt”.

Me: “So what about Burger King?”

DG: “Yes, ban that as well”

Me: “And KFC?”

DG: “Yes!”

Me: “And cheese sandwiches?”

DG: “No! Why?”

Me: “Because they are full of fat and salt, like a burger”

This illustrates one reason why regulators may not want to ban things.  If you have objective criteria and apply them consistently, you may come up with some unintended consequences.  Be careful of what you wish for.

Another, more general, reason is the ‘nanny state’ argument, that will be familiar to all of you. It goes like this. People want to make their own lifestyle choices, even if these have bad personal outcomes, and the government or the regulator should not restrict our freedom to choose.  It’s up to us to make our own mistakes. But nannies aren’t always bad. 

One of Hilaire Belloc’s cautionary tales ends with the advice: “always keep ahold of nurse, for fear of finding something worse”.  

So when is nannying appropriate?

John Stuart Mill, in his famous “harm principle” argued that nannying is appropriate to prevent people harming each other.

Few would disagree that the state should aim to prevent people from killing other citizens or exploiting children for sex.

But I want to go beyond this and suggest a distinction between ‘direct’ and ‘indirect’ harm. Stabbing someone causes direct harm, while imposing a disproportionate burden on a public good on which the population depends causes indirect harm.

Take the example of type 2 diabetes. According to Diabetes UK, about four million people in the UK have type 2 diabetes, and a new case of diabetes is diagnosed every two minutes.  The NHS spends £1m per hour on treating diabetes and as this figure is rising rapidly, it will eventually, and inexorably, overwhelm the NHS budget.

Type 2 diabetes is largely an avoidable lifestyle disease: over 80% of the risk of developing the disease is explained by waist circumference and obesity.

The massive resources used by the NHS in treating diabetes displaces other priorities.  So suppose you needed a hip replacement and you had to wait five years because of the priority given to saving the lives of people with type 2 diabetes, this would be an example of indirect harm.

My question, for discussion, is whether or not similar externalities of poor financial decisions by consumers could cause “indirect harms” and therefore justify regulation. Note that I am not placing responsibility on those suffering from type 2 diabetes, but asking about how our food environments, that are shaped for us by others, affect our freedom to choose.

The food industry uses many techniques, from pricing and advertising, to choice architecture, to influence our choices of what we eat.  Our choices are heavily edited and shaped. If the industry’s profit motive conflicts with a public health agenda and unhealthy foods are aggressively promoted, there’s a case for regulation to protect consumers from harm, not by restricting individual freedom directly, but by restricting the freedom of the food industry to cause harm.  We all accept the argument that there should be restrictions on the tobacco and alcohol industries, and increasingly we accept analogous regulation of the food industry.

Two current examples from this country are the ban on promotion and advertising of high fat, sugar and salt foods to children during peak viewing hours, and the levy on sugary drinks, due to be introduced next April. But rather than ban or tax, is it not simpler to label food as a way of giving people signposts to aid healthy choices?

When I was at the Food Standard Agency (FSA) we introduced, on a voluntary basis, the “traffic light” system of front of pack labelling, as a way of giving people an instance visual snapshot of the fat, salt, sugar and calorie profile of a processed composite food such as a sandwich, a pizza or a ready meal.

The traffic light system may be better than nothing, but it has various shortcomings. First, it has implemented in different ways by different companies, so consumers could get confused.  Some labels tell you what’s in the pack as a whole, some tell you what’s in a typical portion, and some tell you what’s in a 100 g.

Second, evidence from randomised control trials show that it makes a relatively small difference to people’s choices, perhaps because only a quarter of shoppers look at food labels. Third, the people for whom it does make a difference tend to be the “worried well”, those who are already alert to the need to eat a healthy diet, rather than those who most need help.

Add to this the fact that about 50% of the money spent of food is out of the home, where the traffic light system is rarely used, and you can see that labelling is not a panacea for the problem of dietary ill health. Again, I pose a question for discussion: are there parallels here for the labelling and marketing of financial products?

Helping people to make better financial decisions

But there will be many voices that question whether or not regulation necessary at all. Why shouldn’t the food industry produce and sell healthy food, and why shouldn’t the financial services industry accept the responsibility of giving consumers advice that will encourage them to make the best possible decisions about their money?

When it comes to financial services, would consumers trust the industry, given the past record of poor advice and mis-selling of products? Furthermore what does good advice look like: does it simply mirror each person’s preferences or does it seek a “good solution” which may be unpalatable to the consumer?

Our company, Oxford Risk aims to improve the ways in which consumers are offered advice about their investments, by deploying insights from behavioural science. I am sure that you are all very familiar with the application of nudging, or choice architecture.

Encouraging people to make better choices through nudging, and as an alternative to regulation, has been advocated in recent years for many areas of policy, including financial services. The latest annual report from the Behavioural Insights Team or “Nudge Unit”, published last month, lists their key success stories from field scale trials. 

They claim that behavioural insights have been used to produce a 20% reduction in speeding, a 34% increase in acceptances of disadvantaged students to top universities, a tenfold increase in the proportion of savers visiting the Pensionwise website and an 8% decrease in household gas consumption.

On the face of it, these are impressive results, although it remains to be seen how long lasting the effects of nudges are. 

But even if these success stories are sustained, I think nudging has its limits.  When I chaired the Science and Technology Select Committee in the Lords, our enquiry into “behaviour change” looked at two examples, modal shift in transport (getting people out of cars and onto bikes, foot or public transport) and obesity. 

The evidence we heard led us to conclude that in both of these areas, nudges are likely to be of limited effect, compared with more interventionist measures such as investment in infrastructure, taxation or regulation.

Just to give one illustration, Copenhagen, where the proportion of short journeys made on a bike or on foot exceeds 40%, way beyond anything in this country, spent roughly 40 times – yes that’s 40 times - as much per person per year on infrastructure to promote cycling and walking as is spent by the average local authority in this country.

No amount of nudging will compensate for lack of investment in the appropriate infrastructure. But I want to return to financial advice. How can behavioural insights be used to help improve advice and lead to better decisions by consumers?

Without going into detail, here are some general points. First, we should not see behavioural science as an alternative to traditional optimisation models. In biology the actual mechanisms by which animals or plants make decisions are seen as complementary to, and not alternatives to, normative optimality models.  Understanding the mechanisms can help to re-define the optimisation problem, but it does not undermine the normative principle of fitness maximisation.

In the same way, it would be wrong to simply replace financial advice based on normative models with, for example, advice based on the various cognitive biases revealed in experimental studies. But at the same time, awareness of how people actually make decisions must be relevant to the ways in which advice is presented. 

A familiar example is that most of us are better at understanding information presented as frequencies than as percentages. So if you want to explain the health risk of eating bacon, it’s better not to say that by eating a large bacon sandwich every day you will increase your risk of pancreatic cancer by 20% , but rather to say that your risk goes up by 1 in a 400.

More generally, the fact that we usually make decisions based on intuition or emotion (Kahneman’s ‘fast system’) rather than careful reflective evaluation (the ‘slow system’) should be taken into account by advisors

Second, there are some good examples of using insights from behavioural science to improve financial decisions. One classic example is the Save More Tomorrow scheme for pension savings, which goes beyond auto-enrolment to encourage higher levels of saving for retirement. It is designed to overcome the problems of hyperbolic discounting, loss aversion and the status quo bias.

It does this by encouraging people to start early, by introducing increases in contribution at the same time as pay rises so that they are not seen as losses to take home pay, and by setting the default option as remaining in the scheme.

Third, there are also less good examples. For instance investment advice which uses the concept of social norms to guide consumers to invest in particular products by comparing them with other with a similar age and wealth profile. Social norms have been used successfully to reduce energy use by showing people how much energy typical households like theirs use.  Marketing experts have long known that persuading consumers that ‘everyone is buying a certain product’ is a powerful tool, notably in the fashion industry.

The Victorian polymath Francis Galton noted that when a crowd at a country fair guessed the weight of an ox, the average of individual guesses was closer to the correct answer than most individuals were.

However, in the case of investment decisions, there’s no guarantee that others in the comparison group have made good choices, and using social norms could simply encourage new investors to make the same mistakes as others have made.

Beyond nudge

So, is the best way to build behavioural science into advice to use nudges? Auto-enrolment is one of the best known nudge success stories, but it is a one-size-fits-all method that doesn’t take into account the different needs of different individuals.  For some people at some stages in their life, saving for the present may be more important, or at least as important, as saving for the future.

Our view at Oxford Risk is that the best financial decisions will be made by consumers when they have the relevant knowledge, when they are engaged with the decision and when they feel comfortable about making the decision.

Behavioural science, properly applied, can support all three of these requirements for good decisions.  My colleague Greg Davies has coined the term “asymmetric paternalism” for policies designed to help people to make confident decisions for the best outcomes for themselves.  This approach encourages more active engagement than the “libertarian paternalism” of straightforward choice architecture.

I wonder if there might be lessons to be learned from medical practice? When I go to my doctor, she doesn’t simply tell me what the treatment should be, nor does she offer formulaic advice and leave the choice to me.  Instead, she helps me to understand, in easily digestable terms, the implications of alternative treatment choices, in order for us to reach a shared decision about what is best for me.  In this way, my choice is constructed jointly, and the doctor’s role is to ensure that the options are not framed in way that leads in one particular direction but leads to genuinely informed consent.

We don’t call doctors “Medical Advisors” because they are much more than this.  Perhaps we need an alternative descriptor to Financial Advisor, to reflect a more sophisticated approach to advice and decision-making.

The challenge for the financial services industry, both when financial advisors offer a service and for on-line direct to consumer sales, is to harness the power of behavioural science to help people to make decisions about their money that will give them what they want, what they need and what they understand.

Thank you for listening.

Please note, this is a copy of the speech as drafted, which may differ from the version delivered.