Introducing System 3: How We Use Our Imagination to Make Choices

The consumer decision-making process is more than a purely emotional or rational assessment, with a third option powered by the imagination.

In recent years we’ve become used to thinking about decisions as “system 1” or “system 2”. System 1 choices are automatic decisions, made without thinking, based on an immediate emotional or sensory reaction. System 2 is used to stop and rationally calculate the consequences of our choices, and determine the best cost-benefit tradeoff.

But these two processes don’t capture every decision. Indeed they might only encompass a minority of our daily choices.

Recent work in neuroscience and psychology has discovered another way of making choices: with the imagination. Customers imagine their possible futures: the outcomes they would experience after a choice, and how those outcomes will make them feel. The future that makes them feel happiest will be the one they choose. These choices use different parts of the brain than System 1 and 2. They are called System 3 choices.

Think about how you might buy a car. System 1 would suggest that you see a colour, or shape, or brand of car, immediately fall in love with it and buy without thinking. System 2 implies that you calculate the price, financing options, fuel efficiency, resale value – and pick the model that makes the most financial sense.

A System 3 decision would look like this: imagine yourself driving that car. Feel, in your mind, the sensations of the seats and how it drives. Imagine how your partner or your friends would view you in it. Consider, too, the impact on your bank account and what else you would be missing out on to pay for it. How you’d feel about the environmental impact and the safety this model offers your family. How do you feel? Is it good? Maybe you also have another model in mind. Try the same process on that. Does it feel better? The car you feel best in – within this mental simulation – is probably the one you’ll choose.

This System 3 process applies to our big choices in life, but also to smaller ones. At the shelf, considering a new breakfast cereal or a new skincare product, you’ll imagine how it tastes or feels before buying it. You might test out the moisturiser from a sample jar, but you still need to project yourself into the future – will it feel the same when you’re applying it before bed, or after you wake up? Your System 3 imagination combines past and present experiences with possible futures, and works out which it enjoys most.

System 1 still has a place: once you are familiar with a product, you might buy it automatically. And we still use system 2 for lots of financial and practical calculations. Indeed, System 3 incorporates elements of systems 1 and 2 in how it works.

But if you’re:

  • Launching something new
  • Trying out a new communications or pack approach
  • Building a brand
  • Or selling something with consequences beyond just the few moments after purchase
  • Then your customers are probably using System 3 to make their decisions, and you need to use System 3 tools to predict the success of what you’re testing.

Some existing research tools can be used to measure System 3, and new ones are emerging too. Implicit Prospection Tools, newly designed qualitative projection techniques, and Adaptive Concept Tests are among them.

You can find out more about System 3 in our talk at IIeX Behaviour London on 10th May, digging into Leigh’s blog on the science behind System 3 or by searching for “prospection psychology”. It’s likely to be a hot topic in the coming years (use your System 3 to imagine that possible future!) – and you can get ahead of the curve if you learn about it today.

Beyond implicit associations, to implicit choice

Over the last two years implicit association tests have started to become a standard tool in market research. The 2017 GRIT report tells us 80% of researchers are using (53%), or considering (27%) nonconscious research methods, and that:

“Implicit/IAT is perceived to be one of the fastest growing nonconscious methods in the industry”

These methods are based on academic research originally done at Harvard University*, which measured unconscious racism or sexism by timing how fast people respond to different stimuli. Researchers found that the vast majority of people hold unconscious associations based on race, gender or age.

This opens a huge question: if people have prejudiced attitudes, does this mean they act in a racist, sexist or ageist way? The answer is not obvious – and in fact, more recent research summarised here has cast doubt on this. The correlation between racist attitudes and racist behaviour is lower than you might expect.

This led us to wonder about how implicit tools are being used for marketing and branding questions. These tools often uncover implicit brand associations (Coke is associated with “Authentic”, or BMW with “Exciting”). But we are now learning that these associations don’t necessarily drive behaviour. People might think Coke is authentic but not go ahead and buy it.

What you really need to know is not only what people think and feel, but how they will behave.

We realized that brands need a more direct way to predict what their customers will actually do. As a result, we have developed a set of tools for measuring implicit choice, rather than implicit association.

Implicit choice uses the same technique of measuring reaction time, to find out how intuitive people’s choices are, how confident they are in their judgements and how reliable those choices are. But the measures are choices – which can be used to directly predict market performance – rather than associations.

When linked with message priming, implicit choice allows us to test the impact of claims or concepts, and whether they work in changing customer behaviour (not just their attitudes or opinions, but what they will actually buy).

A popular method used by many of our clients is behavioural conjoint**, which combines conjoint analysis with implicit choice. This method tells us which product features or attributes most strongly drive consumer choice: does a price cut work better than a bigger pack or stronger claim? Or how much of a price premium can you earn by getting the messaging right?

No research method is perfect, and implicit choice is still only a proxy for what people choose in the real world. But this method gives much stronger correlations with true behaviour than either implicit associations, or traditional stated-preference survey research. Implicit choice measurement gives the best of both worlds.

Ultimately, choice is the only thing that matters for your bottom line – measure choice and you’ll be able to drive the business outcomes you want.

* Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. (1998). Measuring individual differences in implicit cognition: the implicit association test. Journal of personality and social psychology, 74(6), 1464.
** Caldwell, L. (2015). Making conjoint behavioural. International Journal of Market Research, 57(3), 495-502.

If you found this post interesting, we’d be grateful if you’d nominate us as an Innovative Supplier in the GRIT industry survey. Just click here, answer the survey (it should take 10-15 minutes) and put Irrational Agency as an innovative supplier. In return for completing the survey, GreenBook will give you early access to the report when it’s published.

Book review: The Choice Factory by Richard Shotton

book table

There are few truly universal books on behavioural science: like most of the others, this one has a particular reader in mind. Richard’s reader works in advertising, and it must be a rare advertising executive who still hasn’t heard of behavioural economics. Richard therefore heads straight into the meat of the book with little beating around the rational-agent bush. A couple of connected anecdotes start us off and we quickly get to the first of 25 chapters, each on a single bias, that make up the body of the text.

The book is very readable, and even if you already know what the fundamental attribution error, the pratfall effect and Veblen goods are, you’ll probably still enjoy the stories and quotes that illustrate them. I hadn’t heard of some of the experiments and anecdotes that Rich discusses – and he and his colleagues have carried out many of their own original tests – so even as a professional in the field there is much here that’s worthwhile.

Structuring a book around a list of biases has the advantage of user friendliness. Each chunk is self-contained and easy to get your head around; you can dip in and read a chapter or two without needing to remember a broader framework. The natural counterpart of this is that approach can feel a little shallow. If you’re already familiar with the discipline you may feel there’s not much to learn from another definition of the availability bias. And inevitably several of the “biases” are not really biases: the replication crisis and “habit” are not biases, though these chapters are as useful as any of the others. Another minor drawback of this approach: because the chapters are designed to be read individually, some of the same quotes show up more than once – ever so slightly jarring if you’re reading it all the way through.

Richard in conversation with Rory Sutherland at the launch of the book

The most useful contribution of the book is the original – and very good – set of practical tips at the end of each chapter. If you do work in advertising or marketing there will be a lot to get your teeth into. The first chapter alone gave me three or four ideas that I could see myself applying in the near future. Richard has a good understanding of the culture of advertising, and the book may well help people in ad agencies – or the advertising function of large companies – persuade their colleagues of the efficacy of behavioural principles.

Those in other fields may find less the book less directly practical, but there will probably be something to stimulate you in most chapters. And you can always get good stuff by following Richard on Twitter.

P.S. Full disclosure: Richard interviewed me when writing the book and you’ll see some of what we discussed reflected in the pricing chapters.


 Where do goals come from?

Much of decision-making psychology (and by extension behavioural economics) explores the processes by which people solve a problem or achieve a goal. Usually the papers in this field contrast the rational, expected-utility way to solve these problems with the approaches people actually use in practice.

An important question they rarely address is “Why that goal?” How is it that people choose the particular problem they want to solve, the objective to work towards? In the psychology lab, the answer is easy: the person in a white coat gives it to them. In real life, that doesn’t happen.

Answering this question is essential to developing a comprehensive theory to replace or challenge classical economics. Standard microeconomic theory has a clear, simple answer to this: we always have the same goal, maximising utility. Any other objective (finding the best job, working out how much money to save, picking what to eat, choosing a romantic partner, deciding whether to rob a convenience store – to pick at random from the typical subjects of economics papers) is a means to an end. According to the classical theorist, we choose between these different goals based on which we think will bring the highest marginal utility. Independent goals which don’t conflict with each other are pursued more-or-less simultaneously: I seek a promotion at work during the day while trying to find the ideal spouse at night, choose the best mutual fund at lunchtime and weigh up the risk and reward of the convenience store holdup before bed.

Psychologists, while rightly challenging the claim that I can simultaneously optimise across all these different life goals, don’t propose an alternative way to choose between them. My conscious problem-solving mind can only focus properly on one objective at a time, but which one?

The fast-and-frugal heuristics school gives an argument that simple heuristics are the best way to solve apparently complex problems like catching a baseball, allocating investment money or walking through a crowd, but doesn’t tell me why I want to catch the baseball or invest money in the first place. The heuristics and biases approach tells me that I am anchored on a particular rate of return for my investments but not whether I will spend this afternoon trying to beat that rate or watching football.

You could easily ignore this question and assert that sooner or later I’ll get round to dealing with most of the important problems in life, and that the real work of psychology should be focused on how I’ll tackle them. But there are plenty of counterexamples. Many people never get around to thinking about investments or savings, or not until it’s too late to do anything meaningful about them. Our success in achieving health objectives is strongly influenced by what we spend our time thinking about – unconscious eating and conscious exercise are in conflict. Status quo bias in the labour market and in consumption patterns is responsible for lots of apparently suboptimal behaviour and there’s a strong argument that the cause is a (possibly rational) lack of attention to the goal.

Here is a candidate theory of how we select our goals.

I draw inspiration from a Glöckner and Betsch paper, Modeling option and strategy choices with connectionist networks. Although this paper is within the narrow paradigm I’m critiquing – how do people solve a problem that is exogenously given to them – it contains a model we can borrow to address the broader problem. They propose that the mind answers questions by collecting data and using it to populate a network of nodes representing a model of the problem it is working on. It tests how self-consistent this model is, and if it is highly consistent it is more likely to consider the problem solved. If it is not consistent (e.g. two different answers to the question can still be true within the mental model) the mind seeks out more information to try to increase consistency. As they say:

“One of the basic ideas of Gestalt psychology (e.g., Köhler, 1947) is that the cognitive system tends automatically to minimize inconsistency between given piece of information in order to make sense of the world and to form consistent mental representations”

The unconscious (automatic) mind determines that there are two or more inconsistent ideas simultaneously held, and prompts the conscious (deliberative) systems to gather more information with which to populate the model, in order to try to resolve the conflict between them. This process continues until the mental model reaches a certain level of consistency – in effect, when it stops changing and reaches a stable representation. This representation is taken as the answer to the question.

In a very different context, John Yorke writes:

“The facts change to fit the shape, hoping to capture a greater truth than the randomness of reality can provide.”

I propose that the mind relies on a similar connectionist, associative network to choose which goals to focus on.

This network represents the actions that the decision maker could take, the consequences of those actions, and the reward that would accompany those outcomes. In any situation a person could take thousands of potential actions with tens of thousands of consequences, and it is unlikely the mind (even the highly parallel automatic system) can simultaneously evaluate all of them. Instead, a small subset of those potential actions and outcomes will be activated by sensory stimuli or familiarity: nodes representing regular, repeated actions are likely to remain active much of the time; nodes representing outcomes such as the satisfaction of hunger may be activated by biological need, and other less frequent actions or consequences may be activated by seeing or hearing messages which remind us of them.

Activation automatically spreads from node to node in this network. The network’s connections link actions to their consequences – the action of eating food links to satisfaction of hunger; saving money is linked to a higher bank balance, which in turn links to an emotional payoff from feeling secure; smoking a cigarette links to the quieting of cravings and a feeling of relief. Thus, when an action is activated a consequence will become active; and vice versa, when an outcome is activated the actions which could lead to it will also become active. If only one action-node is activated, the decision maker will take that action. If only one outcome-node is active, the decision maker will choose to pursue that outcome. At this point a goal has been set, and the well-studied processes of decision making will take over.

If nodes representing more than one potential action or outcome have been activated, the automatic system needs to keep working, until it can resolve which one to pursue. This work includes the further spreading of activation to other nodes in the network (the food-eating node could activate nodes that represent spending money and gaining weight, the hunger-satisfaction node could activate alternative actions that lead to the same outcome) which in turn may connect back to some of the same nodes, increasing their activation further. The network might “test out” particular outcomes by activating nodes that represent their second-order consequences. The activation is diluted by this point; the earlier nodes were strongly activated; these ones are weaker.

At this point a similar kind of consistency-testing to that proposed by Glöckner and Betsch comes into play. The activated, imagined actions and outcomes are tested against sensory input and knowledge about the outside world. Are these outcomes plausible? Can I actually take these actions? Are they consistent with what I believe about how the world works? If so, the activation, and the relationship between actions and outcomes, is reinforced. If not, the activation is reduced and the network keeps looking for a consistent, stable, combination of action and outcome.

Eventually, that stable set of active nodes will emerge; or perhaps two or three combinations will continue to compete for attention and plausibility. If so, again following the template of Glöckner and Betsch, the deliberative system comes into play and selects between them. The deliberative mind applies symbolic, logical or linguistic forms of reasoning and decision making instead of the connectionist, activation-driven process of the automatic mind. These symbolic processes (and the biases that can affect them) are the stuff of most decision theory, and I defer to the accumulated body of science to tell us how they work. My claim here is only about the automatic process that selects the options between which we deliberate.

The outcome emerging from this process becomes the goal we consciously seek. It will be paired with an initial action, though that action alone may not be enough to achieve the goal, in which case a planning process of some kind has to take place – again, thoroughly explored by existing decision theory.

This model tells us something about why certain goals or actions might be preferred to others. If an action is particularly salient or easy to imagine, we are more likely to focus on the outcomes that follow naturally from it. If an outcome is particularly consistent with our mental model of the world, we are more likely to take the actions that will cause that outcome. The availability heuristic, effects related to salience and attention, and confirmation bias are all natural outcomes of this emergent-goal process. For now it is a theoretical model, but it is not too hard to imagine empirical tests for it.

Of course, the outcome you choose should not only be consistent with your view of the world, but also be a rewarding one. I agree with both the classical economist and the behaviourist that reward drives us to choose outcomes, and therefore the actions that lead to them. But we clearly do not apply probabilistic, utilitarian calculations to estimate and respond to that anticipated reward, and simple behavioural conditioning is not enough to explain the rich, complex actions and plans we make. In the next post in this series I will suggest a more plausible way to think about reward, how it motivates us to act, and what this means for how we experience life.

On the identity and methods of behavioural economics

The FT has a very good article from Tim Harford today, surveying behavioural economics and asking some important questions about it. People within a field can be so immersed in their unconscious assumptions and practices that it takes an outsider to point out some of the questions they are not asking.

Tim says:

The past decade has been a triumph for behavioural economics…[which] is one of the hottest ideas in public policy….Yet, as with any success story, the backlash has begun. Critics argue that the field is overhyped, trivial, unreliable, a smokescreen for bad policy, an intellectual dead-end – or possibly all of the above. Is behavioural economics doomed to reflect the limitations of its intellectual parents, psychology and economics? Or can it build on their strengths and offer a powerful set of tools for policy makers and academics alike?

Quite. That, of course, is a journalistic question – not one intended to be answered within the article, but designed to provoke the prospect of a good ding-song. But the substantive points come soon. Note that Tim, writing for a generalist FT-reading audience, chooses to address his article to public policy so it doesn’t look like an abstruse argument between academics. But actually it’s about the effectiveness of BE, and economics in general, as a tool at all. Public policy, private decisions, how businesses operate – all can be informed by whatever economic theory we believe in.

…there is something unnerving about a discipline in which our discoveries about the past do not easily generalise to the future…This patchwork of sometimes-fragile psychological results hardly invalidates the whole field but complicates the business of making practical policy.

Indeed – and it divides the field, into those who believe a (more) unified theory is available, and those who believe rational choice is still the main theory available and that behavioural results are only meaningful in relation to that.

The line between behavioural economics and psychology can get a little blurred. Behavioural economics is based on the traditional “neoclassical” model of human behaviour used by economists. This essentially mathematical model says human decisions can usefully be modelled as though our choices were the outcome of solving differential equations. Add psychology into the mix – for example, Kahneman’s insight (with the late Amos Tversky) that we treat the possibility of a loss differently from the way we treat the possibility of a gain – and the task of the behavioural economist is to incorporate such ideas without losing the mathematically-solvable nature of the model.

Consider the example of, say, improving energy efficiency. A psychologist might point out that consumers are impatient, poorly-informed and easily swayed by what their neighbours are doing. It’s the job of the behavioural economist to work out how energy markets might work under such conditions, and what effects we might expect if we introduced policies such as a tax on domestic heating or a subsidy for insulation.

And the problem today is that, without a clear theory, behavioural economists can’t work that out. All they can do is suggest various effects that might happen, and design an experiment to test them. Nothing wrong with that, but it’s a bit ad hoc.

The most well-known critique of behavioural economics comes from a psychologist, Gerd Gigerenzer of the Max Planck Institute for Human Development. Gigerenzer argues that it is pointless to keep adding frills to a mathematical account of human behaviour that, in the end, has nothing to do with real cognitive processes.

David Laibson, a behavioural economist at Harvard…concedes that Gigerenzer has a point but adds: “Gerd’s models of heuristic decision-making are great in the specific domains for which they are designed but they are not general models of behaviour.” In other words, you’re not going to be able to use them to figure out how people should, or do, budget for Christmas or nurse their credit card limit through a spell of joblessness.

We come back again to the need for a general theory, and one of behavioural economics’ regular combatants agrees:

For some economists, though, behavioural economics has already conceded too much to the patchwork of psychology. David K Levine, an economist at Washington University in St Louis, and author of Is Behavioral Economics Doomed? (2012), says: “There is a tendency to propose some new theory to explain each new fact. The world doesn’t need a thousand different theories to explain a thousand different facts. At some point there needs to be a discipline of trying to explain many facts with one theory.”

The challenge for behavioural economics is to elaborate on the neoclassical model to deliver psychological realism without collapsing into a mess of special cases…The question is, how many special cases can behavioural economics sustain before it becomes arbitrary and unwieldy? Not more than one or two at a time, says Kahneman.

Thaler says: “…if you want one unifying theory of economic behaviour, you won’t do better than the neoclassical model, which is not particularly good”

It seems that Kahneman and Thaler actually agree with Levine in a way; all three doubt that behavioural economics can crystallise into a single theory, though only Levine thinks this is a serious problem.

George Loewenstein and Peter Ubel wrote in The New York Times that “behavioural economics is being used as a political expedient, allowing policy makers to avoid painful but more effective solutions rooted in traditional economics.”

This point is different but important: if policymakers expect behavioural economics to be a substitute for regular economics they’ll be disappointed. The two are complementary, and the most important policy contribution of BE may be to tell us which economic incentives will have the biggest impact, and which will have unwanted side-effects, rather than to obviate the need for traditional incentives altogether.

Should we be trying for something more ambitious than behavioural economics? “I don’t know if we know enough yet to be more ambitious,” says Kahneman.

That’s a provocative point. Yet it acknowledges that whatever field eventually manages to incorporate both traditional and behavioural economics may have to be called something different.

Laibson says behavioural economics has only just begun to extend its influence over public policy. “The glass is only five per cent full but there’s no reason to believe the glass isn’t going to completely fill up.

I and many readers of this blog will probably be with Laibson on this point. But perhaps without a new approach, behavioural policy is going to run more and more often into the wall of adhockery – the lack of general theories making us redo things from the ground up in each new situation.

Tim isn’t the only person to write about this recently. For a contrary word, try Chris Dillow’s comment, which makes some good challenges from his usual half-libertarian, half-Marxist point of view.

Then, here are some links and thoughts from Diane Coyle, including “Is behavioural economics the past or the future” by Chris House. Diane hones down one of Tim’s questions into Kao and Velupillai’s distinction between classical and modern behavioural economics: modern assumes people are (biased) optimisers, while classical assumes they are satisficers. This is the same distinction drawn by Gerd Gigerenzer, though his research looks at a broader range of decision-making heuristics, of which satisficing is just one. Diane asks, effectively: is the best mathematical approach to tweak the models of maximisation, or to try to build a new behavioural economics based on heuristics?

Chris House’s post says:

…in 2007-2008 we were again told that behavioral economics would finally come into full bloom. It didn’t happen though. The wave of behavioralists never came.

While this isn’t true in psychology or behavioural policy and marketing – all thriving and fast-growing fields – it is true of economics. My experience is that many new economics undergraduates or entrants to economics PhD programs are intrigued by behavioural ideas, they are often guided by supervisors into more traditional areas where it is easier to define a research question that is going to produce safe, publishable papers. Barkley Rosser, commenting on House’s post, mentions the new journal Review Of Behavioural Economics, which along with other emerging initiatives may help to change this.

Otherwise, Chris raises that same point:

Behavioral economics won’t get very far if it ends up being just a pile of “quirks.” Are these anomalies merely imperfections in a system which is largely characterized by rational self-interest or is there something deeper at play? …if behavioral is to somehow fulfill its earlier promise then there has to be some transcendent principle or insight which comes from behavioral economics that we can use to understand the world.

Then there is the David Levine paper that Tim mentions, “Is Behavioural Economics Doomed?“. In this, Levine says (among many other interesting things!):

For most decisions of interest to economists these external helpers [computers, paper and pencil etc] play a critical role – and no doubt lead to a higher level of rationality in decision making than if we had to make all decisions on the fly in our heads.

What a brave claim! Do we really rule out from the realm of economically interesting decisions all consumer purchases, the consumer’s intuitive feelings about how safe they feel with a certain amount of savings in the bank, and all the decisions about cars, houses and jobs that – although someone might sit and think about them for a while – still involve a big chunk of emotion?

Actually, there is no need to throw out these kinds of decisions in order to meet Levine’s key challenge of “trying to explain many facts with one theory.” He asserts that mainstream economics is already successful at explaining many facts. But perhaps, when he discards all those “uninteresting” decisions it isn’t so hard to explain what’s left. Indeed, it’s those “uninteresting” decisions which classical economics does struggle with, and only behavioural economics can illuminate. Contrary to Levine, I am convinced that these decisions actually make up the majority of important economic events. But I do recognise his critique – echoed by Tim and implicitly by Velupillai and Gigerenzer: that behavioural economics does not offer a full theory to replace that of mainstream economics. However, it has given us good empirical evidence which we could build a theory on.

As well as defining away a large portion of the economy as “not interesting”, Levine also co-opts some of the parts that he does consider interesting, saying they are already handled by mainstream economics: notably the subject of learning. Non-behavioural economists have considered consumers’ imperfect ability to learn the preferences of other consumers, or the rules of the “game” they are playing, as a factor in non-optimal decisions. But psychologists know much more about exactly how people learn than economists do – so a successful model of learning as part of economics can only be built with an openness to psychological research. Where Levine may be right is that behavioural economics will not replace mainstream economics, but instead the two fields will merge – with the behaviour of consumers predicted by a combination of objective economic, and subjective psychological, factors.

Anyway, arguments over the boundaries of disciplines are rarely productive: I don’t really mind if Levine considers a model to be behavioural or not, as long as the model advances the cause of making successful predictions.

The real questions are: does standard economics fail to address some important problems? How good is behavioural economics at addressing them instead? And does behavioural economics need a unified approach in order to address them?

Most of the people mentioned above have different answers to those questions:

  • Levine wants a unified theory – but think we have to exclude many types of “uninteresting” decision in order to get one.
  • Kahneman and Thaler want different theories for several different areas – but those incompatible theories will not be able to deal with the many boundaries where different aspects of economics interact with each other.
  • The classical economists already have a unified theory – but there are many things it can’t explain.
  • Gigerenzer has a philosophy – but no overall theory. And I’m not sure if he expects or really wants a unifying theory any more than Kahneman does (this may be one of the few things they agree on).

[Update: much of this debate was anticipated in this Werner Guth paper of 2007]

My view, which I think concurs with Laibson’s: a single broader theory is possible. I think we’ve hit a theoretical dead end with the traditional maximising agent, so it will have to be based on more psychologically realistic foundations, such as those of Velupillai, Gigerenzer or Bettman, Payne & Johnson. To achieve this, we need to carefully choose the right elements to build into our model of decision-making in a way, so that it can make useful predictions of how those elements might operate. I have a paper coming out later this year which suggests one direction towards this.

Consumers are irrational – a myth

[Transcript of my talk from NewMR: Explode A Myth. The accompanying slide is here and you can listen to the recording of the talk and Q&A here]


If you’re the type of person who tunes into NewMR events, you can hardly have missed the library of behavioural economics books that have poured out of the typewriters of various academics in the last few years. Nudge, Thinking Fast and Slow, and Predictably Irrational have hammered into us the idea that we’re all irrational. We don’t make choices in our best interests and we’re always making mistakes. We don’t even know what we want. How can we be trusted to make our own decisions in life! We’re idiots!

Even the name of my own agency, the Irrational Agency, plays up this idea.

Today I’m going to tell you all this is nonsense. Consumers aren’t irrational at all. Not that they’re rational either…and thank goodness for that.

The fact is: if we really did behave the way economists say we should, we’d all be dead. The only people who make decisions in the way economists tell them to are people with specific types of brain damage. These people cannot use their intuition or feelings to make choices – they have to calculate all the costs and benefits of every decision they make. As a result, they can’t make any practical decisions in life: not even whether to get out of bed in the morning. They end up unable to live outside of an institution.

We’ve all probably experienced this on a small scale – they call it “analysis paralysis”. Any time you’ve found it really hard to make a decision between two options, and you’ve put it off and thought it over and worked it out and still been no closer to deciding – you’ve been in this mode. And eventually you probably picked one path or the other, and chances are it worked out OK.

Imagine you weren’t in a comfortable office (or living room) in a modern economy, but instead in a threatening environment, with only just enough food to survive on, and a wide variety of creatures waiting to kill you. In other words, imagine you were your great-great-great-great-grandmother 300 generations back. Now imagine you stopped to calculate and weigh up the potential costs, benefits and associated probabilities of every decision before making a choice. It wouldn’t be long before you ended up as lion food.

Any “rational” person – rational in the sense that economists would like us to be – would quickly be eaten out of the gene pool. Only those who are willing to make decisions quickly, find shortcuts and do what’s “good enough” would have survived. And those people are your ancestors, and mine, and the ancestors of the people who buy our products.

This isn’t just a matter of whether our brains are big enough, or if we just happen to be a bit too stupid and slow to calculate the right answers. It’s mathematically impossible for any imaginable creature to calculate all the pros and cons and predict the future consequences of their actions and weigh them up and make decisions, every minute or every second of the day. If you dedicated all the computers in the world, programmed by the smartest programmers, helped by all the people in the world and gave them a hundred years to calculate in exact terms the best sandwich for you to order today: they wouldn’t even be able to do that. There is so much information in the world; it’s such a complex place, and everything influences everything else; that nothing and nobody could process it all, even if given aeons to do it in. And certainly not in time to dodge that lion. It’s simply not an option.

So instead of calculating everything, we use heuristics. Heuristics, contrary to the impression given in some books, are not flawed or error-prone ways of thinking. They are brilliantly designed techniques to let you make a good-enough decision in less than a second, 99.9% of the time, without even being aware you’re doing it. The only reason we can navigate the world in a practical way is because we have these heuristics to make our decisions for us.

The heuristics are what let us choose products we know we’ll like. They’re what help us to drive to work safely, or talk to people without having to calculate and plan out every individual sound that every word will be made up of. It’s heuristics that tell us what brands we can trust and let us know we’re not being ripped off by a high price.

And that one time in a thousand that the heuristics don’t give us the right answer? Well that’s a better hit rate than any computer algorithm you can think of. Sure, algorithmic step-by-step calculations can give us the right answer to a small number of carefully defined mathematical problems: and there’s no harm in learning those situations so you know when not to use a heuristic. I wouldn’t recommend using a heuristic to calculate your tax bill. But don’t start believing the heuristics are wrong, or bad, or useless. And above all, don’t let them tell you consumers are irrational. Not even someone who runs an agency named after irrationality. (Sometimes I wish we’d just picked a nice acronym like GFK or TNS. But there you go.)

Don’t let your fantasy of that unattainable, perfect calculating machine be the enemy of recognising what a brilliant object your brain is, to have found its way past those lions on the savannah, through all the dangers of history, and past the supermarket till without buying the National Enquirer.

Consumers aren’t irrational, but you might be irrational if you think they are.

Can George Loewenstein’s curiosity theory finally make behavioural economics work in MR?

Behavioural economics keeps coming up in market research conversations. Surprisingly, I’ve never heard in that context one of the most important names in the discipline: George Loewenstein. Loewenstein’s work provides deep psychological insight into how consumers choose and value the products they buy. And one of his long-term research projects might have unlocked a key secret of why people want what they want: the theory of curiosity.

He is not one of the best known researchers among the general public: unlike Dan Ariely, Richard Thaler or Daniel Kahneman, he hasn’t written a book for popular consumption. But the scope of his writing and his research is as broad and rich as any of theirs. Ariely is well-known for clever experiments; Thaler for policy applications and experiments in behavioural finance; and Kahneman (apart from that Nobel prize) for coming up with some key simplifying models of thinking.

Loewenstein’s research has its own core message: people’s preferences are not straightforward. Despite the claims of standard economics, people seek out information or risk or meaning in a fundamentally different way to how they seek out apples or BMWs. This insight is probably more important for marketing than any of the better known behavioural economics discoveries. Since market research is traditionally focused on discovering people’s preferences (or as we call it in MR, liking), Loewenstein’s work is highly relevant.

Simplistic marketing, like simplistic economics, assumes that we can explain all consumer purchases in a simple way:

  • people have fixed desires
  • they know what those desires are and how strong they are
  • once they become aware of products that can satisfy their desires, they buy those products

In this model, marketing is just a matter of making people aware of your products in a memorable way; and market research is just a matter of finding out what their desires are.

Loewenstein’s research is all about the complex ways in which the things we want do not work like this simple model. He questions the relationship between our underlying long-term preferences and the product choices we make. He shows that it is not enough simply to understand what people like. To gain useful brand insight, we must also understand the process of how their subconscious minds construct choices from those likes, from their knowledge and from emotions, in order to finally pick a product.

He looks at how we trade off pleasure today against pain tomorrow, how our mood influences the choices we make, and how much we know about our own future behaviour. He looks at how our preferences and behaviour are controlled by ideas of fairness, how much we actually know about the attributes of the products we buy, and how much we enjoy the anticipation of consuming something, not just the actual experience of it.

His writing about all of this is engaging and readable, with plenty of fun examples. One of his experiments asks how much you would pay for a kiss from your favourite movie star – now, or in ten years time. Another paper examines why people risk their lives to climb Mount Everest while hating every minute of the physical experience of it. If you want the rigorous mathematical theory behind his ideas, that is there too – but you can skip it and just enjoy the writing.

For me, two particular subjects in his work are relevant to MR:

One of these should be required reading for all market researchers. His paper “A Bias in the Prediction of Tastes” tackles what is perhaps the most important question in MR: do consumers know what they want? Loewenstein demonstrates that they do not; and also that their mistakes are consistent (they underestimate how much they will value a product when they first possess it, but they don’t realise how much they will adapt to pleasure or pain over time). The paper poses a series of important questions that market researchers should ask about the accuracy of consumer responses, such as: Can regular buyers of a product understand the attitudes of people who don’t buy it? Do people realise how much their tastes will change in future? Two followup papers are particularly relevant to researchers in the food industry and other fmcg categories.

The other subject is one Loewenstein has been developing throughout his career. He has built a model of curiosity from a behavioural economics perspective. Why are people curious; why do we so desperately want to know things that will often make us unhappy when we know them; why do we so easily lose interest in something we were once intensely curious about, even without finding out the answer; and why do we care about information before knowing it (who killed JR? What does that person in front of me on the pavement look like?) that we barely give a toss about after we find out?

Curiosity in itself is an important subject. It improves the effectiveness of advertising, explains a lot about consumer behaviour, and has been explored by philosophers as one of the basic aspects of being human.

More fundamentally, Loewenstein’s answer to the curiosity question – that whenever we are conscious of an information gap, we instinctively want to fill it – provides the basis for a powerful general model of consumer behaviour.

Instead of seeing curiosity as a passion or a personality trait, he suggests that it’s merely one example of how we instinctively want to close perceived gaps in our environment. If this same drive applies to other gaps in perception, it could explain our desire for any product and the strategies we follow to satisfy that appetite.

This also implies a difference between wanting and liking something – with curiosity we may want to close that information gap even if we know we won’t like the outcome. What if the same applies to other products?

Richard Thaler may be best at understanding finance, and Dan Ariely at designing experiments, but I’d rate George Loewenstein as the top behavioural economist for illuminating the psychology of consumer behaviour in general. If you’d like to understand, and perhaps influence, consumer behaviour, it’s his papers you should be reading. Most of the papers I’ve referred to, and many more, are collected in his highly recommended book Exotic Preferences: Behavioural Economics and Human Motivation.