First an Introduction – Sarah Sloan.
What Is Cognitive Economics? Understanding the World Through New Types of Data.
Economics isn’t just a number’s game. Human irrationality is so intrinsically tied up in the human need to rationalize that financial decisions are often made when our conscious brains are held for ransom by our emotions. Because of this, the study of money has specific branches devoted to the study of Homo sapiens interacting with money. The dismal science has genetic, experimental, and neurological branches.
Then there is Cognitive Economics, the economics of what is going on in people’s minds.
Cognitive economics is characterized by its unique use of data. Rather than skimming from markets or hooking up sensors to subjects, cognitive economists rely on surveys, interviews, and attitudes. Still, the internal dynamics of cognitive economics still hinges more on the numbers-side of economics, rather than psychology. This area of study can help researchers understand what people are looking for, whether it’s a successful retirement or just general happiness and how policy can shape or reshape that search.
Inverse spoke with Miles Kimball, professor of economics and survey research at the University of Michigan, about his chosen field. A research affiliate at the Populations Studies Center and research associate at the National Bureau of Economic Research, Kimball sometimes moonlights as a columnist for Quartz. He spends a lot of time thinking about the role of cognition in our internal and financial systems.
This interview has been edited and condensed, but not too much because Kimball is super interesting.
Why is this field of study called cognitive economics and how is it an analogy to cognitive psychology?
The definition that I came up with is that cognitive economics is what is in people’s minds. This is basically a branch of behavioral economics.
Behavioral economics is a very broad area of studying all the things that shouldn’t happen according to traditional economic theory.
Economists are trained to identify when someone is doing something strange — their behavior seems confused, they don’t quite understand the situation. The goal of the economist is to talk about people’s motivations, what they’re trying to accomplish; their preferences.
Historically, the first thing that a behavioral economist did was try to document the things people do when their actions look strange from the standpoint of standard economic theory. My way, as a cognitive economist, is to look at the reasons why they have these preferences. The first category of explanation is that standard economics is fine, but there may be something deeper going on that you just didn’t see, even though what you’re doing makes perfect sense according to standard economic theory. Like any scientific discipline, one of the jobs of economics is to understand how the world works. Trying to understand why people do what they do, how society fits together, and how that fits into a policy point of view — economics has taken on the job of helping people get more of what they want. And we can use data to actually get a good idea of what that is. For example, a goal would be to use this data to influence public policy so that people understand when to claim their social security benefits.
So, is the job of cognitive economics, in part, to figure out what people want and then try to help them achieve that?
That’s certainly an element. If people don’t know something — what economists call imperfect information — we now have models that are very good with dealing with that imperfect information processing. There are certainly many choices in life that are really tough, especially in the financial market, that you may not figure out correctly. Deception doesn’t necessarily rely on lying — you can reveal everything in the fine print and still deceive people. How many of us have clicked yes on user agreements without understanding the real cost of what’s happening? Certain institutions of the government, like the Consumer Financial Protection Bureau, incorporates cognitive economics to deliver good results to people who may not have a handle on the complications of financial products.
It’s interesting because the image that people have of companies is that they have tricky products, and from there are able to make big profits off of people. It’s actually trickier than that. It is possible to make profits by tricking people, which will cause more firms to make profits in the industry. At the end of the day what happens is that people who are smarter than average get these products cheaper, and the people who are easily tricked are paying through the nose. You see this with credit card grace periods. People who are really smart about how they use their credit card actually get the zero interest loans. But this is at the expense of the people who go in thinking they’re going to be sensible using their credit card, but then don’t realize how many things are going to come up, that are going to make that hard to do. That’s a simple example, but there are plenty more you can go through! Companies may come off like they’re just trying to make a profit by tricking people, but interestingly enough it ends up being less smart people subsidizing smart people.
In what ways is cognitive economics different than other fields of economic research?
Different branches of economics have different characteristic data types. There is a field called neuroeconomics where you do brain scans on people. You have them make economic decisions, and you utilize scull caps that will record brain activity with EEGs. Somewhat more modestly, cognitive economics is a survey. It can be combined with lab data and neuroeconomics, but its bread and butter is survey data. You ask people what they’re thinking, what they’re feeling, and you have access to their minds by asking.
So surveys are the key?
Well, cognitive economics is all about humans! It’s a branch of behavioral economics, and economics itself is really on the border with psychology. In fact, some people wanted to call this “psychology and economics” but I think cognitive economics is more descriptive. I don’t want to downplay the influence of psychology in economics, I’m just saying that if somehow economists have never read psychology literature, behavioral economics would have still emerged
How do you conduct your research?
By designing surveys and analyzing the answers with a team. Dan Benjamin and I started this initiative and we just finished designing a survey about how people rate presidential candidates and use a scale in a sophisticated way. The idea is to compare whether you would rather have, say, Bernie Sanders become president for sure or wake up on election day with an election between Hillary Clinton and Donald Trump, in which either has a real chance to win.
We work very hard to make the question understandable — it’s a balancing act. On the one hand we have an economic concept we want to get. This is called an expected utility rating. We’re trying to get a rating of exactly, in between your best and worst candidate, where the other candidates are. This is economic theory in a powerful way and we can’t compromise on that. You could maybe think of a question that is worded in an easier way, but then we wouldn’t have an economic concept at the end. Trying to get survey questions that have a certain precision to them is quite a trick.
If you’re only surveying people about what they did or if you’re using data from firms about what they bought, that’s considered standard economics, not cognitive economics. But if you’re asking them about what they’re thinking about, what they want, then it gets to be cognitive economics. We sometimes end up working on one question for a week.
On your blog you have one section titled “So You Want to Save the World.” What role do you think cognitive economics has in making society a more fruitful place for everyone?
The initiative that I mentioned earlier is the Wellbeing Measurement Initiative. We view the economics of happiness as a part of cognitive economics. When asking about what is in people’s minds, it’s not just the math they’re doing, but their feelings while they do this. There has been quite a big push by many governments to essentially have a national well-being measurement. There’s wide recognition that gross domestic product is inadequate in representing the things that people care about. We have to incorporate things like people’s relationship with their family, their romantic relationships, the want to have meaning in life — we could go on and on. For these projects, we sit down and try to design survey questions for everything we can think of that is somewhat at the abstract level. Right now we have a list of 120 — there are a lot of things that people want!
In regard to what governments have done so far, the United Kingdom, for example, has questions that look at how happy you are, how satisfied you are with your life, how anxious you have been, do you feel your life is worthwhile, etcetera. They’ve collected a lot of data on that, but we don’t think those few questions are enough to cover the waterfront. We’re hoping that 120 will do an okay job at measuring how well somebody is.
People look at how money is spent, because money creates data. But this is only one element — that score card needs to include factors like if the person feels they’re doing better than last year; how they feel affected by different government policies. You also need to do randomized trials and try different options to see what makes people feel better off.
You’ve got to face the facts that most government policies, if you have an A/B001FSJCOQ test, doing it one way is going to make it better for some people, and worse for other people — especially when you think about something like taxes. There are only a few ways of making everybody better off, and even then you’ll probably have a few individuals who end up worse off. However, things do get better in society when individuals have statistical agency — you start identifying the subtle ways that can make everybody better off.
Cognitive Economics: How Self-Organization and Collective Intelligence Works – Geoff Mulgan.
The study of self-organizing groups points toward what could be called a cognitive economics.
The organization of thought as a series of nested loops, each encompassing the others, is a general phenomenon. It can be found in the ways in which intelligence is organized in our bodies and within the groups we’re part of. The efficient deployment of energy to intelligence depends on a similar logical hierarchy that takes us from the automatic and mindless (which require little energy) to the intensely mindful (which require a lot). With each step up from raw data, through information to knowledge, judgment, and wisdom, quantity is more integrated with qualitative judgment, intelligence becomes less routine and harder to automate, and crucially, the nature of thought becomes less universal and more context bound. With each step up the ladder, more energy and labor are required.
What Is the Organization in Self-Organization?
Seeing large-scale thought through this lens provides useful insights into the idea of self-organization. The popularity of this idea reflects the twentieth- century experience of the limits of centralized, hierarchical organizations— even if the world is still dominated by them, from Walmart and Google to the People’s Liberation Army and Indian Railways. We know that a central intelligence simply can’t know enough, or respond enough, to plan and manage large, complex systems.
Widely distributed networks offer an alternative. As with the Internet, each link or node can act autonomously, and each part of the network can be a fractal, self-similar on multiple scales.
There are obvious parallels in human systems. The term stigmergy has been coined to describe the ways in which communities—such as Wikipedia editors or open-software programmers—pass tasks around in the form of challenges until they find a volunteer, a clear example of a community organizing itself without the need for hierarchy.
Friedrich Hayek gave eloquent descriptions of the virtues of self- organization, and counterposed the distributed wisdom of the network to the centralized and hierarchical wisdom of science or the state: “It is almost heresy to suggest that scientific knowledge is not the sum of all knowledge. But there is a body of very important but unorganized knowledge: the knowledge of the particular circumstances of time and place. Practically everyone has some advantage over all others because he possesses unique information of which beneficial use might be made, but of which use can be made only if the decisions depending on it are left to him or are made with his . . . cooperation.”
More recently, Frederick Laloux wrote the following lines, capturing a widely held conventional wisdom: “Life in all its evolutionary wisdom, manages ecosystems of unfathomable beauty, ever evolving towards wholeness, complexity and consciousness. Change in nature happens everywhere, all the time, in a self-organizing urge that comes from every cell and every organism, with no need for central command and control to give orders or pull levers.” Here we find the twenty-first- century version of the late nineteenth-century notion of the élan vital, a mystical property to be found in all things.
It’s an appealing view. But self-organization is not an altogether-coherent concept and has often turned out to be misleading as a guide to collective intelligence. It obscures the work involved in organization and in particular the hard work involved in high-dimensional choices. If you look in detail at any real example—from the family camping trip to the operation of the Internet, open-source software to everyday markets, these are only self-organizing if you look from far away. Look more closely and different patterns emerge. You quickly find some key shapers—like the designers of underlying protocols, or the people setting the rules for trading. There are certainly some patterns of emergence. Many ideas may be tried and tested before only a few successful ones survive and spread. To put it in the terms of network science, the most useful links survive and are reinforced; the less useful ones wither. The community decides collectively which ones are useful. Yet on closer inspection, there turn out to be concentrations of power and influence even in the most decentralized communities, and when there’s a crisis, networks tend to create temporary hierarchies—or at least the successful ones do—to speed up decision making. As I will show, almost all lasting examples of social coordination combine some elements of hierarchy, solidarity, and individualism.
From a sufficient distance, almost anything can appear self-organizing, as variations blur into bigger patterns. But from close-up, what is apparent is the degree of labor, choice, and chance that determines the difference between success and failure. The self-organization in any network turns out to be more precisely a distribution of degrees of organization.
The more detailed study of apparently self-organizing groups points toward what could be called a cognitive economics: the view of thought as involving inputs and outputs, costs and trade-offs. This perspective is now familiar in the evolutionary analysis of the human brain that has studied how the advantages of an energy-hungry brain, which uses a quarter of all energy compared to a tenth in most other species, outweighed the costs (including the costs of a prolonged childhood, as children are born long before they’re ready to survive on their own, partly an effect of their large head size).
Within a group or organization, similar economic considerations play their part. Too much thought, or too much of the wrong kind of thought, can be costly. A tribe that sits around dreaming up ever more elaborate myths may be easy pickings for a neighboring one more focused on making spears. A city made up only of monks and theologians will be too. A company transfixed by endless strategy reviews will be beaten in the marketplace by another business focused on making a better product.
Every thought means another thought is unthought. So we need to understand intelligence as bounded by constraints. Cognition, memory, and imagination depend on scarce resources. They can be grown through use and exercise, and amplified by technologies. But they are never limitless.
This is apparent in chaotic or impoverished lives, where people simply have little spare mental energy beyond what’s needed for survival. As a result, they often make worse choices (with IQ falling by well over ten points during periods of intense stress—one of the less obvious costs of poverty). But all of us in daily life also have to decide how much effort to devote to different tasks—more for shopping or your job; more time finding the ideal spouse, career or holiday, frequently with options disappearing the longer you take.
So we benefit from some types of decision becoming automatic and energy free, and using what Kahneman called System 1 and 2. Walking, eating, and driving are examples that over time become automatic. With the passage of time, we pass many more skills from the difficult to the easy by internalizing them. We think without thinking—how and when to breathe, instinctive responses to danger, or actions learned in childhood like how to swim. We become more automatically good at playing a tune on the piano, kicking a football, or riding a bicycle. Learning is hard work, but once we’ve learned the skill, we can do these things without much thought. There are parallels for organizations that struggle to develop new norms and heuristics that then become almost automatic—or literally so when supported by algorithms. This is why so much effort is put into induction, training, and inculcating a standardized method.
Life feels manageable when there is a rough balance between cognitive capacity and cognitive tasks. We can cope if both grow in tandem. But if the tasks outgrow the capacity, we feel incapable. Similarly, we’re in balance if the resources we devote to thinking are proportionate to the environment we’re in. The brain takes energy that would otherwise be used for physical tasks like moving around. In some cases, evolution must have gone too far and produced highly intelligent people who were too weak to cope with the threats they faced. What counts as proportionate depends on the nature of the tasks and especially how much time is a constraint. Some kinds of thought require a lot of time, while others can be instantaneous. Flying aircraft, fighting battles and responding to attack, and flash trading with automated algorithmic responses are all examples of quick thought. They work because they have relatively few variables or dimensions, and some simple principles can govern responses.
Compare personal therapy to work out how to change your life, a multistakeholder strategy around a new mine being built in an area lived in by aboriginal nomads, or the creation of a new genre of music. All these require by their nature a lot of time; they are complex and multilayered. They call out for many options to be explored, before people can feel as well as logically determine which one should be chosen.
These are much more costly exercises in intelligence. But they happen because of their value and because the costs of not doing them are higher.
Here we see a more common pattern. The more dimensional any choice is, the more work is needed to think it through. If it is cognitively multidimensional, we may need many people and more disciplines to help us toward a viable solution. If it is socially dimensional, then there is no avoiding a good deal of talk, debate, and argument on the way to a solution that will be supported. And if the choice involves long feedback loops, where results come long after actions have been taken, there is the hard labor of observing what actually happens and distilling conclusions. The more dimensional the choice in these senses, the greater the investment of time and cognitive energy needed to make successful decisions.
Again, it is possible to overshoot: to analyze a problem too much or from too many angles, bring too many people into the conversation, or wait too long for perfect data and feedback rather than relying on rough-and-ready quicker proxies. All organizations struggle to find a good enough balance between their allocation of cognitive resources and the pressures of the environment they’re in. But the long-term trend of more complex societies is to require ever more mediation and intellectual labor of this kind.
This variety in types of intelligence, the costs they incur, and the value they generate (or preserve) gives some pointers to what a more developed cognitive economics might look like. It would have to go far beyond the simple frames of transaction costs, or traditional comparisons of hierarchies, markets, and networks. It would analyze the resources devoted to different components of intelligence and different ways of managing them—showing some of the trade-offs (for example, between algorithmic and human decision making) and how these might vary according to the environment. More complex and fast-changing environments would tend to require more investment in cognition. It would also analyze how organizations change shape in moments of crisis—for instance, moving to more explicit hierarchy, with less time to consult or discuss, or investing more in creativity in response to a fast-changing environment.
Economics has made significant progress in understanding the costs of finding information, such as in Herbert Simon’s theories of “satisficing,” which describe how we seek enough information to make a good enough decision. But it has surprisingly thin theories for understanding the costs of thought. Decision making is treated largely as an informational activity, not a cognitive one (though greater attention to concepts such as “organization capital” is a move in the right direction).
A more developed cognitive economics would also have to map the ways in which intelligence is embodied in things—the design of objects, cars, and planes—and in systems—water, telecommunications, and transport systems—in ways that save us the trouble of having to think.
It would need to address some of the surprising patterns of collective intelligence in the present, too, many of which run directly counter to conventional wisdom. For example, organizations and individuals appear to be investing a higher, not lower, proportion of their wealth and income in the management of intelligence in all its forms, particularly those operating in competitive environments. Digital technologies disguise this effect because they have dramatically lowered the costs of processing and memory. But this rising proportion of spending appears close to an iron law, and may be a hallmark of more advanced societies and economies. Much of the spending helps to orchestrate the three dimensions of collective intelligence: the social (handling multiple relationships), cognitive (handling multiple types of information and knowledge), and temporal (tracking the links between actions and results).
A related tendency is toward a more complex division of labor to organize advanced forms of collective intelligence. More specialized roles are emerging around memory, observation, analysis, creativity, or judgment, some with new names like SEO management or data mining. Again, this effect has been disguised by trends that appear to make it easier for anyone to be a pioneer and for teenagers to succeed at creating hugely wealthy new companies. Linked to this is a continuing growth in the numbers of intermediaries helping to find meaning in data or link useful knowledge to potential users. This trend has been disguised by the much-vaunted trends toward disintermediation that have cut out a traditional group of middlepersons, from travel agencies to bookshops. But another near iron law of recent decades—the rising share in employment of intermediary roles, and the related rise of megacompanies based on intermediary platforms such as Amazon or Airbnb—shows no signs of stopping. In each case, there appear to be higher returns to investment in tools for intelligence.
A cognitive economics might also illuminate some of the debates under way in education, as education systems grapple with how to prepare young people for a world and labor market full of smart machines able to perform many more mundane jobs. Schools have not yet adopted Jerome Bruner’s argument that the primary role of education is to “prepare students for the unforeseeable future.” Most prefer the transmission of knowledge—and in some cases rightly so, because many jobs do require deep pools of knowledge. But some education systems are concentrating more on generic abilities to learn, collaborate, and create alongside the transmission of knowledge, in part because the costs of acquiring these traits later on are much higher than the costs of accessing knowledge. Th traits generally associated with innovation—high cognitive ability, high levels of task commitment, and high creativity—which were once thought to be the preserve of a small minority, may also be the ones needed in much higher proportions in groups seeking to be collectively intelligent.
An even more ambitious goal for cognitive economics would be to unravel one of the paradoxes that strikes anyone looking at creativity and the advance of knowledge. On the one hand, all ideas, information, and thoughts can be seen as expressions of a collective culture that finds vehicles—people or places that are ready to provide fertile soil for thoughts to ripen. This is why such similar ideas or inventions flower in many places at the same time. It is why, too, every genius who, seen from afar, appears wholly unique looks less exceptional when seen in the dense context of their time, surrounded by others with parallel ideas and methods. Viewed in this way, it is as odd to call the individual the sole author of their ideas as it is to credit the seed for the wonders of the flowers it produces. That some upbringings, places, and institutions make people far more creative and intelligent than others proves the absurdity of ascribing intelligence solely to genes or individual attributes.
But to stop there is also untenable. All thought requires work—a commitment of energy and time that might otherwise have been spent growing crops, raising children, or having a drink with friends. Anyone can choose whether to do that work or not, where to strike the balance between activity and inertia, engagement and indolence. So thought is always both collective and individual, both a manifestation of a wider network and something unique, both an emergent property of groups and a conscious choice by some individuals to devote their scarce time and resources. The interesting questions then center on how to understand the conditions for thought. How does any society or organization make it easier for individuals to be effective vehicles for thought, to reduce the costs and increase the benefits? Or to put it in noneconomic language, how can the collective sing through the individual, and vice versa?
The current state of understanding these dynamics is limited. We know something about clusters and milieus for innovation and thought. It’s clearly possible for the creative and intellectual capability of a place to grow quickly, and using a combination of geography, sociology, and economics, it is easy to describe the transformation of, say, Silicon Valley, Estonia, or Taiwan. Yet there are few reliable hypotheses that can make predictions, and many of the claims made in this area—for example, about what causes creativity—have not stood up to rigorous analysis. For now, this is a field with many interesting claims but not much solid knowledge.