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Nudging and the problem of context dependent preferences

In my recent post on Robert Sugden’s The Community of Advantage: A Behavioural Economist’s Defence of the Market, I noted a couple of papers in which Sugden and Cass Sunstein debated how to make people better off “as judged by themselves” if they have context dependent preferences.

Below is one point and counterpoint that I found useful.

In a reply to Sugden’s paper, Cass Sunstein writes:

2. Mary is automatically enrolled in a Bronze Health Care Plan – it is less expensive than Silver and Gold, but it is also less comprehensive in its coverage, and it has a higher deductible. Mary prefers Bronze and has no interest in switching. In a parallel world (a lot like ours, but not quite identical, Wolf 1990), Mary is automatically enrolled in a Gold Health Care Plan – it is more expensive than Silver and Bronze, but it is also more comprehensive in its coverage, and it has a lower deductible. Mary prefers Gold and has no interest in switching.

3. Thomas has a serious illness. The question is whether he should have an operation, which is accompanied with potential benefits and potential risks. Reading about the operation online, Thomas is not sure whether he should go ahead with it. Thomas’ doctor advises him to have the operation, emphasizing how much he has to lose if he does not. He decides to follow the advice. In a parallel world (a lot like ours, but not quite identical), Thomas’s doctor advises him not to have the operation, emphasizing how much he has to lose if he does. He decides to follow the advice.

In the latter two cases, Mary and Thomas appear to lack an antecedent preference; what they prefer is an artifact of the default rule (in the case of Mary) or the framing (in the case of Thomas). …

These are the situations on which I am now focusing: People lack an antecedent preference, and what they like is a product of the nudge. Their preference is constructed by it. After being nudged, they will be happy and possibly grateful. We have also seen that even if people have an antecedent preference, the nudge might change it, so that they will be happy and possibly grateful even if they did not want to be nudged in advance.

In all of these cases, application of the AJBT [as judged by themselves] criterion is less simple. Choice architects cannot contend that they are merely vindicating choosers’ ex ante preferences. If we look ex post, people do think that they are better off, and in that sense the criterion is met. For use of the AJBT criterion, the challenge is that however Mary and Thomas are nudged, they will agree that they are better off. In my view, there is no escaping at least some kind of welfarist analysis in choosing between the two worlds in the cases of Mary and Thomas. There is a large question about which nudge to choose in such cases (for relevant discussion, see Dolan 2014). Nonetheless, the AJBT criterion remains relevant in the sense that it constrains what choice architects can do, even if it does not specify a unique outcome (as it does in cases in which people have clear ex ante preferences and in which the nudge does not alter them).

Sugden responds:

In Sunstein’s example, Thomas’s preference between having and not having an operation varies according to whether his attention is directed towards the potential benefits of the operation or towards its potential risks. A choice architect can affect Thomas’s choice by choosing how to present given information about benefits and risks. The problem for the AJBT criterion is that Thomas’s judgement about what makes him better off is itself context-dependent, and so cannot be used to determine the context in which he should choose.

In response to the question of what the choice architect ought to do in such cases, Sunstein concludes that ‘there is no escaping at least some kind of welfarist analysis’—that is, an analysis that makes ‘direct inquiries into people’s welfare’. In his comment, Sunstein does not say much about how a person’s welfare is defined or assessed, but many of the arguments in Nudge imply that the method of enquiry is to try to reconstruct the (assumedly context-independent) latent preferences that fully informed choosers would reveal in the absence of psychologically induced error. Sunstein seems to endorse this methodology when he says: ‘It is psychologically fine to think that choosers have antecedent preferences, but that because of a lack of information or a behavioural bias, their choices will not satisfy them’. Here I disagree. In the first part of my paper, which summarises a fuller analysis presented by Infante et al. (2016), I argued that it is not psychologically fine to assume that human choices result from interactions between context-independent latent preferences and behavioural biases. I maintain that the concept of latent preference is psychologically ungrounded.

I have interpreted AJBT, as applied to category (3) cases, as referring to the judgements implicit in choosers’ latent preferences. In his comment, Sunstein offers a different interpretation—that the relevant judgements are implicit in choosers’ actual posterior preferences. Take the case of Thomas and the operation. We are told that, in whichever direction Thomas is nudged, he will be ‘happy’ with his decision, judging himself to be better off than if he had chosen differently. In other words, any nudge that causes Thomas to change his decision can be said to make him better off, as judged by himself. I think Sunstein is going astray here by thinking of nudges as causing changes in preference. Suppose the doctor directs Thomas’s attention towards the benefits of the operation and advises him to have it. Thomas accepts this advice. At the moment of choice, Thomas is thinking about the options in the frame provided by the doctor, and so he thinks he is making the right decision. But suppose that, shortly before he is wheeled into the operating theatre, he looks at some medical website that uses the opposite frame. If his preferences are context-dependent, he may now wish he had chosen differently. Sunstein is not entitled to assume that, after choosers have been nudged, their judgements become context-independent. If the AJBT criterion is to have bite—if, as Sunstein says, it is to ‘discipline the content of paternalistic interventions’—it must adjudicate between the judgements that the chooser makes in different contexts. That is why Thaler and Sunstein need the concept of latent preference—with all its problems.

Robert Sugden’s The Community of Advantage: A Behavioural Economist’s Defence of the Market

There are few books critiquing behavioural economics that I find compelling. David Levine’s Is Behavioral Economics Doomed? attacks too many straw men. Gilles Saint-Paul’s The Tyranny of Utility: Behavioral Science and the Rise of Paternalism is more an attack of the normative foundations of economics than of behavioural science. And in most of Gerd Gigerenzer’s books, while making a strong case that many of the so-called “biases” are better described as good decision-making under uncertainty, Gigerenzer often extends his defence of human decision making too far.

In The Community of Advantage: A Behavioural Economist’s Defence of the Market, Robert Sugden finds a nice balance in his critique. Sugden starts by taking the evidence of behavioural anomalies seriously, reflecting his four decades working in the field. His critique focuses on how the behavioural research has been interpreted and used as part of the “nudge” movement to develop recommendations for the “planner”, “benevolent autocrat” or “choice architect”.

Sugden’s critique has two main thrusts. The first relates to how behavioural economists have interpreted the experimental evidence that our decisions don’t conform with rational choice theory. In the preface, Sugden writes:

I have to say that I have been surprised by the readiness of behavioural economists to interpret contraventions of rational choice theory as evidence of decision-making error. In the pioneering era of the 1980s and 1990s, this was exactly the interpretation of anomalies that mainstream economists typically favoured, and that we behavioural economists disputed. As some of us used to say, it is as if decision-makers are held to be at fault for failing to behave as the received theory predicts, rather than that theory being faulted for failing to make correct predictions.

In particular, Sugden sees behavioural economists as having adopted an approach whereby they see people as having inner-preferences that conform with the rational choice model (“latent preferences”), contained within a “psychological shell”. This shell distorts our decisions through lack of attention, limited cognitive abilities and incomplete self control. As Sugden points out, this approach has almost no relationship with actual psychological processes, and it is questionable whether these latent preferences exist.

The second thrust of Sugden’s critique relates to how the behavioural findings have triggered a public policy response that is largely paternalistic. In the preface, he continues:

I have been less surprised, but still disappointed, by the willingness of behavioural economists to embrace paternalism. And I have felt increasingly uneasy that, in public discourse, ideas from behavioural welfare economics are appealing to a sensibility that is hostile to principles of economic freedom—principles that, for two and a half centuries, have been central to the liberal tradition of economics.

Here Sugden undertakes the rather large task of seeking to displace the dominant normative basis of economics – utilitarianism – with “contractarianism”.

I’ll now cover each of these two arguments in the depth they deserve.

The concept of latent preferences

Decades of behavioural research have presented a challenge to neoclassical economics. Many of its underpinning assumptions about human preferences and decision making simply do not hold. So how can we reconcile the two?

Sugden makes the case that behavioural economists typically approach this problem by thinking of humans as rational beings wrapped in a layer of irrationality. (He draws heavily on his work with Gerardo Infante and Guilhem Lecouteux in Preference purification and the inner rational agent: A critique of the conventional wisdom of behavioural welfare economics (working paper pdf) in making this case.) Sugden pulls apart a number of the seminal papers on nudging, including Thaler and Sunstein’s Libertarian Paternalism (pdf) (the precursor to Nudge) and Colin Camerer, Samuel Issacharoff, George Loewenstein, Ted O’Donaghue, and Matthew Rabin’s Regulation for Conservatives (pdf), in arguing that there is this common approach. For each of them, the underlying “latent preferences” are the benchmark that against which utility is measured, with decisions that do not meet this criteria attributed to error.

Given this, the role of the planner (or “choice architect” as Thaler and Sunstein rebranded the planner in Nudge) is to try to reconstruct a person’s latent preferences. These latent preferences would have been revealed if they had not been affected by limitations of attention, information, cognitive ability or self-control. Sugden calls this reconstruction of latent preferences “preference purification”.

One of Sugden’s central points concerns whether preference purification is possible. For instance, it is only possible if the latent preferences are context independent.

To illustrate the problem of context independence, Sugden asks us to consider Thaler and Sunstein’s famous cafeteria story. Imagine that the relevant prominence or ordering of food in a cafeteria affects people’s choices (and experimental evidence suggests that it does). The cafeteria director could place the fruit more prominently, with the cakes at the back, increasing purchases of fruit and “nudging” the customers to the healthy option.

Suppose the cake is at the front of the display. When the ordinary human “Joe” goes to the cafe, he selects the cake. If the fruit had been at the front, he would have selected the fruit. Has Joe made an error in his choice? We need to ask what his latent preference is. But suppose Joe is indifferent between cake and fruit. He is not misled by labelling or any false beliefs about the products or their effects on their health. He simply feels a desire to eat whatever is at the front of the display. What is the nature of the error?

To help answer this, imagine that SuperReasoner also goes to the cafe. SuperReasoner is just like Joe except that he “has the intelligence of Einstein, the memory of Deep Blue, and the self-control of Gandhi”. (Sugden borrows this combination of traits from Nudge). What happens when SuperReasoner encounters cake and fruit that vary in prominence? Since he is just like Joe, he is indifferent between the two. He also has the same feelings as Joe, so feels a desire to eat whatever is at the front. This is not a failing of intelligence, memory or self-control. There is no error. Rather, the latent preference itself is context dependent. But if latent preferences themselves are context dependent, how do you ever determine what a latent preference is? What is the right context?

When I first read this example, I was unclear how important it was. It was clear that Sugden had found a weakness in the latent preferences approach, but was this something practically important?

I think the answer to that question is yes, and it comes down to the disconnect between the latent preferences approach and the actual decision making processes of humans. Context independent latent preferences in many cases simply do not exist. They only come into existence in certain contexts. And whatever the psychological approach actually is, latent preferences in an inner shell isn’t it.

Even if there were an inner rational agent, advocates of the preference purification approach don’t attempt to understand or explain the decision making process of this inner agent. There is simply an assumption that there is some mode of latent reasoning that satisfies the economists’ principles of rationality, free from the imperfections created by the external psychological shell. (This is also a problem with rational choice theory. As Sugden writes “rational choice is not self-explanatory: if there are circumstances in which human beings behave in accordance with the theory of rational choice, that behaviour still requires a psychological explanation.”)

(As an aside that I won’t go further into today, Sugden and Sunstein continue this debate in a series of papers that are worth reading. See Sugden’s Do people really want to be nudged towards healthy lifestyles?, Sunstein’s response (pdf) and Sugdens rejoinder. Sugden’s rejoinder has another great example of the problems created by context dependent latent preferences that I’ll discuss in another post.)

Sugden does see that one possible defence of the latent preference approach is to define latent preferences as the preferences that this same person will endorse in independently definable circumstances. People will acknowledge these latent preferences even when a lack of self control (akrasia) leads them to act against their better judgment.

Sunstein and Thaler draw on this interpretation in Nudge in their New Year’s resolution test. How many people vow to drink or smoke more when making their resolutions?

As Sugden points out, this is a context dependent preference. People are using the cue of the New Year to make their resolution. In the same way, if they decide later to have an extra glass of wine in a restaurant, they are responding to that particular context.

The issue then becomes which of these are the true preference. I presume Sunstein and Thaler would take the New Year’s resolution. Sugden is less sure. As he writes:

[J]ust as the restaurant gives cues that point in the direction of drinking, so the traditions of New Year give cues that point in the direction of resolutions for future temperance. If an argument based on akrasia is to work, we need to be shown that in the restaurant, the individual acknowledges that her true preferences are the ones that led to her New Year’s resolution and not the ones she is now acting on. In many cases that fit the story of the resolution and the restaurant, the individual in the restaurant will be thinking that resolutions should not be followed too slavishly, that there is a place in life for spontaneity, and that having an extra glass of wine would be an appropriately spontaneous response to the circumstances. A person who thinks like this as she breaks a previous resolution is not acting contrary to what, at the moment of choice, she acknowledges as her true preferences.

So why do behavioural economists tend to see problems such as this as self-control problems? Sugden suggests this is because of their commitment to the model of the inner-rational agent. Any context dependent choice needs to be seen as an error. Sugden has a different view:

If one has no prior commitment to the idea of latent preferences, there is no reason to suppose that Jane has made any mistake at all. The question of how much she should drink may have no uniquely rational answer. Both when she was making New Year’s resolutions and when she was in the restaurant, she had to a strike a balance between considerations that pointed in favour of alcohol and considerations that pointed against it. The simplest explanation of her behaviour is that she struck one balance in the first case and a different balance in the second. This is not a self-control problem; it is a change of mind.

The contractarian perspective

While I have opened with Sugden’s critique of the nudging approach that emerged from his own field of behavioural economics, his agenda is The Community of Advantage is much broader – an alternative normative basis for economics that is consistent with the psychological evidence.

This normative basis is not new. At the beginning of the book Sugden sources it to John Stuart Mill – the book’s title comes from Mill’s description of the market as a “community of advantage”. Mill considered that economic life is, or should be, built on mutually beneficial cooperation. If people participate in relationships of mutual benefit, they will come to understand that they are cooperative partners, not rivals.

Sugden’s uses the term “contractarianism” to describe this normative foundation. Sugden is inspired by James Buchanan in this approach. Buchanan saw economics as not being about how the market should achieve certain means, but rather how the market is a forum by which people can enter into voluntary exchange.

The question for the economist thus becomes what institutional arrangements will maximise the opportunity for mutually beneficial cooperation, or more specifically, what institutional arrangements are in the interest of each individual to accept if everyone else accepts the same. As Sugden shows (through some rather technical proofs), markets tend to intermediate mutually beneficial transactions, so like neoclassical economics, contractarianism provides support for the use of markets. In this argument, he does not rely on the preferences of the agents being integrated, so he avoids the problems of the inadequacy of rational choice theory. In fact, opportunity is defined independently of people’s preferences, so it does not rely on preferences at all.

The contractarian approach does not result in a blunt call for no government action. Possibly the most stark example of this is Sugden’s suggestion that retirement savings might be mandated. Sugden is sceptical that savings shortages are driven by short-term desires to spend, and asks whether the large economic, political and personal uncertainties involved in saving for a retirement decades away are more important. Among other things, people may simply believe that their collective voting power might enable them to secure sufficient transfers from the working population whatever they do.

In this case, Sugden suggests the problem is a collective action problem. What is the credibility of a policy regime in which private savings play a major part if a large proportion of people simply won’t play ball? In a society where the imprudent have votes, some form of mandatory saving might be required to create the sustainable institutional structure to guarantee some minimum living standard.

I struggled through my first read of the book to understand exactly what a contractarian would think about nudging (I am no philosopher), but there was one passage that I felt gave me the closest glimpse:

A typical questioner will describe some case in which (as judged by the questioner) a mild but unchosen nudge would be very beneficial to its nudgees. Perhaps the nudgees are morbidly obese, and the nudge is a government policy that will make unhealthy fast food less readily available. The questioner asks me: What would you do in this case? To which my reply is: What do you mean, what would I do? What is the hypothetical scenario in which I am supposed to be capable of doing something about the diets of my morbidly obese fellow-citizens?

If the scenario is one in which Robert Sugden is in a roadside restaurant and a morbidly obese stranger is sitting at another table ordering a huge all-day breakfast as a mid-afternoon snack, the answer is that I would do nothing. I would think it was not my business as a diner in a restaurant to make gratuitous interventions into other diners’ decisions about what to eat. But of course, this is not the kind of scenario the questioner has in mind. What is really being asked is what I would do, were I a benevolent autocrat. My answer is that I am not a benevolent autocrat, nor the adviser to one. As a contractarian economist, I am not imagining myself in either of those roles. I am advising individuals about how to pursue their common interests, and paternalism has no place in such advice.

Another section of the mindset of the contractarian was also helpful:

Sunstein and Thaler devote a chapter of Nudge to the issue of retirement savings. The content of this chapter is summarized in the final paragraph:

Saving for retirement is something that Humans [as contrasted with ideally rational agents] find difficult. They have to solve a complicated mathematical problem to know how much to save, and then they have to exert a lot of willpower for a long time to execute this plan. This is an ideal domain for nudging. In an environment in which people have to make only one decision per lifetime, we should surely try harder to help them get it right. (Thaler and Sunstein, 2008: 117)

Look at the final sentence. Sunstein and Thaler are telling their readers that we should try harder to help them get their decisions right. But who are the ‘we’ and who are the ‘they’ here? What ‘we’ are supposed to be doing is designing and implementing choice architecture that nudges individuals to save more for retirement; so presumably ‘we’ refers to government ministers, legislators, regulators, human resource directors, and their respective assistants and advisers; ‘they’ are the individuals who should be saving. As an expert adviser on the design of occupational pension schemes, Thaler is certainly entitled to categorize himself as one of the ‘we’. But where do his readers belong? Very few of them will be in any position to design savings schemes, but just about all of them will face, or will have faced, the problem of saving for retirement. From a reader’s point of view, Sunstein and Thaler’s conclusion would be much more naturally expressed as: They should try harder to help us get it right.

Sunstein and Thaler are writing from the perspective of insiders to the public decision-making process: they are writing as if they were political or economic decision-makers with discretionary power, or the trusted advisors of such decision-makers. And they are inviting their readers to imagine that they are insiders too—that they are the people in control of the nudging, not the people who are being nudged.

I suggest that the benevolent autocrat model appeals to people who like to imagine themselves as insiders in this sense.

In contrast, the contractarian approach appeals to people who take an outsider’s view of politics, thinking of public decision-makers as agents and themselves as principals. The sort of person I have in mind does not think that he has been unjustly excluded from public decision-making or debate; he is more likely to say that he has (what for him are) more important things to do with his time. He does not claim to have special skills in economics or politics, and is willing to leave the day-to-day details of public decision-making to those who do—just as he is willing to leave the day-to-day maintenance of his central heating system to a trained technician. But when public decision-makers are dealing with his affairs, he expects them to act in his interests, as he perceives them. He does not expect them to set themselves up as his guardians.

Sugden states – and I agree – that he takes the psychological evidence more seriously than most nudge advocates. But his approach – which doesn’t rely on integrated preferences – does on first glance seem to have some weaknesses. How do people identify these mutually beneficial advantages? If we increase opportunity, do we end up with choice overload?

I covered some of Sugden’s views on choice overload in a previous post, whereby he stated that much concern for choice overload was condescension towards other people’s preferences. But he does take some of the issues with choice overload seriously. For instance, he notes that long menus of retirement or insurance plans lead to poorer choices through the lack of pre-existing preferences and lack of navigational aids (partly the result of public programs needing to be impartial in the way they present options).

Sugden also sees problems with “obfuscation” in the market, whereby firms deliberately price their products or present the pricing information in overly complex ways. They might bait, whereby only a small quantity of stock is available at the advertised price, or provide exploding offers, whereby a decision must be made in a certain timeframe.

Here the contractarian does not seek to close opportunities for exchange, but rather to provide a better institutional structure. This might involve requiring transparency in pricing, such as requiring pricing to be given for complementary bundles of goods (e.g. printers and print cartridges). However, they should not be required to be purchased together. Exploding offers designed to induce quick decisions might be tempered by cooling-off periods. Other product information such as calorie counts might be required on menus. Importantly, these measures are not then taken to have failed if someone continues to purchase the high calorie food.

The question about how effective people are at identifying and capitalising on opportunities for mutual benefit, outside of the choice overload issue, was less clearly addressed. Sugden reviews some of the experimental evidence relating to fairness, and suggests it points to a preference for mutually beneficial exchange (also a subject for another post). But this does not extend to the question of our effectiveness at seeing these opportunities for exchange.

A discussion

If you would prefer to get a flavour of the book in a different manner, below is a video discussion between Sugden, Henry Leveson-Gower and myself on some of the topics in the book.

Do nudges diminish autonomy?

Despite the fact that nudges, by definition, do not limit liberty, many people often have a feeling of discomfort about governments using nudges. I typically find it difficult to elicit from them what precisely is the problem, but often it comes down to the difference between freedom and autonomy.

In an essay Debate: To Nudge or Not to Nudge (pdf), Daniel Hausman and Bryan Welch do a good job of pulling this idea apart:

If one is concerned with autonomy as well as freedom, narrowly conceived, then there does seem to be something paternalistic, not merely beneficent, in designing policies so as to take advantage of people’s psychological foibles for their own benefit. There is an important difference between what an employer does when she sets up a voluntary retirement plan, in which employees can choose to participate, and what she does when, owing to her understanding of limits to her employees’ decision-making abilities, she devises a plan for increasing future employee contributions to retirement. Although setting up a voluntary retirement plan may be especially beneficial to employees because of psychological flaws that have prevented them from saving on their own, the employer is expanding their choice set, and the effect of the new plan on employee savings comes mainly as a result of the provision of this new alternative. The reason why nudges such as setting defaults seem, in contrast, to be paternalist, is that in addition to or apart from rational persuasion, they may “push” individuals to make one choice rather than another. Their freedom, in the sense of what alternatives can be chosen, is virtually unaffected, but when this “pushing” does not take the form of rational persuasion, their autonomy—the extent to which they have control over their own evaluations and deliberation—is diminished. Their actions reflect the tactics of the choice architect rather than exclusively their own evaluation of alternatives.

And not only might nudges diminish autonomy, they might be simply disrespectful.

One reason to be troubled, which Thaler and Sunstein to some extent acknowledge (p. 246/249), is that such nudges on the part of the government may be inconsistent with the respect toward citizens that a representative government ought to show. If a government is supposed to treat its citizens as agents who, within the limits that derive from the rights and interests of others, determine the direction of their own lives, then it should be reluctant to use means to influence them other than rational persuasion. Even if, as seems to us obviously the case, the decision-making abilities of citizens are flawed and might not be significantly diminished by concerted efforts to exploit these flaws, an organized effort to shape choices still appears to be a form of disrespectful social control.

But what if you believe that paternalistic policies are in some cases defensible? Are nudges the milder version?

Is paternalism that plays on flaws in human judgment and decision-making to shape people’s choices for their own benefit defensible? If one believes, as we do, that paternalistic policies (such as requiring the use of seat belts) that limit liberty are sometimes justified, then it might seem that milder nudges would a fortiori be unproblematic.

But there may be something more insidious about shaping choices than about open constraint. For example, suppose, for the purposes of argument, that subliminal messages were highly effective in influencing behavior. So the government might, for example, be able to increase the frequency with which people brush their teeth by requiring that the message, “Brush your teeth!” be flashed briefly during prime-time television programs. Influencing behavior in this way may be a greater threat to liberty, broadly conceived, than punishing drivers who do not wear seat belts, because it threatens people’s control over their own evaluations and deliberation and is so open to abuse. The unhappily coerced driver wearing her seat belt has chosen to do so, albeit from a limited choice set, unlike the hypothetical case of a person who brushes his teeth under the influence of a subliminal message. In contrast to Thaler and Sunstein [authors of Nudge], who maintain that “Libertarian paternalism is a relatively weak and nonintrusive type of paternalism,” to the extent that it lessens the control agents have over their own evaluations, shaping people’s choices for their own benefit seems to us to be alarmingly intrusive.

Hausman and Welch outline three distinctions that can help us think about whether nudges should be permissible (which I am somewhat sympathetic to).

First, in many cases, regardless of whether there is a nudge or not, people’s choices will be shaped by factors such as framing, a status quo bias, myopia and so forth. Although shaping still raises a flag because of the possibility of one agent controlling another, it arguably renders the action no less the agent’s own, when the agent would have been subject to similar foibles in the absence of nudges. When choice shaping is not avoidable, then it must be permissible.

Second, although informed by an understanding of human decision-making foibles, some nudges such as “cooling off periods” (p. 250/253) and “mandated choice” (pp. 86–7/88) merely counteract foibles in decision-making without in any way pushing individuals to choose one alternative rather than another. In this way, shaping apparently enhances rather than threatens an individual’s ability to choose rationally. …

Third, one should distinguish between cases in which shaping increases the extent to which a person’s decision-making is distorted by flaws in deliberation, and cases in which decision-making would be at least as distorted without any intentionally designed choice architecture. In some circumstances, such as (hypothetical) subliminal advertising, the foibles that make people care less about brushing their teeth are less of a threat to their ability to choose well for themselves than the nudging. In other cases, such as Carolyn’s, the choices of some of the students passing through the cafeteria line would have been affected by the location of different dishes, regardless of how the food is displayed.

There remains an important difference between choices that are intentionally shaped and choices that are not. Even when unshaped choices would have been just as strongly influenced by deliberative flaws, calculated shaping of choices still imposes the will of one agent on another.

One funny line about all this, however, is whether it is actually possible to choose “rationally”. Hausman and Welch see this point:

When attempting to persuade people rationally, we may be kidding ourselves. Our efforts to persuade may succeed because of the softness of our smile or our aura of authority rather than the soundness of our argument, but a huge difference in aim and attitude remains. Even if purely rational persuasion were completely impossible—that is, if rational persuasion in fact always involved some shaping of choices as well—there would be an important difference between attempting to persuade by means of facts and valid arguments and attempting to take advantage of loss aversion or inattention to get someone to make a choice that they do not judge to be best. Like actions that get people to choose alternatives by means of force, threats, or false information, exploitation of imperfections in human judgment and decision-making aims to substitute the nudger’s judgment of what should be done for the nudgee’s own judgment.

A New Useless Class?

Yuval Noah Harari writes:

Fears of machines pushing people out of the job market are, of course, nothing new, and in the past such fears proved to be unfounded. But artificial intelligence is different from the old machines. In the past, machines competed with humans mainly in manual skills. Now they are beginning to compete with us in cognitive skills. And we don’t know of any third kind of skill—beyond the manual and the cognitive—in which humans will always have an edge.

At least for a few more decades, human intelligence is likely to far exceed computer intelligence in numerous fields. Hence as computers take over more routine cognitive jobs, new creative jobs for humans will continue to appear. Many of these new jobs will probably depend on cooperation rather than competition between humans and AI. Human-AI teams will likely prove superior not just to humans, but also to computers working on their own.

However, most of the new jobs will presumably demand high levels of expertise and ingenuity, and therefore may not provide an answer to the problem of unemployed unskilled laborers, or workers employable only at extremely low wages. Moreover, as AI continues to improve, even jobs that demand high intelligence and creativity might gradually disappear. The world of chess serves as an example of where things might be heading. For several years after IBM’s computer Deep Blue defeated Garry Kasparov in 1997, human chess players still flourished; AI was used to train human prodigies, and teams composed of humans plus computers proved superior to computers playing alone.

I have written previously that it is overly simplistic to extrapolate from the “freestyle chess” example to a statement that the future is human-machine combinations. This has to be true in some form, even if the sole human role is designer. But when we look at the level of individuals decisions, the evidence in support of human-machine combinations appears somewhat weak.

First, the idea that we can work in effective teams of this type overestimates the capabilities of most humans. Garry Kasparov may not have been defeated by a machine until 1997, but most humans had been inferior to chess computers for decades earlier. Most people should not interfere with their chess playing computer, suggesting difficulty in implementing these models at scale. As Harari notes above, this option may not be available to the unskilled.

Second, these successful pairings appear the exception, rather than the rule. Most of the (admittedly underdeveloped) evidence in this area suggests that when you put an algorithm in the hands of a human, the human is more likely to degrade its performance than if the algorithm was left alone.

But finally, even where the rare skilled human forges a partnership, how long does it remain superior? Harari continues:

Yet in recent years, computers have become so good at playing chess that their human collaborators have lost their value and might soon become entirely irrelevant. On December 6, 2017, another crucial milestone was reached when Google’s AlphaZero program defeated the Stockfish 8 program. Stockfish 8 had won a world computer chess championship in 2016. It had access to centuries of accumulated human experience in chess, as well as decades of computer experience. By contrast, AlphaZero had not been taught any chess strategies by its human creators—not even standard openings. Rather, it used the latest machine-learning principles to teach itself chess by playing against itself. Nevertheless, out of 100 games that the novice AlphaZero played against Stockfish 8, AlphaZero won 28 and tied 72—it didn’t lose once. Since AlphaZero had learned nothing from any human, many of its winning moves and strategies seemed unconventional to the human eye. They could be described as creative, if not downright genius.

Can you guess how long AlphaZero spent learning chess from scratch, preparing for the match against Stockfish 8, and developing its genius instincts? Four hours. For centuries, chess was considered one of the crowning glories of human intelligence. AlphaZero went from utter ignorance to creative mastery in four hours, without the help of any human guide.

AlphaZero is not the only imaginative software out there. One of the ways to catch cheaters in chess tournaments today is to monitor the level of originality that players exhibit. If they play an exceptionally creative move, the judges will often suspect that it could not possibly be a human move—it must be a computer move. At least in chess, creativity is already considered to be the trademark of computers rather than humans! So if chess is our canary in the coal mine, we have been duly warned that the canary is dying. What is happening today to human-AI teams in chess might happen down the road to human-AI teams in policing, medicine, banking, and many other fields.

Harari also argues that even if the computer is not superior by itself, its connectivity and updatability might still mean that it is sensible to replace all the humans.

What’s more, AI enjoys uniquely nonhuman abilities, which makes the difference between AI and a human worker one of kind rather than merely of degree. Two particularly important nonhuman abilities that AI possesses are connectivity and updatability.

For example, many drivers are unfamiliar with all the changing traffic regulations on the roads they drive, and they often violate them. In addition, since every driver is a singular entity, when two vehicles approach the same intersection, the drivers sometimes miscommunicate their intentions and collide. Self-driving cars, by contrast, will know all the traffic regulations and never disobey them on purpose, and they could all be connected to one another. When two such vehicles approach the same junction, they won’t really be two separate entities, but part of a single algorithm. The chances that they might miscommunicate and collide will therefore be far smaller.

Similarly, if the World Health Organization identifies a new disease, or if a laboratory produces a new medicine, it can’t immediately update all the human doctors in the world. Yet even if you had billions of AI doctors in the world—each monitoring the health of a single human being—you could still update all of them within a split second, and they could all communicate to one another their assessments of the new disease or medicine. These potential advantages of connectivity and updatability are so huge that at least in some lines of work, it might make sense to replace all humans with computers, even if individually some humans still do a better job than the machines.

Harari’s article expands to discuss the broader question of whether AI will lead to tyranny. I recommend reading the full piece.

 

Has the behavioural economics pendulum swung too far?

Over at Behavioral Scientist, as part of their “Nudge Turns 10” special issue, is my latest article When Everything Looks Like a Nail: Building Better “Behavioral Economics” Teams. Here’s the opening:

As someone who became an economist via a brief career as a lawyer, I did notice that my kind had privileged access to the halls of government and business. Whether this was because economics can speak the language of dollars, or that we simply claimed that we had all the answers, the economists were often the first consulted (though not necessarily listened to) on how we priced, regulated, and designed our policies, services, and products.

What I lacked, however, was a privileged understanding of behavior. So about a decade ago, with the shortcomings of economics as an academic discipline top of mind, I commenced a Ph.D. to develop that understanding. It was fortuitous timing. Decades of research by psychologists and “misbehaving” economists was creating a new wave of ideas that would wash out of academia and into the public and corporate spheres. Famously encapsulated in Richard Thaler and Cass Sunstein’s Nudge, there was now a recognized need to design our world for humans, not “econs.”

Following Nudge, a second wave found many organizations creating their own “nudge units.” The Behavioural Insights Team (BIT) within 10 Downing Street was the precursor to government behavioral teams around the world. Although the first dedicated corporate behavioral units predated the BIT, a similar, albeit less visible, pattern of growth can be seen in the private sphere. These teams are now tackling problems in areas as broad as tax policy, retail sales, app design, and social and environmental policy.

On net, these teams have been a positive and resulted in some excellent outcomes. But my experience working in and alongside nudge units has me asking: Has the pendulum swung too far? My education and experience have proven to me that economics and the study of human behavior are complements rather than substitutes. But I worry that in many government departments and businesses, behavioral teams have replaced rather than complemented economics teams. Policymakers and corporate executives, their minds rushing to highly available examples of “nudge team” successes, often turn first to behavioral units when they have a problem.

A world in which we take advice only from economists risks missing the richness of human behavior, designing for people who don’t exist. But a world in which policymakers and corporate executives turn first to behavioral units has not been without costs. A major source of these costs comes from how we have been building behavioral teams.

We have been building narrow teams. We have been building teams with only a subset of the skills required to solve the problems at hand. When you form a team with a single skillset, there is the risk that everything will start to look like a nail.

It’s now time for a third wave. We need to build multidisciplinary behavioral units. Otherwise we may have results such as those reflected in the observations below. Some of the observations relate to my own experiences and errors, some are observations by others. To protect identities, confidential projects, and egos (including my own), I have tweaked the stories. However, the lessons remain the same.

You can read the rest here.

I considered a few alternative angles for the special issue article. One was around the question of whether behavioural interventions that look impressive in isolation are less so if we consider the system-wide effects. Another angle I considered, hinted at in the published piece, is around replicability and publication bias in the public sphere. Maybe they can be topics for future articles.

I also considered an alternative introduction, but changed my approach on feedback from a friend who reviewed the first draft. Here’s the old introduction, which takes too long to get to the point and is too narrow for the ultimate thread of the article, but which makes the point about narrow approaches in a stronger way:

Economists have never been shy about applying the economic toolkit to what are normally considered the non-economic aspects of life. They have tackled discrimination, the family, crime, culture, religion, altruism, sports and war, to name a few.

This “economics imperialism” has often been controversial, but (at least in this author’s opinion) left many subjects better off. Some of the subjects benefited from a different approach, with the effort to repel the imperialists creating more robust disciplines.

But at times the economics imperialists simply missed the mark. Often this was because they lacked domain knowledge. Complexities invalidated their underlying assumptions or created a dynamic that they simply didn’t foresee.

Some economists also have a habit of leaving the complexities of their own body of work behind when they wander into new domains. A rich understanding of moral hazard, adverse selection, information asymmetries and principle-agent problems often becomes a simple declaration to let the price mechanism do its job.

One (almost caricatured) illustration of this occurred when Freakonomics authors Steven Levitt and Stephen Dubner met with Prime Minister David Cameron to discuss increasing health expenditure in the United Kingdom’s free healthcare system. As described in Think Like A Freak, they posed a thought experiment:

What if, for instance, every Briton were also entitled to a free, unlimited, lifetime supply of transportation? That is, what if everyone were allowed to go down to the car dealership whenever they wanted and pick out any new model, free of charge, and drive it home?

We expected him to light up and say, “Well, yes, that’d be patently absurd—there’d be no reason to maintain your old car, and everyone’s incentives would be skewed. I see your point about all this free health care we’re doling out!”

Instead, Cameron said nothing, offered a quick handshake and disappeared to “find a less-ridiculous set of people with whom to meet.”

Can Levitt and Dubner have expected a different response? Even if Levitt had a more serious proposal up his sleeve, Levitt and Dubner’s failure to engage seriously with the particular features of the healthcare market rendered the message useless. They had effectively ignored the complexities of the problem and hammered away in the hope they had found a nail.

A few years before the visit by the Freakonomics team, David Cameron’s Conservative Government established the Behavioural Insights Team, or “Nudge unit” within 10 Downing Street. The team was tasked with realising the Government’s intention to find “intelligent ways to encourage, support and enable people to make better choices for themselves”.

Now spun out of the Cabinet Office, the Behavioural Insights Team was the precursor to government based behavioural teams around the world. Although the first dedicated corporate behavioural units pre-dated the Behavioural Insights Team, a similar, albeit slower pattern of growth can be seen in the private sphere. These teams are now tackling issues as broad as tax evasion, customer conversion, domestic violence and climate change.

While the development of these teams has been a positive and resulted in some excellent outcomes, these teams have not been without weaknesses – in fact, some of the same weaknesses suffered by the economics imperialists. The primary one is that when you form a team around a central idea, there is the risk that everything will start to look like a nail.

The three faces of overconfidence

I have complained before about people being somewhat quick to label poor decisions as being due to “overconfidence”. For one, overconfidence has several distinct forms. It is a mistake to treat each as the same. Further, these forms vary in their pervasiveness.

The last time I made this complaint I drew on an article by Don Moore and Paul Healy, “The Trouble with Overconfidence” (pdf). A more recent article by Don Moore and Derek Schatz (pdf) provides some further colour on this point (HT: Julia Galef). It’s worth pulling out a few excerpts.

So what are these distinct forms? Overestimation, overplacement and overprecision. (It’s also useful to disambiguate overoptimism, which I’ll touch on at the end of this post.)

Overestimation is thinking that you’re better than you are. Donald Trump’s claim to be worth $10 billion (White, 2016) represented an overestimate relative to a more credible estimate of $4.5 billion by Forbes magazine (Peterson-Withorn, 2016). A second measure of overconfidence is overplacement: the exaggerated belief that you are better than others. When Trump claimed to have achieved the largest electoral victory since Ronald Reagan (Cummings, 2017), he was overplacing himself relative to other presidents. A third form of overconfidence is overprecision: being too sure you know the truth. Trump displays overprecision when he claims certainty about views which are contradicted by reality. For example, Trump claimed that thousands of Arab Americans in New Jersey publicly celebrated the fall of the World Trade Center on September 11th, 2001, without evidence supporting the certainty of his assertion (Fox News, 2015).

When people are diagnosing overconfidence, they can conflate the three. Pointing out that 90% of people believe they are better than average drivers (overplacement) is not evidence that a CEO was overconfident in their decision to acquire a competitor (possibly overestimation).

Overestimation

People tend to overestimate their performance on hard tasks. But when easy, they tend to underestimate.

In contrast to the widespread perception that the psychological research is rife with evidence of overestimation (Sharot, 2011), the evidence is in fact thin and inconsistent. Most notably, it is easy to find reversals in which people underestimate their performance, how good the future will be, or their chances of success (Moore & Small, 2008). When a task is easy, research finds that people tend to underestimate performance (Clark & Friesen, 2009). If you ask people to estimate their chances of surviving a bout of influenza, they will radically underestimate this high probability (Slovic, Fischhoff, & Lichtenstein, 1984). If you ask smokers their chances of avoiding lung cancer, they will radically underestimate this high probability (Viscusi, 1990).

The powerful influence of task difficulty (or the commonness of success) on over- and underestimations of performance has long been known as the hard-easy effect (Lichtenstein & Fischhoff, 1977). People tend to overestimate their performance on hard tasks and underestimate it on easy tasks. Any attempt to explain the evidence on overestimation must contend with the powerful effect of task difficulty.

Overplacement

In a reverse of the pattern for overestimation, people tend to overplace on easy tasks, but underplace on harder ones.

The evidence for “better-than-average” beliefs is so voluminous that it has led a number of researchers to conclude that overplacement is nearly universal (Beer & Hughes, 2010; Chamorro- Premuzic, 2013; Dunning, 2005; Sharot, 2011; Taylor, 1989). However, closer examination of this evidence suggests it suffers from a few troubling limitations (Harris & Hahn, 2011; Moore, 2007). Most of the studies measuring better-than-average beliefs use vague response scales that make it difficult to compare beliefs with reality. The most common measure asks university students to rate themselves relative to the average student of the same sex on a 7-point scale running from “Much worse than average” to “Much better than average.” Researchers are tempted to conclude that respondents are biased if more than half claim to be above average. But this conclusion is unwarranted (Benoît & Dubra, 2011). After all, in a skewed distribution the majority will be above average. Over 99% of the population has more legs than average.

Within the small set of studies not vulnerable to these critiques, the prevalence of overplacement shrinks. Indeed, underplacement is rife. People think they are less likely than others to win difficult competitions (Moore & Kim, 2003). When the teacher decides to make the exam harder for everyone, students expect their grades to be worse than others’ even when it is common knowledge that the exam will be graded on a forced curve (Windschitl, Kruger, & Simms, 2003). People believe they are worse jugglers than others, that they are less likely than others to win the lottery, and less likely than others to live past 100 (Kruger, 1999; Kruger & Burrus, 2004; Moore, Oesch, & Zietsma, 2007; Moore & Small, 2008). These underplacement results are striking, not only because they vitiate claims of universal overplacement, but also because they seem to contradict the hard-easy effect in overestimation, which finds that people most overestimate their performance on difficult tasks.

Moore and Healy offer an explanation for the different effects of task difficulty on overestimation and overplacement – myopia. I wrote about that in the earlier post.

Overprecision

Overprecision is pervasive but poorly understood.

A better approach to the study of overprecision asks people to specify a confidence interval around their estimates, such as a confidence interval that is wide enough that there is a 90% chance the right answer is inside it and only a 10% chance the right answer is outside it (Alpert & Raiffa, 1982). Results routinely find that hit rates inside 90% confidence intervals are below 50%, implying that people set their ranges too precisely—acting as if they are inappropriately confident their beliefs are accurate (Moore, Tenney, & Haran, 2016). This effect even holds across levels of expertise (Atir, Rosenzweig, & Dunning, 2015; McKenzie, Liersch, & Yaniv, 2008). However, one legitimate critique of this approach is that ordinary people are unfamiliar with confidence intervals (Juslin, Winman, & Olsson, 2000). That is not how we express confidence in our everyday lives, so maybe unfamiliarity contributes to errors.

Overprecision is the most pervasive but least understood form of overconfidence. Unfortunately, researchers use just a few paradigms to study it, and they rely on self-reports of beliefs using questions people are rarely called on to answer in daily life.

(Although not covered in Moore and Schatz’s paper, Gigerenzer also offers a critique that I’ll discuss in a forthcoming post.)

Overoptimism

Moore and Healy don’t touch on overoptimism directly in their paper, but in an interview with Julia Galef on the Rationally Speaking podcast, Moore touches on this point:

Julia: Before we conclude this disambiguation portion of the podcast I want to ask about optimism, which I am using to mean thinking that some project of yours has a greater chance of success than you’re justified in thinking it does. How does that fit into that three‐way taxonomy?

Don: It is an excellent question, and optimism has been studied a great deal. Perhaps the most famous scholars of optimism are Charles Carver and Mike Shier who have a scale that assesses the personality trait of optimism. Their usage of the term is actually not that far from the colloquial usage of the term, where to be optimistic is just to believe that good things are going to happen. Optimism is distinctively about a forecast for the future, and whether you think good things or bad things are going to happen to you.

Interestingly, this trait of optimism seems very weakly related to actual specific measures of overconfidence. When I ask Mike Shier why his optimistic personality trait didn’t correlate with any of my measures of overconfidence he said, “Oh, I wouldn’t expect it to.”

Julia: I would expect it to!

Don: Yeah. My [reaction] actually was, “Well, what the heck does it mean, if it doesn’t correlate with any specific beliefs?”

I think it’s hard to reconcile those in any sort of coherent or rational framework of beliefs. But I have since had to concede that there is a real psychological phenomenology, wherein you can have this free floating, positive expectation that doesn’t commit you to any specific delusional beliefs.

Concern about the “tyranny of choice”? Or condescension towards others’ preferences?

I have been reading Robert Sugden’s book The Community of Advantage: A Behavioural Economist’s Defence of the Market in preparation for an upcoming webinar with Robert about the book, facilitated by Henry Leveson-Gower.

The webinar will be help at 1pm London time and 10pm Sydney time on Monday 3 September. Details about the webinar are here and you can register here. A video will be posted after.

I’ll also post an in-depth review later, but the book is a mix of philosophy, technical economics, and critique of applied behavioural economics. The critiques are great reading, the philosophy is interesting but tougher, and the technical economic sections are for aficionados only.

Here’s one snippet of critique as a taster:

An extreme version of the claim that choice overload is a serious problem in developed economies has been popularized by Barry Schwartz (2004) in a book whose premise is that when the number of options becomes too large, ‘choice no longer liberates, but debilitates. It may even be said to tyrannize’ (2004: 2). Researchers who investigate choice overload sometimes suggest that their findings reveal a fundamental failure of the market system—that it provides too much choice.

[T]he idea that markets offer too much choice seems to have some resonance in public debate, as evidenced by the success of Schwartz’s book and by the fame of Iyengar and Lepper’s experiment with jams. My sense is that it appeals to culturally conservative or snobbish attitudes of condescension towards some of the preferences to which markets cater. This may seem harmless fogeyism, as when Schwarz (2004: 1–2) begins his account of the tyranny of choice by complaining that Gap allows him to choose between too many different types of pairs of jeans (‘The jeans I chose turned out just fine, but it occurred to me that buying a pair of pants should not be a daylong project’). But it often reflects a misunderstanding of the facts of economic life, and a concealed interest in restricting other people’s opportunities to engage in mutually beneficial transactions.

Imagine you are asked to describe your ideal shopping environment. For many people, and I suspect for Schwartz, the description would be something like this. Your Perfect Shop is a small business, conveniently located in your own neighbourhood (perhaps just far enough away that you are not inconvenienced by other customers who might want to park their cars in front of your house). It stocks a small product range, tailored to your particular tastes and interests, but at prices that are similar to those charged by large supermarkets. There are some categories of goods (such as jeans if you are Schwartz) which you sometimes need to buy but whose detailed features do not much interest you. The Perfect Shop stocks a small but serviceable range of such items. There are other categories of goods (breakfast cereal might be an example) for which you have a strong preference for a specific brand and feel no need to try anything different; the Perfect Shop sells a limited range of this type of good, but your favourite brand is always on sale. However, there are a few categories of goods in which you are something of a connoisseur and like to experiment with different varieties. Here, the Perfect Shop offers a wide range of options, imaginatively selected to appeal to people who want to experiment in just the kinds of ways that you do. No shelf space is wasted on categories of goods which you have no desire to buy.

Compared with such an ideal, real shopping may well seem to offer too much choice, not to mention clutter and vulgarity. But, of course, in a world in which there are economies of scale in retailing and people have different tastes and interests, the idea that each of us can have a Perfect Shop is an economic fantasy. A less fantastic possibility is that there are Perfect Shops for some people, but everyone is constrained to use them. Because these shops are well-used, prices can be kept low. But then the viability of what are some people’s Perfect Shops depends on the absence of opportunities for other people to buy what they want. Restricting other people’s opportunities to buy goods that have no appeal to you can be a way of conserving your preferred shopping environment without your having to pay for it. Describing these restrictions as defences against the tyranny of choice can be a convenient camouflage for a form of protectionism.

Gerd Gigerenzer’s Gut Feelings: Short Cuts to Better Decision Making

gut_feelingsFor many years I have been influenced by Gerd Gigerenzer’s arguments about the power of simple heuristics and the underlying rationality to many human decisions. But I have contrasting reactions to different parts of Gerd Gigerenzer’s body of work.

His published collections of essays – Simple Heuristics That Make Us Smart (with Peter Todd and the ABC research group), Adaptive Thinking and Rationality for Mortals – are fantastic, although some people might find them a touch academic.

Gigerenzer’s popular books are more accessible, but the loss of some of the nuance, plus his greater stridency of argument, push them to a point where I find a fair bit to disagree with.

In his most recent book, Risk Savvy, I struggled with how far Gigerenzer extended his arguments about the power of human decision-making. I agree that the heuristics and biases approach can lead us to be overeager in labelling decisions as “irrational” or sub-optimal. “Biased” heuristics can find a better point on the bias-variance trade-off. They are designed to operate in an uncertain world, not in a lab. But there is little doubt that humans err in some cases – particularly in environments with no resemblance to those in which we evolved. Gigerenzer can be somewhat quick to disparage use of data and praise gut instinct in environments where there is little evidence that these instincts work.

Gigerenzer’s earlier Gut Feelings: Short Cuts to Better Decision Making strikes perhaps the best balance between nuance and accessibility. While it still leaves an impression about the accuracy of our instincts that I’m not completely in agreement with, it provides a good overview of how our gut feelings can lead to good decisions.

Gigerenzer defines a gut feeling – which you might also call an intuition or hunch – as a feeling that appears quickly in consciousness, with us unaware of the underlying reasons, but strong enough for us to act on. Gut feelings work through simple rules of thumb that take advantage of the evolved capacities of the brain. The skill of the unconscious is knowing what rule to apply at what time.

Let’s break this down.

The power of simple rules

Gut feelings can be powerful tools despite (and because of) their reliance rules of thumb. Often in decision-making, “less is more”, in that there is a beneficial degree of ignorance, or benefits to excluding information from consideration. The recognition heuristic is an example of this: if you recognise one option but not the other, infer that the recognised option has the higher value. The recognition heuristic only works if recognise one but not the other option.

In contrast, complex strategies can explain too much in hindsight. In an uncertain world where only part of the information is useful for the future, a simple rule that focuses on only the best or a limited subset of information has a good chance of hitting that useful information. Gigerenzer provides plenty of examples of the superiority or utility of simple rules of thumb, a point that many advocates of complex statistical methods and machine learning should hear.

But sometimes Gigerenzer’s examples drift toward becoming straw man competitions. For instance, he describes a competition between two models – multinomial regression and a heuristic called “Take the best” – in predicting school drop-out rates. Take the best operates by looking only at the cue which has the strongest relationship with drop-out rates (such as the attendance rate), and if one is higher than the other, you make a decision at that point. If the cues have the same value, move to the next cue and repeat.

The two models were trained on half the data, and tested against the other half of the data. Take the best achieved 65% accuracy in the training data, and 60% on the test data. In contrast, multinomial regression achieved 72% on training data, but this plunged to 54% on test data. (Gigerenzer only shows a chart in the book – I got the numbers from the related chapter of Simple Heuristics That Make Us Smart.) Multinomial regression overfit the training data.

This victory for Take the best sounds impressive, but there were observations for only 57 schools, with half the data used in training. Of course basing a prediction on a regression with 18 variables and twenty-odd observations is rubbish. I wouldn’t expect anything else. Gigerenzer often frames the victory of simple rules such as Take the Best as surprising to others (and originally to him), which it might be at a general level. But when you look at many of the specific examples and the numbers involved, the surprise doesn’t last long.

There is some more subtlety in the reporting of these results in Simple Heuristics That Make Us Smart, where the prediction of drop out rates was one of 20 “competitions” between Take the Best and multiple regression. The overall gap between Take the Best and multiple regression on the test data was 71% versus 68%, an impressive but narrow victory for Take the Best despite its reliance on far fewer cues.

That said, most of the competitions involved small samples – an area where the simple heuristics excel. Only three of the 20 had more than 30 examples available for training the model. The models also had access to dichotomised, not numerical, values, further decreasing the utility of regression. There is a tie at 76% apiece when numerical values were used. The tie is still an impressive result for the simple Take the Best heuristic, but this is now some way from the headline story we get in Gut Feelings. (Conversely, I should also note that the territory of these competitions was fairly stable, which might give more complex techniques an edge. Move to an uncertain dynamic environment, and the simple heuristics may gain an advantage even if the datasets are much larger.)

How humans use these heuristics

An important part of Gigerenzer’s argument is that these simple heuristics are used by humans. An example he provides is a picture of a boy’s head surrounded by four chocolate bars. Which bar does Charlie want? The one he is looking at. The simple heuristic is that “If a person looks at one alternative (longer than at others), it is likely the one the person desires.”

The gaze heuristic is another example. Someone seeking to catch a ball will run so as to maintain the angle of the ball in their gaze. The gaze heuristic will eventually lead them to where the ball will land. They don’t simply compute where the ball will land and then run there.

The question of whether humans use these heuristics has been tested in the lab. People have been demonstrated to rely heavily on the recognition heuristic when picking winners of tennis matches and football games, particularly where they are unfamiliar with the teams, or in determining which of two cities is larger. Less is more, as if you know all the teams or cities, you can’t use the recognition heuristic. This gives the people using these heuristics surprising predictive power, close (or superior) to more knowledgeable experts.

An interesting question about these heuristics is how someone knows when they should apply a particular heuristic. Gigerenzer notes that the skill of the unconscious is knowing, without thinking, what rule to apply at what time. This is the least satisfactory piece of the book, with little discussion as to how this process might work or be effective. It is fair to say the selection is unconscious – people are particularly poor at explaining what rule they applied – but are they skilful at this selection?

The other question mark relates to the inconsistency of our decisions. As Daniel Kahneman and friends have written about recently, human decisions are often noisy, with decisions varying across occasions. If we are applying heuristics, why do our decisions appear so haphazard in some environments? Does our selection of heuristics only work where we have had the right experience with feedback? More on that below.

Applied gut feelings

A point that Gigerenzer highlights – one of his important contributions to how we should think about the heuristics and biases approach – is that the structure of the environment is central to how well a rule of thumb works. A rule of thumb is not good or bad in itself, but depends on the environment in which it is used.

This point was earlier made by Herbert Simon, with his description of the capabilities of the decision maker, and the environment in which they are used, as blades on a pair of scissors. You cannot assess one without the other.

Where I find the discussion of rules of thumb becomes most interesting is in complex environments where we need to learn the rules of thumb to be applied. The heuristic of following someone else’s gaze to determine what they are talking about is something that one-year olds do. But consider a hospital, where a doctor is trying to determine whether someone is having a heart attack. Or a CEO deciding whether to support a merger.

Gigerenzer points out – as you can also see in work by others such as Gary Klein – that you need feedback to develop expertise. Absent feedback you are likely to fall back on rules that don’t work or that achieve other purposes. Gigerenzer gives the example of judges who are not given feedback on their parole decisions. They then fall back on the heuristic of protecting themselves from criticism by simply following the police and prosecution recommendation.

Gigerenzer offered a few examples where I was not clear on how that expertise could develop. One involves discussion of the benefits of strategies that involve incremental change toward a solution, rather than first computing the ideal solution and acting on it. The gaze heuristic is a good example of this, whereby someone seeking to catch a ball maintains the angle of the ball in their gaze, with this heuristic eventually leading them to where it will land. They don’t simply compute where the ball will land and then run there.

Gigerenzer extends this argument to the setting of company budgets:

Strategies relying on incremental changes also characterize how organizations decide on their yearly budgets. At the Max Planck Institute where I work, my colleagues and I make slight adjustments to last year’s budget, rather than calculating a new budget from scratch. Neither athletes nor business administrators need to know how to calculate the trajectory of the ball or the business. An intuitive “shortcut” will typically get them where they would like to be, and with a smaller chance of making grave errors.

The idea of lower probability of “grave error” might be right. But how does someone learn this skill? And here is Dan Lovallo and Olivier Sibony writing on the same concept:

It has been another long, exhausting budget meeting. As the presenters showed you their plans, you challenged every number, explored every assumption. In the end you raised their targets a little, but, if you’re honest, you have to admit it: the budget this unit will have to deliver next year is not very different from the one they proposed at the beginning of the budget process, which in turn is not very different from the latest forecast for this year.

What happened? The short answer is, you’ve been anchored. Anchoring is the psychological phenomenon that makes a number stick in your mind and influence you — even though you think you’re disregarding it.

I have some sympathy to the Lovallo and Sibony assessment, having sat in numerous organisations where it was near unanimously agreed that the budget needed to be reallocated, but the status quo prevailed. But I’m not overly convinced it was due to anchoring, rather than trenchant self-interest of those who might be affected, and a timidity and desire to avoid conflict on the behalf of the decision makers. It would be interesting to see a study on this. (Maybe it’s out there – I briefly searched, but not particularly hard).

An interesting story in the chapter about medical environments concerned doctors who were required to judge whether someone was having a heart attack. The doctors were doing a generally poor job, defensively sending 90% of people with chest pain to the coronary care unit.

Some researchers developed a process whereby doctors would use a complicated chart with 50-odd probabilities, a long formula and a pocket calculator to determine whether a patient should be admitted to the coronary care unit. The doctors didn’t like it and didn’t understand it, but its use improved their decision-making and reduced overcrowding in the coronary care unit.

The researchers then took the chart and calculator away from the doctors, with the expectation that the decision-making quality would decline back to what it was previously. But the decision quality did not drop. Exposure to the calculator had improved their intuition permanently. What the doctors needed was the cues that they could not learn from experience, but when provided with them, they applied them in a fast and frugal way that matched the accuracy of the more complicated procedure.

As an aside, the above is how the story is told in Gut Feelings, which might have been coloured by some discussion between Gigerenzer and the researchers. My reading of the related article (pdf minus charts) has a different chain of events. The researchers first developed the tool using patient data, and presented their results to the doctors. Seven months later, the tool was trialed. They found that admissions to the coronary care unit had declined following the presentation, but not on introduction of the tool, suggesting the doctors started using the cues after the presentation and could achieve equal superiority through their own decision processes. The paper notes that “Take the Best” and tallying – simply adding up the number of cues – would be good strategies. Gigerenzer takes the analysis further here.

As a second aside, this story is similar to one by Daniel Kahneman tells in Thinking Fast and Slow where military recruiters were asked to use a mechanical process to select candidates. After protesting that they were not robots, Kahneman suggested that after collecting the required data, the recruiters close their eyes, imagine the recruit as a soldier and assign a score of one to five. It turned out the “close your eyes” score was as accurate as the sum of the six factors that were collected, both being much better than the useless interviewing technique they had replaced. Intuition worked, but only after disciplined collection of data (cues).

And as a third aside and contrast, here’s a story from another study (quoted text from here):

During an 18 month period the authors used a computer-based diagnosis system which surpassed physicians in diagnostic accuracy. During the course of this research after each physician made a diagnosis, he or she was informed of the computer’s diagnosis. The diagnostic accuracy of the physicians gradually rose toward that of the computer during the 18 month period. The authors attributed this improvement in part to the “discipline” forced upon the physicians, the constraint of carefully collecting patient information, the “constant emphasis on reliability of clinical data collected, and the rapid ‘feedback’ from the computer,” which may have promoted learning. When the computer system was terminated, the physicians very quickly reverted to their previous lower level of diagnostic accuracy. Apparently discipline and reliability fell victim to creativity and inconsistency.

The rest of the book

Gigerenzer provides plenty of other thought-provoking material about the role of heuristics and gut feeling in various domains. Sometimes it feels a bit shallow Advertising is put down to the recognition heuristic. What about signalling, discussed shortly after in another context? The final couple of chapters relating to moral behaviour and social instincts seemed somewhat out-of-date when looked at next to the burgeoning literature on cultural transmission and learning. But there are enough interesting ideas in those chapters to make them worthwhile. And you can’t expect someone to pin every point down in-depth in a popular book.

So, if you want a dose of Gigerenzer, Gut Feelings is interesting and worth reading. But if you have the patience, I recommend starting with Simple Heuristics That Make Us Smart, Adaptive Thinking and Rationality for Mortals. Then if you want a slightly less “academic” Gigerenzer, move on to Gut Feelings.

Gerd Gigerenzer’s Rationality for Mortals: How People Cope with Uncertainty

RationalityGerd Gigerenzer’s collection of essays Rationality for Mortals: How People Cope with Uncertainty covers most of Gigerenzer’s typical turf: ecological rationality, heuristics that make us smart, understanding risk and so on.

Below are observations on three of the more interesting essays: the first on different approaches to decision making, the second on the power of simple heuristics, and the third on how biologists treat decision making.

Four ways to analyse decision making

In the first essay, Gigerenzer provides four approaches to decision making – unbounded rationality, optimisation under constraints, cognitive illusions (heuristics and biases) and ecological rationality.

1. Unbounded rationality

Unbounded rationality is the territory of neoclassical economics. Omniscient and omnipotent people optimise. They are omniscient in that they can see the future – or at least live in a world of risk where they can assign probabilities. They are omnipotent in that they have all the calculating power they need to make perfect decisions. With that foresight and power, they make optimal decisions.

Possibly the most important point about this model is that it is not designed to describe precisely how people make decisions, but rather to predict behaviour. And in many dimensions, it does quite well.

2. Optimisation under constraints

Under this approach, people are no longer omniscient. They need to search for information. As Gigerenzer points out, however, this attempt to inject realism creates another problem. Optimisation with constraints can be even harder to solve than optimisation with unbounded rationality. As a result, the cognitive power required is even greater.

Gigerenzer is adamant that optimisation under constraints is not bounded rationality – and if we use Herbert Simon’s definition of the term, I would agree – but analysis of this type commonly attracts the “boundedly rational” label.

3. Cognitive illusions – logical irrationality

The next category is the approach in much of behavioural science and behavioural economics. It is often labelled as the “heuristics and biases” program. This program looks to understand the processes under which people make judgments, and in many cases, seeks to show errors of judgment or cognitive illusions.

Gigerenzer picks two main shortcomings of this approach. First, although the program successfully shows failures of logic, it does not look at the underlying norms. Second, it tends not to produce testable theories of heuristics. As Gigerenzer states, “mere verbal labels for heuristics can be used post hoc to “explain” almost everything.”

An example is analysis of overconfidence bias. People are asked a question such as “Which city is farther north – New York or Rome?”, and asked to give their confidence that their answer is correct. When participants are 100 per cent certain of the answer, less than 100 per cent tend to be correct. That pattern of apparent overconfidence continues through lower probabilities.

There are several critiques of this analysis, but one of the common suggestions is that people are presented with questions that are unrepresentative of a typical sample. People typically use alternative cues to answer a question such as the above. In the case of latitude, temperature is a plausible cue. The overconfidence bias occurs because the selected cities are a biased sample where the cue fails more often than expected. If the cities are randomly sampled from the real world, the overconfidence disappears. The net result is that what appears to be a bias may be better explained by the nature of the environment in which the decision is made. (Kahneman and Tversky contest this point, suggesting that even when you take a representative sample, the problem remains.)

4. Ecological rationality

Ecological rationality departs from the heuristics and biases program by examining the relationship between mind and environment, rather than the mind and logic. Human behaviour is shaped by scissors with two blades – the cognitive capabilities of the actor, and the environment. You cannot understand human behaviour without understanding both the capabilities of the decision maker and the environment in which those capabilities are exercised. Gigerenzer would apply the bounded rationality label to this work.

There are three goals to the ecological rationality program. The first is to understand the adaptive toolbox – the heuristics of the decision maker and their building blocks. The second is to understand the environmental structures in which different heuristics are successful. The third is to use this analysis to improve decision making through designing better heuristics or changing the environment. This can only be done once you understand the adaptive toolbox and the environments in which different tools are successful.

Gigerenzer provides a neat example of how the ecological rationality departs from the heuristics and biases program in its analysis of a problem – in this case, optimal asset allocation. Harry Markowitz, who received a Nobel Memorial Prize in Economics for his work on optimal asset allocation, did not use the results of his analysis in his own investing. Instead, he invested his money using the 1/N rule – spread your assets equally across N assets.

The heuristics and biases program might look at this behaviour and note Markowitz is not following the optimal behaviour determined by himself. He is making important decisions without using all the available information. Perhaps it is due to cognitive limitations?

As Gigerenzer notes, optimisation is not always the best solution. Where the problem is computationally intractable or the optimisation solution lacks robustness due to estimation errors, heuristics may outperform. In the case of asset allocation, Gigerenzer notes work showing that 500 years of data would have been required for Markowitz’s optimisation rule to outperform his practice of 1/N. In a world of uncertainty, it can be beneficial to leave information on the table. Markowitz was using a simple heuristic for an important decision, but rightfully so as it is superior for the environment in which he is making the decision.

Simple heuristics make us smart

Gerd Gigerenzer is a strong advocate of the idea that simple heuristics can make us smart. We don’t need complex models of the world to make good decisions.

The classic example is the gaze heuristic. Rather than solving a complex equation to catch a ball, which requires us to know the ball’s speed and trajectory and the effect of the wind, a catcher can simply run to keep the ball at a constant angle in the air, leading them to the point where it will land.

Gigerenzer’s faith in heuristics is often taken to be based on the idea that people have limited processing capacity and are unable to solve the complex optimisation problems that would be needed in the absence of these rules. However, Gigerenzer points out this is perhaps the weakest argument for heuristics:

[W]e will start off by mentioning the weakest reason. With simple heuristics we can be more confident that our brains are capable of performing the necessary calculations. The weakness of this argument is that it is hard to judge what complexity of calculation or memory a brain might achieve. At the lower levels of processing, some human capabilities apparently involve calculations that seem surprisingly difficult (e.g., Bayesian estimation in a sensorimotor context: Körding & Wolpert, 2004). So if we can perform these calculations at that level in the hierarchy (abilities), why should we not be able to evolve similar complex strategies to replace simple heuristics?

Rather, the advantage of heuristics lies in their low information requirements, their speed and, importantly, their accuracy:

One answer is that simple heuristics often need access to less information (i.e. they are frugal) and can thus make a decision faster, at least if information search is external. Another answer – and a more important argument for simple heuristics – is the high accuracy they exhibit in our simulations. This accuracy may be because of, not just in spite of, their simplicity. In particular, because they have few parameters they avoid overfitting data in a learning sample and, consequently, generalize better across other samples. The extra parameters of more complex models often fit the noise rather than the signal. Of course, we are not saying that all simple heuristics are good; only some simple heuristics will perform well in any given environment.

As the last sentence indicates, Gigerenzer is careful not to make any claims that heuristics generally outperform. A statement that a heuristic is “good” is ill-conceived without considering the environment in which it will be used. This is the major departure of Gigerenzer’s ecological rationality from the standard approach in the behavioural sciences, where the failure of a heuristic to perform in an environment is taken as evidence of bias or irrationality.

Once you have noted what heuristic is being used in what environment, you can have more predictive power than in a well-solved optimisation model. For example. an optimisation model to catch a ball will simply predict that the catcher will be at the place and time where the ball lands. Once you understand that they use the gaze heuristic to catch the ball, you can also predict the path that they will take to get to the ball – including that they won’t simply run in a straight line to catch it. If a baseball or cricket coach took the optimisation model too seriously, they would tell the catcher that they are running inefficiently by not going straight to where it will land. Instructions telling them to run is a straight line will likely make their performance worse.

Biologists and decision making

Biologists are usually among the first to tell me that economists rely on unrealistic assumptions about human decision making. They laugh at the idea that people are rational optimisers who care only about maximising consumption.

But the funny thing is, biologists often do the same. Biologists tend to treat their subjects as optimisers.

Gigerenzer has a great chapter considering how biologists treat decision making, and in particular, to what extent biologists consider that animals use simple decision-making tools such as heuristics. Gigerenzer provides a few examples where biologists have examined heuristics, but much of the chapter asks whether biologists are missing something with their typical approach.

As a start, Gigerenzer notes that biologists are seeking to make predictions rather than accurate descriptions of decision making. However, Gigerenzer questions whether this “gambit” is successful.

Behavioral ecologists do believe that animals are using simple rules of thumb that achieve only an approximation of the optimal policy, but most often rules of thumb are not their interest. Nevertheless, it could be that the limitations of such rules of thumb would often constrain behavior enough to interfere with the fit with predictions. The optimality modeler’s gambit is that evolved rules of thumb can mimic optimal behavior well enough not to disrupt the fit by much, so that they can be left as a black box. It turns out that the power of natural selection is such that the gambit usually works to the level of accuracy that satisfies behavioral ecologists. Given that their models are often deliberately schematic, behavioral ecologists are usually satisfied that they understand the selective value of a behavior if they successfully predict merely the rough qualitative form of the policy or of the resultant patterns of behavior.

You could write a similar paragraph about economists. If you were to give the people in an economic model objectives shaped by evolution, it would be almost the same.

But Gigerenzer has another issue with the optimisation approach in biology. As for most analysis of human decision making, “missing from biology is the idea that simple heuristics may be superior to more complex methods, not just a necessary evil because of the simplicity of animal nervous systems.” Gigerenzer writes:

There are a number of situations where the optimal solution to a real-world problem cannot be determined. One problem is computational intractability, such as the notorious traveling salesman problem (Lawler et al., 1985). Another problem is if there are multiple criteria to optimize and we do not know the appropriate way to convert them into a common currency (such as fitness). Thirdly, in many real-world problems it is impossible to put probabilities on the various possible outcomes or even to recognize what all those outcomes might be. Think about optimizing the choice of a partner who will bear you many children; it is uncertain what partners are available, whether each one would be faithful, how long each will live, etc. This is true about many animal decisions too, of course, and biologists do not imagine their animals even attempting such optimality calculations.

Instead the behavioral ecologist’s solution is to find optima in deliberately simplified model environments. We note that this introduces much scope for misunderstanding, inconsistency, and loose thinking over whether “optimal policy” refers to a claim of optimality in the real world or just in a model. Calculating the optima even in the simplified model environments may still be beyond the capabilities of an animal, but the hope is that the optimal policy that emerges from the calculations may be generated instead, to a lesser level of accuracy, by a rule that is simple enough for an animal to follow. The animal might be hardwired with such a rule following its evolution through natural selection, or the animal might learn it through trial and error. There remains an interesting logical gap in the procedure: There is no guarantee that optimal solutions to simplified model environments will be good solutions to the original complex environments. The biologist might reply that often this does turn out to be the case; otherwise natural selection would not have allowed the good fit between the predictions and observations. Success with this approach undoubtedly depends on the modeler’s skill in simplifying the environment in a way that fairly represents the information available to the animal.

Again, Gigerenzer could equally be writing about economics. I think we should be thankful, however, that biologists don’t take their results and develop policy prescriptions on how to get the animals to behave in ways we believe they should.

One interesting question Gigerenzer asks is whether humans and animals use similar heuristics. Consideration of this question might uncover evidence of the parallel evolution of heuristics in other lineages facing similar environmental structures, or even indicate a common evolutionary history. This could form part of the evidence as to whether these human heuristics are evolved adaptations.

But are animals more likely to use heuristics than humans? Gigerenzer suggests the answer is not clear:

It is tempting to propose that since other animals have simpler brains than humans they are more likely to use simple heuristics. But a contrary argument is that humans are much more generalist than most animals and that animals may be able to devote more cognitive resources to tasks of particular importance. For instance, the memory capabilities of small food-storing birds seem astounding by the standards of how we expect ourselves to perform at the same task. Some better-examined biological examples suggest unexpected complexity. For instance, pigeons seem able to use a surprising diversity of methods to navigate, especially considering that they are not long-distance migrants. The greater specialism of other animals may also mean that the environments they deal with are more predictable and thus that the robustness of simple heuristics may not be such as advantage.

Another interesting question is whether animals are also predisposed to the “biases” of humans. Is it possible that “animals in their natural environments do not commit various fallacies because they do not need to generalize their rules of thumb to novel circumstances.” The equivalent for humans is mismatch theory, which proposes that a lot of modern behaviour (and likely the “biases” we exhibit) is due to a mismatch between the environment in which our decision making tools evolved and the environments we exercise them in today.

The difference between knowing the name of something and knowing something

In an excellent article over at Behavioral Scientist (read the whole piece), Koen Smets writes:

A widespread misconception is that biases explain or even produce behavior. They don’t—they describe behavior. The endowment effect does not cause people to demand more for a mug they received than a mug-less counterpart is prepared to pay for one. It is not because of the sunk cost fallacy that we hang on to a course of action we’ve invested a lot in already. Biases, fallacies, and so on are no more than labels for a particular type of observed behavior, often in a peculiar context, that contradicts traditional economics’ simplified view of behavior.

A related point was made by Owen Jones in his paper Why Behavioral Economics Isn’t Better, and How it Could Be:

[S]aying that the endowment effect is caused by Loss Aversion, as a function of Prospect Theory, is like saying that human sexual behavior is caused by Abstinence Aversion, as a function of Lust Theory. The latter provides no intellectual or analytic purchase, none, on why sexual behavior exists. Similarly, Prospect Theory and Loss Aversion – as valuable as they may be in describing the endowment effect phenomena and their interrelationship to one another – provide no intellectual or analytic purchase, none at all, on why the endowment effect exists. …

[Y]ou can’t provide a satisfying causal explanation for a behavior by merely positing that it is caused by some psychological force that operates to cause it. That’s like saying that the orbits of planets around the sun are caused by the “orbit-causing force.” …

[L]oss aversion rests on no theoretical foundation. Nothing in it explains why, when people behave irrationally with respect to exchanges, they would deviate in a pattern, rather than randomly. Nor does it explain why, if any pattern emerges, it should have been loss aversion rather than gain aversion. Were those two outcomes equally likely? If not, why not?

And here’s Richard Feynman on the point more generally (from What Do You Care What Other People Think):

We used to go to the Catskill Mountains, a place where people from New York City would go in the summer. The fathers would all return to New York to work during the week, and come back only for the weekend. On weekends, my father would take me for walks in the woods and he’d tell me about interesting things that were going on in the woods. When the other mothers saw this, they thought it was wonderful and that the other fathers should take their sons for walks. They tried to work on them but they didn’t get anywhere at first. They wanted my father to take all the kids, but he didn’t want to because he had a special relationship with me. So it ended up that the other fathers had to take their children for walks the next weekend.

The next Monday, when the fathers were all back at work, we kids were playing in a field. One kid says to me, “See that bird? What kind of bird is that?”

I said, “I haven’t the slightest idea what kind of a bird it is.”

He says, “It’s a brown-throated thrush. Your father doesn’t teach you anything!”

But it was the opposite. He had already taught me: “See that bird?” he says. “It’s a Spencer’s warbler.” (I knew he didn’t know the real name.) “Well, in Italian, it’s a Chutto Lapittida. In Portuguese, it’s a Bom da Peida. In Chinese, it’s a Chung-long-tah, and in Japanese, it’s a Katano Tekeda. You can know the name of that bird in all the languages of the world, but when you’re finished, you’ll know absolutely nothing whatever about the bird. You’ll only know about humans in different places, and what they call the bird. So let’s look at the bird and see what it’s doing—that’s what counts.” (I learned very early the difference between knowing the name of something and knowing something.)

Knowing the name of a “bias” such as loss aversion isn’t zero knowledge – at least you know it exists. But knowing something exists is a very shallow understanding.

And back to Koen Smets:

Learning the names of musical notes and of the various signs on a staff doesn’t mean you’re capable of composing a symphony. Likewise, learning a concise definition of a selection of cognitive effects, or having a diagram that lists them on your wall, does not magically give you the ability to analyze and diagnose a particular behavioral issue or to formulate and implement an effective intervention.

Behavioral economics is not magic: it’s rare for a single, simple nudge to have the full desired effect. And being able to recite the definitions of cognitive effects does not magically turn a person into a competent behavioral practitioner either. When it comes to understanding and influencing human behavior, there is no substitute for experience and deep knowledge. Nor, perhaps even more importantly, is there a substitute for intellectual rigor, humility, and a healthy appreciation of complexity and nuance.