When to consider boosting: some rules
Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany
Abstract: In recent years, public ofﬁcials have shown a growing interest in
using evidence from the behavioural sciences to promote policy goals. Much
of the discussion of behaviourally informed approaches has focused on
‘nudges’; that is, non-ﬁscal and non-regulatory interventions that steer
(nudge) people in a speciﬁc direction while preserving choice. Less attention
has been paid to boosts, an alternative evidence-based class of non-ﬁscal and
non-regulatory intervention. The goal of boosts is to make it easier for
people to exercise their own agency in making choices. For instance, when
people are at risk of making poor health, medical or ﬁnancial choices, the
policy-maker –rather than steering behaviour through nudging –can take
action to foster or boost individuals’own decision-making competences.
Boosts range from interventions that require little time and cognitive effort
on the individual’s part to ones that require substantial amounts of training,
effort and motivation. This article outlines six rules that policy-makers can
apply in order to determine under which conditions boosts, relative to
nudges, are the preferable form of non-ﬁscal and non-regulatory
intervention. The objective is not to argue that boosts are better than nudges
or vice versa, but to begin to spell out the two approaches’respective
conditions for success.
Submitted 3 December 2016; accepted 7 December 2016
In recent years, policy-makers have shown mounting interest in using behav-
ioural science to make government simpler, less expensive and more effective.
For instance, in 2015, former US President Barack Obama issued an Executive
Order directing federal agencies to use behavioural science to improve their
policies (Executive Order No. 13,707, 2015). A central provision of the
*Correspondence to: Ralph Hertwig, Center for Adaptive Rationality, Max Planck Institute for
Human Development, Berlin, Germany. Email: email@example.com
Behavioural Public Policy (2017), 1: 2, 143–161
©Cambridge University Press doi:10.1017/bpp.2016.14
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Executive Order required the government to consider “how the content,
format, timing, and medium by which information is conveyed affects compre-
hension and action by individuals.”Another provision called on agencies to
devote “particular consideration to the selection and setting of default
options.”Under the Obama administration, behavioural science informed
numerous moves by the US government, in contexts including environmental
protection, health care, energy efﬁciency, anti-obesity policy, consumer protec-
tion, food safety and ﬁnancial reform (Sunstein, 2013).
In the UK, the Behavioural Insights Team (BIT), in operation since 2010, has
also enlisted behavioural science to promote change in a wide range of areas
(Halpern, 2015). Many of BIT’s actions involve clearer communication and
better choice architecture –for example, invoking social norms when
sending out tax reminders (e.g. through a text message telling late payers
that nine out of ten taxpayers paid on time) and prompting drivers paying
their vehicle tax to become organ donors. In 2015, Australia created its own
team, the Behavioural Economics Team of Australia. In the same year, the
World Bank devoted its entire annual report to the subject of behavioural
science, with particular emphasis on the developing world; it subsequently
created a Global Insights Team, dedicated to using psychology and behavioural
insights to improve social outcomes. Behavioural science is now being used or
seriously considered as a policy tool in numerous member states of the
Organisation for Economic Co-operation and Development (OECD); a
comprehensive collection of over 100 case studies of behavioural insights in
practice has recently been compiled and published (OECD, 2017).
Much of the aforementioned discussion of behaviourally informed
approaches has emphasised ‘nudges’; that is, interventions that steer people
in a particular direction while preserving freedom of choice (Thaler &
Sunstein, 2008). Automatic enrolment in a pension plan or in green energy,
for example, count as nudges (Eberling & Lotz, 2015). Freedom of choice is
preserved because the ultimate decision as to whether or not to accept the
default of automatic enrolment and its consequences remains with the individual.
Default rules establish what happens if people do nothing. The use of social
norms, as in BIT’s tax compliance effort, has also been interpreted as nudging
(Thaler & Sunstein, 2008).
Yet behavioural science also provides support for a distinct kind of interven-
tion, namely ‘boosts’(Grüne-Yanoff & Hertwig, 2016; see also the related
‘think’approach by John et al.,2011). The objective of boosts is to improve
people’s competence to make their own choices; the focus is on interventions
that make it easier for people to exercise their own agency. For instance, by
acquiring the ability to understand the statistical health information that is ubi-
quitous in the medical domain, patients can decide for themselves whether the
144 RALPH HERTWIG
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potential beneﬁts of a medical test recommend by their physician (e.g. the pros-
tate-speciﬁc antigen [PSA] test) outweigh the test’s potential costs. The compe-
tence to reason statistically (risk literacy) is an ability that generalises from the
medical to many other domains (Hoffrage et al.,2000). By the same token,
ﬁnancial literacy is a competence that could be boosted by, for instance, teach-
ing individuals simple ﬁnancial and accounting rules (Drexler et al.,2014).
Parents could be equipped with simple strategies enabling them to make the
family meal environment conducive to nutritional health (e.g. modelling
healthy behaviour for their children; Dallacker et al.,2017). These interven-
tions can be interpreted as boosts. The goal of this article is to outline six
rules that policy-makers planning to implement behaviourally informed,
non-ﬁscal and non-regulatory interventions might consider in order to decide
whether to nudge, to boost or to do both.
I will start from what is often called upon as the key criterion in public policy
debate: namely, people’s welfare. This criterion can be operationalised by
means of cost–beneﬁt analyses. From the standpoint of those who reject welfar-
ist approaches, other concerns –above all, individual autonomy and agency –
are important, and I will also consider the role of those concerns. Before
turning to the dimensions entered in policy-makers’cost–beneﬁt analyses,
however, let us ﬁrst consider the differences between boosts and nudges in
What distinguishes boosts from nudges?
Many policy interventions can be used to change behaviour, including man-
dates or bans (restricting or eliminating options), ﬁscal measures (monetary
incentives and disincentives) or non-regulatory and non-ﬁscal measures. In
recent years, the latter category has increasingly been equated with nudging.
Nudges come in different forms. They can be, in the terms of Sunstein
(2016), ‘educative nudges’(e.g. disclosure requirements, warnings, labels or
reminders) or ‘non-educative nudges’(e.g. default rules, ordering of items on
a menu or website or cafeteria design). Non-educative nudges can target or
enlist cognitive or motivational biases (e.g. inertia, procrastination and loss
aversion; see Rebonato, 2012) to change behaviour.
Recently, Grüne-Yanoff and Hertwig (2016; see also Hertwig & Grüne-
Yanoff, in press) proposed that a distinction be drawn, on conceptual
grounds, between boosts and nudges. They deﬁned boosts as interventions that
“[in order] to extend the decision-making competences of laypeople and pro-
fessionals alike …target the individual’s skills and knowledge, the available
When to consider boosting 145
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set of decision tools, or the environment in which decisions are made”
Rather than merely presenting pertinent and accurate information (as educa-
tive nudges do), boosts explicitly seek to foster existing decision-making com-
petences and to develop new ones, thus enabling individuals to translate their
intentions (preferences) into behaviour –that is, to exercise personal agency.
Clearly, information can be instrumental in developing an intention to
engage in a speciﬁc behaviour. Such intentions are not necessarily translated
into action, however. Findings on the ‘intention–behaviour gap’suggest that
conceptual knowledge often does not sufﬁce, but that it needs to be supplemen-
ted with, for instance, procedural knowledge (including self-regulatory strat-
egies; e.g. see Abraham et al.,1998).
Boosts thus differ from nudges in terms of the underlying assumptions about
the potential value of educative efforts, the emphasis placed on the importance
of actually exercising the power of choice (as opposed to being afforded the
opportunity to choose), the levers used in the intervention (Hertwig &
Grüne-Yanoff, in press) and the underlying understanding of human deci-
sion-making (Grüne-Yanoff & Hertwig, 2016). Furthermore, although propo-
nents of non-ﬁscal and non-regulatory approaches often share a commitment
to Herbert Simon’s notion of bounded rationality, their interpretations of its
implications differ. Those in favour of nudges typically invoke the ﬁndings
of Kahneman and Tversky’s heuristics and biases research programme (see
Rebonato, 2012), emphasising decision-makers’systematic cognitive biases
and motivational shortcomings. Those in favour of boosts typically invoke
ﬁndings showing that bounds on people’s time, knowledge and computational
powers do not prevent them from making good decisions, to the extent that
they succeed in employing simple decision strategies in the appropriate con-
texts –that is, where there is a ﬁt between cognition and environment (i.e. eco-
logical rationality; Gigerenzer et al.,2011).
For the reasons outlined above, this article argues that it is both useful and
important to understand boosts as a distinct category of behaviour change
1 Another reason to see boosts as distinct from nudges is that policy-makers, faced with the choice
of whether to enlist cognitive or motivational biases or to boost individual competence (and, in the
process, remove a bias once and for all), may avoid boosting and use, for example, a default rule,
even though it would be in the immediate best interests of both policy-makers and individuals to
boost. The reason is that policy-makers thereby retain the option of enlisting bias in the future.
This possibility emerged from a game-theoretical analysis (Hertwig & Ryall, 2016). Although it is
unclear to what extent the game-theoretic dynamic generalises to real policy-makers’choices, the
possible existence of this dynamic highlights the importance of analysing both categories of
146 RALPH HERTWIG
tools. The goal is not, however, to engage in a terminological debate about
nudges and boosts (for a conceptual analysis, see Grüne-Yanoff & Hertwig,
2016). Instead, this article aims to identify rules that policy-makers could
apply in order to determine the conditions under which boosts, as deﬁned
above, are preferable to nudges.
Welfare, cost–beneﬁt analyses and autonomy
How, at least in theory, do policy-makers choose which intervention to select?
From the standpoint of nudging, the key criterion is welfare. According to
Sunstein’s(2014) broad deﬁnition, the term ‘welfare’refers to “whatever choo-
sers think would make their lives go well”(p. 73). Indeed, in many important
situations (e.g. choices between medical treatment options or savings levels),
what is primarily at stake is the choosers’welfare. Their choices do not
affect others, except perhaps indirectly. One way to approach the welfare ques-
tion is to analyse relevant costs and beneﬁts from the standpoint of the chooser
and the policy-maker, and to select, among the possible interventions, the one
maximising the chooser’s welfare.
Two components of that analysis are the costs of decisions and the costs of
errors for the chooser and the policy-maker. For example, a paradigmatic
nudge such as a default rule (e.g. automatic enrolment in a pension plan) typ-
ically reduces the cognitive and other costs of decisions for choosers, who are
freed from devoting time and attention to the problem. Similarly, as long as the
rule is not especially difﬁcult to devise, it also reduces costs for policy-makers,
whose actions can have a broad and enduring impact in exchange for a rela-
tively small investment (e.g. changing a legal default; see Chetty et al.,2014).
Consider, for illustration, the goal of promoting healthy eating. For the
policy-maker, it would be costly and time consuming to teach children and
adults nationwide how to interpret nutrition labels, or to equip them with
the numerical processing skills that are relevant for food choice (e.g. portion-
size estimation skills; Dallacker et al.,2016). It is likely to be less costly and
more effective to change, where possible, the choice architectures in which
food choices are made (Wansink, 2014). School cafeterias, for instance,
could be redesigned to present the options differently. Salad bars could be
moved away from the wall and placed in front of the cash registers, thus
nudging students to make healthy choices. This and other relatively inexpen-
sive changes to the choice architecture of all school cafeterias (e.g. what is dis-
played at eye level) could reach a large population of choosers.
Although setting up default rules is relatively inexpensive, default rules may
also increase the costs of errors (understood in terms of the number and mag-
nitude of mistakes) if the policy-makers who devise them only know what ﬁts
When to consider boosting 147
the statistically average person, but not what ﬁts the individual. An organ
donation default (Johnson & Goldstein, 2003), for instance, automatically
makes everybody a potential donor, including those who, for whatever
reason, do not wish to be donors. If the latter fail to opt out of the organ dona-
tion default, perhaps because of inertia or perceived social pressure, the default
will lead to errors. (I bracket the fact that the rules for organ donation affect
Boosts can, but need not, impose high costs for choosers and policy-makers.
They can be very low in cognitive costs for the chooser –for instance, when
transparent risk communication is used to foster the understanding of health
statistics (e.g. Hoffrage et al.,2000; Gigerenzer et al.,2007) or when people
are taught to “strategically call on automatic processes”in order to translate
self-declared goals into simple action plans (i.e. harnessing implementation
intentions; Gollwitzer, 1999, p. 493). Alternatively, boosts may require some
hours of instruction and practice –for instance, training ﬁnancial decision-
making skills by teaching people simple ﬁnancial and accounting heuristics
(Drexler et al.,2014) or implementing a 12-hour sexual assault resistance
training programme (including risk assessment strategies) for ﬁrst-year
female university students (Senn et al.,2015). To the extent that only those
people who seek the competence offered by a boost will adopt it, this approach
can be expected to reduce (and perhaps even eliminate) the costs of errors.
The analysis of costs and beneﬁts also requires attention to the effectiveness
of the intervention, usually measured in terms of the magnitude of its impact.
Take, for instance, the objective of increasing people’s retirement savings.
Savings behaviour could be increased by efforts to promote ﬁnancial literacy
(boosting) or by default rules such as automatic enrolment (nudging). There
is an ongoing debate about the effectiveness of these two distinct strategies.
Willis (2011) has argued that ﬁnancial literacy education has, at best, limited
success. Should efforts to promote ﬁnancial literacy indeed fail to meaningfully
promote retirement savings, the argument against ﬁnancial literacy boosts, and
in favour of defaults, will be strengthened.
For the welfarist, integrating factors such as the costs of decisions, the costs
of errors and the effectiveness of the intervention seems, at least in theory,
straightforward: which approach has the highest net beneﬁts? Policy-makers
2 The timing of the boost and the type of boost are two key issues in this debate. ‘Just-in-time’
education tied to speciﬁc behaviours appears to be more effective than educative interventions that
are not coordinated with the intended behaviour (Johnson et al.,2013). Furthermore, ﬁnancial liter-
acy boosts seem to generate more of the desired impact when they take the form of simple heuristics
conveying procedural knowledge (Drexler et al.,2014) than when they are limited to the delivery of
knowledge (e.g. what is compound interest?).
148 RALPH HERTWIG
could quantify the costs and beneﬁts of (say) default rules and compare them
with the corresponding costs and beneﬁts of boosting people’s competences.
From the perspective of political philosophy, of course, many people are not
welfarists. Instead, they emphasise the importance of autonomy.
Paradigmatic nudges such as defaults and boosts preserve autonomy in the
formal sense; they do not prevent people from going their own way. But if
autonomy is an important goal and, in particular, if the policy-maker’s aim
is to promote individual agency, there is an argument in favour of boosts.
This argument is further strengthened by the possibility of errors caused, for
instance, by defaults. For those who emphasise autonomy, the production of
errors by passive rather than active decisions may represent a problem that
cannot be offset by considerations of cost–beneﬁtefﬁciency. More generally,
whenever actual choice and, in particular, informed choice –rather than the
formal opportunity to choose –are believed to be important because they
are conducive to welfare (e.g. in terms of procedural satisfaction; Frey et al.,
2014) or because they are intrinsic goods, boosts should be favoured.
Rules for policy-makers
Next, I will outline some additional, less-discussed criteria that could be
invoked in the process of selecting an intervention. My focus will be on
the choice between boost and nudge interventions. In principle, however,
the following rules could be extended to include other kinds of interventions
as well. I will list six rules. The ﬁrst four relate to what could be interpreted
as necessary requirements for boosting and nudging to succeed, measured in
terms of welfare. If these requirements are not met, this shortcoming cannot
be offset by any other advantage an intervention may have. The remaining
two criteria could and should be included in the cost–beneﬁt calculus out-
lined above and thus balanced against an intervention’s other costs and
Rule 1. If individuals lack the cognitive ability or motivation to acquire new
skills or competences, then nudging is likely to be the more efﬁcient
In order to develop new skills and competences, people need sufﬁcient levels
of motivation and cognitive capacity (Grüne-Yanoff & Hertwig, 2016).
Consider, for example, the ability to make Bayesian inferences, which is
often required in the context of medical choices. Assume that a patient’s
routine PSA screening test has produced a positive result. To understand
the meaning of that test result, several pieces of information are needed: (i)
the base rate of prostate cancer, p(PC); (ii) the probability that the test is
When to consider boosting 149
positive if the person has prostate cancer (sensitivity or true-positive rate of
the test; p[pos|PC]); and (iii) the probability that the test is positive if the
person does not have prostate cancer (false-positive rate or 1 –speciﬁcity;
p[pos|no PC]). On the basis of this information, the patient and his physician
can use Bayes’rule to calculate the positive predictive value (PPV) of a test;
that is, the probability of someone who tests positive actually having prostate
PPV ¼pPCðÞp posjPCðÞ
pPCðÞp posjPCðÞþpnoPCðÞp posjno PCðÞ
For instance, if the base rate is about 6.3% and the test has a sensitivity of
about 21% and a false-positive rate of 6%, the PPV is about 19%, which
means that 81% of positive test results are false-positives (these numbers
are realistic; see Arkes & Gaissmaier, 2012). Many doctors and patients do
not understand these calculations. Consequently, doctors often cannot
advise their patients properly (Gigerenzer et al.,2007). One solution is to
boost doctors’and laypeople’s statistical prowess by training them to trans-
late probabilities (or percentages) into a representation that makes it much
easier to calculate the PPV.
In the present example, that would mean replacing conditional probabilities
by natural frequencies
Imagine 1000 men like you are tested. Of those, 63 will have prostate cancer
and, of those, 13 will test positive. Of the remaining 937 men who do not
have prostate cancer, 56 will also test positive. Thus, 69 men will test posi-
tive. But only 13 of them have prostate cancer. This is the situation you
are in if you test positive; the chance of you actually having prostate
cancer is about one in ﬁve, or 19%.
Sedlmeier and Gigerenzer (2001) trained people to construct such frequency
representations and compared the success of this approach with that of trad-
itional and explicit rule training (i.e. teaching people to insert probabilities
into Bayes’rule, as is typically done in schools). Transfer of learning to new
problems was good in both rule training and representation training.
However, the latter showed greater temporal stability, with no decrease in per-
formance even after 15 weeks. This kind of training in statistical and risk liter-
acy is relatively simple and requires a one-off time investment of less than 2
hours. However, it does necessitate some basic cognitive abilities (e.g.
3 Natural frequencies represent numerical information in terms of frequencies as they are experi-
enced in a series of events. More technically, natural frequencies are frequencies that have not been
normalised with respect to the base rates; that is, they still carry information about base rates.
150 RALPH HERTWIG
numerical and arithmetic skills), as well as the motivation to learn to translate
information from one representation into another. Without those, this statis-
tical reasoning boost will not work.
Motivation and basic cognitive skills are also the prerequisites for another
simple boost: a 5–10-minute expressive writing exercise has been shown to
reduce test anxiety (Ramirez & Beilock, 2011), a widespread phenomenon
(80% of community college students report a moderate to high degree of math-
ematics anxiety) related to negative outcomes such as impaired ﬁnancial plan-
ning. The motivational demands for boosts that require a greater time
investment will, of course, be even greater. In a study using a simple app,
parents who were habitually anxious about mathematics did bedtime mathem-
atics with their children, reading short numerical story problems and answer-
ing questions on topics such as counting, geometry, arithmetic, fractions and
probability over the course of the school year. By the end of the year, these chil-
dren outperformed children in a control group by almost 3 months in mathem-
atics achievement (Berkowitz et al.,2015). This is a great improvement, but it is
conditioned on regular investment of time.
To conclude, if individuals’cognitive resources are, for whatever reason,
severely compromised or their motivation is low, boosts that require one or
both of these resources will fail. In some contexts, policy-makers may
instead be able to choose a boost that requires minimal cognitive competences
(e.g. Gollwitzer, 1999). But if even such competences cannot be assumed, then
other kinds of interventions, such as default rules, appear preferable.
Such problematic cognitive and motivational conditions, however, also pose
a challenge for policy-makers who decide in favour of nudging. Nudging (or
soft paternalism; Sunstein, 2014) is suggested to be less intrusive than man-
dates and bans because it imposes low costs and permits people to easily
reverse the choice, thus maintaining freedom of choice. Reversibility is easy
in theory but may prove difﬁcult in practice, particularly if cognitive and motiv-
ational resources are compromised or unavailable. Under such circumstances,
advocates of default rules must face the possibility that they will stick, even if
they are ill suited to people’s preferences.
Rule 2. If policy-makers are uncertain about people’s goals, if there is marked
heterogeneity of goals across the population or if an individual has conﬂicting
goals, then boosting is the less error-prone intervention
In imposing a default rule, policy-makers (choice architects) seek to steer the
chooser’s behaviour towards his or her ultimate goal, as judged by him or
herself (e.g. greater savings or healthier food choices). This requires the
policy-maker to have information about the chooser’s goals; otherwise, the
default may not ﬁt those goals, leading to errors. But policy-makers do not
When to consider boosting 151
necessarily know what individuals care about (Rebonato, 2012). Experts may
even systematically misconstrue what people want for themselves –for instance,
imposing a treatment-at-all-costs medical model that ignores quality-of-life con-
cerns (Gawande, 2014). Even choosers themselves may not always be aware of
their goals; sometimes they may need to work them out, and to do that, they need
transparent information and the competence to process it. Let us again take par-
ticipation in cancer screening tests as an example (e.g. mammography or PSA).
Depending on the quality of the test (i.e. sensitivity and speciﬁcity) and the sub-
sequent treatment options, an individual may decide to participate in a speciﬁc
screening test (e.g. screening for colorectal cancer), but not another (e.g. screen-
ing for prostate cancer). In light of this test-dependent preference to participate,
nudging people to participate in all cancer screenings –for instance, by using
gain-framed (survival) rather than loss-framed (mortality) messages –would
be ethically controversial. There is a good chance, moreover, that it would not
increase people’s welfare (see Arkes & Gaissmaier, 2012). Here, boosting –in
terms of fostering statistical literacy –would be more appropriate (assuming
the boost is effective).
A related problem arises when goals are heterogeneous across a population
or in conﬂict within a person. One-size-ﬁts-all default rules that do not ﬁtan
individual’s conﬂicting goals (e.g. saving for retirement versus paying for a
child’s education) or people’s diverse situations, concerns or values may never-
theless ‘stick’. Personalised default rules can reduce the risks (Sunstein, 2014),
but choice architects need a great deal of information to design such rules, and
this information may well be lacking.
To conclude, when goals are uncertain, heterogeneous within a person or
across a population or are even in conﬂict within a person, boosting people’s
competence (e.g. risk literacy or decision skills) is likely to be more appropriate
than nudging, because the boost option is less prone to error.
Rule 3. If the working of a nudge requires it to be non-transparent or even
invisible to the person being nudged, then it fails the easy-reversibility test
and is paternalistic
Regulatory and ﬁscal interventions (e.g. a law prohibiting people from riding
motorcycles without helmets or the taxing of cigarettes or soft drinks) are
highly visible. Visibility is an important safeguard against arbitrary and unrea-
sonable government interventions: citizens can scrutinise them and hold the
government accountable for them. Nobody is confused or fooled. Boosts are
visible because they require the audience’s engagement. Many nudges are
also visible and public (e.g. graphic health warnings or automatic enrolment
in savings plans).
152 RALPH HERTWIG
But some nudges, such as the order of items on a menu, may ‘ﬂy under the
radar’of the person being nudged, potentially inﬂuencing their behaviour
without their awareness or the recognition that they could act differently.
For example, people appear not to realise that the amount they eat is substan-
tially driven by the portion size of a snack. As a result, they do not adjust their
subsequent intake to compensate for consuming a larger amount (Wansink,
2014). Similarly, shoppers may not be aware that they pay more attention
to, and are much more likely to buy, products placed at eye level than products
placed above or below it (‘eye level is buy level’). That is, the mechanisms that
retailers use to ‘nudge’shoppers may be invisible to consumers.
If the desired behavioural effects of a public policy nudge, such as the order
of items on a choice menu, depend on people not being told that this interven-
tion had been implemented –because, if informed, they would perceive it to be
manipulative and categorically override it –then a boost has signiﬁcant advan-
tages, at least on ethical grounds, and may for that reason be more appropriate.
How people respond when being told about either an intervention or its under-
lying mechanisms is an empirical question (for evidence, see Loewenstein et al.,
2015), but also raises ethical concerns.
Some have argued that it is not ethically necessary for governments to specify
that an intervention has been implemented, especially if it would make the inter-
vention less effective (see Bovens in House of Lords Science and Technology
Select Committee, 2011). From this standpoint, it sufﬁces that a perceptive
person could discern for him or herself that an intervention is in place.
However, this criterion may be too weak for several reasons. First, a person’s
ability to discern an intervention as such (e.g. a default or a different order) is
distinct from the ability to discern how it changes his or her behaviour –particu-
larly if the direction of the effect is counterintuitive. Second, if most people are, in
practice, unable to discern how an intervention changes their behaviour, its
underlying mechanism will essentially be hidden, rendering it difﬁcult for
people to reverse their choice and compromising their freedom of choice.
Third, if most people who are told about a nudge and its targeted effects
would reject it, then the nudge lacks acceptability and public authorisation.
More generally, if an intervention or its underlying mechanism is invisible, it
does not treat people with respect, and it may undermine their dignity and
agency. In a free society, I believe, public ofﬁcials should generally follow a
version of John Rawls’publicity principle: they should not adopt policies
that they could not defend in public. If a transparent intervention ran into
public opprobrium, there would be good reason not to adopt it.
Furthermore, in a free society, it should ideally be difﬁcult to keep things
hidden, and an intervention that ofﬁcials try to hide will hopefully come into
the open –compromising the entire enterprise.
When to consider boosting 153
Rule 4. If governments do not (always) act benevolently, or if they permit the
private sector to create ‘toxic’choice architectures, then boosting will
provide better protection for individuals
Choice architectures are ubiquitous and their systematic impacts can rarely be
avoided. The private sector nudges, as does the government. There is little
doubt that both private and public choice architectures can have illicit goals
or be manipulative (e.g. Nestle, 2015). Governments inﬂuenced by wealthy
donors or lobbyists, for example, may act in a way that does not have the
welfare of the citizens at heart, but that favours particular interest groups.
Thus, nudges used by public ofﬁcials may, in the worst-case scenario, be coer-
cive and manipulative (Rebonato, 2012). To promote both welfare and auton-
omy under such circumstances, it is desirable for individuals to be equipped
with the competence to see through and to counteract illicit or manipulative
nudges –and toxic choice environments more generally.
Take, for instance, the problem of asymmetric framing in the medical domain.
One widely used industry technique is to report the beneﬁts of medical interven-
tions in the form of relative risks (big numbers) and their harms and side effects
in the form of absolute risks (small numbers). This asymmetry magniﬁes the
representation of beneﬁts and minimises harms. To inoculate patients against
this industry nudge, they should be equipped with the skills to see through
this and other manipulative techniques and to understand both absolute and
relative risks and health statistics more generally (Gigerenzer et al.,2007).
The phenomenon of defensive decision-making is another example of how a
choice environment can become toxic. Although highly prevalent in medicine,
defensive decision-making is not limited to this ﬁeld. In medicine, it describes
the behaviour of doctors who alter their clinical decisions in response to the
threat of being sued for medical malpractice. The option they pursue is not
necessarily the best for the patient, but is motivated by the threat of liability.
Defensive practices include ordering more diagnostic tests, prescribing more
medication than is medically indicated or suggesting unwarranted invasive pro-
cedures. Such practices are prevalent in high-liability specialties (e.g. gynaecol-
ogy, radiology and orthopaedic surgery) in the USA (Studdert et al.,2005), but
are also relatively common in less litigious health environments. Within such
an environment, patients should not blindly follow their doctors’advice;
rather, they also need to understand the relevant numbers (e.g. health
To conclude, citizens need to be enabled to discern and competently navigate
public and commercial choice environments in which choice architects do not
act benevolently and ﬁnancial conﬂicts of interest are endemic. Without boost-
ing people’s skills to see through toxic choice environments, this will be barely
154 RALPH HERTWIG
possible, or its effects limited. Next, I discuss two somewhat neglected dimen-
sions that should be entered into a cost–beneﬁt analysis in order to determine
which intervention maximises welfare.
Rule 5. If the policy-maker aims to foster generalisable and lasting behaviours,
boosting seems, ceteris paribus, to be more expedient
Redesigning school cafeterias to promote healthier food choices is a paradigmatic
example of a public policy nudge (Thaler & Sunstein, 2008), but school cafeterias
are just one context in which high-school students make food choices. Each
context has its own choice architecture, and many are not under the control of
a benevolent choice architect. For instance, many students have countless oppor-
tunities to buy and consume junk food on their way home from school; once
home, they are bombarded with television food advertisements.
If the goal is to promote healthy food choices that promise to be generalis-
able across a wide range of (benevolently or commercially constructed)
choice architectures, including architectures that are harder to reach by
nudges than by boosts (e.g. the family dinner table; Dallacker et al.,2017),
then boosts (e.g. in the form of a set of simple, memorable rules for eating
wisely; Pollan, 2009) are more likely to succeed than nudges. Indeed, one of
the advantages of boosts is that people can apply them independently of the
given choice architecture. A nudge in the form of cafeteria design, in contrast,
is restricted to the speciﬁc context in which it is implemented.
In theory, boosts aim to produce long-lasting behavioural effects by instilling a
new competence or fostering anexisting one. This isless clear in the caseof nudges.
One-time nudges (e.g. the organ donation opt-out default or automatic enrolment
in a savings plan or green energy) may ceaseto have the desired behavioural effect
as soon as they are removed. In contrast, many-time nudges that affect behaviour
repeatedly (e.g. daily food choices in a rearranged cafeteria) may produce
behavioural routines through learning (e.g. healthy food choices). These routines
may ‘survive’the removal of the initial choice architecture and generalise to other
In the service of lasting behaviours, let me also emphasise the importance of
self-nudging. Self-nudging means that people intentionally nudge themselves in
order to self-regulate their behaviour and break self-destructive habits (akin to
Thomas Schelling’s self-commitment devices). For example, people can
rearrange their kitchen so that sweets and snacks are placed out of convenient
reach and healthier foods are stored at eye level. A user can set his or her
browser to open with a news page rather than a sports page. When the
nudger and the nudged are one and the same person, as in the case of self-
nudging, autonomy and agency remain intact.
When to consider boosting 155
To nudge him or herself, however, the individual has to have the same
insights into the nudging intervention and the underlying mechanism as the
choice architect (unlike in the conception of nudging of Thaler & Sunstein,
2008). The nudge thus becomes a boost insofar as individuals ﬁrst need to
be educated about the rationale of the self-nudging intervention, its mechan-
isms and effects and how those effects can be harnessed. Indeed, this is the
approach that is often taken in behaviour change programmes in health psych-
ology (e.g. Michie et al.,2008).
Rule 6. If there is substantial danger of unanticipated (unpredictable) and
undesired consequences of a nudging or boosting intervention, then consider
the respective alternative
The risk of unintended and undesirable behavioural consequences (‘behav-
ioural spillovers’; Dolan & Galizzi, 2015) is an insufﬁciently explored dimen-
sion of some forms of nudging. For example, past good deeds can liberate
individuals to engage in behaviours that are immoral, unethical or otherwise
problematic –behaviours that they would otherwise avoid. Something akin
to this ‘moral self-licensing’(e.g. Monin & Miller, 2001) may also occur
when people are nudged in certain ways. For instance, if a choice architecture
nudges individuals to make healthier food choices by, for instance, rearranging
foods in the cafeteria (order, eye level, etc.), people who had salad for lunch
may feel liberated to consume more calories later (afternoon snacks or
dinner) than they otherwise would have done. In the ﬁnancial domain, auto-
matic enrolment in pension plans has been shown to substantially increase par-
ticipation (Chetty et al.,2014), but people who feel safe in the knowledge that
they are investing in their retirement may feel liberated to engage in more dis-
cretionary spending then they otherwise would have done.
Whether a nudge has such unintended negative behavioural effects is anempir-
ical question, as is the magnitude of any such effects. Should they exist for a
speciﬁc nudge, however, then a boost may be the more advisable intervention.
Although it may be tempting to arguethat boosts are less likely to haveunintended
negative consequences than nudges –because boosts are less likely to be designed
as local repairs with unanticipated consequences in someother domain –the very
nature of unintended consequences is that they are difﬁcult to anticipate.
Therefore, boosts, like nudges, need to be evaluated in terms of their intended
and unintended consequences.
Finally, some unintended consequences may, of course, be desirable in nature.
For instance, people who are nudged repeatedly to adopt healthier food choices
may extend this behaviour to other domains (e.g. exercising). As with negative spill-
over effects, whether such effects occur –and for what forms of nudging or boosting
156 RALPH HERTWIG
–is an empirical question. If positive spillover effects occur, they would, of course,
add a desirable and little-explored dimension to both forms of policy intervention.
The main goal of this article was to place a spotlight on boosts –a distinct kind
of behaviourally informed policy intervention that has, in my view, received
too little attention in work on choice architectures and in public policy
circles. When people are at risk of making poor choices, one important
response is to boost their competences. Boosting can be done in many ways,
such as by ensuring transparent communication (e.g. that appeals to, rather
than confounds, people’s intuitions), by offering information and education
(e.g. brief, comprehensible statements of fact), by instilling or fostering
speciﬁc cognitive or behavioural competences (such as ﬁnancial and accounting
rules of thumb [Drexler et al.,2014], simple food choice rules [e.g., Pollan,
2009], risk literacy skills [Gigerenzer et al.,2007] and strategic use of goal-
attainment strategies [Gollwitzer, 1999]) or by helping people to overcome
their anxieties and motivational problems (e.g. Berkowitz et al.,2015).
Such boosts, which aim to give choosers agency, make assumptions about
people’s cognitive and motivational abilities. If people cannot or are not moti-
vated to engage with them, boosts will not be effective interventions. From the
point of view of policy-makers, some boosts may lack cost effectiveness to the
extent that they require choice architects to invest signiﬁcant time and money.
Yet boosts have the advantage –at least in theory –of producing more lasting
behaviours (rather than short-term changes). The risk of imposing a one-size-
ﬁts-all solution on a heterogeneous population is smaller. They are more likely
to offer competences that generalise beyond a benevolently designed choice
architecture to commercially constructed and even toxic choice architectures.
To the extent that boosts promote people’s capacity to choose for themselves,
they also have obvious advantages when measured against concerns for auton-
omy. Arguments in favour of boosts are clearly weakest when individuals have
low levels of competence or motivation and when effective boosts would, from
the point of view of the policy-maker, be costly to implement. Furthermore,
boosts and nudges need to be evaluated against their intended as well as unin-
tended adverse consequences.
Niches for boosts, nudges and both
Nudges and boosts are not perfect substitutes. For instance, no nudge has been
proposed to reduce mathematics and test anxiety (Ramirez & Beilock, 2011;
Berkowitz et al.,2015) or to foster risk literacy (Gigerenzer et al.,2007).
When to consider boosting 157
Conversely, to my knowledge, no boost has been designed to control vehicle
speeding (cf. the nudge of painting narrower white lines on roads to create
the visual illusion of speed; Thaler & Sunstein, 2008) or to increase students’
enrolment in medical plans (cf. the nudge of automatic enrolment). Yet there
are many behavioural domains in which policy-makers can choose between
nudging and boosting, including the contexts of self-control problems (e.g.
food choice), ﬁnancial decisions and medical decisions. The rules outlined in
this article are intended to give policy-makers some guidance in this choice.
In many domains, the two approaches may well complement each other and
thus amplify the desired behavioural effects. For instance, the redesign of
school cafeterias could easily be combined with the provision of simple but
smart food choice rules (Pollan, 2009).
Boosting does not equal more schooling
Although some boosts (e.g. expressive writing to reduce test anxiety and
representation training for statistical information; Ramirez & Beilock, 2011;
Sedlmeier & Gigerenzer, 2001) could easily be included in school curricula,
boosting, as understood here, is not identical to traditional formal education
and schooling for several reasons. First, boosts, like nudges, need to be
rooted in empirical evidence. Each of the (small selection of) boosts outlined
in this article has been informed by laboratory or ﬁeld studies. Although
what happens in the classroom is increasingly subject to empirical testing, it
seems fair to say that not all instructional techniques are empirically validated.
Second, the primary goal of boosts is not to offer accurate declarative knowl-
edge and general procedural competences such as reading, writing, grammar
and algebra. Instead, boosts offer competences in domains that school curric-
ula either do not explicitly address or fail to deal with effectively (e.g. ﬁnancial
decision-making, healthy food choice, medical decisions, self-control pro-
blems, anxieties and speciﬁc risk assessment and prevention skills; Senn
et al.,2015). Finally, boosts should be able to enhance such procedural compe-
tences with limited time and effort, rather than through years of schooling.
Ongoing empirical evaluation
Let me conclude with an observation on the role of the behavioural sciences.
The nudge approach has done the immeasurable service of drawing policy-
makers’attention to the evidence mustered by the behavioural sciences.
More such evidence is needed. In outlining the conditions under which
nudging or boosting appear to be the more appropriate interventions, this
article has raised a number of empirical questions. For instance, do speciﬁc
nudges or boosts have unintended (from the choice architect’s point of view)
158 RALPH HERTWIG
adverse effects? What is the magnitude of any such effects? How do people
respond when told about a speciﬁc intervention –with acceptance or resist-
ance? These and related questions suggest that an ongoing evaluation of any
such intervention is needed. Of course, the same also holds for policy interven-
tions other than the non-regulatory and non-ﬁscal ones discussed here.
This manuscript beneﬁted from helpful comments from Sabrina Artinger, Mattea
Dallacker, Gerd Gigerenzer, Niklas Keller, Jutta Mata, Björn Meder, Cass Sunstein and
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