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When to consider boosting: some rules for policy-makers



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.
When to consider boosting: some rules
for policy-makers
Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany
Abstract: In recent years, public ofcials 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-scal and non-regulatory interventions that steer
(nudge) people in a specic direction while preserving choice. Less attention
has been paid to boosts, an alternative evidence-based class of non-scal 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 nancial choices, the
policy-maker rather than steering behaviour through nudging can take
action to foster or boost individualsown decision-making competences.
Boosts range from interventions that require little time and cognitive effort
on the individuals 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-scal 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 approachesrespective
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:
Behavioural Public Policy (2017), 1: 2, 143161
©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 efciency, anti-obesity policy, consumer protec-
tion, food safety and nancial 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 BITs 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 BITs 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
thinkapproach by John et al.,2011). The objective of boosts is to improve
peoples 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
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potential benets of a medical test recommend by their physician (e.g. the pros-
tate-specic antigen [PSA] test) outweigh the tests 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,
nancial literacy is a competence that could be boosted by, for instance, teach-
ing individuals simple nancial 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-scal 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, peoples welfare. This criterion can be operationalised by
means of costbenet 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-makerscostbenet analyses,
however, let us rst consider the differences between boosts and nudges in
more detail.
What distinguishes boosts from nudges?
Many policy interventions can be used to change behaviour, including man-
dates or bans (restricting or eliminating options), scal measures (monetary
incentives and disincentives) or non-regulatory and non-scal 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 dened boosts as interventions that
[in order] to extend the decision-making competences of laypeople and pro-
fessionals alike target the individuals skills and knowledge, the available
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set of decision tools, or the environment in which decisions are made
(p. 152).
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 specic behaviour. Such intentions are not necessarily translated
into action, however. Findings on the intentionbehaviour gapsuggest that
conceptual knowledge often does not sufce, 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-scal and non-regulatory approaches often share a commitment
to Herbert Simons notion of bounded rationality, their interpretations of its
implications differ. Those in favour of nudges typically invoke the ndings
of Kahneman and Tverskys heuristics and biases research programme (see
Rebonato, 2012), emphasising decision-makerssystematic cognitive biases
and motivational shortcomings. Those in favour of boosts typically invoke
ndings showing that bounds on peoples 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 t 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-makerschoices, the
possible existence of this dynamic highlights the importance of analysing both categories of
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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 dened
above, are preferable to nudges.
Welfare, costbenet 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
Sunsteins(2014) broad denition, the term welfarerefers 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 chooserswelfare. Their choices do not
affect others, except perhaps indirectly. One way to approach the welfare ques-
tion is to analyse relevant costs and benets from the standpoint of the chooser
and the policy-maker, and to select, among the possible interventions, the one
maximising the choosers 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 difcult 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 ts
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the statistically average person, but not what ts 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
third parties.)
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 processesin 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 nancial decision-
making skills by teaching people simple nancial and accounting heuristics
(Drexler et al.,2014) or implementing a 12-hour sexual assault resistance
training programme (including risk assessment strategies) for rst-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 benets 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 peoples retirement savings.
Savings behaviour could be increased by efforts to promote nancial 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 nancial literacy education has, at best, limited
success. Should efforts to promote nancial literacy indeed fail to meaningfully
promote retirement savings, the argument against nancial 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 benets? 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 specic behaviours appears to be more effective than educative interventions that
are not coordinated with the intended behaviour (Johnson et al.,2013). Furthermore, nancial 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?).
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could quantify the costs and benets of (say) default rules and compare them
with the corresponding costs and benets of boosting peoples 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-makers 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 costbenetefciency. 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 rst 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 costbenet calculus out-
lined above and thus balanced against an interventions 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 efcient
In order to develop new skills and competences, people need sufcient 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 patients
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
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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 specicity;
p[pos|no PC]). On the basis of this information, the patient and his physician
can use Bayesrule 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 doctorsand laypeoples 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
as follows:
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 ve, 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 Bayesrule, 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.
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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 510-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 nancial 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 individualscognitive 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 difcult 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 peoples preferences.
Rule 2. If policy-makers are uncertain about peoples goals, if there is marked
heterogeneity of goals across the population or if an individual has conicting
goals, then boosting is the less error-prone intervention
In imposing a default rule, policy-makers (choice architects) seek to steer the
choosers 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 choosers goals; otherwise, the
default may not t those goals, leading to errors. But policy-makers do not
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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 specicity) and the sub-
sequent treatment options, an individual may decide to participate in a specic
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 peoples 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 conict within a person. One-size-ts-all default rules that do not tan
individuals conicting goals (e.g. saving for retirement versus paying for a
childs education) or peoples 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 conict within a person, boosting peoples
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 scal 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 audiences engagement. Many nudges are
also visible and public (e.g. graphic health warnings or automatic enrolment
in savings plans).
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But some nudges, such as the order of items on a menu, may y under the
radarof the person being nudged, potentially inuencing 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 nudgeshoppers 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 signicant 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 sufces 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 persons
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 difcult 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 ofcials should generally follow a
version of John Rawlspublicity 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 difcult to keep things
hidden, and an intervention that ofcials try to hide will hopefully come into
the open compromising the entire enterprise.
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Rule 4. If governments do not (always) act benevolently, or if they permit the
private sector to create toxicchoice 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 inuenced 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 ofcials 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 benets 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 magnies the
representation of benets 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 eld. 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 doctorsadvice;
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 nancial conicts of interest are endemic. Without boost-
ing peoples skills to see through toxic choice environments, this will be barely
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possible, or its effects limited. Next, I discuss two somewhat neglected dimen-
sions that should be entered into a costbenet 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 specic 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 survivethe removal of the initial choice architecture and generalise to other
dietary decisions.
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 Schellings 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
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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 rst 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 insufciently 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 nancial 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
specic 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 difcult 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
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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, peoples intuitions), by offering information and education
(e.g. brief, comprehensible statements of fact), by instilling or fostering
specic cognitive or behavioural competences (such as nancial 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
peoples 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 signicant 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-
ts-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 peoples 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
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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), nancial 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 eld 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. nancial
decision-making, healthy food choice, medical decisions, self-control pro-
blems, anxieties and specic 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-
makersattention 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 specic
nudges or boosts have unintended (from the choice architects point of view)
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adverse effects? What is the magnitude of any such effects? How do people
respond when told about a specic 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-scal ones discussed here.
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When to consider boosting 161
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... 7 As boosts aim at improving deliberative thinking, they require additional cognitive resources to be successful. Some believe (see for example Hertwig, 2017) that if target individuals lack either motivation or cognitive resources, nudges can be more effective than boosts in achieving a specific behaviour change (Hertwig, 2017). ...
... 7 As boosts aim at improving deliberative thinking, they require additional cognitive resources to be successful. Some believe (see for example Hertwig, 2017) that if target individuals lack either motivation or cognitive resources, nudges can be more effective than boosts in achieving a specific behaviour change (Hertwig, 2017). ...
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... This is the first systematic evaluation of these four behavioural tools, and also first formal evaluation of nudge+. As such, we contribute to a growing comparative literature on nudging versus alternate forms of behaviour change (van Roekel et al., 2022;John et al., 2022;Krawiec et al., 2021;Hertwig, 2017;Bradt, 2019;Franklin et al., 2019). First, we test "nudge+" (Banerjee and John, 2021). ...
... These interventions include deliberative policies that educate people to think about their available and affordable choice alternatives before they engage in optimal choice processes. Finally, we test "boosts" (Grüne-Yanoff and Hertwig, 2016;Hertwig, 2017;Hertwig and Grüne-Yanoff, 2017). Boosting relies on building people's competencies to enable them to make better decisions. ...
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Nudges have been increasingly deployed to deliver climate policies in the last decade. Recent evidence shows nudges are hard to scale–up. So can we use nudges more effectively, or should we rely on other tools of behaviour change? We argue that reflective strategies can enhance nudges by encouraging agency and ownership in citizens. We test this by systematically comparing nudges to reflective interventions like thinks, boosts, and nudge+ over orders of low-carbon meals using an online experiment with 3,074 participants in the United Kingdom. We find all behavioural interventions increase intentions for climate-friendly diets, but encouraging reflection prior to nudging (“nudge+”) strengthens these treatment effects. There is no evidence of negative behavioural spillovers as measured by participants’ donations to pro-social charities. There is potential for reflective policies in promoting climate citizenship.
... A nudge for good, and in the right direction Sunstein, 2008, 2021), once considered to be cost-effective (Benartzi et al., 2017) and attractive to organisations globally (OECD, 2017;Ball and Head, 2021), is now proving to under-deliver on its goals. Some relate these shortcomings to the design of these interventions (Hertwig, 2017;Mongin and Cozic, 2018;Tor, 2020;Banerjee and John, 2021). Others, for example, Chater and Loewenstein (2022) conclude, our focus on individual behaviour change ("i-frame"), rather than systemic changes ("s-frame"), has led behavioural public policy astray. ...
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Behavioural public policy is increasingly interested in scaling-up experimental insights to deliver systemic changes. Recent evidence shows some forms of individual behaviour change, such as nudging, are limited in scale. We argue, we can scale up individual behaviour change by accounting for nuanced social complexities in which human responses to behavioural public policies are situated. We introduce the idea of the "social brain", as a construct to help practitioners and policymakers facilitate a greater social transmission of pro-social behaviours. The social brain encompasses complex human relationships interacted with elements of the choice architecture. It includes three main components: (1) individual actors, representing nodes in the social brain, who interact with other actors through (2) verbal and non-verbal cues, and who are affected by the (3) physical environment in which they belong. Ignoring the social brain runs the risk of fostering localised behavioural changes, through individual actors, which are neither scalable nor lasting. We identify pathways to facilitate changes in the social brain: either through path dependencies or critical mass shifts in individual behaviours, moderated by the brain's property of social cohesion and multiplicity of situational and dispositional factors. In this way, behavioural changes stimulated in one part of the social brain can reach other parts and evolve dynamically. We recommend designing public policies that engage different parts of the social brain as a whole.
... governance issue that will have to be worked out between government, civil society and the food industry in the future. • increase the real wage of workers so that they can have access to food of better nutritional quality (Penne & Goedemé 2020); • ban certain food produced with substances harmful to the body (Levasseur 2020) • to introduce changes in social norms to avoid the reproduction of gender roles that put women in a high vulnerability situation of suffering obesity • to introduce educational boosts (Grüne-Yanoff & Hertwig 2016;Hertwig 2017;Hertwig & Ryall 2016) to raise nutritional education in agents. ...
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... The design of choice architecture is one approach that takes into account these insights into human behaviour, and the interventions undertaken should take into account that there are many forces that interact when we decide how to behave in a given situation. Research on choice architecture has shown that in quite a number of cases it is possible to structure the decision-making environment in such a way that the expressed interests of individuals are more closely linked to social goals [102]. This research is still ongoing and also concerns testing the support and effects of behavioral interventions to promote sustainable behaviors related to GHG emission reduction. ...
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... Last, we included two interventions aimed at boosting participants' ability to make informed decisions (31,32). These interventions targeted possible sources of misunderstanding in the game situation so that decision-makers were empowered to align their choices with their preferences and to make good, autonomous decisions. ...
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... Differently from nudges, boosts are interventions that do not target behaviour, but competencies, with the aim to empower individuals to make complex decisions (Grüne-Yanoff and Hertwig, 2016;Hertwig, 2017;Hertwig and Ryall, 2020), like investing in EE. As an example, training providing some basic financial concepts, in addition to knowledge on energy-related issues, can boost the necessary skills to make complex calculations, helping appreciate the benefits of EE and make a well-informed investment decision (Blasch et al., 2017). ...
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The Climate Change urgency requires a swift reduction of energy consumption. One way to achieve this is through increased energy efficiency. Over the past decades, the debate on how to encourage energy efficiency has been guided by the physical-technical-economic model, which has a strong focus on technologies and cost savings, and in which human behaviour has been seen as a trivial factor. However, the advent of behavioural economics has started enabling the integration of the human factor also into energy efficiency policy. Still, this integration is only in its infancy. While the perspectives taken by economics and behavioural sciences enable to capture the individual dimension of energy efficiency as a problem of individual choice, the collective and social aspect of energy efficiency is still largely overlooked on the energy policy agenda. With its emphasis on how social structures interpenetrate individual actions and construction of reality, sociology offers an additional important insight that goes beyond the identification of barriers-drivers underlying investment choices. This paper aims to increase policy makers' awareness of complementary disciplinary resources, on which they can draw to better define and address the problems associated to energy efficiency. Second, it provides a case to develop an interdisciplinary perspective as a basis to develop a more scientifically valid and socially relevant energy efficiency policy advice.
Behavioral policies Nudge and Boost are often advocated as good candidates for evidence-based policy. Nudges present or “frame” options in a way that trigger people’s decision-making flaws and steer into the direction of better choices. Nudge aims to do this without changing the options themselves. Boosts also present choices in alternative ways without changing options. However, rather than steering, Boosts are aimed to increase people’s competences. Nudge and Boost originated in extensive research programs: the “heuristics-and-biases program” and the “fast-and-frugal heuristics program,” respectively. How exactly do Nudge and Boost policies relate to the theories they originated from in the first place? Grüne-Yanoff and Hertwig labeled this a question of “theory-policy coherence” and propose to use it for determining the evidence-base of Nudge and Boost. I explore the question: “In how far is theory-policy coherence in Nudge and Boost relevant for evidence-based policymaking?.” I argue that the implications of (weaker or stronger) theory-policy coherence are relevant in two ways. First, Grüne-Yanoff and Hertwig show that theory-policy coherence between Nudge and Boost and the research programs is not as strong as often assumed. It is crucial for the evidence-based policymaker to realize this. Assuming theory-policy coherence while it does not exist or is weaker than assumed can lead to an incorrect assessment of evidence. Ultimately it can even lead to adoption of policies on false grounds. Second, the concept of theory-policy coherence may assist the policymaker in the search and evaluation of (mechanistic) evidence. However, in order to do so, it is important to consider the limitations of theory-policy coherence. It can neither be employed as the (sole) criterion with which to determine how well-grounded a policy is in theory, nor be the (sole) basis for making comparative evaluations between policies.
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In recent years, policy makers worldwide have begun to acknowledge the potential value of insights from psychology and behavioral economics into how people make decisions. These insights can inform the design of nonregulatory and nonmonetary policy interventions—as well as more traditional fiscal and coercive measures. To date, much of the discussion of behaviorally informed approaches has emphasized “nudges,” that is, interventions designed to steer people in a particular direction while preserving their freedom of choice. Yet behavioral science also provides support for a distinct kind of nonfiscal and noncoercive intervention, namely, “boosts.” The objective of boosts is to foster people’s competence to make their own choices—that is, to exercise their own agency. Building on this distinction, we further elaborate on how boosts are conceptually distinct from nudges: The two kinds of interventions differ with respect to (a) their immediate intervention targets, (b) their roots in different research programs, (c) the causal pathways through which they affect behavior, (d) their assumptions about human cognitive architecture, (e) the reversibility of their effects, (f) their programmatic ambitions, and (g) their normative implications. We discuss each of these dimensions, provide an initial taxonomy of boosts, and address some possible misconceptions.
In recent years, ‘Nudge Units’ or ‘Behavioral Insights Teams’ have been created in the United States, the United Kingdom, Germany, and other nations. All over the world, public officials are using the behavioral sciences to protect the environment, promote employment and economic growth, reduce poverty, and increase national security. In this book, Cass R. Sunstein, the eminent legal scholar and best-selling co-author of Nudge, breaks new ground with a deep yet highly readable investigation into the ethical issues surrounding nudges, choice architecture, and mandates, addressing such issues as welfare, autonomy, self-government, dignity, manipulation, and the constraints and responsibilities of an ethical state. Complementing the ethical discussion, The Ethics of Influence: Government in the Age of Behavioral Science contains a wealth of new data on people’s attitudes towards a broad range of nudges, choice architecture, and mandates.
The authors present and test a new method of teaching Bayesian reasoning, something about which previous teaching studies reported little success. Based on G. Gigerenzer and U. Hoffrage's (1995) ecological framework, the authors wrote a computerized tutorial program to train people to construct frequency representations (representation training) rather than to insert probabilities into Bayes's rule (rule training). Bayesian computations are simpler to perform with natural frequencies than with probabilities, and there are evolutionary reasons for assuming that cognitive algorithms have been developed to deal with natural frequencies. In 2 studies, the authors compared representation training with rule training; the criteria were an immediate learning effect, transfer to new problems, and long-term temporal stability. Rule training was as good in transfer as representation training, but representation training had a higher immediate learning effect and greater temporal stability.
Three experiments supported the hypothesis that people are more willing to express attitudes that could be viewed as prejudiced when their past behavior has established their credentials as nonprejudiced persons. In Study 1, participants given the opportunity to disagree with blatantly sexist statements were later more willing to favor a man for a stereotypically male job. In Study 2, participants who first had the opportunity to select a member of a stereotyped group (a woman or an African American) for a category-neutral job were more likely to reject a member of that group for a job stereotypically suited for majority members. In Study 3, participants who had established credentials as nonprejudiced persons revealed a greater willingness to express a politically incorrect opinion even when the audience was unaware of their credentials. The general conditions under which people feel licensed to act on illicit motives are discussed.
Background: In adults, lower numeracy is associated with poorer nutrition label comprehension and higher BMI. It remains unclear whether parental numeracy also impacts children's body weight. Purpose: We examined the relationship between parental numeracy and children's BMI z-scores and analyzed whether weight-related numerical information processing skills-specifically, portion-size estimation skills, comprehension of nutrition labels, and comprehension of growth charts-mediated that relationship. Design and methods: Numeracy, portion-size estimation skills, comprehension of nutrition labels, and comprehension of growth charts were assessed in face-to-face interviews with 320 parents of children aged 6-12 years in Germany. Parent and child body weight were measured; parents reported both their own height and that of their children. Results: Lower parental numeracy was significantly associated with having a child who was either underweight (β = 0.126, P = 0.048) or overweight (β = -0.299, P < 0.001). Lower parental numeracy was also associated with poorer portion-size estimation skills (r = -0.08, P = 0.023) and inferior comprehension of growth charts (r = 0.33, P < 0.001) and nutrition labels (r = 0.26, P < 0.001). However, these weight-related numerical information processing skills did not mediate the association between parental numeracy and children's BMI. Conclusion: This study is the first to find lower parental numeracy to be a risk factor for children being either over- or underweight. However, portion-size estimation skills, comprehension of nutrition labels, and comprehension of growth charts did not mediate the association between parental numeracy and children's BMI. The present findings thus winnow down the set of mechanisms potentially underlying this association. Parental numeracy is an as yet largely overlooked factor that can be targeted when developing interventions to prevent and treat malnutrition and to achieve and maintain a healthy body weight in children.
How can governments persuade citizens to act in socially beneficial ways? Thaler and Sunstein's book Nudge drew on work from behavioural economics to claim that citizens might be encouraged through 'light touch interventions' (i.e.nudges) to take action. In this ground-breaking successor to Nudge, Peter John and his colleagues argue that an alternative approach also needs to be considered, based on what they call a 'think' strategy. Their core idea is that citizens should themselves deliberate and decide their own priorities as part of a process of civic and democratic renewal. The authors not only set out these divergent approaches in theory but they offer evidence from a series of experiments to show how using techniques from 'nudge' or 'think' repertoires work in practice and how that practice is made effective. The book is unique in exploring an expanding field of policy and social science interest - changing civic behaviour, using insights from another growing field of social science interest - the rise of experimental methods.
Based on a series of pathbreaking lectures given at Yale University in 2012, this powerful, thought-provoking work by national best-selling author Cass R. Sunstein combines legal theory with behavioral economics to make a fresh argument about the legitimate scope of government, bearing on obesity, smoking, distracted driving, health care, food safety, and other highly volatile, high-profile public issues. Behavioral economists have established that people often make decisions that run counter to their best interests-producing what Sunstein describes as "behavioral market failures." Sometimes we disregard the long term; sometimes we are unrealistically optimistic; sometimes we do not see what is in front of us. With this evidence in mind, Sunstein argues for a new form of paternalism, one that protects people against serious errors but also recognizes the risk of government overreaching and usually preserves freedom of choice. Against those who reject paternalism of any kind, Sunstein shows that "choice architecture"-government-imposed structures that affect our choices-is inevitable, and hence that a form of paternalism cannot be avoided. He urges that there are profoundly moral reasons to ensure that choice architecture is helpful rather than harmful-and that it makes people's lives better and longer.
With a randomized field experiment of 587 first-graders, we tested an educational intervention designed to promote interactions between children and parents relating to math. We predicted that increasing math activities at home would increase children's math achievement at school. We tested this prediction by having children engage in math story time with their parents. The intervention, short numerical story problems delivered through an iPad app, significantly increased children's math achievement across the school year compared to a reading (control) group, especially for children whose parents are habitually anxious about math. Brief, high-quality parent-child interactions about math at home help break the intergenerational cycle of low math achievement.