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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.
Behavioural public policies for the social brain
Sanchayan Banerjee1, Siddhartha Mitra2
Short Abstract
We can scale up behaviour change by accounting for nuanced social complexities in
which human responses to behavioural public policies are situated. We introduce the
“social brain”, encompassing complex human relationships interacted with elements
of choice architecture. It includes individual actors, representing nodes in the social
brain, who interact with other actors through verbal and non-verbal cues, and are
affected by the physical environment in which they belong. We identify pathways to
facilitate changes in the social brain: through path dependencies or critical mass
shifts, moderated by the social brain’s cohesiveness. Behavioural changes stimulated
in social brain can evolve dynamically. [100 words]
Long Abstract
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
Corresponding author: Department of Geography and Environment, London School of Eco-
nomics and Political Science, United Kingdom. Click here for homepage
Department of Economics, Jadavpur University, India. Click here for
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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. [224 words]
Keywords:Critical mass,Path dependency,Scaling up,Social brain,Social cohesion
Conflict of Interests: None.
Word count: Main text [8,029 words], References [1,839 words], Entire manuscript
[10,270 words]
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1 Introduction
Behavioural public policy is increasingly interested in scaling–up experimental
insights to deliver systemic changes (Al-Ubaydli et al., 2017a,b; Al-Ubaydli and
List, 2017; Al-Ubaydli et al., 2017, 2019, 2021; John, 2021). Recent evidence shows
some forms of individual behaviour change, such as nudging, are limited in scale
(Beshears and Kosowsky, 2020; DellaVigna and Linos, 2022; Mertens et al., 2022).
A nudge for good, and in the right direction (Thaler and 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. While we agree on the need to scale–up and deliver systemic
changes, we believe attention should continue to be given to the “i-frame”. In fact,
we suggest using the “i–frame” better, by accounting for nuanced social complexities
to scale–up human behaviour change in ways that reach the “s–frame”. This is the
objective of this paper. We outline behavioural public policies for the “social brain”,
a collective that helps us accommodate the interplay between human actors, and
between the greater social complexities in which these actors respond to behavioural
public policies, in designing better, scalable interventions.
We define the social brain as a vast set of invisible and dynamic linkages (call
forces), not just within the part of the human mind dealing with social behavior,
but also between a group of humans in any social or physical environment, mediated
by cues of verbal and non-verbal communication, and catalysed by the presence
of non-human animate and inanimate objects. The social brain considers human
behaviours as a result of individuals’ own characteristics, the dispositional factors
that explain their biases and ir(rationalities), and a social complex to which they
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belong, including situational factors which ultimately affect their decision–making.
Using this construct, it is possible to show that most tools of behaviour change
have attempted to identify and correct human biases by putting an undue emphasis
on only one aspect of the social complex, its choice architecture. In this frame,
elements of choice are presented differently to people (Johnson, 2022). This is where
nudges and their variants shine. Yet, there remains much more to explore within
the social complex. For example, can human interrelationships be used a medium
of change, not just in isolation, but in conjunction with the choice architecture
itself? If we capitalise on social cues, the drivers of these human interrelationships,
and alter the delivery points of these interventions, we might be able to facilitate
greater behavioural shifts. Alternatively, can we adapt behavioural tools to suit the
social complex better, to relay ecological rationality (Todd and Gigerenzer, 2012)?
For example, a nudge that fails in one social complex can work in another, with
relevant modifications to suit its social complex. While such an ask for behavioural
public policy is reasonable, engaging in these localisations can become a daunting
task without a construct. We contribute here, as we theorise into the social brain
to reconcile different behavioural pulleys that can motivate and direct individual
behaviour changes collectively to deliver maximum scalable impact.
However, the idea of this construct is not completely new. We have seen several
fragmented applications of the social brain, albeit not formally. For example, Michie
et al. (2013) and Michie and West (2013) reviewed behaviour change theories to
encompass the role of social and physical environments, applications of which have
shown how physical environments can prime people to perform certain behaviours
(Kay et al., 2004); sometimes fostering smarter heuristics (Gigerenzer and Gaissmaier,
2011). Recently, scholars have also turned towards a discussion of how human
emotions can drive behaviour change (Laffan et al., 2021; Rela, 2022). There is
also work acknowledging the role of contexts and situated environments in changing
human behaviours (Laffan et al., 2021; Lades et al., 2021). While these developments
in behavioural public policy speak to the merits of recognising a broader social
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complex, like in the social brain, there lacks a coordinated effort to develop and direct
interventions that systematically accounts for these complexities. Consequently,
our work is motivated by this gap to formalise the social brain which gives us
opportunities to employ means of behaviour change that facilitate a greater social
transmission of pro–social behaviours.
The social brain has wider connotations in acknowledging the existence of the
behavioural person. It can be viewed as a continuous fabric on which humans reside
as significant specks, with the welfare of each human affected by the quality and
properties of the fabric over its entire expanse. Behavioural scientists, concerned
primarily with human welfare, can shore up its levels by identifying and mending
swathes in the fabric needing repair, not just by attending to the humans concerned.
The rest of the paper is set out as follows: we motivate the idea of the social
brain by briefly reviewing competing theories in social sciences. We then provide a
comprehensive definition of the social brain and outline its properties and constituents.
We analyse human interrelationships through the lens of the social brain and explain
how we can use different parts of this social brain to promote scalable good behaviours.
Set up this way, we deduce testable hypotheses in using the social brain to improve
human interactions. Finally, we outline an approach to treat the social brain in
different socioeconomic contexts and steer welfare improving choices by fostering
amicable human interrelationships. There remains limitations to how much of
this can be realised just at once. But when this is done, we will rely not only on
changing individual human behaviours, as if it were the result of their own biases,
but also devise forms of change that consider the wider social complex in which such
behavioural responses are situated.
Competing theories of holistic behaviour change
The social brain, first seen as an evolutionary hypothesis relating to the biological
chain of cognitive development (Dunbar and Shultz, 2007; Dunbar, 2009), lacks a
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robust definition (Al´os-Ferrer, 2018). In its earliest exposition, it represented the
social correlate of primate cognition, quantified as the size of the brain. In essence,
to accommodate complex ‘Machiavellian’ relationships, our brains grew in size. More
recently, however, this definition has expanded to accommodate “any set of brain
structures and functions [that are] related to the perception and evaluation of the
social environment and how that perception and evaluation affects social decision
making” (Al´os-Ferrer, 2018, 246-247). The relationship between human behaviours,
realised from the social brain, and the social complex is thought to be bi–directional,
with forces at play that shape and reinforce each other. But this idea of the social
brain has been seen to echo in many other social science theories.
Consider, for instance, the idea that our mind can be influenced by factors
that reside outside the human body, in the socio-physical environments around us,
which has been centrepiece to the theory of the extended mind hypothesis. Active
externalism, in which the mind and external factors create a two–way interaction,
a coupled system of some sorts, was proposed by Clark and Chalmers (1998).
Cognitive processes, as such, are defined over the extended mind, rather than what
one has bodily control over. In recent times, technology has made such an idea more
relevant, with techno–human interactions being proposed as modes of behaviour
change (Krpan and Urban´ık, 2020). In addition, theories of situated cogntion (see
Lindblom and Ziemke (2003)) have resonated similar ideas of extrinsic influences,
albeit from the actions of other human beings. In this way, our mind can anticipate
and respond to other actors. These theses of the extended mind form a natural basis
to conceptualise our social brain. We think of the brain to encompass linkages with
extrinsic entities, such as other human actors, or the social and physical environments,
or even both, that can influence the mind.
The idea that such linkages are of paramount importance to behaviour change
were first coherently identified through the wheel of behaviour change, put for-
ward by Michie et al. (2011, 2014). Opportunities, relating to the C(apability)
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O(pportunity) M(otivation)–B(ehaviour) model of behaviour change, correspond to
physical opportunities, made available through environmental context and resources,
or social opportunities that reflect social influences and norms (Cane et al., 2012).
In a recent review of restructuring built environments, Wilkie et al. (2018) find such
physical opportunities facilitate the transmission of healthy behaviours, enabled by
behavioural policy interventions. And that is not to be confused with alterations of
the choice architecture simply. While elements of choice inhibit some behaviours and
motivate others in individuals, physical and social opportunities help diffuse intended
behaviour changes to more than one individual at a time, much like population–based
intervention strategies.
Such a population approach, where interventions are designed to target groups
wholly, rather than individually, to become beneficiaries of the treatment, was
introduced by Rose (2001) in applications of preventive medicine. In his conceptuali-
sation of delivering improved public health outcomes, often considered as one of the
‘absolute truths’ (Adams and White, 2005), he thought of altering social contexts to
minimise underlying health risks to all members of the population. These population
approaches, in addition to individual behaviour change strategies, have been effective
in changing diets, increasing levels of physical activity and curbing smoking rates
(Mozaffarian et al., 2012). The review of these competing theories point towards the
relevance of a construct—like the one we are about to propose—that will identify
multiple facets of social behaviour change. The individual mind and its extensions,
in its social and physical surroundings, with cues to relay information, are building
blocks of such a construct. In what follows next, we will outline the idea of the
social brain with these elements and discuss its properties.
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3 The social brain
3.1 A comprehensive redefinition
The conventional definition of the social brain points to the human brain’s neuro-
circuitry for processing social cues (Lieberman, 2013). These can be verbal and
non-verbal social cues, such as speech, body language, and expressions. The percep-
tion of these cues through human senses and their processing in the brain thereafter
occupies a significant chunk of a human’s waking and non-waking hours. As Lieber-
man (2013) suggests, these social cues not only produce pleasure and pain in the
individual but are a major conduit for influences, generating changes in behaviour
and acceptance of new ideas.
Each human brain can, therefore, be considered as an emitter and receiver of
social cues. Just like an ant releases pheromones to signal and interact with fellow
ants, we humans use non-chemical social cues, an outcome of chemical activity in the
brain, to behave around and influence one another. When viewed spatially, humans
form a network through which ideas, influences, and moods (anger, sorrow, and
happiness) traverse in the society, with significant implications for the welfare of
its corresponding social groups. Thus, it is possible to use this network to catalyse
behavioural improvements thought the regulation of these cues, amongst others, till
these improvements acquire the critical mass for their more universal adoption.
Turning to the cues as forces operating in the social brain, almost no idea or
thought generated by an individual can be completely original: received ideas (or
cues) are treated as inputs and processed by the brain to generate an output which
is then passed onto other human brains. This process can be conscious or not. Thus,
the society or the relevant collection of individuals, other than the considered one,
influence the ideas and behaviours of an individual. Each individual, as a processor
of such influences, contributes potentially to the modification of a social influence:
by processing information available in neurons, establishing neural connections inside
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the human brain, which yields an influential thought as an addition to the flow of
social communication. This spreads through the network that is the social brain.
But this network of human brains is embedded in a sea of objects, natural and
man-made, which, in turn, has implications for the functioning of the individual
brains and therefore the network. While individual humans are specks on the fabric
that is the social brain, and their social cues the transmitters that link them together
in co–determining human behaviours; the quality and properties of each swathe
of the fabric are also deterministic of human behaviour marking the relationships
corresponding to that swathe. This is what has been traditionally considered, in
parts3, as the ‘elements of choice’, which the choice architect manoeuvres.
When viewed as a whole, the idea being conveyed through the social brain as a
construct is that individual actions and behaviours depend on many factors located
outside the confines of themselves, and beyond their immediate choice architecture.
Realisation of this often–forgotten truism can help us to fix suboptimality in human
behaviours and welfare levels without unnecessarily blaming human actors, whose
actions are driven by their histories of origin (genes), nurture, and social interactions,
by tinkering with the nature of their social groups and location of the objects around
them, and by undertaking appropriate servicing of these factors. Changes in objects
or their design, often an artefact of tools of behaviour change like nudges, have
featured prominently. But this is only one dimension of change. We need to focus
on the social groups and factors that define their relationships, conditional on these
objects: the choice architecture.
Consider, for example, a thought experiment below. Sam (Samuel/Samuela) is
a fitness enthusiast and believes in maintaining a good and healthy life style. Sam
eats healthy food, except for the occasional times, when Sam finds themselves to
be in a certain context, such as when they meet a friend or go out on a vacation.
The policymaker notices Sam only during these specific contexts when Sam indulges
In traditional “i-frame” based choice architecture, social planners focus on the immediate
environment of the individual.
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in unhealthy (say, junk food) choices. If the policy maker decides to nudge Sam’s
behaviour unilaterally, without an explicit understanding of the contextual factors
around Sam, scaling–up the nudge is limited in its future applications. And in the
event it fails, is it reasonable to blame the nudge? No, its not. Why? There is a wider
social construct around Sam which tempts and influences Sam’s behaviours. Be-
haviour change interventions should be mindful of these social complexities. Consider
another example. Travellers are often in a bind. They want to travel sustainably,
but the price of sustainable transport might be higher than non–sustainable alter-
natives. Tools of behaviour change might fade in effects compared to the strong
(dis)incentive imposed by these high prices. In this situation, is it reasonable to
conclude, based on their actions (un)altered by the nudge, that the traveller does not
have environmental preferences? A behavioural public policy that does not recognise
this social construct will also be limited in its impact. Even further, consider, all
our actions which result jointly as interactions with other human actors, or due to
the environment (not simply choice architecture) we happen to be in. Adopting the
social brain acknowledges these influences which are important for ‘horizontally’ and
‘vertically’ scaling up behaviour change (Al-Ubaydli et al., 2021). More importantly,
it gives policymakers an opportunity to design more effective interventions to begin
with that suit the social complex people find themselves in.
Let’s argue this with some more examples. Experimental evidence suggests
greenery and proximity to water bodies have a calming impact on humans and
therefore on human interactions, with a salutary influence on their focus and
creativity (Nichols, 2014; Dolan, 2014; Dolan and Kahneman, 2015; Braubach et al.,
2017). There are positive changes that are triggered by a walkway surrounded by
greenery inside a company’s premises: the calming effect of being exposed to nature
implies that aggressive interpersonal behaviour threatening employee productivity
is curbed. Employees walking in groups in the lap of nature develop close bonds
and cooperation that boosts productivity. Mobile phones have a complex impact on
human behaviour and well being, helping humans to keep in touch with emotionally
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close members while being physically separated; and enhancing the number of
contacts (see Chen et al. (2013)) while giving rise to addictions and obsessions with
digital content that can significantly and adversely impact human relationships.
Inside offices, employees can be made to feel at ease through various gestures and
facilities which enhance their productivity: resting areas for workers and provision
of a certain amount of privacy to name a few. These objects impact the quality
and material of the social fabric. Aided with these objects, some cues are picked up
faster than others which can result in improved behaviours.
The social planner must, therefore, assume the role of a visionary
in acknowl-
edging these different dimensions to behaviour change, just like a pool shark, who in
stroking a single ball on the billiard table is able, because of their clarity of vision in
the sport, to correctly anticipate the resulting interaction of all the balls on that table
and the consequent outcomes. In billiards (planning), the skill and awareness of the
player (planner), in anticipating the movements of various balls (humans) through
the chain reaction triggered by a given stroke and then choosing and executing
the optimal stroke among various alternatives, matters significantly. Modifying
behaviour at the level of the individual, a unit of the social brain, necessarily forms
the basis for wider changes within the social brain, given the inter–relatedness of
human behaviour. As such, we envisage new form of behavioural spillovers, not
simply between actions of the same human, but between actions of different human
actors, which then reinforces the system of behaviour change. These are important
feedback loops that the policymaker must account for when thinking of delivering
scalable, systemic changes.
To summarise, the social brain is a network of individuals (their minds), im-
mersed in a significant sea of (in)animate objects in their social and physical
environments, through which social cues travel. Set up this way, the network of
individuals bears an uncanny resemblance to the neural network inside a human
brain, hence the name “social brain”. We visualise this construct in Figure 1. Each
4this is a conventional welfarist argument, for details see Sugden (2013).
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node in social brain fabric is analogous to individual human actors in our society,
exemplified by persons A and B, such that one person is connected to many others.
The effect of these actors on each other, in turn, are determined by the proximity of
the nodes at which they are located, though given the state of modern technology,
the correct reference would be a measure of connectedness by social, familial, or
ideological proximity rather than physical proximity only. These invisible bonds,
the visible thread between the nodes in Figure 1, are channels of communication
between human actors. For example, the red line demonstrates the connectedness
between person A and B. These are the social cue transmitters that link these actors
together. Together with these strong human influences, are non-human objects
which not only affect and relay the cues but also affect the processing of cues in the
social brain, modifying behaviours and moods and catalysing ideas. These, in turn,
are manifested by the quality and properties of the fabric. All of it taken together
makes up the social brain.
Figure 1: The Social Brain Fabric
Take, for example, the connotation of A and B lying at the same altitude on
the social fabric. It can signal similar reference points on stratas of human society,
which would be different if they were at different heights, such as A on a peak and
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B in a valley. Such differences could embody differences in socio-economic statuses.
In turn, these differences would affect how cues might travel between them when
a behaviour change is initiated, for ultimately, we want to see a population–level
change. The strength of their inter-personal relationships also matter, when we want
to scale–up behaviours. A behavioural intervention that ignores these features of the
social brain fabric risks failure to deliver meaningful behavioural change or suffers
from unwanted distributional consequences.
3.2 Properties
The social brain as a tangle of forces, visible and invisible, enables our society to
behave as a living organism. We argue that when a behavioural policy is targeted
at one node of the social brain, without acknowledging these invisible forces, and
its other nodes, the outcomes can be misleading; just like Bart fails to acknowledge
Coriolis effects when he is told that toilets drain the opposite way in the two
. Even further, not all forces result in social cues between similar actors
that are of equal intensity. For example, feelings of love or hate, significantly differ
in mobilisation, an instrument of social change. Or, partisan beliefs imply that
people cannot come together on issues of common interest to effectuate change. A
social planner must not only acknowledge these forces in trying to deliver change
but also distinguish between them, prioritising some, based on their significance and
need. This would help the social planner to trace the path of an intended stimulus
through the social brain and determine how and when an intervention will lead to
a scalable behavioural change in the population. As we show, understanding this
hinges on the properties of the social brain, which we outline next.
3.2.1 Path Dependence
The social brain can be path dependent in determining its social linkages. When
individual actors receive new information, through tools of behaviour change (see
5The Simpsons, Season 6, Episode 16 “Bart v/s Australia”
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John (2013)), they process it, based on what they already know. At times, they
process it based on how they think they would like to use it (for details, see Wickens
and Carswell (2021)). Simply delivering new information might not help facilitate
behaviour change, if we do not recognise the ways in which humans react to them.
Even worse, sometimes, too much information can be welfare–reducing (Sunstein,
2020). As such, policymakers working in this social brain need to understand
what norms and values are acquired by and are instilled in people before they
deliver interventions. They further need to evaluate how information from others is
received, including positions in the social hierarchy, reliability, and responsiveness
(Lieberman, 2013). All of this will reduce frictions from the social fabric of the brain,
in overcoming barriers from inefficient path dependencies to scale systemic changes
(Schrey¨ogg et al., 2011; Barnett et al., 2015; Bednar and Page, 2018). Since, the
past has a prominent place in determining human behaviour and cues, and instils
inertia in the behaviour of individuals, care must be also taken in not prescribing
alterations to the choice architecture, ignorant of these path dependencies in social
linkages, because unlearning changes can become equally difficult.
3.2.2 Critical Mass Effects
In direct contrast to path dependence is the theory of critical mass (Oliver, 2013b).
While path dependence, through inertia, locks–in human actors into certain be-
haviours; there are a few small fissile reactions, characterising the functioning of
the social brain, which endow it with a certain dynamism and capacity for fast and
sweeping radicalism. Examples relate to the fast adoption of the motor car which
replaced horses and horse drawn carriages as the primary means of road transport
towards the beginning of the 20
century: some bold humans (the pioneers) intro-
duced and adopted this innovation which then led to its use by some almost equally
enterprising individuals (the imitators). Together these two developments implied
that a critical mass of people had quickly adopted the innovation and was sending
out social cues encouraging people to purchase motor cars, thus revolutionizing this
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aspect of consumer behaviour.
The computer revolution also reveals the importance of the critical mass: slow
adoption to begin with and then an explosion in the developed world in the 1980s
and then in the developing countries in the beginning of the 21st century. This
economic dynamism, as theorised in creative destruction (Schumpeter, 1942), is
nothing but a behavioural property of the social brain. More recently, social media
trends, such as TikTok display similar vigour. When a cue in the social brain reaches
its critical mass, it can lead to domino effects, a rapid spillover among individuals.
More complicated models of attainment of critical mass are possible: for example,
what happens if a person embracing the change is in many cases not that enthusiastic
about sending the ‘message for change’ to others? Social policy planners need to
be aware of the parameters
characterizing models of ‘critical mass’ in planning
behavioural change in a society. Designing interventions that stand to facilitate the
domino effects can be key to treating the social brain.
3.2.3 Dynamic evolution
The social brain, defined by its path-dependency or critical mass effects, is constantly
changing. A good way to visualise the activity within the social brain is to see it as
a dynamically evolving unit, one embodying a continuum of dose–response feedbacks
between the actors, the social cues, and its social fabric. As such, any behaviour
observed for a particular human at a given node in the social brain appears to be
nothing short of randomness. If this behaviour is viewed in isolation, it can appear to
be misleading and noisy (Kahneman et al., 2021), just like the policymaker watching
Sam make one–off unhealthy choices. But on careful introspection, they should be
Let us assume that each human is in touch with n more and that a proportion of these humans
responds positively to a request for change sent over a period. Then we see that
respond positively over t time periods to a message originally sent out in the first of these t periods,
given that those who embrace the change always send out messages to others. Of vital importance
is the magnitude of
, which depends on the nature of the request and how well the message is
crafted, as well as the size of n, which depends on the state of technology as well as population
density and literacy. If n and
are large, fast, and sweeping changes are possible as the system
reaches a critical mass quickly.
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able to find contextual evidence, and systematic, unidentifiable patterns, like the
influence of Sam’s friends or the vacation in tempting these behaviours. Collectively,
these behaviours are informative of a wide array of changes that are happening
in the social brain. It is here that policymakers need to repeat experiments, to
see temporal effects, not only with an eye for replication, but also to make sense
of differences that arise as time passes by (Kahneman, 2014; Feest, 2019), for the
social brain has dynamically evolved. Theories of human behaviour change must,
therefore, be able to account for these changes. A sequence of behaviours resulting
in the social brain can then inform policymakers of the behavioural trends to be
influenced, rather than one-off changes which cannot be sustained if left alone.
3.3 What influences the social brain?
Next, we turn to the factors that influence the social brain and its constituents,
namely, the actors, their social cues, and the underlying fabric. These are explained
3.3.1 Social Cohesion
The impact of any behavioural activity on the actors in the social brain is influenced
by the degree of the social cohesion between them. Studies have shown that a
greater cohesiveness is often related with a greater tendency to perform related
behaviours (Beal et al., 2003). In other words, if some cues are relayed faster than
others, cohesiveness could be one of the facilitators. Further, cohesion can also
motivate habit formation, good or bad (Van der Weiden et al., 2020). Consumption
of unhealthy food when friends get together in pubs, like with Sam, for example, is
a result of social cohesion among the cohort members. This cohesion amplifies the
perceived social approval of the behaviour and hastens its evolution into a habit,
repeated by the individual automatically without thought. Hence, when looking to
enforce a change in the social brain, social cohesiveness can be used for traction.
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Using behavioural interventions in conjunction with such cohesion will scale–up
treatment effects.
3.3.2 Situational and dispositional factors
A human actor, in the social brain, is a node with access to a fraction of the multiple
nodes in a setup. On receiving a cue, the human brain processes it using its powers
of analysis to make sense of it and add value to it. The individual can react to
stimulus received, a process that has been referred to as ‘perspective transformation’
in driving behaviour change (Banerjee and John, 2021). This, however, depends on
the level of situational and dispositional factors accessible to humans. These factors
therefore stand to influence the properties of the social brain. The situational factors
refer to influences in the local social and physical environment, beyond the control
of the actors. Sometimes, this can involve elements of choices, such as those tweaked
by a nudge. The dispositional factors are merely one’s own preferences or inherent
nature and qualities: socio-economic preferences could be an example (Bernheim
and Rangel, 2009).
Let us see how these might work. In formulating a cue, the reflective capacity
of the individual, in turn affected by their intellectual and emotional intelligence,
matters. The influence this individual exercises through their cues would also
depend on many factors in the social surroundings, an artefact of the social fabric.
Nonetheless, it is the interaction of these situational and dispositional factors which
dynamically determines factors influencing the social brain. An example is the
socio-economic status of an actor in society, which is often inherited and not earned,
combined with reputation, which is an outcome of past deeds. For example, reconsider
Sam who is also risk-averse (dispositional) and finds themselves in a race course
(situational), and whose nature and class determines who they befriend at that
point. Any behaviour undertaken by Sam would be influenced by these factors,
independently, and in interaction with each other. Thus, influencing the social brain,
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can also be facilitated by designing interventions which act on these situational and
dispositional factors. It is mostly here that nudges and related behavioural cues,
like boosts and nudge+ have been conceptualised to work.
3.4 Testable hypotheses
Set up this way, we now put forward three testable hypotheses of generating scalable
behaviour change by relying on the properties and influences of the social brain.
Hypothesis 1
: A behaviour change intervention targeted at a population will lead
to significantly larger treatment effects when social cohesion between human
actors in the social brain is stronger.
Hypothesis 2
: A behaviour change intervention targeted at a population will
lead to significantly larger treatment effects when localised social and physical
environments are conducive for diffusion of cues such that there are fewer
Hypothesis 3
: A behaviour change intervention that is targeted at nodes which
are conducive to deliver critical mass shifts will lead to significantly larger
treatment effects than nodes which are stagnant.
The first hypothesis highlights the role of greater social cohesiveness among
human actors in the social brain. Scaling–up behaviour change (horizontally) will be
faster when nodes in the social brain are connected by stronger forces (such as social
cues) within themselves. Thus, similar forms of behaviour change interventions
will have varying effectiveness when applied to different nodes of the human brain.
The social planner must exercise caution in targeting optimal nodes of application.
Alternatively, social planners must also pay attention to increase social cohesiveness
at a given node to increase the transmissibility of any human behaviour change.
Next, hypothesis 2 highlights the role of situational factors in facilitating human
behaviour change. A tool of behaviour change will be more effective when the
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social fabric in which such tools are situated are conducive to the change. If the
neighbouring social complex does not accommodate new reformed human behaviours,
actors will find it hard to sustain them. Thus reducing costs of frictions within the
social complex will increase effectiveness of behaviour change. Alternatively, social
planners must attend to tailoring contextual factors so that behavioural changes
introduced by behavioural tools are reinforced in the local environment, enabling
mass shifts and adoption. In our example of sustainable transport decisions, this will
imply subsiding sustainable modes of transport. Finally, hypothesis 3 highlights the
modality of effecting behaviour change. Given priors about human actors, and their
social cues, it is easier to deliver critical shifts at some nodes than others. Social
planners must therefore account for intensive and extensive margins of change and
deliver behavioural interventions while being mindful of nodes which can be most
conducive to reach critical masses.
4 Policies for the social brain: Applications
The social brain tells us that no human behaviour sits in vacuum just with the
individual person. It has behavioural insights for the policy maker who must now
holistically target different parts of the social brain to effectuate any positive social
changes. In this section, we outline a few examples to illustrate the applicability of
the social brain construct.
4.1 Regrouping actors in the social brain
Consider the following thought experiment. Have you ever wondered, what were to
happen if academic members of a research discipline, say Economics, were made to
collaborate on one joint research project? Are we to lose gains to be made from the
solitude of the lone genius when they are working in groups? Assuming away all
differences in research specialisation(s), it would not be surprising to find limited
to no academic output from this cohort. Academic disenchantment (Blackmore,
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2014), wastage of talent resulting from a quest for superiority (Nicholls, 1989) and
dehumanisation of academia ((Cornelis, 2014) might be to blame. And this is
not true only for academics. Workplace revenge (Tripp and Bies, 2009) has been
well documented. In these settings, what matters, therefore, is not how we can
steer these individuals to their welfare improving choices but how we can re–frame
the social brain to maximise the overall output from the cohort. Perhaps, even a
group of low–merit workers can engage to produce better output than this cohort of
high–performing academics. Let’s formalise this.
Imagine being assigned as the head of Academia. In the simplest world,
Academia has only two tenure–track staff members, call them High-performing
(H) and Low-performing (L). H and L are appointed to produce novel, high-impact
research for their department. Both have a fixed amount of labour at their disposal,
which they can use to produce new research (R) or demotivate the other using verbal
cues (D). Demotivation by one translates into reduced research output for the other
since it involves some emotional costs to it. Let D indicate the net demotivation,
accounting for any positive interaction spillovers between H and M. As a newly
appointed head, you are tasked to either assign H and L to a shared office space or
allow them to hot desk. Hotdesking involves lost time in output produced for any
absent staff but saves on the emotional costs of demotivation. What do you do?
Let us consider the possibilities under hot desking. For convenience, we will
assume some production functions, but this simple exercise is self–contained in itself.
RH= 20TH
RL= 10TL
As the production functions indicate, when made to hot desk, by putting in time
T, H and L produce research work, in a ratio of 2:1 indicating their differences in
merit. Both together can invest resources up to T units as available to hot desk in
Academia. There is no demotivation produced since H and L do not interact with
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one another. Optimality warrants an entire office space allocation to H to maximise
output produced. On ground of fairness, if office space is equally allocated (T/2),
we end up with a sub-optimal research output (= 15
). Understanding how people
communicate and affect each other in the social brain can, therefore, improve overall
output. Choice architects might also propose changes to what the office orientation
might look. If the social fabric changes, the outcomes are bound to change. For
example, under diminishing marginal productivity of labour, it might be optimal to
allocate a positive number of hours to both H and L, but in different portions of the
day to avoid demotivation.
It is also possible to consider cases when there are different levels of demotivation
induced by each of these agents. While we do not pursue this example any further,
it is easy to anticipate when one worker equally demotivates the other, while also
producing the highest output of research, any allocation of time between them will
be sub–optimal. In this, it is best to re–group these actors so that their social
connection are limited. Our example here highlights the role of social cues between
nodes which are often seen to influence many decisions. These have been discussed
in a wide range of setting such shifts in socially embedded preferences and norms to
tackle fertility behaviours (Dasgupta and Dasgupta, 2017), understanding market
reactions by studying human pro-social feelings (Dasgupta and Dasgupta, 2016;
Smith and Wilson, 2019; Oliver, 2021).
The social cohesion glue: An example of a female literacy
scheme in India
Consider the problem caused by low levels of female literacy, empowerment and
education in many developing countries which has adverse implications for the
status and welfare of women as well as the state of the economy, given that women
potentially constitute
40% of the workforce of the economy
. In India the
government has recently undertaken a programme for education, empowerment and
7see World Bank (2020)
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security of women and girls, called Beti Bachao Beti Padhao which translates into
“Educate the Girl to Save Her” scheme. The programme was motivated by the
continuous decline in the Child Sex Ratio (CSR), the number of girls per 1000 of
boys in the 0-6 years age category, since 1961. Between 1991 and 2011 this ratio has
shown alarming decrease from 945 to 918, an indicator of female dis-empowerment;
and a consequence of pre-birth discrimination, manifested in gender biased sex
selection, and post birth discrimination against girls. Therefore, the Government
of India has devised a programme that would attempt to drastically reduce this
discrimination by fostering attitudinal changes in adult men and women towards
girl children as well as educate and empower the girl child.
The success of a programme such as this depends on the content of messaging
and the channels chosen to convey messages. The properties of the social brain
discussed earlier indicate that the functioning of any given part of the social brain,
a collection of human brains serving as different nodes for receiving and producing
cues, is dependent on social cohesion within that part. In regard to this programme
targeting female literacy, it is important to realize that India is marked by diversity
in languages spoken, religions and faiths followed and the compartmentalization of
the society into castes. Thus, the idea would be to consider the society (social brain)
as carved into different sub-categories, with each such sub-category characterized
by commonality in language, caste and religion. Sub-categories so defined can
be expected to have high social cohesion: a member chosen from each part as
an ambassador for the female literacy campaign would be acceptable to all other
members and therefore their messages to other members will be understood well
and quickly without any misunderstandings. On the other hand, if an ambassador
to a sub-category is chosen from outside that sub-category – for example, a high
caste ambassador to a low caste group – the messaging from that ambassador to the
group will not be able to take advantage of the social cohesion within the group.
There might be problems relating to comprehension of the message, both in regard
to speed and accuracy.
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Second, as mentioned in our discussion of properties of the social brain, it is
important to take into account situational and dispositional factors characterizing
humans. We must consider which situational factors can positively impact the
messaging: for example, the use of national and regional radio and television
for carrying the messages in the appropriate language at timings which attract
the maximum number of viewers/listeners; and strategically placed billboards, for
example in city and town centres, where the message will be viewed by many people
and transmitted to their relatives and acquaintances. Not everyone might have
access to televised communication channels, in which case village level campaigns
will help deliver the cues. Further, an important part of disposition in regard to
the programme at hand is attitude towards women which varies according to the
sub-category in question. Success of this policy and its messaging to a sub-category
would depend on this attitude and the appropriateness of the choice of ambassador
– for example, in male dominated societies where empowerment of women is poor
and their ability to influence decision making weak, it might be a good idea to use
both male and female ambassadors vigorously as role models advocating the cause;
in others women ambassadors might suffice. Finally, a dispositional factor which
always determines the optimality of choice of ambassador for messaging, irrespective
of the nature of the programme, is the social status and reputation of the person
being considered for ambassador. Thus, it is important to choose ambassadors who
are well known, reputed and role models, a factor which would generate acceptance
of the message and the desired modification in attitude and behaviour (Dolan et al.,
2012; Gladwell, 2016) Thus, any policy implementation should be preceded by a
thorough study of the its population. It is important to take cognizance of who
the actors can be, such that the right node of the social brain can be targeted to
effectuate the process of change. In this, the role of actors, their situational and
dispositional influences should be considered.
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Improving social and human capital: An example to reach
critical masses for change
Consider building on the social and human capital available to an economy. This
is done best by a programme that shapes habits in children, such as reading, the
benefits of which have been rigorously identified. For example, children in the
UK who do not manage to learn to read are more economically vulnerable than
others. Functional literacy can increase income by 16% (see ROGO (2017)). Reading
enhances comprehension, imagination and therefore creativity and is therefore closely
linked to innovation; it also stimulates relaxation and well being. It is necessary to
emphasise that while one of the advantages of reading is that it helps students to
build up human capital in regard to a particular occupation, equally important is the
observation that it builds the general capacity of students to comprehend, synthesise
and analyse texts on their own, a capacity which comes in handy in adult life when
a person explores a new subject, driven by curiosity and interest or compulsion.
These schemes that build on social and human capital, in turn, strengthen
different elements of the social brain. First, they can improve the dispositional ability
of human actors to reach critical mass shits. For example, educated samples have
been found to better adopt healthy behaviours and recommendations (De Vries et al.,
2008). Education can also improve the social cohesion glue, which can then manifest
in good human behaviours. For example, Cutler and Lleras-Muney (2010) show that
social networks contribute substantially to building better health behaviours.
5 Treating the social brain: A general guide
In many ways, the social planner is a doctor who is entrusted to treat and cure the
society of its social ills. These policymakers, like our doctors, have tools at their
disposal to change human behaviours (Hood, 1983; Hood and Margetts, 2007; Oliver,
2013a). However, like every sensible doctor, they must understand the cause of an
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ill, before identifying the best way to start treating it. A successful prognosis of
the disease also rests on whether it is localised or not, for the spread can determine
appropriate interventions and treatment channels. And that’s not all, for they must
ensure that it simply does not come back once the diagnosis and treatment is over.
To extend this analogy further, recently, we have seen many treatments which
have failed to deliver this persistence. Ultimately, the social ill has returned, partly or
fully. We need to scale–up our treatments. We also need to think about persistence
of these treatments. It is here that our construct of the social brain can work
as a guide for policymakers to warrant appropriation of behavioural treatments
beyond individual actors of change and their physical environments. We have no
qualms with the surgical toolkits of effecting change. There are ample, albeit it’s an
important discussion to ascertain which works best (Johnson et al., 2019; Banerjee,
2021). However, what we feel has been missing in practice is the attention to the
wider social complex that influences many of these behavioural public policies and
human behavioural responses to them. in putting these toolkits to use. The context
of initiating behavioural change matters, a point that was raised by Hallsworth
and Kirkman (2021) in their discussion of a standard behavioural problem–solving
exercise. As our social brain posits, there are channels, human interrelationships, and
social cues, which can amplify or dampen the effects of standard behavioural tools.
The policymaker, therefore, must carefully inspect these. Some guiding questions
can be helpful.
For example, what is the appropriate node to initiate behaviour change? The
node is important because it denotes the position of actors in a society who will be
key to receiving behavioural change and transmitting them in the form of social cues,
enabling critical shifts for population masses. The policymaker can avail statistics
to inform their choice. For example, the reach of the node, can be measured by
their social network, per se. Similarly, the rate of transmission of social cues, can
be measured by numbers of trickle down, a function of the social network too. It
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might be a case that different nodes have to be targeted to begin this chain reaction.
However, it might also be a case that nodes need re–grouping, just like our example
in Academia showed. Rearranging social networks to increase social cohesion is a
way to diffuse any treatment quickly.
Similarly, who constitutes these nodes are important. Their characteristics,
mainly in their situational and dispositional factors, will yield information on
their capacities to facilitate social cues. Sometimes, specific needs for social cue
transmission will determine the appropriate actors to target in the social brain,
such in our example of building social and human capital with children. Moreover,
the social brain is characterised by human interrelationships that are based on
communication signals. Manifesting social cues therefore can engage right channels
of transmission, through role models or messengers, that will match the nodes where
change in being initiated. This was shown in our example with the girl child education
policy in India. And not to forget, it involves the fabric, our physical environment,
with its objects, that can need mending too, but we have already discussed this,
elements of choice, our so called choice architecture, can be restructured to facilitate
behaviour change.
A lot of what is proposed in the social brain needs vast amount of data, a
limitation we face currently. It is for the same reason, other desirable changes are still
waiting in line to take off, such as personalisations to improve the delivery of micro–
based behavioural interventions (Mills, 2022). Knowing about all the actors, which
form these nodes, their social networks, and how they communicate with cues, and
their social and physical environments in which they are based in, are fundamental
requirements to corroborate the social brain. That might never be possible, but we
can simply think of aggregate statistics, sample–based measurements, which can
inform the application of any tool of behaviour change. Fundamentally, thinking
about the social brain can move us towards ways of designing public policies that
go beyond individuals solely. In scaling them up, the scope of behavioural public
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policies can be increased by tapping into connections that humans share with each
other, the relationships they have and the cues they use to communicate. The social
brain simply suggests us ways of doing it by using choice architecture in conjunction
with them.
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Over the past decade, choice architecture interventions or so-called nudges have received widespread attention from both researchers and policy makers. Built on insights from the behavioral sciences, this class of behavioral interventions focuses on the design of choice environments that facilitate personally and socially desirable decisions without restricting people in their freedom of choice. Drawing on more than 200 studies reporting over 440 effect sizes (n= 2,148,439), we present a comprehensive analysis of the effectiveness of choice architecture interventions across techniques, behavioral domains, and contextual study characteristics. Our results show that choice architecture interventions overall promote behavior change with a small to medium effect size of Cohen’s d= 0.43 (95% CI [0.38, 0.48]). In addition, we find that the effectiveness of choice architecture interventions varies significantly as a function of technique and domain. Across behavioral domains, interventions that target the organization and structure of choice alternatives (decision structure) consistently outperform interventions that focus on the description of alternatives (decision information) or the reinforcement of behavioral intentions (decision assistance). Food choices are particularly responsive to choice architecture interventions, with effect sizes up to 2.5 times larger than those in other behavioral domains.Overall, choice architecture interventions affect behavior relatively independently of contextual study characteristics such as the geographical location or the target population of the intervention. Our analysis further reveals a moderate publication bias toward positive results in the literature. We end with a discussion of the implications of our findings for theory and behaviorally informed policy making.
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Although there has been a proliferation of research and policy work into how nudges shape people's behaviour, most studies stop far short of consumer welfare analysis. In the current work, we critically reflect on recent efforts to provide insights into the consumer welfare impact of nudges using willingness to pay and subjective well-being reports and explore an unobtrusive approach that can speak to the immediate emotional impacts of a nudge: automatic facial expression coding. In an exploratory lab study, we use facial expression coding to assess the short-run emotional impact of being presented with calorie information about a popcorn snack in the context of a stylised ‘Cinema experience’. The results of the study indicate that calorie information has heterogeneous impacts on people's likelihood of choosing the snack and on the emotions they experience during the moment of choice which varies based on their level of health-consciousness. The information does not, however, affect the emotions people go on to experience while viewing movie clips, suggesting that the emotional effects of the information are short-lived. We conclude by emphasising the potential of automatic facial expression coding to provide new insights into the immediate emotional impacts of nudges and calling for further research into this promising technique.
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Nudge plus is a modification of the toolkit of behavioral public policy. It incorporates an element of reflection – the plus – into the delivery of a nudge, either blended in or made proximate. Nudge plus builds on recent work combining heuristics and deliberation. It may be used to design prosocial interventions that help preserve the autonomy of the agent. The argument turns on seminal work on dual systems, which presents a subtler relationship between fast and slow thinking than commonly assumed in the classic literature in behavioral public policy. We review classic and recent work on dual processes to show that a hybrid is more plausible than the default-interventionist or parallel-competitive framework. We define nudge plus, set out what reflection could entail, provide examples, outline causal mechanisms, and draw testable implications.
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Understanding the determinants of pro-environmental behaviour is key to addressing many environmental challenges. Economic theory and empirical evidence suggest that human behaviour is partly determined by people's economic preferences which therefore should predict individual differences in pro-environmental behaviour. In a pre-registered study, we elicit seven preference measures (risk taking, patience, present bias, altruism, positive reciprocity, negative reciprocity, and trust) and test whether they predict pro-environmental behaviour in everyday life measured using the day reconstruction method. We find that only altruism is significantly associated with everyday pro-environmental behaviour. This suggests that pro-social aspects of everyday pro-environmental behaviour are more salient to people than the riskiness and intertemporal structure of these behaviours. We also show in an exploratory analysis that different clusters of everyday pro-environmental behaviours are predicted by patience, positive reciprocity, and altruism, indicating that these considerations are relevant for some, but not other, pro-environmental behaviours.
Nudge interventions have quickly expanded from academic studies to larger implementation in so‐called Nudge Units in governments. This provides an opportunity to compare interventions in research studies, versus at scale. We assemble a unique data set of 126 RCTs covering 23 million individuals, including all trials run by two of the largest Nudge Units in the United States. We compare these trials to a sample of nudge trials in academic journals from two recent meta‐analyses. In the Academic Journals papers, the average impact of a nudge is very large—an 8.7 percentage point take‐up effect, which is a 33.4% increase over the average control. In the Nudge Units sample, the average impact is still sizable and highly statistically significant, but smaller at 1.4 percentage points, an 8.0% increase. We document three dimensions which can account for the difference between these two estimates: (i) statistical power of the trials; (ii) characteristics of the interventions, such as topic area and behavioral channel; and (iii) selective publication. A meta‐analysis model incorporating these dimensions indicates that selective publication in the Academic Journals sample, exacerbated by low statistical power, explains about 70 percent of the difference in effect sizes between the two samples. Different nudge characteristics account for most of the residual difference.
Behavioural science has been effectively used by policy makers in various domains, from health to savings. However, interventions that behavioural scientists typically employ to change behaviour have been at the centre of an ethical debate, given that they include elements of paternalism that have implications for people's freedom of choice. In the present article, we argue that this ethical debate could be resolved in the future through implementation and advancement of new technologies. We propose that several technologies which are currently available and are rapidly evolving (i.e., virtual and augmented reality, social robotics, gamification, self-quantification, and behavioural informatics) have a potential to be integrated with various behavioural interventions in a non-paternalistic way. More specifically, people would decide themselves which behaviours they want to change and select the technologies they want to use for this purpose, and the role of policy makers would be to develop transparent behavioural interventions for these technologies. In that sense, behavioural science would move from libertarian paternalism to liberalism, given that people would freely choose how they want to change, and policy makers would create technological interventions that make this change possible.
In this article, I contend that the behavioural effects that tend to be labelled as errors by most behavioural economists, and as such have served as the justification for a paternalistic direction in behavioural public policy (i.e. policy intervention that aims to protect people from imposing harms on themselves), are in an ecological sense not errors at all. While acknowledging that modern societies are very different from the types of societies in which these effects evolved, I argue that we still cannot conclude that attempts to modify people’s choices in accordance with these so-called errors will improve the lives of those targeted for behaviour change, particularly given the varied and multifarious private objectives and desires that people pursue. Where people are imposing no substantive harms on others, I maintain that policy makers should restrict themselves to protecting and fostering the fundamental motivational force of reciprocity, which serves to benefit the group (which could be the whole society) and, by extension, most of the people who comprise the group, irrespective of their own personal desires in life. However, when one party to any particular exchange actively uses the behavioural affects to benefit themselves but imposes harms on the other party to the exchange, the concept of a free and fair reciprocal exchange has been violated. In these circumstances, there is an intellectual justification to introduce behavioural-informed regulations – a form of negative reciprocity – against activities that impose unacceptable harms on others. My arguments thus call for behavioural public policy to preserve individual autonomy within an overarching policy framework that nurtures reciprocity whilst at the same time regulates against behavioural-informed practices that impose substantive harms on others, rather than focusing on reducing the harms that people supposedly impose on themselves. This would be a major switch in emphasis for one of the most important developments in public policy in modern times.
This chapter goes beyond classic nudges in introducing public policy practitioners and researchers worldwide to a wide range of behavioural change interventions like boosts, thinks, and nudge pluses. These policy tools, much like their classic nudge counterpart, are libertarian, internality targeting and behaviourally informed policies that lie at the origin of the behavioural policy cube as originally conceived by Oliver. This chapter undertakes a review of these instruments, in systematically and holistically comparing them. Nudge pluses are truly hybrid nudge-think strategies, in that they combine the best features of the reflexive nudges and the more deliberative boosts (or, think) strategies. Going forward, the chapter prescribes the consideration of a wider policy toolkit in directing interventions to tackle societal problems and hopes to break the false synonymity of behavioural based policies with nudge-type interventions only.