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1
An Introduction to Behavioral Economics *
* This introduction was originally published in the Behavioral Economics Guide 2014. To
learn more about the subject, please download our free Behavioral Economics Guide
2015 and Behavioral Economics Guide 2016.
By Alain Samson, Ph.D.
Think about the last time you purchased a customizable product. Perhaps it was a laptop
computer. You may have decided to simplify your decision making by opting for a popular
brand or the one you already owned in the past. You may then have visited the
manufacturer’s website to place your order. But the decision making process did not stop
there, as you now had to customize your model by choosing from different product
attributes (processing speed, hard drive capacity, screen size, etc.) and you were still
uncertain which features you really needed. At this stage, most technology manufacturers
will show a base model with options that can be changed according to the buyer’s
preferences. The way in which these product choices are presented to buyers will influence
the final purchases made and illustrates a number of concepts from behavioral economic
(BE) theories.
First, the base model shown in the customization engine represents a default choice. The
more uncertain customers are about their decision, the more likely it is that they will go
with the default, especially if it is explicitly presented as a recommended configuration.
Second, the manufacturer can frame options differently by employing either an ‘add’ or
‘delete’ customization mode (or something in between). In an add mode, customers start
with a base model and then add more or better options. In a delete frame, the opposite
process occurs, whereby customers have to deselect options or downgrade from a fullyloaded model. Past research suggests that consumers end up choosing a greater number
of features when they are in a delete rather than an add frame (Biswas, 2009). Finally, the
option framing strategy will be associated with different price anchors prior to
customization, which may influence the perceived value of the product. If the final
configured product ends up with a £1500 price tag, its cost is likely to be perceived as more
attractive if the initial default configuration was £2000 (fully loaded) rather than £1000
(base). Sellers will engage in a process of careful experimentation to find a sweet spot—an
option framing strategy that maximizes sales, but set at a default price that deters a
minimum of potential buyers from considering a purchase in the first place.
Rational Choice
In an ideal world, defaults, frames, and price anchors would not have any bearing on
consumer choices. Our decisions would be the result of a careful weighing of costs and
benefits and informed by existing preferences. We would always make optimal decisions. In
the 1976 book The Economic Approach to Human Behavior, the economist Gary S. Becker
famously outlined a number of ideas known as the pillars of so-called ‘rational choice’
theory. The theory assumes that human actors have stable preferences and engage in
maximizing behavior. Becker, who applied rational choice theory to domains ranging from
crime to marriage, believed that academic disciplines such as sociology could learn from
the ‘rational man’ assumption advocated by neoclassical economists since the late 19th
century. The decade of the 1970s, however, also witnessed the beginnings of the opposite
flow of thinking, as discussed in the next section.
Prospect Theory
While economic rationality influenced other fields in the social sciences from the inside out,
through Becker and the Chicago School, psychologists offered an outside-in reality check to
prevailing economic thinking. Most notably, Amos Tversky and Daniel Kahneman published
a number of papers that appeared to undermine ideas about human nature held by
mainstream economics. They are perhaps best known for the development of prospect
theory (Kahneman & Tversky, 1979), which shows that decisions are not always optimal.
Our willingness to take risks is influenced by the way in which choices are framed, i.e. it is
context-dependent. Have a look at the following classic decision problem:
Which of the following would you prefer:
1. A) A certain win of $250, versus
B) A 25% chance to win $1000 and a 75% chance to win nothing?
2. How about:
C) A certain loss of $750, versus
D) A 75% chance to lose $1000 and a 25% chance to lose nothing?
Tversky and Kahneman’s work shows that responses are different if choices are framed as
a gain (1) or a loss (2). When faced with the first type of decision, a greater proportion of
people will opt for the riskless alternative A), while for the second problem people are more
likely to choose the riskier D). This happens because we dislike losses more than we like an
equivalent gain: Giving something up is more painful than the pleasure we derive from
receiving it.
Bounded Rationality
Long before Tversky and Kahneman’s work, 18th– and 19th-century thinkers were already
interested in the psychological underpinnings of economic life. Scholars during the
neoclassical revolution at the turn of the 20th century, however, increasingly tried to
emulate the natural sciences, as they wanted to differentiate themselves from the then
“unscientific” field of psychology (see summary in Camerer, Loewenstein and Rabin, 2011).
The importance of psychologically informed economics was later reflected in the concept
of ‘bounded rationality’, a term associated with Herbert Simon’s work of the 1950s.
According to this view, our minds must be understood relative to the environment in which
they evolved. Decisions are not always optimal. There are restrictions to human
information processing, due to limits in knowledge (or information) and computational
capacities (Simon, 1982; Kahneman, 2003).
Gerd Gigerenzer’s work on “fast and frugal” heuristics later built on Simon’s ideas and
proposed that the rationality of a decision depends on structures found in the
environment. People are “ecologically rational” when they make the best possible use of
limited information-processing abilities, by applying simple and intelligent algorithms that
can lead to near-optimal inferences (Gigerenzer & Goldstein, 1996).
While the idea of human limits to rationality was not a radically new thought in economics,
Tversky and Kahneman’s ‘heuristics and biases’ research program made important
methodological contributions, in that they advocated a rigorous experimental approach to
understanding economic decisions based on measuring actual choices made under
different conditions. About 30 years later, their thinking entered the mainstream, resulting
in a growing appreciation in scholarly, public, and commercial spheres.
Limited Information: The Importance of Feedback
Bounded rationality’s principle of limited knowledge or information is one of the topics
discussed in the 2008 book Nudge. In the book, Thaler and Sunstein point to experience,
good information, and prompt feedback as key factors that enable people to make good
decisions. Consider climate change, for example, which has been cited as a particularly
challenging problem in relation to experience and feedback. Climate change is invisible,
diffuse, and a long-term process. Pro-environmental behavior by an individual, such as
reducing carbon emissions, does not lead to a noticeable change. The same is true in the
domain of health. Feedback in this area is often poor, and we are more likely to get
feedback on previously chosen options than rejected ones.
The impact of smoking, for example, is at best noticeable over the course of years, while its
effect on cells and internal organs is usually not evident to the individual. Traditionally,
generic feedback aimed at inducing behavioral change has been limited to information
ranging from the economic costs of the unhealthy behavior to its potential health
consequences (Diclemente et al., 2001). More recent behavior change programs, such as
those employing smartphone apps to stop smoking, now usually provide positive and
personalized behavioral feedback, which may include the number of cigarettes not smoked
and money saved, along with information about health improvement and disease
avoidance.
“Irrational” Decision Making: The Example of the Psychology of Price
Boundedly rational choices, made due to limits in our thinking processes, especially those
we make as consumers, are illustrated well in Dan Ariely’s popular science book Predictably
Irrational. A good portion of the research he discusses involves prices and value
perception. One study asked participants whether they would buy a product (e.g. a cordless
keyboard) for a dollar amount that was equal to the last two digits of their US social
security number. They were then asked about the maximum they would be willing to pay.
In the case of cordless keyboards, people in the top 20% of social security numbers were
willing to pay three times as much compared to those in the bottom 20%. The experiment
demonstrates anchoring, a process whereby a numeric value provides a non-conscious
reference point that influences subsequent value perceptions (Ariely, Loewenstein, &
Prelec, 2003).
Ariely also introduces the concept of the zero price effect, namely when a product is
advertised as ‘Free’, consumers perceive it as intrinsically more valuable. A free chocolate is
disproportionately more attractive relative to a $0.14 chocolate than a $0.01 chocolate is
compared to one priced at $0.15. To a ‘rational’ economic decision maker, a price difference
of 14 cents should always provide the same magnitude of change in incentive to choose
the product (Shampanier, Mazar, & Ariely, 2007). Finally, price is often taken as an indicator
of quality, and it can even serve as a cue with physical consequences, just like a placebo in
medical studies. One experiment, for instance, gave participants a drink that purportedly
helped mental acuity. When people received a discounted drink their performance in
solving puzzles was significantly lower compared to regular-priced and control conditions
(Shiv, Carmon, & Ariely, 2005).
Predictably Irrational and Nudge alerted the public to a new breed of economists
influenced by the study of behavioral decision making that was pioneered by Kahneman
and Tversky’s work (sometimes referred to as ‘choice under uncertainty’). The psychology
of homo economicus—a rational and selfish individual with relatively stable preferences—
has been challenged, and the traditional view that behavior change should be achieved by
informing, convincing, incentivizing or penalizing people has been questioned (Thaler &
Sunstein, 2008). The field associated with this stream of research and theory is behavioral
economics (BE), which suggests that human decisions are strongly influenced by context,
including the way in which choices are presented to us. Behavior varies across time and
space, and it is subject to cognitive biases, emotions, and social influences. Decisions are
the result of less deliberative, linear, and controlled processes than we would like to
believe.
Dual-System Theory
Daniel Kahneman uses a dual-system theoretical framework (which established a foothold
in cognitive and social psychology of the 1990s) to explain why our judgments and
decisions often do not conform to formal notions of rationality. System 1 consists of
thinking processes that are intuitive, automatic, experience-based, and relatively
unconscious. System 2 is more reflective, controlled, deliberative, and analytical. Judgments
influenced by System 1 are rooted in impressions arising from mental content that is easily
accessible. System 2, on the other hand, monitors or provides a check on mental
operations and overt behavior—often unsuccessfully.
Availability and AJect
System 1 is ‘home’ of the heuristics (cognitive shortcuts) we apply and responsible for the
biases (systematic errors) we may be left with when we make decisions (Kahneman, 2011).
System 1 processes influence us when prior exposure to a number affects subsequent
judgments, as evident in the anchoring effects discussed previously (Tversky & Kahneman,
1974). One of the most universal heuristics is the availability heuristic. Availability serves as
a mental shortcut if the possibility of an event occurring is perceived as higher simply
because an example comes to mind easily (Tversky & Kahneman, 1974); for instance, a
person may deem pension investments too risky as a result of remembering a family
member who lost most of her retirement savings in the recent recession. Readily available
information in memory is also used when we make similarity-based judgments, as evident
in the representativeness heuristic.
Finally, another ‘general purpose’ heuristic is that of affect, namely good or bad feelings
that surface automatically when we think about an object. Applying the affect heuristic can
lead to black-and-white thinking, which is particularly evident when people think about an
object under conditions that hamper System 2 reflection, such as time pressure. For
example, consumers may consider food preservatives’ benefits as low and costs as high,
thus leading to a significant negative risk-benefit correlation (Finucane, Alhakami, Slovic, &
Johnson, 2000).
The role of affect in risky or uncertain situations is also evident in the risk-as-feelings model
(Loewenstein, Weber, Hsee, & Welch, 2001). ‘Consequentialist’ accounts of decision making
tend to focus on expectations along with the likelihood and desirability of possible
outcomes. The risk-as-feelings perspective explains behavior in situations where emotional
reactions to risk differ from cognitive evaluations. In these situations, behavior tends to be
influenced by anticipatory feelings, emotions experienced in the moment of decision
making.
Salience
Availability and affect are processes internal to the individual that may lead to bias. The
external equivalent of these processes is salience, whereby information that stands out, is
novel, or seems relevant is more likely to affect our thinking and actions (Dolan et al., 2010).
For example, a technological device can be framed as being 99% reliable or having only a
1% failure rate, thereby emphasizing either positive or negative information. Salience also
underlies heuristic judgments that rely on external cues. Some psychologists have derived
effort-reducing heuristics that simplify consumer decision making. The brand name
heuristic, for example, suggests that salient cues in the form of brand names can be used
to infer quality (Maheswaran, Mackie, & Chaiken, 1992). In terms of degrees of visual
salience, one study found a congruence effect between price and font size, where showing
a lower sale price in a small print size relative to the regular price resulted in greater
purchase likelihood than presenting the sale price in a relatively large font (Coulter &
Coulter, 2005). Finally, the salience of options can also be manipulated by rearranging the
physical environment; for instance, a change as simple as moving water bottles closer to
the cashier in a cafeteria has been shown to increase the salience and convenience of this
healthier drink choice and thereby significantly boost water sales (Thorndike, Sonnenberg,
Riis, Barraclough, & Levy, 2012).
Status Quo Bias and Inertia
While many heuristics and biases are the result of quick impressions, the automatic
character of System 1 is also reflected in a human aversion to change. One aspect in this
respect is evident in the formation of habits, automatic behavioral patterns that are the
result of repetition and associative learning (Duhigg, 2012). The preference for things to
remain the same, such as a tendency not to change behavior unless the incentive to do so
is strong, has been termed the “status quo bias” (Samuelson & Zeckhauser, 1988). Inertia is
one form of people’s propensity to remain at the status quo (Madrian & Shea 2001), a wellknown manifestation of which includes low rates of pension plan enrolment when people
have to make the effort to sign up (‘opt-in’). In this case, an effective way to increase
enrolment rates is to change the default—what happens when people do not make an
active choice. Inertia, procrastination, and a lack of self-control are problems that make
changes in default options from opt-in to opt-out an effective strategy, so, instead of having
to take action to enroll (opt-in), people now have to make an effort to dis-enroll (opt-out)
(Thaler & Sunstein, 2008). Nudging with defaults is one of the primary tools of the ‘choice
architect’ (Goldstein, Johnson, Herrman, & Heitmann, 2008).
Temporal Dimensions
Another important domain of BE introduces a time dimension to human evaluations and
preferences. This area acknowledges that people are biased towards the present and poor
predictors of future experiences, value perceptions, and behavior.
Time Discounting and Present Bias
According to time-discounting theories, present events are weighted more heavily than
future ones (Frederick, Loewenstein & O’Donoghue, 2002); for example, many people
prefer to receive £100 now over £110 in a month’s time. Discounting is non-linear, and its
rate is not constant over time. People’s preference for receiving £100 a week from now
versus £110 a month and one week from now will not be the same as their preference for
receiving £100 a year from now versus £110 a year and one month from now. Although the
gap is one month in both cases, the value of events that are farther in the future falls more
slowly than those closer to the present (Laibson, 1997).
In addition to inertia, future discounting is another key problem that explains low
retirement savings rates. One piece of research suggests that behavioral change could be
achieved by helping people connect with their future selves. In the study, people who saw
an age-progressed avatar of themselves were more likely to accept future financial rewards
over immediate ones (Hershfield et al., 2011).
DiversiLcation Bias and the Empathy Gap
Time inconsistency also occurs when our present self fails to predict accurately the
preferences of our future self, a point illustrated well by diversification bias (Read, &
Loewenstein, 1995). When shopping for multiple future consumption episodes, I may
choose the variety pack of cereal, only to realize two weeks later that I would have enjoyed
my breakfasts more if I had just stuck to my favorite kind. In the case of food,
diversification bias should be particularly strong if you make your purchasing decision
when you’re satiated (e.g. right after a meal). This inability to appreciate fully the effect of
emotional and physiological states on decision making is known as the (hot-cold) empathy
gap, a term coined by George Loewenstein, one of the founders of the field of behavioral
economics. Hot states include a number of visceral factors, ranging from negative emotions
associated with high levels of arousal (e.g. anger or fear) to feeling states (e.g. pain) and
drive states (e.g. thirst, cravings related to addiction, or sexual arousal) (Loewenstein, 2000).
The best known illustration occurs in sexual decision making, whereby men in a ‘cold’,
unaroused state often predict that they will use a condom during their next sexual
encounter, but when they are in an aroused ‘hot state’ they may fail to do so (Ariely &
Loewenstein, 2006).
Forecasting and Memory
When we make plans for the future, we are often too optimistic. For example, we are
subject to committing the planning fallacy by underestimating how long it will take us to
complete a task and ignoring past experience (Kahneman, 2011). Similarly, when we try to
predict how we will feel in the future, we may overestimate the intensity of our emotions
(Wilson & Gilbert, 2003). The level of happiness that I expect to feel during my next
vacation, for example, is likely to be higher than how I will rate it during the actual
experience. There are different explanations for this error, including how we remember
past events. My memory of a past holiday is likely to be non-representative of the holiday
overall (Morewedge, Gilbert, & Wilson, 2005), and I may evaluate my last vacation based on
the most pleasurable points and its end, for example, rather than the average of every
moment of the experience (the peak-end-rule; Kahneman & Tversky, 1999). Finally, as my
vacation days go by, I will simply get used to it and my happiness will level out. According to
the concept of hedonic adaptation, changes in experiences tend only to induce happiness
temporarily as we get used to new circumstances (Frederick & Loewenstein, 1999).
Social Dimensions
Contrary to the homo economicus view of human motivation and decision making, BE does
not assume that humans make choices in isolation, or to serve their own interest. Aside
from cognitive and affective (emotional) dimensions, an important area of BE also
considers social forces, in that decisions are made by individuals who are shaped by—and
embedded in—social environments.
Trust and Dishonesty
Trust, which is one of the explanations for discrepancies between actual behavior and that
predicted by a model of self-interested actors, makes social life possible and permeates
economic relationships. It has been related to positive economic outcomes, such as
macro-level economic growth (Zak & Knack, 2001) and micro-level intrinsic motivation and
work performance (Falk & Kosfeld, 2006).
While trust can make us vulnerable, and thereby reflects risk preferences, it may also be
the result of social preferences (Fehr, 2009). For instance, it has been linked to the concept
of “betrayal aversion” (Bohnet, Greig, Herrmann, & Zeckhauser, 2008): People take greater
risks when they are faced with a given probability of bad luck than the same probability of
being cheated by another person.
In human relationships, deception is often considered a violation of trust, while in standard
economics, dishonesty can be seen as a natural by-product of actors with self-interested
motives. However, the BE perspective does not consider humans to be more honest;
rather, it takes a more social-psychological perspective by showing that dishonesty is not
just about tradeoffs between external incentives (such as material gain) and costs (such as
punishments). Dishonesty is the product of situations as well as both internal and external
reward mechanisms, which often involves self-deception—the reframing of dishonest acts
(e.g. not declaring all of your income to the tax authorities) in a way that makes them
appear less dishonest (Mazar & Ariely, 2006).
Fairness and Reciprocity
Behavioral research on individual decision making in social contexts often relies on
experimental games. Along with behavioral decision theory, behavioral game theory is the
second major theoretical area found in behavioral economics. Typically, these games
endow participants with rewards (e.g. tokens), which then change hands based on choices
made by individuals within the rules of the game. This occurs over the course of one or
more rounds of playing. The outcome of the game is evident in the way rewards are split
between players, and the results often show that people have inequity aversion, i.e. they
prefer fairness over inequality in many contexts (Fehr & Schmidt, 1999).
Fairness is related to a human desire for reciprocity, our tendency to return another’s
action with another equivalent action. Reciprocity, however, can have positive and negative
aspects. As Ernst Fehr’s work in this area has shown, people’s responses to positive actions
are often kinder than a self-interest model would predict, but on the flipside it can also lead
to punitive responses to negative actions (Fehr & Gaechter, 2000). In the real world,
charities sometimes use reciprocity to their advantage. For example, one field experiment
investigating donation behavior showed that people who received a large gift with a
donation solicitation letter had a 75 percent higher donation frequency compared to a ‘no
gift’ baseline condition (Falk, 2004).
Social Norms
The sociologist Alvin Gouldner referred to reciprocity as a “generalized moral norm”
(Gouldner, 1960). Social norms are implicit or explicit behavioral expectations or rules
within a society or group of people (Dolan et al., 2010), and they are an important
component of identity economics, which considers economic actions to be the result of
both monetary incentives and people’s self-concepts (Akerlof & Kranton, 2010). Our
preferences are not simply a matter of basic tastes; they are also influenced by norms, as
manifested in gender roles, for example.
Norms vary across cultures and contexts. For example, while market norms would dictate
that payment is required for a good or service, social norms are quite different—would you
offer to pay a family member for the meal that he has prepared for you (Ariely, 2008)?
Sometimes social norms of exchange such as reciprocity and market norms co-exist in the
same sphere. For instance, while market exchange norms dictate that I will charge a client
for a consulting job, I may also give that client free advice, on some occasions, in the hope
that the favor will be reciprocated in the future.
Social norms signal appropriate behavior or actions taken by the majority of people
(although what is deemed ‘appropriate’ is itself subject to continual change). Along with
informational feedback (e.g. the amount of money saved by not drinking alcohol),
descriptive normative feedback (e.g. how one’s drinking level compares to the national
average) is often used in health behavior change programs (Diclemente et al., 2001), while
non-profit organizations sometimes use normative information to affect donation levels.
One study compared contribution levels for a public radio fundraiser in the US. When
potential donors were provided with social information signaling norms (e.g. “We had
another member, they contributed $300”), they saw up to a 12% increase in average
contribution amounts (Shang & Croson, 2009).
Consistency and Commitment
Human susceptibility to feedback about social norms is related to our desire to maintain a
positive view of who we are as a person. When the outcome of an action threatens this
desire, we may change our behavior, though we often simply change our attitudes or
beliefs. When this happens, we usually resort to ‘rationalization’, which is a form of
cognitive dissonance reduction (Festinger, 1957). Unlike the rational choice view of human
decision making, where preferences guide choices, rationalization implies the opposite:
Sometimes preferences can justify actions after the fact (March, 1978). Cognitive
dissonance theory is an illustration of the human need for a continuous and consistent
self-image (Cialdini, 2008). In an effort to align future behavior, being consistent is best
achieved by making a commitment, especially if it is done publicly. Thus, pre-committing to
a goal is one of the most frequently applied behavioral devices to achieve positive change.
The ‘Save More Tomorrow’ program, aimed at helping employees save more money,
illustrates a number of behavioral biases and remedies, including commitment (Thaler &
Benartzi, 2004). The program gives employees the option of pre-committing to a gradual
increase in their savings rate in the future, each time they get a raise. The program avoids
the perception of loss that would be felt with a reduction in disposable income, because
consumers commit to saving future increases in income. People’s inertia makes it more
likely that they stick with the program, because they have to opt out to leave.
Summary and Implications
Behavioral economics (BE) uses psychological experimentation to develop theories about
human decision making and has identified a range of biases as a result of the way people
think and feel. BE is trying to change the way economists think about people’s perceptions
of value and expressed preferences. According to BE, people are not always self-interested,
benefits maximizing, and costs minimizing individuals with stable preferences—our
thinking is subject to insufficient knowledge, feedback, and processing capability, which
often involves uncertainty and is affected by the context in which we make decisions. Most
of our choices are not the result of careful deliberation. We are influenced by readily
available information in memory, automatically generated affect, and salient information in
the environment. We also live in the moment, in that we tend to resist change, are poor
predictors of future behavior, subject to distorted memory, and affected by physiological
and emotional states. Finally, we are social animals with social preferences, such as those
expressed in trust, reciprocity and fairness; we are susceptible to social norms and a need
for self-consistency.
Interdisciplinary Context
The field of BE is situated in a larger landscape of social and behavioral sciences, including
cognitive and social psychology, and developments in the domain of neuroscience have
opened up promising avenues for BE informed by better understanding of the human
brain (Camerer, Loewenstein, & Prelec, 2005). It has been argued that BE would benefit
from greater connections with other behavioral sciences, such as anthropology, which may
be particularly important for domains that incorporate human interaction, especially
behavioral game theory (Gintis, 2009). In a related vein, psychologists interested in the
evolutionary origins of phenomena studied by behavioral economists have investigated
behavioral biases in monkeys (Lakshminarayanan, Chen, & Santos, 2011).
Some evolutionary psychologists have challenged assumptions about the rationality that
underlies BE, in that seemingly ‘irrational’ judgments and decisions may have been
functionally adaptive in our ancestral environment. The use of heuristic shortcuts, for
example, is an efficient means for humans to make use of limited knowledge and
processing capabilities. According to Herbert Simon, people tend to make decisions by
satisficing (a combination of sufficing and satisfying) rather than optimizing (Gigerenzer &
Goldstein, 1996), where decisions are often simply good enough in light of the costs and
constraints involved.
Evolutionary perspectives have also been applied to decision framing, showing that framing
effects in a classic ‘lives lost’ versus ‘lives saved’ risky decision problem can change with the
number of lives at stake. An “irrational” risk preference reversal effect is present when 600
or 6000 are involved, but it disappears when the number is reduced to 6 or 60. The
evolutionary view holds that our thinking patterns evolved in hunter-gatherer
environments that involved small groups (Rode & Wang, 2000).
Generalizability
More cross-cultural research will be needed to establish the degree of universality
associated with behavioral theories (Etzioni, 2011). Research on analytic (Western
European) versus holistic (East Asian) thinking styles implies that tensions between the
psychology of homo economicus and homo sapiens should be much more pronounced in
Western-European cultural regions, especially the US. In East-Asian cultures, reasoning
tends to be influenced more by contexts, since people are more likely to use their intuition
if it is in conflict with formal rationality and to accept variations in behavior across
situations (Nisbett, Peng, Choi & Norenzayan, 2001). In collectivist cultures that foster
interdependent self-construals, individuals see themselves as more connected to others,
and unlike the selfish homo economicus, Eastern individuals are more likely to attend to
other people and make decisions in the context of harmonious interdependence (Markus &
Kitayama, 1991).
In both scholarly and applied areas of BE, and the behavioral sciences more generally,
there has been an emerging interest in taking the study of decision making out of the
(mostly American) university lab and into real-world settings. The usefulness of
experiments limited to student samples has been questioned and online experimentation
with diverse samples has become more common (Goodman, Cryder, & Cheema, 2013).
Some authors have identified external validity (generalizability) issues when psychological
studies initially performed in a lab are replicated in the field (Mitchell, 2012). In both
business (Davenport, 2009) and the public sector (Haynes, Service, Goldacre, & Torgerson,
2012), a ‘test and learn’ approach based on field experimentation is now advocated as a
valuable way to test behavioral hypotheses.
Applications: BE and Behavior Change
The implications of BE are far-reaching, and its ideas have been applied to various
domains, including personal and public finance, health, energy, public choice, and
marketing. Richard Thaler and Cass Sunstein became involved in US government policy as
early as 2008, during Barack Obama’s presidential campaign. In 2010, the UK government
set up the ‘Behavioural Insights Team’ (BIT), a special unit dedicated to applying behavioral
science to public policy and services. News broke in 2013 about a similar nudge unit being
set up by the US government. The communications arm of the UK government, COI (now
defunct), also took on board BE insights, in order to enhance their communications efforts.
Practitioners at COI used BE ideas to complement traditional approaches gleaned from
psychology that tend to focus on people’s awareness, attitudes, and self-efficacy in
producing behavior change (COI, 2009).
Most psychologists and economists would probably agree with Tim Harford’s observation
that BE appears to have become a catch-all term for any type of psychology applied to realworld problems (Hartford, 2014); many of the nudges tested by the UK’s BIT, for example,
are social-psychological in nature (e.g. attempting to increase organ donation rates through
social proof). We do not need to rely on complex and often quite mathematical insights
from BE to inspire behavior change policies, but the field of economics has always
influenced public policy to a greater extent than psychology. The application of a
‘behavioral economics’ label to existing ideas from psychology appears to have proven
effective. Despite BE’s boundary disputes, the popularity of the behavioral sciences has
widened practitioners’ conceptual toolkit, encouraged research that is concerned with
actual behavior, and begun to foster a ‘test and learn’ culture among governments and
corporations alike.
When behavioral science is asked to tackle practical issues, conducting experiments prior
to implementing interventions is indispensable. George Loewenstein and Peter Ubel have
noted that behavioral economics is sometimes “asked to solve problems it wasn’t meant to
address” (Loewenstein & Ubel, 2010). Unhealthy eating and energy consumption problems,
for example, can be dealt with effectively with traditional economic interventions, such as
price and tax changes. BE therefore needs to be considered alongside rather than as a
replacement for traditional interventions.
In the private sector, BE has reinvigorated practitioners’ interest in psychology, particularly
in marketing, consumer research, as well as business and policy consulting. Part 3 of this
Guide provides a selection of papers written by practitioners in those areas.
Ethical issues
When BE is used to influence decisions, unavoidable questions about ethics arise. The
liberal (or ‘soft’) paternalist approach of applying nudges in the public sphere argues that
interventions occur for the good of the individual or society as a whole (Thaler & Sunstein,
2008). However, the practice and philosophy behind nudges are not without criticism, since
interventions occur without the awareness of the public on both the level of policy
implementation and the psychological processes involved (Dunt, 2014). Thaler and
Sunstein maintain that changing choice architecture preserves individuals’ freedom to
choose and that there are no such things as neutrally presented choices in the first place.
Clear rules of conduct and transparency will benefit nudgers in both public and private
spheres. A recent opinion poll suggests that the global public is more supportive of the
nudge approach (making behaviors more difficult or expensive) than ‘shoving’ (mandatory
legislation) (Branson et al., 2012). The same survey also found public support for legislation
against companies, for example in the area of promoting healthy food choices or acting in
an environmentally sustainable way.
Debates about using BE (and behavioral science more generally) to influence consumers
will have to consider consumer expectations about companies in contrast to governments,
notions of free will, psychological processes in consumer decision making, and the wider
context of marketing ethics and traditional marketing approaches. Do nudges directed at
consumers undermine people’s ability to choose freely, or do they merely steer consumers
in a particular way (e.g. buying Brand A vs B) through actions that are already goal-directed
(e.g. buying a soft drink)? Furthermore, does people’s ability to reflect on their actions and
their expectations of commercial self-interest in the marketplace make them sufficiently
vigilant to control and correct their choices, if necessary? Finally, is BE applied to marketing
radically new (most marketers would point out that it is not), or has it simply expanded
managers’ existing selling technique toolkit while allowing them to better understand
human behavior and systematize marketing and research practice?
If you’d like to learn more about behavioral economics, including recent developments,
applications, and challenges, please download our free Behavioral Economics Guide
2015 and Behavioral Economics Guide 2016
2016.
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