Emerging Trends in The Social and Behavioral Sciences

Choice Architecture

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Choice Architecture

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Choice Architecture
ADRIAN R. CAMILLERI and RICK P. LARRICK

Abstract
“Choice architecture” is a metaphor capturing the idea that all choices occur within
a structure of contextual and task features. These features in turn help to “construct”
a person’s choice. In this chapter, we summarize the academic literature on three
types of choice architecture tools—defaults, information restructuring, and information feedback—and document some real-world examples where these tools have
been applied as successful “nudges.” We end the chapter with a discussion of some
key challenges and opportunities associated with this new field—including the need
for customized choice architecture and the political acceptability of the use of choice
architecture—and highlight some avenues for future research.

CHOICE ARCHITECTURE
The concept of “choice architecture” is borne from the discovery that
people’s preferences are not stable, but malleable. This discovery has
emerged from several decades of behavioral decision research. A memorable demonstration—called partition dependence—is an elegant illustration
of malleable preferences. Partition dependence is the cognitive tendency
to give equal weight to different categories in a judgment or choice task.
Because categories can often be reconfigured, people’s judgments and
preferences likewise change. For example, in one experiment, people were
much more likely to select fruits and vegetables from a menu when the list
of foods was grouped in terms of “fruits,” “vegetables,” and “cookies &
crackers” than when the same list of foods was grouped in terms of “fruits &
vegetables,” “cookies,” and “crackers” (Fox, Ratner, & Lieb, 2005). This
study shows that different choices can be made depending on how people
subjectively partition a set of options or how those options are otherwise
grouped.
Experimental findings such as partition dependence reveal that preferences can differ across ostensibly equivalent choice situations. These results
prove that people are not purely rational decision makers with stable,
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.

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well-understood preferences. Rather, people’s preferences are influenced
by many of the contextual and task features associated with a choice that
should not rationally influence choice. The list of these features is broad—in
this article, we summarize in detail only a few—but they include the
number of options, the default option, the similarity of options, the presence
of compromise or “decoy” options, the type of attributes, the number of
attributes, the correlation between the attributes, the scale the attributes are
expressed upon, and the type of response mode. In general, these contextual
and task features can change what information is used, how that information
is processed, and how an evaluation is expressed.
“Choice architecture” is a metaphor which captures the idea that all
choices occur within a structure that is rich with contextual and task
features. Together, these features help to “construct” a person’s choice
(Lichtenstein & Slovic, 2006; Payne, Bettman, & Johnson, 1992). The concept
of choice architecture makes it evident that there is no such thing as a neutral
choice architecture: Every single choice—from selecting tonight’s dinner to
selecting a lifelong partner—is embedded within an environment that will
influence choice. It is also important to be clear that the concept does not
deny that people’s choices are a function of their values, attitudes, goals,
and preferences; it simply adds choice architecture as another contributing
factor.
The vast majority of choices are made in environments in which no designer
ever deliberately chose the choice architecture. More recently, however, people have become increasingly interested in how the choice architecture can
be explicitly designed to influence choice. Those who engage in such work
are called choice architects. Of course, marketers have been in the choice architecture business for centuries with the specific goal of selling products and
services for profit. A new class of choice architects has emerged with goals
focused on improving decisions for the benefit of the individual and society. The ethos of this new class of choice architect, heralded by Richard H.
Thaler and Cass R. Sunstein (2008), is that by knowing how people think,
choice architects can design choice structures that make it easier for people
to choose what is best for themselves, their families, and their society, and all
without restricting freedom of choice (Johnson et al., 2012). For example, partition dependence reveals a potential intervention that can be implemented
in order to “nudge” consumers toward choosing healthier fruits and vegetables while still permitting the complete freedom to choose the less healthy
cookies.
The general goal of a nudge is to use psychological insights to help people
make better decisions by designing better choice architecture. Such architecture usually includes presenting more (or sometimes less) information or by
restructuring information to make it more easily processed. Policy makers

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are especially interested in identifying nudges that align individual choices
with societal benefits, particularly in the domains of financial savings,
health, and the environment. Nudges can have their effect through different
mechanisms and can be thought of in terms of outcome nudges and process
nudges (Dietvorst, Milkman, & Soll, 2014). Outcome nudges tend to tap a
general psychological tendency and shift people’s preferences in the direction of an outcome selected by the architect. For example, people’s tendency
to select a fuel-efficient vehicle increases when fuel economy is expressed
by multiple correlated metrics such as miles per gallon (MPG), annual fuel
cost, and greenhouse gas emissions because people “count” these attributes
and are impressed that they all favor the efficient car (Ungemach, Camilleri,
Johnson, Larrick, & Weber, 2014). Process nudges are those that allow people
to better process information and thereby allow them to make choices
more aligned with their personal values, attitudes, and goals. For example,
people’s tendency to select a fuel-efficient vehicle increases when presented
with a greenhouse gas rating but, crucially, only for those people who hold
pro-environmental attitudes (Ungemach et al., 2014). Therefore, process
nudges can serve as “signposts” that remind people about their values and
goals and also point them toward the options that best satisfy them.
TOOLS OF CHOICE ARCHITECTURE
There exist a number of excellent resources summarizing different choice
architecture tools (Johnson et al., 2012), debiasing techniques (Soll, Milkman,
& Payne, 2014), and practical guides for nudging (Ly, Mazar, Zhao, & Soman,
2013). In this section, we aim to summarize some of the most important tools
together with some examples of where these tools have been applied as successful nudges.
DEFAULTS
A default is the option that will be enacted automatically if someone does
not actively intervene to change it. The presence of a default represents one
of the strongest forms of choice architecture (Smith, Johnson, & Goldstein,
2013). One classic demonstration shows that there exist very large differences
in organ donation rates between very similar European countries that vary in
whether organ donation is the default or not (Johnson & Goldstein, 2003). For
example, at the time of the study, the donation rate was 12.0% in Germany but
99.9% in nearby Austria. On average, where organ donation was the default
more than 99% of the population was an organ donor, whereas where organ
donation was not the default the rate was less than 30% of the population.

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The effectiveness of a default stems from at least three sources (Smith et al.,
2013): First, people tend to assume that the default has been singled out intentionally as a recommendation or a signal as to what most people choose.
Second, a default may be psychologically interpreted as an option that is in
some sense already possessed by the person and therefore giving it up could
be perceived as a loss, which is potentially painful, and also opens the door
to feelings of anticipated regret. Third, it takes more effort to change a default
than to keep a default even when the effort required is as little as clicking on
a button.
One of the most successful applications of defaults has been retirement
savings. The program studied by Madrian and Shea (2001) altered whether
employees had to opt-in to a retirement program or were automatically
enrolled and had to opt out. Those participating in the retirement increased
from roughly 50% to over 85%.
INFORMATION RESTRUCTURING
Humans are limited in their capacity for processing information (Miller, 1956;
Newell & Simon, 1972). Therefore, a general principle in the design of useful
choice architecture is to present information in a format that is easily processed. Often this requires an understanding of the goals that people are
attempting to achieve as well as an understanding of their processing limitations. A good example is the introduction of unit pricing information, which
recognizes that consumers often have a goal of purchasing the cheapest product available but struggle to calculate which product is most cost effective
when products vary in size. Unit pricing information rescales a product’s
price in terms of a single, standardized unit that is common across products
of the same type (e.g., “price per ounce”). When unit pricing information is
presented for a range of competing products, then consumers are more easily
able to achieve their goal of purchasing the most economical product (Russo,
1977).
When purchasing a vehicle, consumers are often concerned with the fuel
economy of the vehicle, primarily because they are interested in minimizing their future fuel costs. In the United States, fuel economy is expressed in
terms of MPG. Although a vehicle with a higher MPG is always more efficient
than a vehicle with lower MPG, equal increases in MPG are not equal in gas
savings (Larrick & Soll, 2008), yet people look at differences in MPG to guess
gas savings. For example, over 100 miles, most people think that the gas savings of improving MPG from 20 to 50 is greater than the savings of improving
MPG from 10 to 20 because the first change is both a larger difference and
percentage increase. Yet, the first change saves only 3 gallons and the second change saves 5 gallons. This nonlinear relationship is not captured by

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MPG and few have the knowledge or processing capacity to make the translation. The solution is to think about gallons of gas used over some meaningful distance. The 2013 US fuel economy and environment label, which was
designed by National Highway Traffic Safety Administration and Environmental Protection Agency, has embraced this solution by including a “gallons per 100 miles” metric. More recent research suggests that consumer’s
tendency to select a fuel-efficient vehicle increases when fuel economy is
expressed as the cost of gas over 100,000 miles (Camilleri & Larrick, 2014).
The cost metric feeds directly into the consumer’s cost-minimization goal
and the scale is helpful because it roughly corresponds to the average US
driver’s lifetime vehicle mileage.
The principle of restructuring information in order to nudge people toward
better decisions is also exemplified by the 2009 US Credit Card Accountability Responsibility and Disclosure (CARD) Act. One of the mandates of the
CARD Act is that credit card holders must receive a monthly statement that
provides information about how different levels of payment strategies, such
as the minimum payment per month, will affect the payoff time. One new feature of the statement is that it is required to state the minimum payment that
must be made per month in order to eliminate the balance in 3 years. Research
has shown that this new statement leads to credit card holders toward better judgments, although the benefits are reduced for those less numerate
and when charges continue to be added to the card (Soll, Keeney, & Larrick,
2013).
Restructuring information has also been an important tool in the battle
against obesity. As part of the Patient Protection and Affordable Care Act
of (2010), the US Food and Drug Administration (FDA) has proposed
guidelines for mandatory menu labeling of calorie information for US
chain restaurants with at least 20 locations. The aim of this policy is to
provide consumers with relevant information at the point of purchase
with the hope that this information will lead consumers to make healthier food choices (Downs, Loewenstein, & Wisdom, 2009). Examination
of choice behavior following the introduction of calorie information on
menus reveals a relatively modest decrease in the average number of
calories consumed (Kiszko, Martinez, Abrams, & Elbel, 2014). Other work
suggests that alternative choice architectures could be more helpful to
consumers trying to process the additional information. For example, one
study found that consumers selected a meal with fewest calories when the
menu items with calorie information were ordered from low to high and
color coded with red and green to signify poorer and better food choices,
respectively (Liu, Roberto, Liu, & Brownell, 2012). More generally, the
multiple traffic light system on food labels has most consistently helped
consumers identify healthier products (Hawley et al., 2013). Restaurants

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also have the option of providing calorie ranges for entrees that are customizable. Recent evidence shows that in such contexts consumers can
underestimate their actual calorie intake unless the calorie range endpoints are labeled with example items (Liu, Bettman, Uhalde, & Ubel,
2014).
INFORMATION FEEDBACK
The ability to causally link action and outcome in order to learn and change
behavior is a fundamental law of human nature (Thorndike, 1898). Therefore,
an important decision faced by the choice architect is what data to feedback
and how to visualize that data so that it is timely and easily processed. An
interesting example is the Power-Aware Cord, which Time Magazine named
as one of the best inventions of 2010. The Power-Aware Cord is a power strip
that lights up while it is drawing power and becomes brighter as more power
is used (Gustafsson & Gyllenswärd, 2005). The tool provides instant and intuitive feedback about energy use. In contrast to this ambient feedback, other
products such as the “Eco-Eye” provide direct, real-time numerical feedback
regarding the money that is being spent on electricity consumption (Pierce,
Odom, & Blevis, 2008).
The capability to provide in-home electricity displays—digital devices that
can give near-real-time information about electricity usage, in some cases
with appliance-disaggregated consumption information—has provided
a new field in which appropriate choice architecture must be developed.
According to one summary report, direct feedback of energy usage can
produce between 5% and 15% consumption savings (Darby, 2006). Interestingly, there appears to be a divergence between the types of feedback
information that consumers prefer (appliance-specific and dollar-feedback)
and the types of feedback information that are actually effective for learning
about appliance energy use (aggregated kilowatt hour; Krishnamurti, Davis,
Wong-Parodi, Wang, & Canfield, 2013). This suggests that consumers may
become overwhelmed with the disaggregated information. Potentially
because of this, another study found an advantage for a simple display
screen that used only ambient face designs in which the size and shape of
the face’s smile varied depending on power usage (Chiang, Mevlevioglu,
Natarajan, Padget, & Walker, 2014).
Another approach to providing feedback is to present consumers with
information about their usage relative to some benchmark. Comparisons
of any kind are useful because they help turn outcomes that are hard to
evaluate, such as energy use, into something that can be judged as a success
or failure (Kahneman & Tversky, 1979). Avoiding failure is very motivating.
In one study, households were left with a door hanger that reported the

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household’s energy consumption compared to the average household
in their neighborhood during a specific period (Schultz, Nolan, Cialdini,
Goldstein, & Griskevicius, 2007). For some households, this descriptive
norm information was coupled with injunctive norm information: If the
household had consumed less than the average household then a happy face
was drawn, whereas if the household had consumed more than the average
household then an unhappy face was drawn. On average, presentation
of the descriptive norm moved all households’ electricity consumption
toward the average: those using more energy than the average reduced
their consumption, whereas those using less increased their consumption.
This undesirable boomerang effect observed in the low-use households was
eliminated when the descriptive norm information was coupled with the
injunctive norm information (i.e., the happy face). These insights have been
implemented by the software company Opower, which partners with utility
providers around the world to promote energy efficiency. Opower sends
home energy report letters comparing electricity use to similar neighbors
together with injunctive norm information such as smiley faces as well as
personalized ways to save energy and money. The program has been widely
considered to be a great success and, according to one estimate, the average
program has reduced energy consumption by 2.0% (Allcott, 2011a).
Another important comparison is a goal, which refers to a specific quantifiable level of performance (Locke & Latham, 1990) and is a salient reference
point against which to judge success and failure (Heath, Larrick, & Wu, 1999).
For example, one study found that marathon runners’ finish times significantly bunch up near round numbers that serve as natural goals such as the
4-h mark (Allen, Dechow, Pope, & Wu, 2014). The American Internet start-up,
stickK.com, has utilized the power of explicit goals by allowing users to set
up a public “commitment contract” in which they agree to achieve a certain
goal, such as losing weight, or else a legally binding contract will be triggered that sends their money to third parties, including “enemies.” The site
also encourages the use of referees: people who help monitor the progress of
the contract. According to stickK’s analysis of 125,000 contracts over 3 years,
the success rate for people who do not name a referee or set financial stakes is
only 29% but it rises to 80% when a contract includes a referee and financial
stakes.
KEY CHALLENGES AND OPPORTUNITIES FOR FUTURE RESEARCH
As choice architecture grows as a dedicated area of study, it will face new
challenges and opportunities. We briefly review these in terms of theoretical
challenges, practical challenges, and application opportunities.

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THEORETICAL CHALLENGES AND OPPORTUNITIES
Choice architecture draws on a wide range of psychological insights both
in identifying decision making shortcomings that need to be improved and
in the tools for helping decision makers. This presents a major challenge:
Can there be a theory of choice architecture that is organized around a few
basic constructs? This is one of the major issues for future research. We suspect that the answer to this question will be “no”—but that frameworks for
designing interventions can be created. Frameworks will focus on diagnosing the relevant psychological limitations for a given task (such as myopia in
retirement savings, saving for college, and investments in energy efficiency),
the task stage (search for information, choice of options, post-decision implementation), and the range of solutions that can be chosen (externally selected
defaults, personal pre-commitments, etc.). We would propose that such a
framework would require a cost–benefit analysis that assesses both ease of
implementation for a solution and the likelihood of effectiveness.
Perhaps the main theoretical challenge for choice architecture is to move
away from identifying “one-size-fits-all” solutions to making them contingent on the interests and abilities of the decision maker. Some examples of
choice architecture already have this quality. The notion of a “sign post” (in
which the same information is translated to several possibly relevant goals,
such as translating an automobile’s gas consumption to gas cost and greenhouse gas impact) is an aspect of choice architecture that reminds consumers
of their personal values (which may differ between people) and allows them
to act on those values. Taking this a step further, choice architecture could
allow a decision maker to personalize his or her own information from a
menu (such as what information to look at, how it is scaled, etc.). Instead
of receiving gas costs expressed as a fixed distance, prospective car buyers
could tailor the distance to their own expected driving. In many situations,
however, complete personalization may defeat the purpose of choice architecture. Research in judgment and decision making has shown that people
often “don’t know what they don’t know.” If there is a danger of neglecting
important information, the choice architect could mandate the inclusion of
specific pieces of information while allowing for personalized tailoring. For
example, a structured process for guiding retirement savings decisions could
require the decision maker to see an actuarial estimate of his or her life span
(including a ninetieth fractile) and an illustration of compounding over that
period of time for an early investor and a late investor. Decision makers could
then personalize it further with health information, personal interests (e.g.,
preferred retirement age), and risk preferences for investments.
A second approach to avoiding the “one-size-fits-all” solution is to use data
provided by the decision maker to tailor aspects of the architecture to the

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individual. For example, Smith et al. (2013) propose that instead of giving
everyone the same default, there can be made “smart defaults” that use simple algorithms embedded in a website or app to calculate the best match
for an individual. These defaults could be used for retirement investing or
in health care by having an individual fill out basic demographic information (age, gender, family size, etc.). One new technology that embraces this
approach is the “smart” Nest Thermostat, which learns when it should turn
on and off from past consumer behavior and responds to changes in the environment such as someone turning on the oven. The program would then
recommend a best fit given a person’s profile, but this is not binding, and the
individual would be welcome to search further and explore more options.
Smith et al. (2013) note that businesses may face a conflict between serving
the interests of a consumer and maximizing their own profit—it might be in
the interest of an insurance company to steer a consumer toward a plan with
a high premium, especially if the consumer is likely to incur low medical
costs. The authors propose that an enlightened company that serves its customers interests best will perform better over the long term; however, they
also acknowledge that in some cases a third party (such as the government
or a consumer advocacy agency) will need to run or oversee the system to
help ensure it meets the consumer’s interests.
PRACTICAL CHALLENGES AND OPPORTUNITIES
Several practical challenges are emerging as choice architecture receives
more academic and public attention. On the academic side, the field of
choice architecture lies at the intersection of different fields and disciplines,
especially psychology—which spawned the basic behavioral insights underlying choice architecture—and behavioral economics—which is interested
in quantifying, understanding, and remedying systematic deviations from
rationality in market behavior. The challenge here is that the two disciplines have many differences in terms of language, methods, and, most
fundamentally, assumptions about how to approach problems. For example,
economists are more inclined than psychologists to focus on incentives and
to prefer formal, elegant models of behavior. But there is also an opportunity
here. The heart of behavioral economics lies in psychology. There are
strong prospects for a continuing intellectual partnership. Psychologists
specialize in generating new behavioral insights, often using laboratory
samples with subjects of convenience; economists specialize in streamlining
behavioral claims to their most testable form and then testing them in large
archival data or randomized controlled studies in the field. For example, the
economist Hunt Allcott has tested both the effectiveness of descriptive norm
interventions in energy use (Allcott, 2011a) and the degree to which people’s

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confusion about “MPG” would change market behavior in car purchases
(Allcott, 2011b).
As choice architecture increasingly guides policy decisions made by some
national and state governments, there is a basic question of the public and
political acceptableness of such uses. Thaler and Sunstein (2008) argue that
using a choice architecture intervention, such as a default, to help consumers
is more “libertarian” than many policy choice options such as mandates or
bans that restrict choice. Although the state is “paternalistic” in trying to
help individuals make better decisions for themselves and society, the use
of defaults preserves individual autonomy. In addition, choice architecture
interventions are often relatively low cost to implement and involve no use
of taxes, making them more palatable to politicians and media pundits who
hold conservative fiscal views. Nevertheless, there has been some political
pushback on using choice architecture interventions (The Economist, 2014).
The most important audience who must accept the use of choice architecture in policy is the voter affected by those policies. This is a growing area
of research: How much do people trust or distrust different sources of intervention (e.g., the government, a corporation, or a third party, such as a professional association)? And can other mechanisms, such as disclosure of the
intervention or the option to opt out of the intervention (Smith et al., 2013),
make the intervention more acceptable?
CONCLUSION
The idea of choice architecture starts with the finding from psychology
that preferences are malleable and adds the logical claim that choices are
never made in a vacuum—there is always a structure. But was the structure
designed well or by accident? The role of the choice architect is to take
insights from psychology to build a better structure—to help guide decision
makers to better choices for themselves and society. Many applications
have already been found in financial decisions, health decisions, and
environmental decisions. More remain to be invented and “built.”
REFERENCES
Allcott, H. (2011a). Consumers’ perceptions and misperceptions of energy costs. The
American Economic Review, 101(3), 98–104.
Allcott, H. (2011b). Social norms and energy conservation. Journal of Public Economics,
95(9), 1082–1095.
Allen, E. J., Dechow, P. M., Pope, D. G., & Wu, G. (2014). Reference-dependent preferences: Evidence from marathon runners. Working paper http://www.nber.org/
papers/w20343.pdf.

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Camilleri, A. R., & Larrick, R. P. (2014). Metric and scale design as choice architecture
tools. Journal of Public Policy & Marketing, 33(1), 108–125.
Chiang, T., Mevlevioglu, G., Natarajan, S., Padget, J., & Walker, I. (2014). Inducing
[sub] conscious energy behaviour through visually displayed energy information:
A case study in university accommodation. Energy and Buildings, 70, 507–515.
Darby, S. (2006). The effectiveness of feedback on energy consumption: A review for DEFRA
of the literature on metering, billing and direct displays. Oxford, England: Environmental Change Institute, University of Oxford.
Dietvorst, B., Milkman, K. L., & Soll, J. B. (2014). Outcome nudges and process
nudges: Diverse preferences call for process nudges. Working paper.
Downs, J. S., Loewenstein, G., & Wisdom, J. (2009). Strategies for promoting healthier
food choices. American Economic Review, 99(2), 159–164.
Fox, C. R., Ratner, R. K., & Lieb, D. S. (2005). How subjective grouping of options
influences choice and allocation: Diversification bias and the phenomenon of partition dependence. Journal of Experimental Psychology: General, 134(4), 538.
Gustafsson, A. & Gyllenswärd, M. (2005). The Power-Aware Cord: Energy awareness
through ambient information display (pp. 1423–1426). CHI ’05 extended abstracts
on human factors in computing systems.
Hawley, K. L., Roberto, C. A., Bragg, M. A., Liu, P. J., Schwartz, M. B., & Brownell,
K. D. (2013). The science on front-of-package food labels. Public Health Nutrition,
16(3), 430–439.
Heath, C., Larrick, R. P., & Wu, G. (1999). Goals as reference points. Cognitive Psychology, 38(1), 79–109.
Johnson, E. J., & Goldstein, D. G. (2003). Do defaults save lives? Science, 302(5649),
1338–1339.
Johnson, E. J., Shu, S. B., Dellaert, B. G., Fox, C., Goldstein, D. G., Häubl, G., … Weber,
E. U. (2012). Beyond nudges: Tools of a choice architecture. Marketing Letters, 23(2),
487–504.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under
risk. Econometrica, 47, 263–291.
Kiszko, K. M., Martinez, O. D., Abrams, C., & Elbel, B. (2014). The influence of calorie
labeling on food orders and consumption: A review of the literature. Journal of
Community Health. http://www.ncbi.nlm.nih.gov/pubmed/24760208
Krishnamurti, T., Davis, A. L., Wong-Parodi, G., Wang, J., & Canfield, C. (2013). Creating an in-home display: Experimental evidence and guidelines for design. Applied
Energy, 108, 448–458.
Larrick, R. P., & Soll, J. B. (2008). The MPG illusion. Science, 320(5883), 1593–1594.
Lichtenstein, S., & Slovic, P. (2006). The construction of preference. New York, NY: Cambridge University Press.
Liu, P. J., Bettman, J. R., Uhalde, A. R., & Ubel, P. A. (2014). ‘How many calories are in my burrito?’ Improving consumers’ understanding of energy (calorie) range information. Public Health Nutrition. http://www.ncbi.nlm.nih.gov/
pubmed/24762709
Liu, P. J., Roberto, C. A., Liu, L. J., & Brownell, K. D. (2012). A test of different menu
labeling presentations. Appetite, 59(3), 770–777.

12

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Locke, E. A., & Latham, G. P. (1990). A theory of goal setting and task performance. Upper
Saddle River, NJ: Prentice-Hall, Inc.
Ly, K., Mazar, N., Zhao, M., & Soman, D. (2013). A practitioner’s guide to nudging. Research report series: Behavioral economics in action, Rotman School of
Management, University of Toronto, http://www.cucentral.ca/cusr/Resources/
0303_REP_Soman_web_final.pdf.
Madrian, B. C., & Shea, D. F. (2001). The power of suggestion: Inertia in 401 (k) participation and savings behavior. The Quarterly Journal of Economics, 116(4), 1149–1187.
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on
our capacity for processing information. Psychological Review, 63(2), 81.
Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ:
Prentice-Hall.
Payne, J. W., Bettman, J. R., & Johnson, E. J. (1992). Behavioral decision research: A
constructive processing perspective. Annual Review of Psychology, 43(1), 87–131.
Pierce, J., Odom, W., & Blevis, E. (2008). Energy aware dwelling: A critical survey
of interaction design for eco-visualizations. In Proceedings of the 20th Australasian
Conference on Computer-Human Interaction: Designing for Habitus and Habitat (pp.
1–8).
Russo, J. E. (1977). The value of unit price information. Journal of Marketing Research,
14(2), 193–201.
Schultz, P. W., Nolan, J. M., Cialdini, R. B., Goldstein, N. J., & Griskevicius, V. (2007).
The constructive, destructive, and reconstructive power of social norms. Psychological Science, 18(5), 429–434.
Smith, N. C., Johnson, E. J., & Goldstein, D. G. (2013). Choice without awareness:
Ethical and policy implications of defaults. Journal of Public Policy & Marketing,
32(2), 159–172.
Soll, J. B., Keeney, R. L., & Larrick, R. P. (2013). Consumer misunderstanding of credit
card use, payments, and debt: Causes and solutions. Journal of Public Policy & Marketing, 32(1), 66–81.
Soll, J. B., Milkman, K. L., & Payne, J. W. (2014). A user’s guide to debiasing. Handbook
of judgment and decision making. New York, NY: John Wiley & Sons, Inc.
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth,
and happiness. New Haven, CT: Yale University Press.
The Economist. (February 7, 2014). The market for paternalism: Nudge unit leaves
kludge unit. Retrieved from, http://www.economist.com/blogs/freeexchange/
2014/02/market-paternalism
Thorndike, E. L. (1898). Animal intelligence: An experimental study of the associative
processes in animals. Psychological Monographs: General and Applied, 2(4), i–109.
Ungemach, C., Camilleri, A. R., Johnson, E. J., Larrick, R. P., & Weber, E. U. (2014).
Translated attributes as a choice architecture. Working paper.

DR. ADRIAN R. CAMILLERI SHORT BIOGRAPHY
Dr. Adrian R. Camilleri is currently a lecturer in marketing at the Royal
Melbourne Institute of Technology (RMIT). He holds a bachelor’s degree in

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13

psychology, a Master’s degree in organizational psychology, and a PhD in
psychology, all from the University of New South Wales. He has recently
completed postdoctoral training at Duke University, where he was supported
by the competitive American Australian Fellowship and Australian Endeavour Fellowship.
Adrian is an expert in the field of behavioral decision making, with special
interests in risky decisions, environmental decisions, and choice architecture.
His research attempts to understand how people’s choices are influenced by
the way that information is presented and reveal what information formats
produce the best choices. Adrian’s research has been published in highly
reputed academic journals including Cognition, Journal of Public Policy &
Marketing, and Judgment and Decision Making. You can learn more about
him at http://adriancamilleri.net/
RICK P. LARRICK SHORT BIOGRAPHY
Rick P. Larrick is the Michael W. Krzyzewski University Professor in Leadership and a Professor of Management and Organizations at Duke University’s
Fuqua School of Business. He serves as the faculty director for Fuqua’s Center
for Energy, Development, and the Global Environment (EDGE) and is a faculty affiliate of the Center for Research on Environmental Decisions (CRED)
located at Columbia University.
Larrick’s research interests include individual, group, and organizational
decision making. Specific areas of research examine environmental decision
making, the wisdom of crowds, and choice architecture. His research on the
“MPG Illusion” (Larrick & Soll, 2008, Science) influenced the redesign of the
EPA fuel economy label in 2013, which added a new metric (gallons per 100
miles metric) that described gas consumption. Larrick received his PhD in
social psychology from the University of Michigan in 1991. Before joining
Duke in 2001, he taught at Northwestern’s Kellogg Graduate School of Management (1991–1993) and at the University of Chicago’s Graduate School of
Business (1993–2001). Larrick received his BA in psychology and economics
from the College of William and Mary.

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

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Choice Architecture

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Choice Architecture
ADRIAN R. CAMILLERI and RICK P. LARRICK

Abstract
“Choice architecture” is a metaphor capturing the idea that all choices occur within
a structure of contextual and task features. These features in turn help to “construct”
a person’s choice. In this chapter, we summarize the academic literature on three
types of choice architecture tools—defaults, information restructuring, and information feedback—and document some real-world examples where these tools have
been applied as successful “nudges.” We end the chapter with a discussion of some
key challenges and opportunities associated with this new field—including the need
for customized choice architecture and the political acceptability of the use of choice
architecture—and highlight some avenues for future research.

CHOICE ARCHITECTURE
The concept of “choice architecture” is borne from the discovery that
people’s preferences are not stable, but malleable. This discovery has
emerged from several decades of behavioral decision research. A memorable demonstration—called partition dependence—is an elegant illustration
of malleable preferences. Partition dependence is the cognitive tendency
to give equal weight to different categories in a judgment or choice task.
Because categories can often be reconfigured, people’s judgments and
preferences likewise change. For example, in one experiment, people were
much more likely to select fruits and vegetables from a menu when the list
of foods was grouped in terms of “fruits,” “vegetables,” and “cookies &
crackers” than when the same list of foods was grouped in terms of “fruits &
vegetables,” “cookies,” and “crackers” (Fox, Ratner, & Lieb, 2005). This
study shows that different choices can be made depending on how people
subjectively partition a set of options or how those options are otherwise
grouped.
Experimental findings such as partition dependence reveal that preferences can differ across ostensibly equivalent choice situations. These results
prove that people are not purely rational decision makers with stable,
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

well-understood preferences. Rather, people’s preferences are influenced
by many of the contextual and task features associated with a choice that
should not rationally influence choice. The list of these features is broad—in
this article, we summarize in detail only a few—but they include the
number of options, the default option, the similarity of options, the presence
of compromise or “decoy” options, the type of attributes, the number of
attributes, the correlation between the attributes, the scale the attributes are
expressed upon, and the type of response mode. In general, these contextual
and task features can change what information is used, how that information
is processed, and how an evaluation is expressed.
“Choice architecture” is a metaphor which captures the idea that all
choices occur within a structure that is rich with contextual and task
features. Together, these features help to “construct” a person’s choice
(Lichtenstein & Slovic, 2006; Payne, Bettman, & Johnson, 1992). The concept
of choice architecture makes it evident that there is no such thing as a neutral
choice architecture: Every single choice—from selecting tonight’s dinner to
selecting a lifelong partner—is embedded within an environment that will
influence choice. It is also important to be clear that the concept does not
deny that people’s choices are a function of their values, attitudes, goals,
and preferences; it simply adds choice architecture as another contributing
factor.
The vast majority of choices are made in environments in which no designer
ever deliberately chose the choice architecture. More recently, however, people have become increasingly interested in how the choice architecture can
be explicitly designed to influence choice. Those who engage in such work
are called choice architects. Of course, marketers have been in the choice architecture business for centuries with the specific goal of selling products and
services for profit. A new class of choice architects has emerged with goals
focused on improving decisions for the benefit of the individual and society. The ethos of this new class of choice architect, heralded by Richard H.
Thaler and Cass R. Sunstein (2008), is that by knowing how people think,
choice architects can design choice structures that make it easier for people
to choose what is best for themselves, their families, and their society, and all
without restricting freedom of choice (Johnson et al., 2012). For example, partition dependence reveals a potential intervention that can be implemented
in order to “nudge” consumers toward choosing healthier fruits and vegetables while still permitting the complete freedom to choose the less healthy
cookies.
The general goal of a nudge is to use psychological insights to help people
make better decisions by designing better choice architecture. Such architecture usually includes presenting more (or sometimes less) information or by
restructuring information to make it more easily processed. Policy makers

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are especially interested in identifying nudges that align individual choices
with societal benefits, particularly in the domains of financial savings,
health, and the environment. Nudges can have their effect through different
mechanisms and can be thought of in terms of outcome nudges and process
nudges (Dietvorst, Milkman, & Soll, 2014). Outcome nudges tend to tap a
general psychological tendency and shift people’s preferences in the direction of an outcome selected by the architect. For example, people’s tendency
to select a fuel-efficient vehicle increases when fuel economy is expressed
by multiple correlated metrics such as miles per gallon (MPG), annual fuel
cost, and greenhouse gas emissions because people “count” these attributes
and are impressed that they all favor the efficient car (Ungemach, Camilleri,
Johnson, Larrick, & Weber, 2014). Process nudges are those that allow people
to better process information and thereby allow them to make choices
more aligned with their personal values, attitudes, and goals. For example,
people’s tendency to select a fuel-efficient vehicle increases when presented
with a greenhouse gas rating but, crucially, only for those people who hold
pro-environmental attitudes (Ungemach et al., 2014). Therefore, process
nudges can serve as “signposts” that remind people about their values and
goals and also point them toward the options that best satisfy them.
TOOLS OF CHOICE ARCHITECTURE
There exist a number of excellent resources summarizing different choice
architecture tools (Johnson et al., 2012), debiasing techniques (Soll, Milkman,
& Payne, 2014), and practical guides for nudging (Ly, Mazar, Zhao, & Soman,
2013). In this section, we aim to summarize some of the most important tools
together with some examples of where these tools have been applied as successful nudges.
DEFAULTS
A default is the option that will be enacted automatically if someone does
not actively intervene to change it. The presence of a default represents one
of the strongest forms of choice architecture (Smith, Johnson, & Goldstein,
2013). One classic demonstration shows that there exist very large differences
in organ donation rates between very similar European countries that vary in
whether organ donation is the default or not (Johnson & Goldstein, 2003). For
example, at the time of the study, the donation rate was 12.0% in Germany but
99.9% in nearby Austria. On average, where organ donation was the default
more than 99% of the population was an organ donor, whereas where organ
donation was not the default the rate was less than 30% of the population.

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

The effectiveness of a default stems from at least three sources (Smith et al.,
2013): First, people tend to assume that the default has been singled out intentionally as a recommendation or a signal as to what most people choose.
Second, a default may be psychologically interpreted as an option that is in
some sense already possessed by the person and therefore giving it up could
be perceived as a loss, which is potentially painful, and also opens the door
to feelings of anticipated regret. Third, it takes more effort to change a default
than to keep a default even when the effort required is as little as clicking on
a button.
One of the most successful applications of defaults has been retirement
savings. The program studied by Madrian and Shea (2001) altered whether
employees had to opt-in to a retirement program or were automatically
enrolled and had to opt out. Those participating in the retirement increased
from roughly 50% to over 85%.
INFORMATION RESTRUCTURING
Humans are limited in their capacity for processing information (Miller, 1956;
Newell & Simon, 1972). Therefore, a general principle in the design of useful
choice architecture is to present information in a format that is easily processed. Often this requires an understanding of the goals that people are
attempting to achieve as well as an understanding of their processing limitations. A good example is the introduction of unit pricing information, which
recognizes that consumers often have a goal of purchasing the cheapest product available but struggle to calculate which product is most cost effective
when products vary in size. Unit pricing information rescales a product’s
price in terms of a single, standardized unit that is common across products
of the same type (e.g., “price per ounce”). When unit pricing information is
presented for a range of competing products, then consumers are more easily
able to achieve their goal of purchasing the most economical product (Russo,
1977).
When purchasing a vehicle, consumers are often concerned with the fuel
economy of the vehicle, primarily because they are interested in minimizing their future fuel costs. In the United States, fuel economy is expressed in
terms of MPG. Although a vehicle with a higher MPG is always more efficient
than a vehicle with lower MPG, equal increases in MPG are not equal in gas
savings (Larrick & Soll, 2008), yet people look at differences in MPG to guess
gas savings. For example, over 100 miles, most people think that the gas savings of improving MPG from 20 to 50 is greater than the savings of improving
MPG from 10 to 20 because the first change is both a larger difference and
percentage increase. Yet, the first change saves only 3 gallons and the second change saves 5 gallons. This nonlinear relationship is not captured by

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MPG and few have the knowledge or processing capacity to make the translation. The solution is to think about gallons of gas used over some meaningful distance. The 2013 US fuel economy and environment label, which was
designed by National Highway Traffic Safety Administration and Environmental Protection Agency, has embraced this solution by including a “gallons per 100 miles” metric. More recent research suggests that consumer’s
tendency to select a fuel-efficient vehicle increases when fuel economy is
expressed as the cost of gas over 100,000 miles (Camilleri & Larrick, 2014).
The cost metric feeds directly into the consumer’s cost-minimization goal
and the scale is helpful because it roughly corresponds to the average US
driver’s lifetime vehicle mileage.
The principle of restructuring information in order to nudge people toward
better decisions is also exemplified by the 2009 US Credit Card Accountability Responsibility and Disclosure (CARD) Act. One of the mandates of the
CARD Act is that credit card holders must receive a monthly statement that
provides information about how different levels of payment strategies, such
as the minimum payment per month, will affect the payoff time. One new feature of the statement is that it is required to state the minimum payment that
must be made per month in order to eliminate the balance in 3 years. Research
has shown that this new statement leads to credit card holders toward better judgments, although the benefits are reduced for those less numerate
and when charges continue to be added to the card (Soll, Keeney, & Larrick,
2013).
Restructuring information has also been an important tool in the battle
against obesity. As part of the Patient Protection and Affordable Care Act
of (2010), the US Food and Drug Administration (FDA) has proposed
guidelines for mandatory menu labeling of calorie information for US
chain restaurants with at least 20 locations. The aim of this policy is to
provide consumers with relevant information at the point of purchase
with the hope that this information will lead consumers to make healthier food choices (Downs, Loewenstein, & Wisdom, 2009). Examination
of choice behavior following the introduction of calorie information on
menus reveals a relatively modest decrease in the average number of
calories consumed (Kiszko, Martinez, Abrams, & Elbel, 2014). Other work
suggests that alternative choice architectures could be more helpful to
consumers trying to process the additional information. For example, one
study found that consumers selected a meal with fewest calories when the
menu items with calorie information were ordered from low to high and
color coded with red and green to signify poorer and better food choices,
respectively (Liu, Roberto, Liu, & Brownell, 2012). More generally, the
multiple traffic light system on food labels has most consistently helped
consumers identify healthier products (Hawley et al., 2013). Restaurants

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

also have the option of providing calorie ranges for entrees that are customizable. Recent evidence shows that in such contexts consumers can
underestimate their actual calorie intake unless the calorie range endpoints are labeled with example items (Liu, Bettman, Uhalde, & Ubel,
2014).
INFORMATION FEEDBACK
The ability to causally link action and outcome in order to learn and change
behavior is a fundamental law of human nature (Thorndike, 1898). Therefore,
an important decision faced by the choice architect is what data to feedback
and how to visualize that data so that it is timely and easily processed. An
interesting example is the Power-Aware Cord, which Time Magazine named
as one of the best inventions of 2010. The Power-Aware Cord is a power strip
that lights up while it is drawing power and becomes brighter as more power
is used (Gustafsson & Gyllenswärd, 2005). The tool provides instant and intuitive feedback about energy use. In contrast to this ambient feedback, other
products such as the “Eco-Eye” provide direct, real-time numerical feedback
regarding the money that is being spent on electricity consumption (Pierce,
Odom, & Blevis, 2008).
The capability to provide in-home electricity displays—digital devices that
can give near-real-time information about electricity usage, in some cases
with appliance-disaggregated consumption information—has provided
a new field in which appropriate choice architecture must be developed.
According to one summary report, direct feedback of energy usage can
produce between 5% and 15% consumption savings (Darby, 2006). Interestingly, there appears to be a divergence between the types of feedback
information that consumers prefer (appliance-specific and dollar-feedback)
and the types of feedback information that are actually effective for learning
about appliance energy use (aggregated kilowatt hour; Krishnamurti, Davis,
Wong-Parodi, Wang, & Canfield, 2013). This suggests that consumers may
become overwhelmed with the disaggregated information. Potentially
because of this, another study found an advantage for a simple display
screen that used only ambient face designs in which the size and shape of
the face’s smile varied depending on power usage (Chiang, Mevlevioglu,
Natarajan, Padget, & Walker, 2014).
Another approach to providing feedback is to present consumers with
information about their usage relative to some benchmark. Comparisons
of any kind are useful because they help turn outcomes that are hard to
evaluate, such as energy use, into something that can be judged as a success
or failure (Kahneman & Tversky, 1979). Avoiding failure is very motivating.
In one study, households were left with a door hanger that reported the

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7

household’s energy consumption compared to the average household
in their neighborhood during a specific period (Schultz, Nolan, Cialdini,
Goldstein, & Griskevicius, 2007). For some households, this descriptive
norm information was coupled with injunctive norm information: If the
household had consumed less than the average household then a happy face
was drawn, whereas if the household had consumed more than the average
household then an unhappy face was drawn. On average, presentation
of the descriptive norm moved all households’ electricity consumption
toward the average: those using more energy than the average reduced
their consumption, whereas those using less increased their consumption.
This undesirable boomerang effect observed in the low-use households was
eliminated when the descriptive norm information was coupled with the
injunctive norm information (i.e., the happy face). These insights have been
implemented by the software company Opower, which partners with utility
providers around the world to promote energy efficiency. Opower sends
home energy report letters comparing electricity use to similar neighbors
together with injunctive norm information such as smiley faces as well as
personalized ways to save energy and money. The program has been widely
considered to be a great success and, according to one estimate, the average
program has reduced energy consumption by 2.0% (Allcott, 2011a).
Another important comparison is a goal, which refers to a specific quantifiable level of performance (Locke & Latham, 1990) and is a salient reference
point against which to judge success and failure (Heath, Larrick, & Wu, 1999).
For example, one study found that marathon runners’ finish times significantly bunch up near round numbers that serve as natural goals such as the
4-h mark (Allen, Dechow, Pope, & Wu, 2014). The American Internet start-up,
stickK.com, has utilized the power of explicit goals by allowing users to set
up a public “commitment contract” in which they agree to achieve a certain
goal, such as losing weight, or else a legally binding contract will be triggered that sends their money to third parties, including “enemies.” The site
also encourages the use of referees: people who help monitor the progress of
the contract. According to stickK’s analysis of 125,000 contracts over 3 years,
the success rate for people who do not name a referee or set financial stakes is
only 29% but it rises to 80% when a contract includes a referee and financial
stakes.
KEY CHALLENGES AND OPPORTUNITIES FOR FUTURE RESEARCH
As choice architecture grows as a dedicated area of study, it will face new
challenges and opportunities. We briefly review these in terms of theoretical
challenges, practical challenges, and application opportunities.

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

THEORETICAL CHALLENGES AND OPPORTUNITIES
Choice architecture draws on a wide range of psychological insights both
in identifying decision making shortcomings that need to be improved and
in the tools for helping decision makers. This presents a major challenge:
Can there be a theory of choice architecture that is organized around a few
basic constructs? This is one of the major issues for future research. We suspect that the answer to this question will be “no”—but that frameworks for
designing interventions can be created. Frameworks will focus on diagnosing the relevant psychological limitations for a given task (such as myopia in
retirement savings, saving for college, and investments in energy efficiency),
the task stage (search for information, choice of options, post-decision implementation), and the range of solutions that can be chosen (externally selected
defaults, personal pre-commitments, etc.). We would propose that such a
framework would require a cost–benefit analysis that assesses both ease of
implementation for a solution and the likelihood of effectiveness.
Perhaps the main theoretical challenge for choice architecture is to move
away from identifying “one-size-fits-all” solutions to making them contingent on the interests and abilities of the decision maker. Some examples of
choice architecture already have this quality. The notion of a “sign post” (in
which the same information is translated to several possibly relevant goals,
such as translating an automobile’s gas consumption to gas cost and greenhouse gas impact) is an aspect of choice architecture that reminds consumers
of their personal values (which may differ between people) and allows them
to act on those values. Taking this a step further, choice architecture could
allow a decision maker to personalize his or her own information from a
menu (such as what information to look at, how it is scaled, etc.). Instead
of receiving gas costs expressed as a fixed distance, prospective car buyers
could tailor the distance to their own expected driving. In many situations,
however, complete personalization may defeat the purpose of choice architecture. Research in judgment and decision making has shown that people
often “don’t know what they don’t know.” If there is a danger of neglecting
important information, the choice architect could mandate the inclusion of
specific pieces of information while allowing for personalized tailoring. For
example, a structured process for guiding retirement savings decisions could
require the decision maker to see an actuarial estimate of his or her life span
(including a ninetieth fractile) and an illustration of compounding over that
period of time for an early investor and a late investor. Decision makers could
then personalize it further with health information, personal interests (e.g.,
preferred retirement age), and risk preferences for investments.
A second approach to avoiding the “one-size-fits-all” solution is to use data
provided by the decision maker to tailor aspects of the architecture to the

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individual. For example, Smith et al. (2013) propose that instead of giving
everyone the same default, there can be made “smart defaults” that use simple algorithms embedded in a website or app to calculate the best match
for an individual. These defaults could be used for retirement investing or
in health care by having an individual fill out basic demographic information (age, gender, family size, etc.). One new technology that embraces this
approach is the “smart” Nest Thermostat, which learns when it should turn
on and off from past consumer behavior and responds to changes in the environment such as someone turning on the oven. The program would then
recommend a best fit given a person’s profile, but this is not binding, and the
individual would be welcome to search further and explore more options.
Smith et al. (2013) note that businesses may face a conflict between serving
the interests of a consumer and maximizing their own profit—it might be in
the interest of an insurance company to steer a consumer toward a plan with
a high premium, especially if the consumer is likely to incur low medical
costs. The authors propose that an enlightened company that serves its customers interests best will perform better over the long term; however, they
also acknowledge that in some cases a third party (such as the government
or a consumer advocacy agency) will need to run or oversee the system to
help ensure it meets the consumer’s interests.
PRACTICAL CHALLENGES AND OPPORTUNITIES
Several practical challenges are emerging as choice architecture receives
more academic and public attention. On the academic side, the field of
choice architecture lies at the intersection of different fields and disciplines,
especially psychology—which spawned the basic behavioral insights underlying choice architecture—and behavioral economics—which is interested
in quantifying, understanding, and remedying systematic deviations from
rationality in market behavior. The challenge here is that the two disciplines have many differences in terms of language, methods, and, most
fundamentally, assumptions about how to approach problems. For example,
economists are more inclined than psychologists to focus on incentives and
to prefer formal, elegant models of behavior. But there is also an opportunity
here. The heart of behavioral economics lies in psychology. There are
strong prospects for a continuing intellectual partnership. Psychologists
specialize in generating new behavioral insights, often using laboratory
samples with subjects of convenience; economists specialize in streamlining
behavioral claims to their most testable form and then testing them in large
archival data or randomized controlled studies in the field. For example, the
economist Hunt Allcott has tested both the effectiveness of descriptive norm
interventions in energy use (Allcott, 2011a) and the degree to which people’s

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

confusion about “MPG” would change market behavior in car purchases
(Allcott, 2011b).
As choice architecture increasingly guides policy decisions made by some
national and state governments, there is a basic question of the public and
political acceptableness of such uses. Thaler and Sunstein (2008) argue that
using a choice architecture intervention, such as a default, to help consumers
is more “libertarian” than many policy choice options such as mandates or
bans that restrict choice. Although the state is “paternalistic” in trying to
help individuals make better decisions for themselves and society, the use
of defaults preserves individual autonomy. In addition, choice architecture
interventions are often relatively low cost to implement and involve no use
of taxes, making them more palatable to politicians and media pundits who
hold conservative fiscal views. Nevertheless, there has been some political
pushback on using choice architecture interventions (The Economist, 2014).
The most important audience who must accept the use of choice architecture in policy is the voter affected by those policies. This is a growing area
of research: How much do people trust or distrust different sources of intervention (e.g., the government, a corporation, or a third party, such as a professional association)? And can other mechanisms, such as disclosure of the
intervention or the option to opt out of the intervention (Smith et al., 2013),
make the intervention more acceptable?
CONCLUSION
The idea of choice architecture starts with the finding from psychology
that preferences are malleable and adds the logical claim that choices are
never made in a vacuum—there is always a structure. But was the structure
designed well or by accident? The role of the choice architect is to take
insights from psychology to build a better structure—to help guide decision
makers to better choices for themselves and society. Many applications
have already been found in financial decisions, health decisions, and
environmental decisions. More remain to be invented and “built.”
REFERENCES
Allcott, H. (2011a). Consumers’ perceptions and misperceptions of energy costs. The
American Economic Review, 101(3), 98–104.
Allcott, H. (2011b). Social norms and energy conservation. Journal of Public Economics,
95(9), 1082–1095.
Allen, E. J., Dechow, P. M., Pope, D. G., & Wu, G. (2014). Reference-dependent preferences: Evidence from marathon runners. Working paper http://www.nber.org/
papers/w20343.pdf.

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Camilleri, A. R., & Larrick, R. P. (2014). Metric and scale design as choice architecture
tools. Journal of Public Policy & Marketing, 33(1), 108–125.
Chiang, T., Mevlevioglu, G., Natarajan, S., Padget, J., & Walker, I. (2014). Inducing
[sub] conscious energy behaviour through visually displayed energy information:
A case study in university accommodation. Energy and Buildings, 70, 507–515.
Darby, S. (2006). The effectiveness of feedback on energy consumption: A review for DEFRA
of the literature on metering, billing and direct displays. Oxford, England: Environmental Change Institute, University of Oxford.
Dietvorst, B., Milkman, K. L., & Soll, J. B. (2014). Outcome nudges and process
nudges: Diverse preferences call for process nudges. Working paper.
Downs, J. S., Loewenstein, G., & Wisdom, J. (2009). Strategies for promoting healthier
food choices. American Economic Review, 99(2), 159–164.
Fox, C. R., Ratner, R. K., & Lieb, D. S. (2005). How subjective grouping of options
influences choice and allocation: Diversification bias and the phenomenon of partition dependence. Journal of Experimental Psychology: General, 134(4), 538.
Gustafsson, A. & Gyllenswärd, M. (2005). The Power-Aware Cord: Energy awareness
through ambient information display (pp. 1423–1426). CHI ’05 extended abstracts
on human factors in computing systems.
Hawley, K. L., Roberto, C. A., Bragg, M. A., Liu, P. J., Schwartz, M. B., & Brownell,
K. D. (2013). The science on front-of-package food labels. Public Health Nutrition,
16(3), 430–439.
Heath, C., Larrick, R. P., & Wu, G. (1999). Goals as reference points. Cognitive Psychology, 38(1), 79–109.
Johnson, E. J., & Goldstein, D. G. (2003). Do defaults save lives? Science, 302(5649),
1338–1339.
Johnson, E. J., Shu, S. B., Dellaert, B. G., Fox, C., Goldstein, D. G., Häubl, G., … Weber,
E. U. (2012). Beyond nudges: Tools of a choice architecture. Marketing Letters, 23(2),
487–504.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under
risk. Econometrica, 47, 263–291.
Kiszko, K. M., Martinez, O. D., Abrams, C., & Elbel, B. (2014). The influence of calorie
labeling on food orders and consumption: A review of the literature. Journal of
Community Health. http://www.ncbi.nlm.nih.gov/pubmed/24760208
Krishnamurti, T., Davis, A. L., Wong-Parodi, G., Wang, J., & Canfield, C. (2013). Creating an in-home display: Experimental evidence and guidelines for design. Applied
Energy, 108, 448–458.
Larrick, R. P., & Soll, J. B. (2008). The MPG illusion. Science, 320(5883), 1593–1594.
Lichtenstein, S., & Slovic, P. (2006). The construction of preference. New York, NY: Cambridge University Press.
Liu, P. J., Bettman, J. R., Uhalde, A. R., & Ubel, P. A. (2014). ‘How many calories are in my burrito?’ Improving consumers’ understanding of energy (calorie) range information. Public Health Nutrition. http://www.ncbi.nlm.nih.gov/
pubmed/24762709
Liu, P. J., Roberto, C. A., Liu, L. J., & Brownell, K. D. (2012). A test of different menu
labeling presentations. Appetite, 59(3), 770–777.

12

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Locke, E. A., & Latham, G. P. (1990). A theory of goal setting and task performance. Upper
Saddle River, NJ: Prentice-Hall, Inc.
Ly, K., Mazar, N., Zhao, M., & Soman, D. (2013). A practitioner’s guide to nudging. Research report series: Behavioral economics in action, Rotman School of
Management, University of Toronto, http://www.cucentral.ca/cusr/Resources/
0303_REP_Soman_web_final.pdf.
Madrian, B. C., & Shea, D. F. (2001). The power of suggestion: Inertia in 401 (k) participation and savings behavior. The Quarterly Journal of Economics, 116(4), 1149–1187.
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on
our capacity for processing information. Psychological Review, 63(2), 81.
Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ:
Prentice-Hall.
Payne, J. W., Bettman, J. R., & Johnson, E. J. (1992). Behavioral decision research: A
constructive processing perspective. Annual Review of Psychology, 43(1), 87–131.
Pierce, J., Odom, W., & Blevis, E. (2008). Energy aware dwelling: A critical survey
of interaction design for eco-visualizations. In Proceedings of the 20th Australasian
Conference on Computer-Human Interaction: Designing for Habitus and Habitat (pp.
1–8).
Russo, J. E. (1977). The value of unit price information. Journal of Marketing Research,
14(2), 193–201.
Schultz, P. W., Nolan, J. M., Cialdini, R. B., Goldstein, N. J., & Griskevicius, V. (2007).
The constructive, destructive, and reconstructive power of social norms. Psychological Science, 18(5), 429–434.
Smith, N. C., Johnson, E. J., & Goldstein, D. G. (2013). Choice without awareness:
Ethical and policy implications of defaults. Journal of Public Policy & Marketing,
32(2), 159–172.
Soll, J. B., Keeney, R. L., & Larrick, R. P. (2013). Consumer misunderstanding of credit
card use, payments, and debt: Causes and solutions. Journal of Public Policy & Marketing, 32(1), 66–81.
Soll, J. B., Milkman, K. L., & Payne, J. W. (2014). A user’s guide to debiasing. Handbook
of judgment and decision making. New York, NY: John Wiley & Sons, Inc.
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth,
and happiness. New Haven, CT: Yale University Press.
The Economist. (February 7, 2014). The market for paternalism: Nudge unit leaves
kludge unit. Retrieved from, http://www.economist.com/blogs/freeexchange/
2014/02/market-paternalism
Thorndike, E. L. (1898). Animal intelligence: An experimental study of the associative
processes in animals. Psychological Monographs: General and Applied, 2(4), i–109.
Ungemach, C., Camilleri, A. R., Johnson, E. J., Larrick, R. P., & Weber, E. U. (2014).
Translated attributes as a choice architecture. Working paper.

DR. ADRIAN R. CAMILLERI SHORT BIOGRAPHY
Dr. Adrian R. Camilleri is currently a lecturer in marketing at the Royal
Melbourne Institute of Technology (RMIT). He holds a bachelor’s degree in

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13

psychology, a Master’s degree in organizational psychology, and a PhD in
psychology, all from the University of New South Wales. He has recently
completed postdoctoral training at Duke University, where he was supported
by the competitive American Australian Fellowship and Australian Endeavour Fellowship.
Adrian is an expert in the field of behavioral decision making, with special
interests in risky decisions, environmental decisions, and choice architecture.
His research attempts to understand how people’s choices are influenced by
the way that information is presented and reveal what information formats
produce the best choices. Adrian’s research has been published in highly
reputed academic journals including Cognition, Journal of Public Policy &
Marketing, and Judgment and Decision Making. You can learn more about
him at http://adriancamilleri.net/
RICK P. LARRICK SHORT BIOGRAPHY
Rick P. Larrick is the Michael W. Krzyzewski University Professor in Leadership and a Professor of Management and Organizations at Duke University’s
Fuqua School of Business. He serves as the faculty director for Fuqua’s Center
for Energy, Development, and the Global Environment (EDGE) and is a faculty affiliate of the Center for Research on Environmental Decisions (CRED)
located at Columbia University.
Larrick’s research interests include individual, group, and organizational
decision making. Specific areas of research examine environmental decision
making, the wisdom of crowds, and choice architecture. His research on the
“MPG Illusion” (Larrick & Soll, 2008, Science) influenced the redesign of the
EPA fuel economy label in 2013, which added a new metric (gallons per 100
miles metric) that described gas consumption. Larrick received his PhD in
social psychology from the University of Michigan in 1991. Before joining
Duke in 2001, he taught at Northwestern’s Kellogg Graduate School of Management (1991–1993) and at the University of Chicago’s Graduate School of
Business (1993–2001). Larrick received his BA in psychology and economics
from the College of William and Mary.

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