Beyond nudges: Tools of a choice architecture

Abstract

The way a choice is presented influences what a decision-maker chooses. This paper outlines the tools available to choice architects, that is anyone who present people with choices. We divide these tools into two categories: those used in structuring the choice task and those used in describing the choice options. Tools for structuring the choice task address the idea of what to present to decision-makers, and tools for describing the choice options address the idea of how to present it. We discuss implementation issues in using choice architecture tools, including individual differences and errors in evaluation of choice outcomes. Finally, this paper presents a few applications that illustrate the positive effect choice architecture can have on real-world decisions.

Full text

Beyond nudges: Tools of a choice architecture
Eric J. Johnson &Suzanne B. Shu &
Benedict G. C. Dellaert &Craig Fox &
Daniel G. Goldstein &Gerald Häubl &
Richard P. Larrick &John W. Payne &Ellen Peters &
David Schkade &Brian Wansink &Elke U. Weber
Published online: 25 May 2012
#Springer Science+Business Media, LLC 2012
Abstract The way a choice is presented influences what a decision-maker chooses.
This paper outlines the tools available to choice architects, that is anyone who present
people with choices. We divide these tools into two categories: those used in
structuring the choice task and those used in describing the choice options. Tools
for structuring the choice task address the idea of what to present to decision-makers,
Mark Lett (2012) 23:487–504
DOI 10.1007/s11002-012-9186-1
Preparation of this article has been supported by NIA Grant 5R01AG027934 to the first author.
E. J. Johnson (*):E. U. Weber
Center for Decision Science, Columbia Business School, Columbia University, New York, NY, USA
e-mail: ejj3@columbia.edu
S. B. Shu :C. Fox
Anderson School of Management, UCLA, Los Angeles, CA, USA
B. G. C. Dellaert
Department of Business Economics, Erasmus School of Economics, Erasmus University, Rotterdam,
Netherlands
D. G. Goldstein
Yahoo! Research and London Business School, London, UK
G. Häubl
School of Business, University of Alberta, Edmonton, AB, Canada
R. P. Larrick :J. W. Payne
The Fuqua School of Business, Duke University, Durham, NC, USA
E. Peters
Psychology Department, The Ohio State University, Columbus, OH, USA
D. Schkade
Rady School of Management, UCSD, San Diego, CA, USA
B. Wansink
Applied Economics and Management Department, Cornell University, Ithaca, NY, USA

and tools for describing the choice options address the idea of how to present it. We
discuss implementation issues in using choice architecture tools, including individual
differences and errors in evaluation of choice outcomes. Finally, this paper presents a
few applications that illustrate the positive effect choice architecture can have on real-
world decisions.
Keywords Choice architecture .Decision support .Options and alternatives .
Describing attributes
Choice architecture, a term coined by Thaler and Sunstein (2008), reflects the fact
that there are many ways to present a choice to the decision-maker, and that what is
chosen often depends upon how the choice is presented. Choice architects have
significant, if perhaps underappreciated, influence, much like the architect of a
building who affects the behaviors of the building’s inhabitants through the place-
ment of doors, hallways, staircases, and bathrooms. Similarly, choice architects can
influence choice in many ways: by varying the presentation order of choice alter-
natives, the order attributes and their ease of use, and the selection of defaults, to
name just a few of the design options available. While it is tempting to think that
choices can be presented in a “neutral”way (“Just the facts, Ma’am”), the reality is
that there is no neutral architecture—any way a choice is presented will influence
how the decision-maker chooses. Consider, perhaps, the best-known example. All
choice presentations have a (usually implicit) default, even if the default is no choice
is made, preserving the status quo. The default option will be chosen more often than
if another option is designated the default. Thus, everyone, from a parent presenting
bedtime options to a child to a government providing pension options to its citizens,
influences choices and is a choice architect.
In this brief paper, we provide examples of the tools available to a choice architect.
We do not provide a theoretical account of why these tools affect choice, nor suggest
a normative analysis of how a choice architecture ought to be designed or when to use
which tool. Our goals are modest: to provide an initial roadmap and to identify,
describe, and categorize many of the tools of a choice architecture with a few brief
illustrative applications. We divide our list of tools into two broad categories, those
used in structuring the choice task and those used in describing the choice options.
These two categories correspond, roughly, to the idea of what to present to decision-
makers and how to present it. We then turn to the challenges and opportunities these
tools raise in implementation and provide some examples of effective application of
choice architecture tools. Table 1provides a summary of the tools and some relevant
examples that are discussed throughout this paper.
1 Structuring the choice task
1.1 Number of alternatives
One of the starkest decisions facing a choice architect is the question of how
many alternatives (choice options) to present to the decision maker. Should the
488 Mark Lett (2012) 23:487–504

decision maker be presented with one option at time, or two options, three
options, or even 10, 20, or 100 or more options simultaneously? There are
times when a person has too few options, such as when the original Ford
Model T were available in any color—as long as it was black. At other times,
there can be the danger of too many options, what others have called the
tyranny of choice (Schwartz 2004) or choice overload (Iyengar and Lepper 2000;
Jacoby 1984). For example, the number of Medicare drug benefit plans available to
US seniors now exceeds 100 in some states, possibly overwhelming the processing
capacity of many elderly decision makers (Kling et al. 2011), and investors can often
Table 1 Summary of the tools
Problem Choice architecture tools Examples
Setting up the task
Alternative overload Reduce number of alternatives Medicare drug plans (Kling et al. 2011),
investments (Cronqvist and Thaler 2004)
Technology and decision aids Sorting on attributes (Lynch and Ariely
2000), mobile devices and applications
(Cook and Song 2009), smart energy grids
Decision inertia Use defaults Investments (Cronqvist and Thaler 2004;
Madrian and Shea, 2001), insurance
(Johnson et al. 1993), organ donation
(Johnson and Goldstein 2003)
Myopic
procrastination
Focus on satisficing Planning errors (Koehler 1991;
Weber et al. 2007; Shu 2008),
job search (Iyengar et al. 2006)
Limited time windows Gift certificates (Shu and Gneezy 2010),
retirement planning (O’Donoghue and
Rabin 1999), tax credits
Long search process Decision staging Automobile customization (Levav et al.
2010), product evaluation (Häubl et al.
2010)
Describing the options
Naïve allocation Partitioning of options Investments (Langer and Fox 2005;
Bardolet et al. 2009), food menus
(Fox 2005), automobile attributes
(Martin and Norton 2009)
Attribute overload Attribute parsimony and labeling Good/bad labels for numeric information
(Peters et al. 2009)
Non-linear attributes Translate and rescale for better
evaluability
Credit card repayments (Soll et al. 2011),
gas mileage ratings (Larrick and Soll 2008)
Implementation issues
Individual differences Customized information Politics and energy conservation
(Hardisty et al. 2010), numeracy
and decision making (Peters et al. 2006;
Sagara 2009)
Outcome valuation Focus on experience Focusing and satisfaction
(Schkade and Kahneman 1998),
cooling off periods
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face hundreds of options for retirement funds (Cronqvist and Thaler 2004). The trend
in the marketplace is for more, not fewer options to be presented to the consumer, and
additional options can also complicate choice in unexpected ways. In cafeterias, for
example, additional foods can be “trigger foods,”resulting in seemingly unrelated
additions that would otherwise have not been made (Hanks et al. 2012). In studies of
high school cafeterias, the presence bananas and green beans decreased sales of ice
cream while the presence of sugary sides, such as fruit cocktail and applesauce,
increased sales of cake and chips.
To answer the question of how many options to present, the choice architect needs
to balance two criteria: first that more options increase the chances of offering a
preference match to the consumer, and second that more options places a greater
cognitive burden on consumers because of the additional need to evaluate options.
Thus, to answer this question of balance, we should be concerned about the willing-
ness of the decision-maker to engage in the choice process, the decision-maker’s
satisfaction with the decision process, and more generally the nature of the processes
that will be used to make the decision. Finally, as discussed later, the answer is
contingent upon characteristics of the individual decision-maker. Older adults, for
instance, with less processing capacity seem to prefer less choice than younger adults
(Reed et al. 2008).
Despite the vast amount of research examining the effects of a number of alter-
natives on decision behavior (see Payne et al. 1993; Scheibehenne et al. 2010), the
issue of balancing different objectives makes it hard to identify a simple recommen-
dation for the optimal number of alternatives to present. However, some general
guidelines apply. One wants the fewest number of options that will encourage a
reasoned consideration of tradeoffs among conflicting values and yet not seem too
overwhelming to the decision maker. Yet too few options may generate context-
specific preferences, a well-known phenomenon in choice, where the presence or
absence of one option influences what is chosen. One recommendation that balances
these considerations is that four or five non-dominating options may represent
reasonable initial values for the choice architect given these tradeoffs. One could
also proceed by starting with this limited choice set, but also provide the decision-
maker with the option of considering more options, if desired.
1.2 Technology and decision aids
More and more of the choices we make involve the use of some form of information
technology (Murray et al. 2010). This technology may be introduced to assist in the
choice task. For instance, we increasingly choose what to buy, what activities to
participate in, or what to attend to via some form of desktop or mobile computer
interface. Moreover, we may use technology-based tools such as search engines or
product recommendation systems to help us identify attractive choice alternatives that
we were not aware of, and to filter out ones that are not of interest to us (Bodapati
2008; Häubl and Murray 2006; Xiao and Benbasat 2007). We can also enlist the
assistance of interactive decision aids that help us compare choice alternatives in
terms of their attractiveness on various feature dimensions (Häubl and Trifts 2000;
Lynch and Ariely 2000). Yet another way in which the choices we make are
increasingly facilitated by technology is the automatic personalization of user
490 Mark Lett (2012) 23:487–504

interfaces to reflect our preferences (Hauser et al. 2009; Price et al. 2006). This
interaction with decision technology is likely to increase in future years as computing
devices become more unobtrusively integrated into our daily environment (see, e.g.,
Cook and Song 2009; Streitz et al. 2007).
Research has demonstrated that decision aids such as product recommendation
systems can be highly beneficial to consumers, enabling them to find products that
better match their preferences while at the same time reducing search effort (Häubl
and Trifts 2000). However, these tools can also predictably influence consumers’
choices through very subtle architectural features such as the set of other products that
are presented alongside a recommended alternative (Cooke et al. 2002) or which
product attributes are made more salient by the system (Häubl and Murray 2003).
Thus, technology-based decision aids could be designed to steer consumers towards
choosing products, services, or activities that are individually and/or socially desirable—
i.e., healthy, environmentally friendly, etc.—without restricting their freedom to choose.
Given that consumers appear to show little resistance to such influence when it benefits
profit-seeking sellers (Häubl and Murray 2006), they should be even more willing to
accept these interventions when these are in their own and/or society’s interest.
1.3 Defaults
One of the most powerful and popular tools available to the choice architect is the use of
defaults. Defaults are settings or choices that apply to individuals who do not take active
steps to change them (Brown and Krishna 2004). Collections of default settings or
“default configurations”determine the way consumers initially encounter products,
services, or policies, while “reuse defaults”come into play with subsequent uses of a
product. At the finest level, a single question can have a “choice option default”,which
on electronic forms can take the shape of a pre-checked box (Johnson et al. 2002).
Defaults have been shown to have strong effects on real-world choices in domains
including investment (Cronqvist and Thaler 2004; Madrian and Shea 2001), insurance
(Johnson et al. 1993), organ donation (Johnson and Goldstein 2003), and marketing
and beyond (Goldstein et al. 2008). They appeal to a wide audience in their ability to
guide choice, while at the same time preserving freedom to choose, and are often regarded
as prototypical instruments of libertarian paternalism (Sunstein and Thaler 2003).
Through default-setting policies, choice architects can exert influence over resulting
choices (Goldstein et al. 2008). The palette of policies includes simple defaults
(choosing one default for all), random defaults (assigning a configuration at random,
for instance, as an experiment), forced choice (withholding the product or service by
default, and releasing it to the recipient only after an active choice is made), and
sensory defaults (those which change according to what can be inferred about the
user, for example, web sites that change language dependent on country of origin of
the visitor). Products and services that are frequently purchased can use either of
persistent defaults (where past choices are remembered) or reverting defaults (which
forget the last changes made to the default configuration). They also can use predictive
defaults (which intelligently alter reuse defaults based on observation of the user).
Choice architects should be mindful of the ethical risks involved in setting defaults
(Smith et al. 2010). The ethical acceptability of using a default to guide choice has
much to do with the reason why the default is having an effect (see Dinner et al. 2011
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for a discussion of those reasons). When consumers are aware that defaults may be set
as recommendations in some cases, or manipulation attempts in other cases (Brown
and Krishna 2004), they exhibit a level of “marketplace metacognition”that suggests
they successfully retain autonomy and freedom of choice. However, if defaults have
an effect because consumers are not aware that they have choices, or because the
transaction costs of changing from the default are too high, defaults impinge upon
liberty. An often-prudent policy, though not a cure-all, is to set the default to the
alternative most people prefer when making an active choice, without time pressure,
in the absence of any default. Running an experiment on a sample of the population
can determine these preferences, and can be done in little time and at low cost in this
age of Internet experimentation.
1.4 Choice over time
The intertemporal structure of a task has important implications for both the decision-
maker and the choice architect. Many of the choices individuals face involve out-
comes that unfold over long periods of time, which affects choice tasks in three
specific ways. First, individuals tend to be myopic and prefer to receive positive
outcomes early, leading them to yield to immediate temptations and heavily discount-
ing future outcomes (Ainslie 2001; Loewenstein and Elster 1992). Second, uncer-
tainty about the future can cause individuals’preferences for future outcomes to be
unclear, such that certain types of outcomes are systematically over- or under-
weighted. For example, uncertainties in life expectancy (Payne et al. 2012) can affect
decisions about financial products with future payouts such as savings, annuities, and
reverse mortgages (Brown 2007; Börsch-Supan 2003; Davidoff et al. 2005). Simi-
larly, uncertainties about the likelihood and extent of global climate change seem to
reduce the political will for mitigation (Hansen 2009). In dealing with this uncertain-
ty, the decision maker can become overly focused on certain highly salient or
desirable future outcomes and fail to consider satisfactory second-best alternatives
(Koehler 1991; Shu 2008). Third, individuals are often overly optimistic about the
future and assume that they can accomplish more than they actually will. They expect
to have both more time and money in the future than they do today and overestimate
the probability that desired outcomes will occur as planned (Kahneman and Lovallo
1993; Zauberman and Lynch 2005).
Tools are available to the choice architect to address each type of intertemporal
bias. One option seems to be order of consideration. Drawing attention to the delayed
options can refocus the decision-maker, generating more patient choices (Weber et al.
2007). One can also refocus the decision-maker toward “satisficing”by considering
second-best outcomes, which can lead to less choice deferral and higher choice
satisfaction (Shu 2008; Iyengar et al. 2006). Placing limited windows for opportuni-
ties can overcome the tendency to think that the future holds more resources. For
example, big city residents who procrastinate visiting local landmarks due to an
assumption they can do it later may benefit by assuming the role of being tourists
whose limited window for sightseeing motivates them to action (Shu and Gneezy
2010). Other examples include putting expiration dates on policy initiatives like home
energy efficiency improvement tax credits (http://www.irs.gov/newsroom/article/0,,
id0206871,00.html) or offering only limited windows for making changes to
492 Mark Lett (2012) 23:487–504

retirement savings plans (O’Donoghue and Rabin 1999). In general, tools that
translate aspects of the choice into immediate salient outcomes are more suc-
cessful than those that attempt to manipulate heavily discounted future costs
and benefits (Soman et al. 2005).
1.5 How task structure affects the search process
The structuring of the decision task not only affects the way in which consumers
decide between choice options, it also has implications for how decision-makers
successively explore the option space, both in choosing what information to examine
and what information to ignore as they narrow down their choice set. As an example,
consider the differences in search when making a single choice versus making a series
of configuration decisions. This distinction has received relatively little attention in
the literature, but has implications for the way in which information is searched,
choices are made, and how they are justified. In a typical choice context, a consumer
needs to decide between a relatively small set of alternatives and typically is asked to
choose only one product (e.g., buying grocery products, buying clothing, choosing a
certain service provider, etc.). Alternately, consumers are confronted with configu-
rators (that is software systems for selecting the options for highly customizable
products, such as for cars) and may use different strategies to deal with the complex-
ity of multiple decisions that need to be made than in the simpler classical choice
context. These strategies affect consumer choice outcomes differently and therefore
suggest different tools of choice architecture. Levav et al. (2010) found that consum-
ers are more likely to choose default levels of attributes when they begin with
attributes that offer a greater number of configuration options than when they begin
with attributes that offer a smaller number of options.
Another way that consumers’definition of the task affects choice is the
common finding that they first screen alternatives on the basis of a subset of
attributes and only then make alternative-based comparisons for the remaining
set of alternatives after screening (Hauser and Wernerfelt 1990; Payne 1975). In
this context, formatting the screening stage by facilitating comparisons on one
attribute but not others will lead to a stronger preference for options favored by the
focal attribute (Diehl et al. 2003).
One way of understanding how consumers search information is to analyze the
role of search costs. Normatively, consumers should consider the total distribution of
alternatives in the market and the cost for inspecting each alternative, and then
compare the (most attractive) alternatives they encounter to a reservation value to
determine when to stop searching (Weitzman 1979). Recent research shows that
consumers are prone to make sub-optimal decisions in these search decisions
(Häubl et al. 2010;Shu2008; Zwick et al. 2003). Formatting the decision task can
help the decision-maker do better; for example, sorting alternatives in order of
expected attractiveness can be an effective way to improve search outcomes
(Dellaert and Häubl 2012; Häubl and Trifts 2000). Providing easier upfront
information about the distribution of product values in the marketplace, such
as informing buyers about the range of possible prices they may encounter, is
also helpful to consumers who are not familiar with the market (Rosenfield et
al. 1983; Shu 2008).
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2 Describing choice options
2.1 Partitioning options and attributes
One important facet of choice architecture is the way in which the set of options,
attributes, or events are partitioned into groups or categories. This seemingly innoc-
uous feature of a choice environment can have a dramatic impact on choice behavior.
Prior research in diverse domains has shown that partitioning creates vivid categories
that can influence allocations involving simultaneous choices (Fox et al. 2005). For
instance, employees tend to allocate their retirement investments evenly over various
categorical options such as stocks, bonds, and real estate when they are separated into
categories than when they are listed together (Thaler and Sunstein 2008). Similar
effects have been documented in lab-based investments (Langer and Fox 2005) and in
charitable donation decisions (Fox et al. 2005). Recent studies have also shown that
the physical partitioning of a shopping cart and on-line order forms can alter the mix
of products a person purchases. For instance, studies with grocers have shown that
altering the amount of a shopping cart reserved for fruits and vegetables ended up
altering how much was purchased (Wansink et al. 2012).
When people allocate a limited resource (e.g., money, attention, probabilistic belief,
importance weights) over a fixed set of possibilities (e.g., investment opportunities,
consumption options or time periods, events or attributes), they are typically biased
toward even allocation over each group or category that has been identified. Thus, in
personal investment, people tend toward allocating 1/nof their savings to each of the n
options that are singled out in a 401(k) plan (Benartzi and Thaler 2001); in consumer
choice, people thus tend to seek variety when choosing multiple goods for future
consumption (Read and Loewenstein 1995; Simonson 1990), and they tend to favor
spreading out consumption over different time periods (Loewenstein and Prelec 1993);
in distributive justice, people tend to favor equal allocation of benefits and burdens
among individuals unless there is a compelling alternative criterion (Messick 1993);
in decision analysis, people are biased toward assigning equal probabilities to each
event that could occur (Fox and Clemen 2005; Fox and Rottenstreich 2003) and equal
importance weights to each attribute that is explicitly identified (Weber et al. 1988).
This pervasive tendency toward even allocation provides a powerful tool to choice
architects: judgments and choices can be strongly influenced by the particular groups
or categories into which the set of possibilities is partitioned. Thus, by assigning
favored investment options to separate superordinate categories (e.g., domestic and
international stock index funds to choice sets A and B) and disfavored investment
options to a single superordinate category (e.g., several high-load exotic mutual funds
to choice set C), one can nudge greater investment into the favored options (see
Bardolet et al. 2009; Langer and Fox 2005). By segregating healthy food menu
options into separate menu categories (e.g., “fruits”,“vegetables”) and integrating
unhealthy options into a single menu category (e.g., “cookies and candies”), one can
nudge participants to choose a greater number of healthy options and smaller number
of unhealthy options; likewise, by segregating later time periods into separate cate-
gories and integrating earlier time periods into a single category, one can induce
greater patience in consumption (Fox et al. 2005). By splitting more important
attributes (e.g., “practicality”of an automobile) into a greater number of sub-
494 Mark Lett (2012) 23:487–504

categories (e.g., “safety”,“gas mileage”,“warranty”) and combining less important
attributes into a single category (e.g., “Stylishness—design, stereo, horsepower”),
one can increase the importance given to the more important attributes when con-
sumers choose among product offerings (Martin and Norton 2009).
A unique virtue of using partitioning to nudge decision-makers toward desired
behaviors is that the impact of this intervention will tend to be strongest among
decision-makers with weaker intrinsic preferences or beliefs and diminish or disap-
pear among those with stronger intrinsic preferences or beliefs (Fox et al. 2005). For
instance, in one study wine novices asked to choose among several different varieties
of white wine were more likely to diversify over grape if wines were grouped by
grape type, and they were more likely to diversify over country of origin when wines
were grouped in that manner; this effect was greatly attenuated among wine experts
(Fox et al. 2005). Thus, partitioning will tend to exert the strongest paternalistic
influence on those who need the greatest guidance and will have the weakest effect on
those who require the least guidance.
2.2 Designing attributes
People choose between alternatives by weighing their pros and cons on different
attributes, and choice architects influence behavior when particular attributes are made
more or less salient. For example, car buyers will consider attributes such as style, cost,
safety, reliability, capacity, and fuel economy. These attributes may be important in their
own right or because they help decision-makers achieve more fundamental objectives
(Keeney 1996). An ideal decision incorporates all of the relevant attributes and weighs
them to the degree that allows decision-makers to achieve their objectives. The choice
architect can help people attend to and use attributes accurately by adhering to the
principles of parsimony, linearity, comparability, and evaluability. In addition, decision
architects may choose to make some attributes, such as those with externalities that
might otherwise be neglected, more salient by using the tools of attribute translation and
attribute expansion, all described in the remainder of this section.
Decision-makers often must make choices by using attribute information to predict
their satisfaction with different alternatives. Here the choice architect’s first available
tool is parsimony. Just as individuals may be overwhelmed by too much choice, they
may also be overwhelmed by too many attributes and simplify their decision by focusing
on only one. Decision-makers can understand more information and weigh important
information better in choices that require less cognitive effort, especially for less
numerate consumers (Peters et al. 2007). As in choosing the number of alternatives,
reducing cognitive effort can be achieved by using smaller sets of attributes and
highlighting the meaning of only the most important attributes. This must be balanced
by the need of including all attributes. Of course attributes may differ across decision-
makers, but while the choice of attributes can be based on a “typical consumer,”
technology allows tailoring by letting consumers choose attributes from a menu.
Linearity is also an important tool for improving accuracy. A decision attribute
may have a non-linear relationship to a more fundamental objective. For example,
people expect that monthly credit payments have a roughly linear relationship to
payback period; in reality, payback period increases sharply when monthly payments
barely cover interest (Soll et al. 2011). To correct this misperception, new credit card
Mark Lett (2012) 23:487–504 495

statements must now list the monthly payment needed to eliminate a balance in
3 years. Similarly, several measures of energy efficiency, such as miles per gallon
(MPG) ratings for cars and SEER ratings for air conditioners, have a reciprocal (i.e.,
non-linear) relationship to energy consumption. However, people use differences in
these numbers to estimate energy savings. As a result, they undervalue the energy
savings from improving inefficient cars (Larrick and Soll 2008). This misperception
can be fixed by converting MPG to an energy consumption measure such as “gallons
per 100 miles”(GPM) that is linear in energy savings.
The final two tools for improving accuracy are ways of increasing comparability
and evaluability. For decisions where the same attribute (e.g., cost) is expressed in
two different ways (e.g., for different time periods) across different contexts (e.g.,
annual newspaper subscriptions, monthly cable bills, and per use music downloads),
placing the activities or products on the same scale allows decision-makers to
compare their relative value more accurately. For highly quantitative information that
can be difficult for people to process because the numbers are challenging or the
domain is unfamiliar, numbers become more easily evaluated if they are broken into
categories, such as grades, or if they have endpoints clearly labeled as good or bad.
Peters et al. (2007) found that decision-makers could integrate more information into
judgments when numbers were supplemented with evaluative labels and showed that
the labels facilitated information processing by allowing affective reactions to be
accessed more quickly. As an example, newly proposed EPA labels contain informa-
tion about carbon dioxide emissions, but no one is familiar with what is a “good”or
bad”level of CO
2
. The labels try to remedy this problem by rating a given car on a 1
to 10 scale that is linear in CO
2
reduction.
For situations where the choice architect wishes to increase the impact of certain
attributes, the tools of attribute translation and attribute expansion can be helpful.
Research by Bond et al. (2008) showed that people bring to mind only half the
objectives they care about in a decision. Thus, there is a benefit in explicitly mapping
an attribute to its consequences for other objectives. For example, a car’s gas consump-
tion is directly related to the cost of driving the car and to the CO
2
emissions from the
car. However, people may fail to translate gas consumption to either scale because the
math is challenging or they do not recognize the consequence on that objective without a
reminder. The newly proposed EPA labels translate gas consumption to both driving
costs and CO
2
emissions to draw more attention to these objectives. A second factor that
increases the use of an attribute is changing the scale on which it is expressed. For
example, a car’s gas consumption can be expressed over short distances (100 miles) or
long distances (10,000 miles). Expanding the denominator makes the numerators larger
and makes the differences between alternatives appear larger, leading these expanded
attributes to receive more weight in choice (Burson et al. 2009).
3 Issues in implementing choice architectures
3.1 Individual differences
Choice architecture at its best promises better decisions, healthier lives, and improved
finances. But some early “nudges”have gone wrong simply because a nudge can have
496 Mark Lett (2012) 23:487–504

multiple effects that may depend on characteristics of the decision-maker. For example,
informing households about their relative energy use led to an average 2 % decrease in
energy usage, but the change depended on the household’s political affiliation. Liberal
households reduced their consumption, while Republicans increased theirs, presumably
due to differences in environmental concerns (Costa and Kahn 2010).
Individual differences can influence how choice architectures play out in the
market. As the eminent learning theorist Hobart Mowrer once said, “To understand
or predict what a rat will learn to do in a maze, one has to know both the rat and the
maze”(Mowrer 1960, p. 10). In similar fashion, choice architects will have to design
decision environments faced by decision-makers in light of knowledge about the
decision environment (this is already being done) but also with knowledge about the
characteristics of targeted decision-makers and how they will process and draw
meaning from information, or what their goals are. In some cases, the right choice
architecture may differ by these individual characteristics. Policy makers insensitive
to this possibility may find that their best efforts at choice architecture leave some
individuals without intended assistance and produce unintended consequences in
other cases. Further complicating the policy maker’s efforts is a “curse of knowledge”
under which choice architects may anchor first on what they themselves know or
want, and then insufficiently adjust for other people’s knowledge levels or prefer-
ences (Nickerson 1999,2001).
The implication is that the intuitions of choice architects will not always be enough
and that choice architectures should be tested in diverse populations of interest.
However, we already know quite a bit about how individual differences influence
decisions and how they interact with situations. An understanding of what individual
differences might be important to particular content domains (e.g., cultural cognitions
in environmental domains) or types of decision problems (e.g., numeracy in decisions
with unfamiliar numeric information) can be brought to bear already on the emerging
science of choice architecture.
For example, a series of studies have been conducted examining the interaction of
numeracy with how information is presented or framed. We know that requiring less
cognitive effort will help decision makers understand more information and weigh
important information more in choices, and this is particularly true for less numerate
consumers (Peters et al. 2006). Attaching affective meaning to numeric information
allows decision makers to integrate more information and, for those who are less
numerate, results in reduced reliance on less relevant, emotional sources of informa-
tion such as mood states (Peters et al. 2007,2009). However, while the use of an
organizing framework helped less numerate consumers to better comprehend infor-
mation that was summarized in the framework, it hurt their comprehension of other
information. On the other hand, highly numerate decision-makers may overuse
number comparisons when such information is provided. A one-size-fits-all approach
to choice architecture will not always work, particularly in diverse and sometimes
highly politicized environments.
3.2 Evaluating outcomes
How can we tell if a choice architecture intervention has helped a decision-maker?
One answer is to consider the decision-maker’s experience of the selected choice
Mark Lett (2012) 23:487–504 497

outcomes. Most theories of choice (implicitly) assume that the utility of an outcome
estimated ex ante equals its utility when it is experienced ex post. However, a
growing body of research has documented numerous ways in which people can fail
to accurately predict how they will feel about the outcomes of their choices (Hsee and
Hastie 2006; Loewenstein and Schkade 1999). People often overestimate the impact
of differences in income on well being (Kahneman et al. 2006) and underestimate the
impact of an empty stomach on their grocery shopping decisions (Nisbett and
Kanouse 1968). A related problem is the underprediction of adaptation to enduring
changes (Schkade and Kahneman 1998). For example, Gilbert et al. (1998) demon-
strated that people greatly overestimate the duration of their emotional responses to
the denial of academic tenure and the breakup of a romance. But there is evidence that
people who have experience in a situation make more accurate predictions about
adaptation (Schkade and Kahneman 1998).
Some features of existing policy and choice environments reflect at least a tacit
knowledge of these phenomena. For example, many consumer protection laws
provide “cooling off”periods, during which a consumer can cancel a choice
without penalty. One function that experienced agents and advisors often serve
is to encourage a decision-maker to consider not only the features of an option
that are salient at the time of choice, but also those that will be more important
when the outcomes are experienced. These and other interventions that bear on
the decision-maker’s knowledge about their future outcomes should be considered
part of the decision architect’s toolkit.
4 Applications of the tools of choice architecture
The concept of choice architecture has already diffused into several economic and
public policy domains where individuals regularly experience suboptimal decisions.
Choice architecture tools have been applied to issues of consumer savings, organ
donation, medical decision-making, consumer health and wellness, and climate
change mitigation. Here, we focus on three primary domains: decisions that impact
the environment, consumer financial decisions, and eating decisions.
Consider first the domain of decisions that impact our environment. This may
include decisions regarding energy consumption (appliances, transportation, heating
and cooling), water use (including showers, gardening, swimming pools, rice farm-
ing), and land use (deforestation, types of agriculture, urban planning). Some envi-
ronmental decisions have substantial long-run implications. Potential climate change
risks are perhaps the greatest sustainability challenge, and require drastic reductions
in greenhouse gas (GHG) emissions through reduced energy consumption and better
efficiency and conservation measures. Producing these reductions can offer both
financial gains for consumers and societal gains for the environment, seemingly a
win–win situation. While purely economic solutions have been attempted, the psy-
chological biases that are a barrier to adoption make this a domain where behavioral
change may prove more effective (Weber 2012, in press).
A second application domain is the area of consumer financial decision-making.
We have already seen the impact of choice architecture on both innovative product
offerings and in public policy. The Save More Tomorrow plan (Benartzi and Thaler
498 Mark Lett (2012) 23:487–504

2004) first reframes the decision to save; instead of reducing consumption now, the
participant decides how much of a future increase in salary will be allocated to
savings. By moving the commitment into the future, the intervention also reduces
the impact of impatience. Finally, the plan makes the increased saving the default.
Together, these three changes in choice architecture have significantly increased
savings behavior and have generated widespread adoption. A change of default in
debit and credit cards has also recently produced a significant change in the structure
of profits in that industry. The 2009 CARD Act changes the default for over-limit fees
on debit and credit cards, requiring an opt-in choice to enable the bank to pay bills
over the amount in the consumer’s account. These fees, often greater than $30,
generated large profits for credit card companies. One firm, Bank of America,
took a write-off of greater than $10.4 billion in the value of its credit card unit
(Schwartz 2010).
Finally, consider the domain of eating decisions. While many individuals spend
significant amounts of time, effort, and money in attempts to modify their diet, most
eating behavior occurs without much conscious thought. Yet given that people make
an average of 200 to 300 decisions regarding food consumption in any given day
(Wansink and Sobal 2007), it is no wonder that individuals might make decisions that
are out of line with their health goals and desires. In order to reduce the cognitive
requirements of so many decisions, individuals may rely on heuristics or decision-
rules to guide food choice and consumption decisions (Wansink 2010; Wansink et al.
2009). These habitual behaviors can become rigid and unresponsive to changes in
understanding of health and nutrition. For instance, doubling the price of an all-you-
can-eat pizza buffet led people to eat more pizza even though the diminishing returns
to taste and perceived quality quickly dropped (Just and Wansink 2011). It led them to
eat more and enjoy it less. It is these types of dynamics that make eating behavior a
prime context for behavioral economic interventions and research.
More generally, standard interventions suggested by classical economic analysis
can backfire, if they ignore the factors we have discussed. For example, providing
information about a particular issue does increase attention toward that issue, but the
intervention can have unintended consequences such as reducing attention about
other important issues (Weber 1997) or increasing focus on only a single corrective
action to the exclusion of others (Weber 2006). Thus, it may be useful to modify
standard utility theory of time and risk preferences, to allow consideration of psy-
chological processes and effects in combination with economic incentives (Just and
Wansink 2009).
For each domain, one may wonder whether choice architecture can leverage
economic solutions. Prior to the advent of choice architecture, traditional models in
economics have suggested three policy levers: altering prices, providing information,
and placing restrictions on purchasing and other behavior. However, many of the
issues examined in psychology and economics, such as excessive discounting, status
quo effects, and information processing limitations can prevent such solutions from
being effective. In financial decisions, purely economic incentives are not enough to
improve choices; for example, even company matching on 401(k) contributions is not
enough to achieve 100 % participation in savings programs. In food consumption,
observed behavior also cannot be reconciled with standard economic models: Alter-
ing prices and providing information is generally ineffective in altering consumption
Mark Lett (2012) 23:487–504 499

(Mytton et al. 2007). For the environment, economic solutions have included regu-
lating behavior (through building codes and CAFE efficiency standards) and raising
the price of energy (e.g., a carbon tax in some countries other than the USA), often
without substantial effects.
An emerging literature has tried to incorporate some psychology into economic
models of environmental choices, financial decisions, and food consumption. While
this literature represents a starting point, much remains to be done in terms of
incorporating the tools of choice architecture into these domains, and creating and
using suitable individual data to calibrate these models for policy purposes. Many of
the choice architecture tools described in this paper were designed to change behavior
in these three areas. Additional situational changes, such as changing the wallpaper
used on a web site (Mandel and Johnson 2002), the social setting of the decision
(Milch et al. 2009), the information process or mode used for making the
decision (Weber and Lindemann 2007), the framing of the outcomes (Tversky
and Kahneman 1981), or even the label of a choice attribute (Hardisty et al. 2010)
may all have desirable effects. Such behavioral interventions are not necessarily
objectionable to the decision-maker even when they are unaware of the impacts
of these interventions on their own behavior (Johnson and Goldstein 2003;
Wan s i n k 2012). Rather, the individual may believe they are better off for the
intervention if the intervention encourages good behavior while not prohibiting bad
behavior. One possible response to the charge that the choice architect is influencing
behavior without the decision-maker’s awareness is full disclosure of decision design:
Choice formats could be accompanied by a description of the potential influences that
might accompany the way the choice is posed. Such full disclosure of choice
architecture is rarely done today, but its effects deserve further study with the goal
of making open disclosure a routine responsibility for choice architects.
The behavioral economics of environmental sustainability, financial decisions, and
eating affects us all in multiple ways multiple times a day. The good news is that the
same factors that lead us to make a mindless suboptimal or unhealthy choice can
often be reversed to help us make a mindless better choice. Behavioral economics
offers a means to encourage more optimal behavior without inducing the resistance
and reactance often associated with restrictive policies (Just and Wansink 2009).
References
Ainslie, G. (2001). Breakdown of will. Cambridge: Cambridge University Press.
Bardolet, D., Fox, C. R., & Lovallo, D. (2009). Naïve diversification and partition dependence in capital
allocation decisions: field and experimental evidence. Strategic Management Journal (in press).
Benartzi, R., & Thaler, R. (2001). Naïve diversification strategies in retirement saving plans. American
Economic Review, 91, 475–482.
Benartzi, S., & Thaler, R. (2004). Save more tomorrow: using behavioral economics to increase employee
savings. Journal of Political Economy, 112, S164–S187.
Bodapati, A. V. (2008). Recommendation systems with purchase data. Journal of Marketing Research, 45,
77–93.
Bond, S. D., Carlson, K. A., & Keeney, R. L. (2008). Generating objectives: can decision makers articulate
what they want? Management Science, 54,56–70.
Börsch-Supan, A. (2003). Life-cycle savings and public policy: a cross-national study of six countries. New
York: Academic.
500 Mark Lett (2012) 23:487–504

Brown, J. R. (2007). Rational and behavioral perspectives on the role of annuities in retirement planning.
(NBER Working Paper No. 13537). National Bureau of Economic Research, Inc, Cambridge, MA.
Brown, C. L., & Krishna, A. (2004). The skeptical shopper: a metacognitive account for the effects of
default options on choice. Journal of Consumer Research, 31, 529–539.
Burson, K. A., Larrick, R. P., & Lynch, J. G., Jr. (2009). Six of one, half dozen of the other: expanding and
contracting numerical dimensions produces preference reversals. Psychological Science, 20, 1074–1078.
Cook, D. J., & Song, W. Z. (2009). Ambient intelligence and wearable computing: sensors on the body, in
the home, and beyond. Journal of Ambient Intelligence and Smart Environments, 1,83–86.
Cooke, A. D. J., Sujan, H., Sujan, M., & Weitz, B. A. (2002). Marketing the unfamiliar: the role of context
and item-specific information in electronic agent recommendations. Journal of Marketing Research,
39, 488–497.
Costa, D. L., & Kahn, M. E. (2010). Energy conservation nudges and environmentalist ideology: evidence
from a randomized residential electricity field experiment. National Bureau of Economic Research,
Inc, Cambridge, MA. (NBER Working Paper No. 15939).
Cronqvist, H., & Thaler, R. (2004). Design choices in privatized social security systems: learning from the
Swedish experience. American Economic Review, 94, 424–428.
Davidoff, T., Brown, J. R., & Diamond, P. A. (2005). Annuities and individual welfare. American
Economic Review, 95, 1573–1590.
Dellaert, B. G. C., & Häubl, G. (2012). Searching in choice mode: consumer decision processes in product
search with recommendations. Journal of Marketing Research. doi:10.1509/jmr.09.0481.
Diehl, K., Kornish, L. J., & Lynch, J. G. (2003). Smart agents: when lower search costs for quality
information increase price sensitivity. Journal of Consumer Research, 30,56–71.
Dinner, I., Johnson, E. J., Goldstein, D. G., & Liu, K. (2011). Partitioning default effects: why people
choose not to choose. Journal of Experimental Psychology. Applied, 17, 332–341.
Fox, C. R., & Clemen, R. T. (2005). Subjective probability assessment in decision analysis: partition
dependence and bias toward the ignorance prior. Management Science, 51, 1417–1432.
Fox, C. R., & Rottenstreich, Y. (2003). Partition priming in judgment under uncertainty. Psychological
Science, 14, 195–200.
Fox, C. R., Bardolet, D., & Lieb, D. (2005). Partition dependence in decision analysis, resource allocation,
and consumer choice. In: R. Zwick, & A. Rapoport (Ed.). Experimental business research, volume III.
Springer, Dordrecht, 3:229–251.
Fox, C. R., Ratner, R. K., & Lieb, D. (2005). How subjective grouping of options influences choice and
allocation: diversification bias and the phenomenon of partition dependence. Journal of Experimental
Psychology. General, 134, 538–551.
Gilbert, D. T., Pinel, E. C., Wilson, T. D., Blumberg, S. J., & Wheatley, T. P. (1998). Immune neglect: a
source of durability bias in affective forecasting. Journal of Personality and Social Psychology, 875,
617–638.
Goldstein, D. G., Johnson, E. J., Herrman, A., & Heitmann, M. (2008). Nudge your customers toward
better choices. Harvard Business Review, 86,99–105.
Hanks, A. S., Just, D. R., & Wansink, B. (2012). Trigger foods alter vegetable and fruit selection in school
lunchrooms. Agricultural and Resource Economics Review, 41,114–123.
Hansen, J. (2009). Storms of my grandchildren: the truth about the coming climate catastrophe and our last
chance to save humanity. New York: Bloomsbury.
Hardisty, D. J., Johnson, E. J., & Weber, E. U. (2010). A dirty word or a dirty world? Attribute framing,
political affiliation, and query theory. Psychological Science, 21,86–92.
Häubl, G., & Murray, K. B. (2003). Preference construction and persistence in digital marketplaces: the role
of electronic recommendation agents. Journal of Consumer Psychology, 13,75–91.
Häubl, G., & Murray, K. B. (2006). Double agents: assessing the role of electronic product recommenda-
tion systems. Sloan Management Review, 47,8–12.
Häubl, G., & Trifts, V. (2000). Consumer decision making in online shopping environments: the effects of
interactive decision aids. Marketing Science, 19,4–21.
Häubl, G., Dellaert, B. G. C., & Donkers, B. (2010). Tunnel vision: local behavioral influences on
consumer decisions in product search. Marketing Science, 29, 438–455.
Hauser, J. R., & Wernerfelt, B. (1990). An evaluation cost model of consideration sets. Journal of
Consumer Research, 16, 393–408.
Hauser, J. R., Urban, G. L., Liberali, G., & Braun, M. (2009). Website morphing. Marketing Science, 28,
202–223.
Hsee, C. K., & Hastie, R. (2006). Decision and experience: why don't we choose what makes us happy?
Trends in Cognitive Sciences, 10,31–37.
Mark Lett (2012) 23:487–504 501

Iyengar, S. S., & Lepper, M. R. (2000). When choice is demotivating: can one desire too much of a good
thing? Journal of Personality and Social Psychology, 79(6), 995–1006.
Iyengar, S., Wells, R., & Schwartz, B. (2006). Doing better but feeling worse: looking for the “best”job
undermines satisfaction. Psychological Science, 17, 143–150.
Jacoby, J. (1984). Perspectives on information overload. Journal of Consumer Research, 10, 432–435.
Johnson, E. J., & Goldstein, D. G. (2003). Do defaults save lives? Science, 302, 1338–1339.
Johnson, E. J., Hershey, J., Meszaros, J., & Kunreuther, H. (1993). Framing, probability distortions, and
insurance decisions. Journal of Risk and Uncertainty, 7,35–53.
Johnson, E. J., Bellman, S., & Lohse, G. L. (2002). Defaults, framing, and privacy: why opting in is not
equal to opting out. Marketing Letters, 13,5–15.
Just, D. R., & Wansink, B. (2009). Better school meals on a budget: using behavioral economics and food
psychology to improve meal selection. Choices, 24,1–6.
Just, D. R., & Wansink, B. (2011). The flat-rate pricing paradox: conflicting effects of ‘all-you-can-eat’
buffet pricing. The Review of Economics and Statistics, 93, 193–200.
Kahneman, D., & Lovallo, D. (1993). Timid choices and bold forecasts: a cognitive perspective on risk
taking. Management Science, 39,17–31.
Kahneman, D., Krueger, A., Schkade, D., Schwarz, N., & Stone, A. (2006). Would you be happier if you
were richer? A focusing illusion. Science, 312, 1908–1910.
Keeney, R. L. (1996). Value-focused thinking: identifying decision opportunities and creating alternatives.
European Journal of Operational Research, 92, 537–549.
Kling, J. R., Mullainathan, S., Shafir, E., Vermeulen, L., & Wrobel, M. V. (2011). Misprediction in
choosing Medicare drug plans. Harvard University Press, Cambridge.
Koehler, D. J. (1991). Explanation, imagination, and confidence in judgment. Psychological Bulletin, 110,
499–519.
Langer, T., & Fox, C. R. (2005). Bias in allocation among risk and uncertainty: partition-dependence, unit
dependence, and procedure dependence. University of Muenster. Working paper.
Larrick, R. P., & Soll, J. B. (2008). The MPG illusion. Science, 320, 1593–1594.
Levav, J., Heitmann, M., Herrmann, A., & Iyengar, S. (2010). Order in product customization decisions:
evidence from field experiments. Journal of Political Economy, 118, 274–299.
Loewenstein, G. F., & Elster, J. (1992). Choice over time. New York: Sage.
Loewenstein, G. F., & Prelec, D. (1993). Preferences for sequences of outcomes. Psychological Review,
100,91–108.
Loewenstein, G. F., & Schkade, D. (1999). Wouldn’tit be nice? Predicting future feelings. In D. Kahneman, E.
Diener, & N. Schwarz (Eds.), Well-being: the foundations of hedonic psychology (pp. 85–105). New
York: Sage.
Lynch, J. G., & Ariely, D. (2000). Wine online: search costs affect competition on price, quality, and
distribution. Marketing Science, 19,83–103.
Madrian, B. C., & Shea, D. F. (2001). The power of suggestion: inertia in 401(k) participation and savings
behavior. Quarterly Journal of Economics, 116, 1149–1187.
Mandel, N., & Johnson, E. J. (2002). When Web pages influence choice: effects of visual primes on experts
and novices. Journal of Consumer Research, 29, 235–245.
Martin, J. M., & Norton, M. I. (2009). Shaping online consumer choice by partitioning the web. Psychology
and Marketing, 26, 908–926.
Messick, D. M. (1993). Equality as a decision heuristic. In J. Baron & B. A. Mellers (Eds.), Psychological
perspectives on justice. Theory and applications (pp. 11–31). New York: Cambridge University Press.
Milch, K. F., Appelt, K. C., Weber, E. U., Handgraaf, M. J. J., & Krantz, D. H. (2009). From individual
preference construction to group decisions: framing effects and group processes. Organizational
Behavior and Human Decision Processes, 108, 242–255.
Mowrer, O. H. (1960). Learning theory and behavior. Hoboken: Wiley.
Murray, K. B., Liang, J., & Häubl, G. (2010). ACT 2.0: the next generation of assistive consumer
technology research. Internet Research, 20, 232–254.
Mytton, O., Gray, A., Rayner, M., & Rutter, H. (2007). Could targeted food taxes improve health? Journal
of Epidemiology and Community Health, 61, 689–694.
Nickerson, R. S. (1999). How we know—and sometimes misjudge—what others know: imputing one's
own knowledge to others. Psychological Bulletin, 125, 737–759.
Nickerson, R. S. (2001). The projective way of knowing: a useful heuristic that sometimes misleads.
Current Directions in Psychological Science, 10, 168–172.
Nisbett, R. E., & Kanouse, D. E. (1968). Obesity, hunger, and supermarket shopping behavior. Proceedings
of the American Psychological Association Annual Convention, 3, 683–684.
502 Mark Lett (2012) 23:487–504

O’Donoghue, T., & Rabin, M. (1999). Procrastination in preparing for retirement. In A. Henry (Ed.), Behavioral
dimensions of retirement economics (pp. 125–156). Washington D.C.: Brookings Institution.
Payne, J. W. (1975). Task complexity and contingent processing in decision making: an information search
and protocol analysis. Organizational Behavior and Human Performance, 16, 366–387.
Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive decision maker. Cambridge: Cambridge
University Press.
Payne, J. W., Sagara, N., Shu, S. B., Appelt, K. C., & Johnson, E. J. (2012). “Life expectation as a
constructed belief: evidence of a live-to or die-by framing effect.”Working paper, Columbia
University.
Peters, E., Västfjäll, D., Slovic, P., Mertz, C. K., Mazzocco, K., & Dickert, S. (2006). Numeracy and
decision making. Psychological Science, 17, 407–413.
Peters, E., Hibbard, J., Slovic, P., & Dieckmann, N. F. (2007). Numeracy skill and the communication,
comprehension, and use of risk and benefit information. Health Affairs, 26, 741–748.
Peters, E., Dieckmann, N. F., Västfjäll, D., Mertz, C. K., Slovic, P., & Hibbard, J. (2009). Bringing meaning
to numbers: the impact of evaluative categories on decisions. Journal of Experimental Psychology.
Applied, 15, 213–227.
Price, B., Greiner, R., Häubl, G., & Flatt, A. (2006). Automatic construction of personalized customer
interfaces. Proceedings of the 11th International Conference on Intelligent User Interfaces (IUI-06).
Read, D., & Loewenstein, G. (1995). Diversification bias: explaining the discrepancy in variety seeking
between combined and separated choices. Journal of Experimental Psychology. Applied, 1,34–49.
Reed, A., Mikels, J. A., & Simon, K. I. (2008). Older adults prefer less choice than young adults.
Psychology and Aging, 23, 671–675.
Rosenfield, D. B., Shapiro, R. D., & Butler, D. A. (1983). Optimal strategies for selling an asset.
Management Science, 29, 1051–1061.
Sagara, N. (2009). Consumer understanding and use of numeric information in product claims. (Doctoral
dissertation). University of Oregon. Retrieved from ProQuest. (Publication No. AAT 3395194)
Scheibehenne, B., Greifeneder, R., & Todd, P. M. (2010). Can there ever be too many options? A meta-
analytic review of choice overload. Journal of Consumer Research, 37, 409–425.
Schkade, D., & Kahneman, D. (1998). Does living in California make people happy? A focusing illusion in
judgments of life satisfaction. Psychological Science, 9, 340–346.
Schwartz, B. (2004). The paradox of choice: why more is less. New York: Harper.
Schwartz, N. D. (2010). Bank of America reports $7.3 billion loss, citing charges. The New York Times.
http://www.nytimes.com/2010/10/20/business/20bank.html.
Shu, S. B. (2008). Future-biased search: the quest for the ideal. Journal of Behavioral Decision Making, 21,
352–377.
Shu, S. B., & Gneezy, A. (2010). Procrastination of enjoyable experiences. Journal of Marketing Research,
47, 933–944.
Simonson, I. (1990). The effect of purchase quantity and timing on variety-seeking behavior. Journal of
Marketing Research, 27(2), 150–162.
Smith, N. C., Goldstein, D. G, & Johnson, E. J. (2010). Choice without awareness: ethical and policy
implications of defaults. Working paper.
Soll, J. B., Keeney, R. L., & Larrick, R. P. (2011). Consumer misunderstanding of credit card use,
payments, and debt: causes and solutions. Working paper.
Soman, D., Ainslie, G., Frederick, S., Li, X., Lynch, J., Moreau, P., et al. (2005). The psychology of
intertemporal discounting: why are distant events valued differently than proximal ones? Marketing
Letters, 16, 347–360.
Streitz, N., Kameas, A., & Mavrommati, I. (2007). The disappearing computer: interaction design, system
infrastructures and applications for smart environments. Heidelberg: Springer.
Sunstein, C. R., & Thaler, R. H. (2003). Libertarian paternalism is not an oxymoron. The University of
Chicago Law Review, 70, 1159–1202.
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: improving decisions about health, wealth and happiness.
New Haven: Yale University Press.
Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211,
453–458.
Wansink, B. (2010). From mindless eating to mindlessly eating better. Physiology and Behavior, 100, 454–463.
Wansink, B. (2012) Package size, portion size, serving size…market size: the unconventional case for half-
size servings. Marketing Science, 31,54–57.
Wansink, B., & Sobal, J. (2007). Mindless eating: the 200 daily food decisions we overlook. Environment
and Behavior, 39, 106–123.
Mark Lett (2012) 23:487–504 503

Wansink, B., Just, D. R., & Payne, C. R. (2009). Mindless eating and healthy heuristics for the irrational.
American Economic Review, 99, 165–169.
Wansink, B., Soman, D., Herbst, K. C., & Payne, C. R. (2012). Partitioned shopping carts: assortment
allocation cues that increase fruit and vegetable purchases. Cornell University. Working paper.
Weber, E. U. (1997). Perception and expectation of climate change: precondition for economic and
technological adaptation. In: M. Bazerman, D. Messick, A. Tenbrunsel, K. Wade-Benzoni (Ed.),
Psychological perspectives to environmental and ethical issues in management (pp. 314–341).
Jossey-Bass, San Francisco.
Weber, E. U. (2006). Experience-based and description-based perceptions of long-term risk: why global
warming does not scare us (yet). Climatic Change, 70, 103–120.
Weber, E. U. (2012). Doing the right thing willingly: behavioral decision theory and environmental policy.
In E. Shafir (Ed.), The behavioral foundations of policy. Princeton University Press, Princeton.
Weber, E. U., & Lindemann, P. G. (2007). From intuition to analysis: making decisions with our head, our
heart, or by the book. In H. Plessner, C. Betsch, & T. Betsch (Eds.), Intuition in judgment and decision
making (pp. 191–208). Mahwah: Erlbaum.
Weber, M., Eisenführ, F., & von Winterfeldt, D. (1988). The effect of splitting attributes in multiattribute
utility measurement. Management Science, 34, 431–445.
Weber, E. U., Johnson, E. J., Milch, K., Chang, H., Brodscholl, J., & Goldstein, D. (2007). Asymmetric
discounting in intertemporal choice: a query theory account. Psychological Science, 18, 516–523.
Weitzman, M. (1979). Optimal search for the best alternative. Econometrica, 47, 641–654.
Xiao, B., & Benbasat, I. (2007). E-Commerce product recommendation agents: use, characteristics, and
impact. MIS Quarterly, 31, 137–209.
Zauberman, G., & Lynch, J. G. (2005). Resource slack and propensity to discount delayed investments of
time versus money. Journal of Experimental Psychology. General, 134,23–37.
Zwick, R., Rapoport, A., King, A., Lo, C., & Muthukrishnan, A. V. (2003). Consumer search: not enough
or too much? Marketing Science, 22, 503–519.
504 Mark Lett (2012) 23:487–504