Psychology, Behavioral Economics, and Public Policy

Abstract

Economics has typically been the social science of choice to inform public policy and policymakers. In the current paper we contemplate the role behavioral science can play in enlightening policymakers. In particular, we provide some examples of research that has and can be used to inform policy, reflect on the kind of behavioral science that is important for policy, and approaches for convincing policy-makers to listen to behavioral scientists. We suggest that policymakers are unlikely to invest the time translating behavioral research into its policy implications, and researchers interested in influencing public policy must therefore invest substantial effort, and direct that effort differently than in standard research practices. Copyright Springer Science + Business Media, Inc. 2005

Full text

eScholarship provides open access, scholarly publishing
services to the University of California and delivers a dynamic
research platform to scholars worldwide.
University of California
Peer Reviewed
Title:
Psychology, behavioral economics, and public policy
Author:
Amir, O
Ariely, D
Cooke, A
Dunning, D
Epley, N
Gneezy, U
Koszegi, B
Lichtenstein, D
Mazar, N
Mullainathan, S
Prelec, D
Shafir, E
Silva, J
Publication Date:
12-01-2005
Publication Info:
Postprints, Multi-Campus
Permalink:
http://escholarship.org/uc/item/5bv261k5
Additional Info:
The original publication is available at www.springerlink.comideas.repec.org/s/kap/mktlet.html
Keywords:
public policy, psychology, behavioral economics
Abstract:
Economics has typically been the social science of choice to inform public policy and policymakers.
In the current paper we contemplate the role behavioral science can play in enlightening
policymakers. In particular, we provide some examples of research that has and can be used to
inform policy, reflect on the kind of behavioral science that is important for policy, and approaches
for convincing policy-makers to listen to behavioral scientists. We suggest that policymakers
are unlikely to invest the time translating behavioral research into its policy implications, and
researchers interested in influencing public policy must therefore invest substantial effort, and
direct that effort differently than in standard research practices.

Behavioral Economics, Psychology, and Public Policy
On Amir (Yale University), Dan Ariely (MIT), Alan Cooke (University of
Florida), David Dunning (Cornell University), Nicholas Epley (Harvard University),
Botond Koszegi (University of California, Berkeley), Donald Lichtenstein (University of
Colorado, Boulder), Nina Mazar (MIT), Sendhil Mullainathan (Harvard University),
Drazen Prelec (MIT), Eldar Shafir (Princeton University), Jose Silva (University of
California, Berkeley)
1
Abstract: Economics has been for a longtime the social science of choice when it
comes to informing policy and policymakers. In the current paper we contemplate what
role can behavioral sciences play in enlightening policymakers. In particular we focus on
the following three questions 1) What kind of behavioral science is important for policy?
2) What are some possible directions for behavioral policy research? And 3) What are
some possible approaches to get policy-makers to listen to behavioral scientists. The
final picture we draw is one where policymakers are unlikely to invest the time
translating behavioral research into its policy implications. Thus, to truly influence
policy, researchers will have to invest substantial effort, and moreover that this effort will
have to be directed differently from standard research practices. .
1
The authors would like to acknowledge the insightful contribution of Uri Gneezy to this paper.

Behavioral Economics, Psychology, and Public Policy
Imagine waking up one morning, turning on the radio and hearing on the NPR
news that the president of the US has issued the following statement:
“After consulting my chief psychologist, I am confident our new
well-being policy will make us 34% happier as a society at almost
no cost – all based on simple reframing”
While the statement above is unlikely to be uttered by any publicly elected
official during our lifetime, this type of statement represents our hope that one day
psychologists and behavioral economists could become more central and substantive
contributors to public policy. Our hope is motivated by two observations: The first is that
over the past two centuries the study of human behavior has yielded many important and
counterintuitive insights. The second is that, despite this knowledge of human nature and
behavior, these findings rarely find their way to the most important potential applications
of this knowledge – public policy.
The failure of psychology and behavioral economics to influence public policy is
particularly painful and frustrating in light of the success of its sibling, economics, as the
basis for policy recommendations. It is not that economics has nothing to offer policy –
economics indeed provides policy-makers with vital tools. Rather, the success of
economics clearly demonstrates that policy-makers are looking to academic fields for

guidance in setting their policies, and given this general willingness to accept advice, it is
unfortunate that behavioral scientists are not providing their own perspectives. One
daunting example of the disconnect of policy from behavioral findings is the design of
military prisons in recent US operations. Had the thirty year old results of Zimbardo’s
famous Stanford prison experiment (1971) been included in their design, military
confinement facilities may have been better equipped to perform well.
In this paper we review some of the recent behavioral improvements to traditional
economics. While such enhancements may question the accuracy and even validity of
several commonly used models, they have by and large been ignored in policy making.
We then describe some of the very few cases in which such behavioral understandings
actually improved policies. Finally, we consider the possible ways to increase the impact
of behavioral research on public policy by outlining the hurdles and possible avenues of
such influence.
Why Should Behavioral Research be Included in Policy?
The main reason behavioral science should be part of the policy debate is that it
provides in some cases a perspective that is vastly different from economics. The most
notable difference has to do with assumptions about rationality. Whereas economics
assumes that individuals and organizations are rational agents, behavioral science does
not. In fact, much of the work in the fields of behavioral decision making has been aimed
at empirically demonstrating deviations from rationality due to cognitive and perceptual
aspects of human architecture. Similarly, much work in behavioral economics has been
aimed at relaxing these assumptions within the standard economic tools.

A major shortcoming of traditional economic models is that they assume too
much about the capabilities of people making decisions. For example, economic research
emphasizes the importance of retirement savings programs to the future welfare of
workers (Ando & Modigliani, 1963). And indeed, many offerings have been created to
enable people to act upon their best interests and save money for their retirement period.
The common finding, though, is that too many employees without pre-defined pension
plans do not save nearly enough for their future (Benartzi & Thaler, 2004). This is despite
the obvious economic benefit of doing so. Behavioral researchers have demonstrated
many possible reasons for this phenomenon, among them the sheer number of investment
options causing inaction (Huberman, Iyengar, & Jiang, 2003), the huge impact of the
default decision (Samuelson & Zeckhauser, 1988), the greater impact of losses then gains
(Kahneman & Tversky, 1979), and the intertemporal asymmetry between the costs and
benefits of the decision (Thaler & Shefrin, 1981). Understanding some of these
behavioral antecedents to individuals’ poor decision making, led to the creation of an
innovative savings policy called “Save More Tomorrow”. In this policy, individuals are
not asked to invest a portion of their salary immediately, but to commit to a future saving
that would be taken out of a portion of their raise (Benartzi & Thaler, 2004). In this way,
many of the behavioral (irrational) difficulties of committing a part of one’s salary
towards some future benefit are mitigated, and a greater proportion of employees save for
their future.
Another example of the importance of behavioral understandings in overcoming
individual shortcomings is… fertilizers …. ##########

Finally, some policies exist and are enforced merely because we know no better
ones. The case of police lineups is one such example. Police lineups in most places
constitute of a witness attempting to recognize one person out of a group of potential
suspects standing next to each other in a line (hence the name). While these lineups are
popular, they suffer from the disturbing possibility of a false recognition – mistakenly
identifying an innocent person as the perpetrator. Using insights about the manner in
which individuals form judgments, Gary Wells and colleagues recently suggested an
improvement to the lineup system whereby suspects would be evaluated sequentially
instead of simultaneously (Turtle, Lindsay, & Wells, 2003; Wells, Malpass, Lindsay,
Fisher, Turtle, & Fulero, 2000). In one telling example, Lindsay and Wells (1985)
convincingly demonstrated that the probability of a false recognition of an innocent
suspect is reduced dramatically when the mechanisms of presentation changes. Instead
of the common practice of presenting multiple individuals simultaneously (which creates
a tendency to identify one of the people as the suspect), a better approach is to present the
individuals one at a time, making sequential judgments regarding their guilt. This
improved methodology is already being used in Ontario and New Jersey.
While there are a few other cases in which behavioral knowledge was effectively
used to improve public policy, they are not many. The factors contributing to such
success stories may lie in the domain of behavior, in the manner in which the behavioral
knowledge was attained, in the manner in which the policy change was attempted, or in a
combination of these factors. We thus attempt to highlight the factors that may contribute
to a successful enhancement of public policy through behavioral science.

What kind of behavioral science is important for policy?
In principle, behavioral science could be an important starting point for
policymaking. What is less clear is what kind of behavioral science is best suited to
inform policy-making. If the kind of behavioral science that is ideal for policy-making
were different from the behavioral science currently practiced, how would it need to be
changed in order to better fit this role? Some aspects that might be important
differentiators between commonplace behavioral science and a policy-oriented behavioral
science are: 1) whether the goal of the research is to study general principles or narrowly
defined behaviors, 2) whether the goal of the research is to study human nature or to
solve a particular problem, and finally, 3) as a consequence of the goal of the research,
does the technology used in the research (e.g., the type of stimuli) lend itself to theory
construction or to application.
The distinction between a theoretical science and an applied discipline is
particularly important as it becomes clearer that the accurate answer to many of the
questions concerning how people behave in certain situations is “it depends.” In fact, as
more knowledge about human behavior accumulates, it becomes evident that the
particular circumstances that define the choice environment have tremendous impact on
the action of the individual, even small ones. The characteristics of the decision
environment that have been shown to influence behavior include the framing of the task
(Tversky & Kahneman, 1981), the particular options that are in the choice set (Huber,
Payne, & Puto, 1982; Simonson, 1989), the type of response that is asked for (Tversky,
Sattath, & Slovic, 1988), the number of alternatives that are given (Iyengar & Lepper,

2000) or may potentially be reached (Amir & Ariely, 2004), the temporal nature of the
decision (Laibson, 1997), the emotional aspects of the decision (Loewenstein, Hsee,
Weber, & Welsh, 2001; Slovic, Finucane, Peters, & MacGregor, 2002; Slovic, Griffin, &
Tversky, 2002), the order in which the alternatives are presented (Russo, Medvec, &
Meloy, 1996), etc. Under this “it depends” kind of world, it is hard for any scientist to
give a single answer about how individuals are expected to behave; yet this is exactly the
input that policy-makers need most in order to better craft optimal policy.
A related question is whether the best role behavioral scientists could play in the
policy arena is to search for general principles or to concoct exact recipes for how to
address any specific policy issue. Despite Kurt Lewin’s claim that “There is nothing so
useful as a good theory,” and James Maxwell’s claim that “There is nothing more
practical than a good theory,” there is still a large gap between finding general principles
and using those principles to prescribe particular policies. While theories and general
principles are clearly useful, it is also clear that policymakers themselves are not going to
conduct the research needed to translate these general constructs into specific policies,
and that if behavioral scientists want their knowledge to be translated, they have to take
the initiative and conduct the research that would bridge theory and the applied setting.
Taking these extra steps means not only doing more applied work, but also becoming an
expert in the particular policy domain one wants the research to apply to (savings,
healthcare, taxes, education, police lineups, etc.). Without such expertise, the researcher
might not be able to understand the nuances of the situation and may therefore conduct
research that would miss some of the central aspects of the application domain. Finally,

it is naïve to expect policymakers to read academic journals, and the applied research
should be disseminated in channels that are easily accessed by policymakers – including
the popular press and personal communications.
A final related aspect has to do with the technology of research. From a scientific
perspective it is almost always better to pick stimuli that would allow the researcher to
directly and unambiguously attribute the effects to the theoretical construct. To achieve
this goal, the selection of stimuli often includes artificial stimuli that are not common in
the marketplace. For example, the use of simple gambles of the form win $x with
probability y, has been instrumental in exploring decision making under uncertainty, but
is clearly more abstract than any of the stimuli individuals encounter in their daily lives.
Decisions regarding investment portfolios, insurance policies, and lottery tickets are
carried out in somewhat different environments than simple gambles. As a consequence,
decisions about these classes of stimuli have the potential to play out very differently. To
make research in behavioral science more applicable, the stimuli used should reflect the
richness of the environment they are meant to represent. Such selection of stimuli will
increase the ability to generalize results to the setting of the policy, while at the same
time to also increase the face validity of the study, which will help “sell” the work to
policymakers. One downside of more realistic stimuli is that they simultaneously
manipulate multiple factors, thus mudding the theoretical interpretation of the causes for
the effects. A second downside is that the use of realistic stimuli can cause individuals to
evaluate the stimuli based on existing schemas they already have from their past
experience – altering their effects from one instantiation to the next.

Some possible directions for behavioral policy research
In this section we would like to point out a few possible directions for policy-
oriented behavioral research. Before discussing such possible directions it is important to
make a few comments about paternalism. To the extent that behavioral research on
policy is successful, policy-makers will be equipped with tools to increase the
effectiveness of policies. For example, behavioral research might help create policies
that would increase savings, decrease drunk driving, increase the number of kids that
upper middle class families have, or increase the expected duration of marriages. While
behavioral research is likely to make such policies more effective, it is still not clear that
the government should implement them. The question of paternalism, control, and
manipulation of the citizens is a complex and delicate one that is beyond the scope of the
current discussion – yet at the same time, the question of paternalism is central to the
issue of research into policy, because any successful research could potentially increase
paternalism. Individuals who have strong anti-paternalistic views may decide at this
point that they do not want to increase the potential for paternalism and hence do not
want to take part in any research related to policy. While this is a valid perspective, it is
worth pointing out that policies made without research are not necessarily less
paternalistic; it is only that they involve less understanding of the effectiveness of the
policies. For example, the use of framing may make policies more effective, but also
more paternalistic. However, current policies already employ framing, with or without
understanding its exact effects.

Returning to the question of possible directions for policy-oriented behavioral
research, we start with research directions that we predict will create the lowest levels of
resistance and opposition from policymakers, as well as from their advisers – such as
economists). We term these research directions “small interventions,” and bundle under
this title all the possible effects that lay people, including all of those who are not familiar
with the behavioral literature, would predict not to have any effects on behavior. We
reason that if policy makers predict that changes of the small interventions type will lead
to no or small effects, they would be less likely to resist them. An example for such
research is the work on effects of defaults of organ donations (Johnson & Goldstein,
2003), showing that the proportion of people who have organ donor status in countries
where the default is that everyone is a donor (and people have to opt out if they don’t
want to be a donor) is over 90%, while the proportion of people who have organ donor
status in countries where the default is that everyone is a non-donor (and people have to
opt in if they want to be a donor) is below 20%. There are other cases in which the
power of defaults can be harnessed to do good – it can be used to help people contribute
to their 401K plans, to their Roth accounts, to enroll people in healthcare, gyms, etc.
(again with all the problems related to paternalism).
Another example of possible small interventions could be based on context effects
such as the asymmetric dominance effect (Huber et al., 1982), and the compromise effect
(Simonson & Tversky, 1992). The work looking at context effects has repeatedly
demonstrated that the alternatives provided in the choice set, even if they are not chosen,
can have substantial effects on the options that are chosen. In the domain of policy, these

effects could be used to influence the choices individuals make on a range of topics from
healthcare plans, to the selection of public officials, and even to convince people that they
are not paying much income tax. A third example could be based on anchoring
(Kahneman & Tversky 1974; Epley & Gilovich, 2001). It has been repeatedly
demonstrated that asking people to answer a question about their willingness to pay (for
example: would you pay an amount equal to the last two digits of your social security
number for this box of chocolate), can have a substantial effect on their true willingness
to pay for the good when elicited later using an incentive compatible procedure. In
general, people don’t believe that answering a hypothetical question about their
willingness to pay can actually change their willingness to pay, and this is why anchoring
could also be a part of the small interventions category. In the policy domain, anchoring
can be used to “help” people contribute more to charity, increase their savings, etc.
A second direction for policy-oriented behavioral research involves the
application of the established arsenal of behavioral effects and result – finding ways to
use these ideas for improving existing, or coming up with new policies. For example,
past research has shown that when a stack of newspapers is offered for sale using the
honor system, asking people to leave the correct amount if they take a newspaper, at the
end of the day there are more missing newspapers than money. The results also show
that if a mirror is placed behind the stack of newspapers such that the people taking the
newspapers can see their reflection, the discrepancy between the amount of missing
newspapers and money left is reduced (%%% ref %%%). Using such devices to increase
self-awareness could have far reaching implications if we were to apply this principle to

driving (reducing the tendency not to obey traffic rules), to personal tax returns
(decreasing tax evasion), and to dishonesty at the workplace.
Another example of an application of a well-documented result to the domain of
policy involves an examination of the framing of tax reduction on spending. In a recent
paper, Epley, Idson, and Mak (2004) examined why the effect of the 2002 tax return on
the economy was smaller than anticipated. Based on a series of experiments the authors
conclude that if the tax reduction had been framed as a “bonus” rather than a “rebate,”
people would have spent significantly more of it. More generally, framing can be used in
many situations ranging from framing the propositions citizens vote on during election
times, to Medicare prescription options, and even to the question of how to trade-off
personal freedom for security.
A final example of an application of established results relate to the “hot cold
empathy gap” (Loewenstein, 1996). This work has demonstrated that when people are in
a “cold” and non-emotional state, they are unable to accurately predict how they
themselves would behave if they were in a “hot” emotional state. Drawing on personal
experience, it is commonly observed that people who go food shopping while hungry
usually buy too much food, and moreover that they do not seem to learn from their past
experiences. A more controlled examination of this idea was provided by (Ariely &
Loewenstein, 2004) where they asked subjects to indicate the likelihood that when
aroused they will have safe sex, and the likelihood that when aroused they will behave
themselves immorally in order to secure sexual gratification. The male respondents who

answered these questions in a cold state indicated that they were unlikely to take risks of
unprotected sex and that they would not engage in morally questionable behavior in order
to obtain sexual gratification. On the other hand, when sexually roused, the same
participants gave dramatically different responses. Indicating that they would take risks
of unprotected sex and engage in morally questionable behavior in order to obtain sexual
gratification. Such “heat of the moment” effects and the intra-personal empathy gap can
have substantial implications for policy. In the domain of sexual education, these results
question the current practices, suggesting that more effective approaches to safe sex
education and to the availability of contraceptives should be considered. A more distant
example involves the relationship between actual voting behavior and opinions expressed
away from the voting booth. When voting or expressing opinion, people are likely to be
less accurate if their emotional state at the time of the opinion expression (which is
usually a cold state) is different from the emotional state of the experience in question
(which is sometimes a hot state). For example, voting about the Big Dig construction
project in Boston might yield different results if the voters were to express their opinions
while sitting comfortably in their offices vs. sitting in a hot humid day in a traffic jam.
Some possible approaches to get policy-makers to listen
The first approach we would like to promote is the grassroots approach. The idea
here is that instead of hoping that someone in Washington DC will read behavioral papers
or invite behavioral scientists to provide advice on policy issues, a better way might be to
start at local communities. Starting at the communities we live in has the advantages that
we know more of the environment, we are closer geographically, the stakes are lower

(which should make it simpler to try something new), and hopefully the bureaucracy is
less potent, generating lower hurdles for implementation. Moreover, to the extent that a
change in local policy is successful it could be spread by people who are using this policy
in their day-to-day lives. One example of a successful grassroots approach is the
abovementioned change to the policy of police lineup promoted by Gary Wells and
colleagues (Turtle, Lindsay, & Wells, 2003; Wells, Malpass, Lindsay, Fisher, Turtle, &
Fulero, 2000). Using the grassroots approach, researchers related to this project were
individually involved in educating policemen and judges about their findings.
Consequently, improved policy was introduced in Ontario and New Jersey, not only
getting police to adopt this procedure but also getting judges to start demanding that
police use this procedure regularly.
A second direction for behavioral policy type of research involves influencing
policy via economics. The idea here is to use the established path from economics to
policy – attempting to modify economics to be more descriptively accurate, and from
there influencing policy. A prime example for this type of approach is prospect theory
(Kahneman & Tversky 1979), which formalized the idea that judgments and preferences
were reference dependent, and has since spurred many applications. One recent example
is the abovementioned work by Epley and colleagues (2004) on the effects of the framing
of tax-returns. As another example, Ariely, Koszegi and Mazar (2004) provide
experimental evidence for the dependence of consumers’ maximum willingness to pay
(WTP) on the prices they expect to see in the marketplace – challenging the assumption
that demand (WTP) is an independent force from production (supply) (see also, Amir,

Ariely, & Carmon, 2004). Their results show that as the price distribution for products
increases in magnitude (i.e., a shift in the supply curve), so does consumers’ willingness
to pay (i.e., shifting the demand curve). They then go further and illustrate how
neoclassical economists, who assume that the forces of supply and demand are
independent, will be led astray when they calculate the effects of policy changes, such as
taxation, on consumption. In particular, they show that the assumption of independence
will overestimate the effects of taxation, and that this overestimation will increase as the
dependency of supply on demand increases. If these results were to hold more generally,
and if this idea were to be incorporated in the economics models attempting to estimate
the effects of policy changes, the estimation might be more accurate.
A third direction for behavioral policy type of research involves influencing
policy via law. As in the example of the eye-witness research (e.g., Wells, et al., 2000), or
the recently evolving field of behavioral-law-and-economics (Sunstein, 2000; Jolls &
Sunstein 2004), legal academia influences both judges and lawyers (i.e., grassroots) and
regulators, and thus may potentially provide the right bridge for the existing gap between
behavioral research and policy. For example, Jolls & Sunstein (2004) consider the
potential to correct behavioral biases through corrective regulation. However, as may be
apparent by the currently narrow scope of overlap between behavioral research and the
field of law, some topics and principles are more easily applicable and useful for
informing public policies than others.

The final and most challenging approach to induce policymakers to listen is to
directly do research on policy. As behavioral scientists we are very used to pilot testing
our ideas – knowing all too well that we cannot possibly think about all the possible
things that could go wrong with our design, and use the pilot data to validate or modify
our thinking. Moreover, we are also painfully aware of the effort and cost of running
experiments and use pilot testing to minimize the waste of money and time. It is
amazing, to say the least, that when it comes to policy there are no pilot tests. If
anything, we would expect there to be many more pilot testing in policy given the
complexity of the conditions, the high uncertainty, and in particular given the incredible
cost. How is it that the government cuts taxes by billions of dollars without any pilot
test? Why not give the residents of Iowa (just as an example) one of four levels of tax
cuts for a year or two and see the effect? Wouldn’t this be much more efficient and
beneficial in the long run? The main point of behavioral policy research is that in many
cases it is hard to make inferences from particular studies to a real policy question and
that the only way to truly determine the effectiveness of policies is to engage in policy
testing as an experimental endeavor. Obviously this idea is going to be difficult for
policy makers to accept since it is so different from the way they currently go about
making policy decisions, but we can dream that one day the Congress will debate the
experimental design of a policy-experiment to test the effects of increased funding to
higher educational institutes on welfare.
Summary

There is no question that the insights from research in psychology and behavioral
economics could be very useful to inform policy decisions. If the designers of the prison
systems would have been more familiar with the work of Zimbardo (1971), the travesty
at the Abu Ghraib Prison (as well as in others) might have been prevented. Despite the
importance of behavioral insights, the picture we draw here on the relationship between
behavioral science and public policy is not a very optimistic one. In fact, the obstacles
facing behavioral researchers who want to influence policy are substantial.
Because of these obstacles we highly recommend that behavioral scientists who
want to take this path choose policy domains they are passionate about – hopefully this
passion will carry them throughout the process and give them the required energy. A
second advantage of general interest in a particular domain stems from the idea that in
order to conduct experiments that can inform policies the experimental setup must take
into account the factors that are most relevant to the policies in question. Without
domain-specific knowledge academic researchers are likely to miss some of the
important elements. Thus, it is clear to us that to influence policies individual researchers
have themselves acquired specific knowledge and expertise in the policy domain.
The experimental setup to answer policy questions should also be considered. For
example, research stimuli should have high face validity. The experimental design
should closely resemble reality, even at a cost of ability to pinpoint the causes of the
results. While some may argue that this is only a cosmetic issue, it is still crucial. Using
ecologically valid stimuli is also instrumental in creating more precise recommendations

to policy makers. An additional cosmetic issue relates to the ways researchers present
themselves. We find it hard to imagine that one day the President of the US will consult
his or her psychological advisor (or at least publicly admit to doing this). The popular
image of psychology usually conjures up the images of psychotherapy, Freud, and the
leather couch—and as such does not necessarily create a positive image for policy.
Psychologists can potentially improve their position by calling themselves behavioral
scientists, or coming up with a new and more impressive title (behavioral policy
science?).
While the overall picture we draw may seem daunting in its implications for how
difficult it is for behavioral researchers to truly influence policy, the battle is not yet lost
and as more researchers join this initiative, treading this path is likely to become easier.
On a practical level, we have two specific recommendations. The first is for behavioral
scientists to participate in the policy making maelstrom of Washington DC in the same
way that economists do. This is not easy, but being willing to spend a few years in DC
and taking the steps to do so is likely to yield progress. The second is to start small.
Instead of imagining that Congress will read your latest paper and decide to change their
policy, try to approach local institutions around your community (local government,
school boards, local police etc.), as they are more likely to adopt changes and the
likelihood of a grassroots movement succeeding at this point seems to us to be much
higher.

References
Amir, O. & Ariely, D. (2004). The Pain of Deciding: Indecision, Procrastination, and
Consumer Choice Online. Unpublished manuscript.
Amir, O. Ariely, D., & Carmon, Z. (2005). The Locus and Appropriateness of Monetary
Evaluations: Why Monetary Assessments do not Reflect Predicted Utility.
Unpublished manuscript.
Ando, A., Modigliani, F., (1963). The ‘Life Cycle’ Hypothesis of Saving: Aggregate
Implications and Tests. American Economic Review, 53, p. 55–84.
Ariely, D., Koszegi, B., & Mazar, N. (2004). Price-Sensitive Preferences.Unpublished
manuscript.
Ariely, D., & Loewenstein, G. (2004). In the Heat of the Moment.Unpublished
manuscript.
Benartzi, S. & Thaler, R. (2004). Save More Tomorrow: Using Behavioral Economics in
Increase Employee Savings. Journal of Political Economy, 112(1), p. 164-187.
Epley, N., Idson, L., & Mak, D. (2004). Rebate or Bonus? The Impact of Income
Framing on Spending and Saving. Unpublished manuscript.
Epley, N., & Gilovich, T. (2001, Sep). Putting adjustment back in the anchoring and
adjustment heuristic: Differential processing of self-generated and experimenter-
provided anchors. Psychological Science, pp. 391-396.
Fish, S. (2004). Why We Built the Ivory Tower. The New York Times, May 21
st
.
Huber, J., Payne, J., & Puto, C. (1982). Adding Asymmetrically Dominated Alternatives:
Violations of Regularity and the Similarity Hypothesis. Journal of Consumer
Research (pre-1986), 9(1), 90.
Huberman, G., Iyengar, S., & Jiang, W. (2003). Defined Contribution Pension Plans:
Determinants of Participation and Contribution Rates. Working paper, Columbia
Business School.
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.
Johnson, E. J., & Goldstein, D. (2003, Nov 21). Do defaults save lives? Science, pp.
1338-1339.
Jolls, C., & Sunstein, C.R. (2004). Debiasing Through Law. Unpublished manuscript.
Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decisions Under
Risk. Econometrica, 47, p. 263-291.
Laibson, D. (1997). Golden Eggs and Hyperbolic Discounting. Quarterly Journal of
Economics, 62, May, 443-477.
Lindsay, R. C. L., & Wells, G. L. (1985). Improving eyewitness identification from
lineups: Simultaneous versus sequential lineup presentations. Journal of Applied
Psychology, 70, 556--564.
Loewenstein, G. (1996, Mar). Out of control: Visceral influences on behavior.
Organizational Behavior and Human Decision Processes, p. 272.
Loewenstein, G. F., Hsee, C. K., Weber, E. U., & Welsh, N. (2001, Mar). Risk as
feelings. Psychological Bulletin, pp. 267-286.

Russo, E.J., Medvec, V.H., & Meloy, M.G. (1996). The Distortion of Information During
Decisions. Organizational Behavior and Human Decision Processes, 66(1), 102-
110.
Samuelson, W., & Zeckhauser R.J. (1988). Status Quo Bias in Decision Making. Journal
of Risk and Uncertainty, 1 (March), p. 7–59.
Simonson, I. (1989). Choice Based On Reasons: The Case Of Attraction And Comprom.
Journal of Consumer Research, 16(2), 158.
Simonson, I., & Tversky, A. (1992). Choice in context: Tradeoff contrast and
extremeness aversion. Journal of Marketing Research, 29(3), 281-295.
Slovic, P., Finucane, M., Peters, E., & MacGregor, D. G. (2002). The affect heuristic. In
T. Gilovich (Ed.), Heuristics and biases: The psychology of intuitive judgment
(pp. 397-420). New York, NY, US: Cambridge University Press.
Slovic, P., Griffin, D., & Tversky, A. (2002). Compatibility effects in judgment and
choice. In T. Gilovich (Ed.), Heuristics and biases: The psychology of intuitive
judgment (pp. 217-229). New York, NY, US: Cambridge University Press.
Sunstein, C.R. (2000). Behavioral Law and Economics. Editor, Cambridge University
Press.
Thaler, R. H., & Shefrin, H.M. (1981). An Economic Theory of Self-Control. Journal of
Political Economy, 89 (April), p. 392–406.
Turtle, J.W., Lindsay, R.C.L. & Wells, G.L. (2003). Best practice recommendations for
eyewitness evidence procedures: New ideas for the oldest way to solve a case.
The Canadian Journal of Police and Security Services, 1, 5-18.
Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of
choice. Science, 211(4481), 453-458.
Tversky, A. & Kahneman, D. (1974). Judgment Under Uncertainty: Heuristics and
Biases. Science, 185, 1124-1131.
Tversky, A., Sattath, S., & Slovic, P. (1988). Contingent weighting in judgment and
choice. Psychological Review, 95(3), 371-384.
Wells, G. L., Malpass, R. S., Lindsay, R.C.L., Fisher, R.P., Turtle, J. W., & Fulero, S.
(2000). From the lab to the police station: A successful application of eyewitness
research. American Psychologist, 55, 581-598.
Zimbardo, P.G. (1971). The power and pathology of imprisonment. Congressional
Record. (Serial No. 15, October 25, 1971). Hearings before Subcommittee No. 3,
of the Committee on the Judiciary, House of Representatives, Ninety-Second
Congress, First Session on Corrections, Part II, Prisons, Prison Reform and
Prisoner's Rights: California. Washington, DC: U.S. Government Printing
Office.