Behavioral Public Administration ad fontes: A Synthesis of Research on Bounded Rationality, Cognitive Biases, and Nudging in Public Organizations

Published date01 May 2019
Date01 May 2019
Public Administration Review,
Vol. 79, Iss. 3, pp. 304–320. © 2018 by
The American Society for Public Administration.
DOI: 10.1111/puar.12994.
304 Public Administration Review May | J une 2 019
R. Paul Battaglio, Jr.
University of Texas at Dallas
Paolo Belardinelli
Bocconi University
Nicola Bellé
Sant’Anna School of Advanced Studies
Paola Cantarelli
Sant’Anna School of Advanced Studies
Behavioral Public Administration ad fontes:
A Synthesis of Research on Bounded Rationality,
Cognitive Biases, and Nudging in Public Organizations
Abstract: This article provides a comprehensive overview of how policy makers, practitioners, and scholars can
fruitfully use behavioral science to tackle public administration, management, and policy issues. The article
systematically reviews 109 articles in the public administration discipline that are inspired by the behavioral sciences
to identify emerging research trajectories, significant gaps, and promising applied research directions. In an attempt
to systematize and take stock of the nascent behavioral public administration scholarship, the authors trace it back to
the seminal works of three Nobel Laureates—Herbert Simon, Daniel Kahneman, and Richard Thaler—and their
work on bounded rationality, cognitive biases, and nudging, respectively. The cognitive biases investigated by the
studies reviewed fall into the categories of accessibility, loss aversion, and overconfidence/optimism. Nudging and choice
architecture are discussed as viable strategies for leveraging these cognitive traps in an attempt to alter behavior for the
better, among both citizens and public servants.
Evidence for Practice
• Understanding how public decisions may predictably go wrong is imperative to improve the architecture of
public organizations and services.
• Cognitive biases systematically affect public policy and management decisions.
• Behavioral science illuminates the gap between how people should behave and how they actually behave, thus
moving beyond traditional models of full rationality in decision making.
• Nudging and choice architecture represent viable tools for policy makers and public managers interested in
altering the behaviors of citizens and public employees, respectively, for individual and societal better.
“A choice architect has the responsibility for
organizing the context in which people make
decisions ... there is no such thing as a ‘neutral’
—Richard H. aler and Cass R. Sunstein (2008, 1).
Decisions in the public sector are often
shaped by a complex array of forces. This
is especially true in the fluid environment
of the information age, when public managers are
inundated with countless challenges (e.g., Kelman,
Sanders, and Pandit 2016). Ready access to data,
adaptable technology, and an ever-combative political
environment contribute to the complexity of decision
making in the public sector. Taking action on public
policy means public managers must overcome not
only these complexities in their environment but also
their own cognitive limitations and moral impasses.
Understanding how supposedly irrelevant factors of
choice architecture may alter public decision making
in predictable ways (Thaler 2017; Thaler and Sunstein
2008) is an increasingly germane topic for further
research (Gordon, Kornberger, and Klegg 2009;
Kelman, Sanders, and Pandit 2016; Moynihan, Herd,
and Harvey 2014; Vlaev et al. 2016). Influencing
public managers’ decision processes from a more
informed assessment of cognitive biases and libertarian
paternalism has the potential to improve effectiveness
through strategic choices that shape goal attainment.
A more robust analysis of the micro-level foundations
(e.g., Jones 2003) of public decision making is timely,
given the numerous and often insurmountable
complex influences facing public managers (Kelman,
Sanders, and Pandit 2016). Behavior modification
techniques offer a better understanding of how we
might influence decision making through heuristics
that nudge public managers in ways that result
in outcomes that are more favorable. Structuring
decision making in ways that positively influence
cognitive biases has the potential to moderate
complexity in the public sector environment,
subsequently reducing learning, psychological, and
Nicola Bellé is assistant professor in the
Institute of Management of the Sant’Anna
School of Advanced Studies, Pisa, Italy.
His research focuses on behavioral public
administration and management.
Paolo Belardinelli is a PhD candidate
in public policy and administration at
Bocconi University, Milan, Italy. His research
focuses on behavioral public policy and
R. Paul Battaglio, Jr. is professor of
public and nonprofit management at the
University of Texas at Dallas and co–editor
in chief of
Public Administration Review
His research interests include public human
resource management, organization
theory, behavioral public administration,
comparative public policy, and research
Paola Cantarelli is a postdoc at the
Sant’Anna School of Advanced Studies, Pisa,
Italy. Her research focuses on behavioral
management and work motivation in
mission-driven organizations.
Research Article
Behavioral Public Administration ad fontes 305
compliance costs (Cantarelli, Bellé, and Belardinelli 2018). From
a practical standpoint, such cognitive strategies have the potential
to nudge public managers in a direction that improves individual
performance, overall productivity, and informs evidence-based
policy (Clement 1987; Vlaev et al. 2016).
A robust discussion has begun on decision-making biases in public
management, administration, and policy. Most scholars have
investigated how citizens make informed assessments of government
policies (see, e.g., Andersen and Hjortskov 2015; Geys and Sørensen
2017; Grosso, Charbonneau, and Van Ryzin 2017; Jilke, Van Ryzin,
and Van de Walle 2016; Marvel 2015b; Olsen 2017b). Fewer
studies, however, have reviewed the decision processes of public
managers (e.g., Bellé, Cantarelli, and Belardinelli 2017) and policy
makers (e.g., Moynihan and Lavertu 2012). This research supports
the need for continued systematic literature reviews of cognitive
biases as a means for providing useful information for how we might
affect individuals’ estimates, judgments, preferences, and behaviors.
Indeed, recent work conducted by the Organisation for Economic
Co-operation and Development (OECD) has highlighted the
usefulness of behavioral and cognitive sciences toward sustainable
public administration. Analyzing 159 case studies from 60
public bodies in 23 states and 2 international institutions, the
OECD (2017) reported that attempts to use behavioral insights
to inform policies are underway across numerous policy areas,
including, consumer behavior, education, energy, environment,
finance, health and safety, labor market, service delivery, taxes, and
However, seeing through a glass darkly may be a more apt
assessment of the broader contribution of behavioral sciences to
public administration. A systematic review at this time in the field
provides an opportunity for moving beyond how systematic errors
work to pinpointing which have proven useful in our research and
the means for moving toward more fruitful heuristic interpretations.
Thus, our study aims to provide greater clarity in an effort to
avoid wrong assumptions about the use of choice architecture to
curtail biases in decision making. Specifically, this article provides
a research synthesis of the public administration, management,
and policy studies linked to the work of the three Nobel Laureates:
Herbert Simon, Daniel Kahneman, and Richard Thaler. By looking
at their seminal works on bounded rationality, cognitive biases,
nudging and choice architecture, our comprehensive synthesis traces
behavioral public administration scholarship back to its sources (ad
fontes) and highlights promising research directions and practical
implications. Simon’s theory of bounded rationality provides a
more circumspect assessment of factors that limit rational choices
by individuals. Kahneman’s work, which is empirically driven and
descriptive in nature rather than normative, unveils a number of
heuristics that people adopt to make difficult decisions and a series
of cognitive biases that systematically lead us astray. Thaler’s nudge
theory systematizes the use of behavioral science to influence high-
stake choices through low-powered incentives, thus paving the way
toward libertarian paternalism.
Bounded Rationality, Cognitive Biases, and Nudging
Several decades of behavioral research have buttressed Simon’s
(1947, 1956) claim that we are endowed with bounded rationality
and, in the face of information that is either intractable or
incomplete, tend to find solutions that are adequate rather
than optimal (e.g., Olsen 2015b). Simon’s conceptualization
of this “satisficing” strategy paved the way for later work aimed
at providing a more realistic representation of human decision
processes compared with postulates by rationalistic models, such
as Bernoulli’s ([1738] 1954) expected utility theory. In particular,
Kahneman and Tversky’s prospect theory (1979) has extended
Bernoulli’s utility theory along several dimensions. First, they
demonstrate that utility does not depend exclusively on the amount
of wealth one has at any given time, but rather on whether that
wealth is the result of a gain or a loss from a particular reference
point—an irrelevant supposition according to rational choice
models. Another supposedly irrelevant factor—whether the same
piece of information is framed in terms of prospective losses or
in terms of prospective gains—makes individuals risk seeking,
thus violating the tenet of risk aversion that underpins expected
utility theory (Tversky and Kahneman 1981). The fact that these
deviations from rational decision making tend to be systematic—
hence predictable—under specific conditions (e.g., Kahneman
2011) brings with it the possibility of strategically exploiting
cognitive biases, for better or worse (Thaler and Sunstein 2008).
Rational choice models and behavioral theories portray two different
types of agents, which Thaler and Sunstein (2008) identify as “Econs”
and “Humans,” respectively. Econs do not follow fashion and make
unbiased estimates. More precisely, their estimates are not necessarily
perfect, because this would convey omniscience; rather, when their
judgments are wrong, they are not systematically so in a predictable
direction. Conversely, Humans are social animals (i.e., they are
influenced by the behaviors of others) and make predictable errors.
Kahneman (2011) traces systematic patterns of deviation from
rational decision making to system 1 thinking, which, along with
the perceptual system, presides over intuition. The perceptual
system processes percepts, deals with stimulations in the moment
during which they are administered, and is stimulus-bound.
System 1, instead, deals with conceptual representations; can refer to
past, present, and future; and is evoked by language. Both are fast,
automatic, effortless, slow learning, and associative. They produce
impressions of the attributes of objects that are to be evaluated
automatically, involuntary, and even without the need of being
verbally overt. Reasoning, instead, happens in system 2 thinking,
which is slow, controlled, effortful, flexible, and rule governed. Like
system 1, system 2 uses cues; can refer to past, present, and future;
and is evoked by language. System 2 thinking produces judgments
based on either intuitions or deliberate reasoning. Judgments
are an intentional and explicit process, regardless of whether
they are verbally expressed or not (e.g., Kahneman 2002, 2011).
Overall, individuals make decisions through one of the following
mechanisms, in order of likelihood: (1) an intuitive judgment is
elicited and endorsed by system 2; (2) an intuitive judgment is
evoked and serves as an anchor to be adjusted by system 2 in light
of other situational features; (3) a deliberate judgment is created
by system 2 because no intuitive judgment is accessible; or (4) a
deliberate judgment is generated by system 2 because the intuitive
judgment that came to mind is identified as incorrect. System 1 and
2 are also known as automatic and reflective systems, respectively
(Thaler and Sunstein 2008).

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