Respond to Risk Assessment Questions & Cite Sources

Briefly respond to all the following questions. Make sure to
explain and backup your responses with facts and
examples. This assignment should be in APA format and have to
include at least two references.
Faced with the need to deliver risk ratings for your
organization, you will have to substitute the organization’s risk
preferences for your own. For, indeed, it is the organization’s
risk tolerance that the assessment is trying to achieve, not each
assessor’s personal risk preferences.
1. 1. What is the risk posture for each particular system as it
contributes to the overall risk posture of the organization?
2. 2. How does each attack surface – its protections if any, in
the presence (or absence) of active threat agents and their
capabilities, methods, and goals through each situation—add up
to a system’s particular risk posture?
3. 3. In addition, how do all the systems’ risks sum up to an
organization’s computer security risk posture?
Complexity, Governance & Networks (2014) 71–78 71
DOI: 10.7564/14-CGN9
Complexity Theory and Its Evolution in Public
Administration and Policy Studies
L. Douglas Kiel
University of Texas at Dallas, School of Economic, Political

and Policy Sciences
800 W. Campbell Road, Richardson, TX 75080 USA, 972-883-
2019
E-mail: [email protected]
This paper traces the evolution of the application of the
complexity sciences in the literature
of public administration and public policy. A four stage
evolutionary model is used to track the
development of this literature. The evolution of the literature
has now reached a third evolution-
ary stage. This third stage is a proliferant stage in the
development of the literature in which
applications and knowledge production has increased
dramatically.
Keywords: Complexity sciences, evolution, emergence,
convergence, proliference, divergence,
public administration and policy studies.
1. Introduction
We are fortunate to live in a historical period of both rapid and
substantial change.
This is a period in which human knowledge accumulates at rates
that exacerbate efforts
to accommodate and store this knowledge. Keeping up with the
accumulated knowledge
requires a singular focus that is rare in a world of continuous
partial attention. The accel-
eration of knowledge as a global resource is also reflected in the
acceleration of knowledge
regarding complexity studies in public administration and
public policy. The period of
early research in this area has evolved, over the last quarter
century, into a period of pro-

liferation in which scholars have applied an increasing array of
methods to examine the
behaviors of complex governance, administrative, and policy
systems.
In this paper I strive to explore the evolution of complexity
studies in public admin-
istration and public policy. In particular, I view the knowledge
accumulation in this area as
an evolutionary process. Tracing the evolution of what and how
we know does not reduce
the uncertainties, errors, or omissions in knowledge production
processes, but it does pro-
vide a means of exploring the field of complexity studies as an
evolving enterprise.
This paper is comprised of four sections. First, I present
Schumacher’s (1986) evo-
lutionary model as a means for appreciating the birth and
maturation of complexity stud-
ies in public administration and policy. Next, the literature of
the complexity sciences
in public administration and public policy is placed within the
evolutionary sequence of
Article_14-9.indd 71 26-06-2014 17:32:40
72 L. Douglas Kiel / Complexity Theory and Its Evolution
Schumacher’s model. This analysis provides means for
assessing the relative maturity of
this academic enterprise and for assessing its current
evolutionary stage.

Any review of the current state of an academic field requires
some sense of the
evolution of that field. Complexity studies in public
administration can be traced back ap-
proximately a quarter of century, to Kiel’s early studies (e.g.,
Kiel, 1989, 1993, 1994). The
sources cited in this paper are a small sample of the growing
and evolving literature on
the applications of complexity theory in public administration
and policy. The realization
of this very journal, Complexity, Governance & Networks,
clearly represents the evolving
maturity of the field.
How can we best understand the evolution of the applications of
complexity theory
in public administration and policy? The scholarly literature
provides some frameworks
for understanding the evolution of an academic field of study.
Kuhn (1970) suggests that
scientific paradigms are challenged from within themselves,
through a Hegelian conflict
of ideas, which first instigates incremental change, but at one
point the entire paradigm
shifts and a new paradigm achieves dominance. If complexity
theorists in public admin-
istration and policy seek dominance, it is likely to be a mild
dominance based on a few
important insights, rather than an effort to alter the entire
landscape of public administra-
tion and policy studies. Important insights such as the pervasive
nature of nonlinear and
dynamic phenomena in our field do not necessitate a revolution.
These insights do sug-
gest, however, that the simplifying requirements of the
dominant linear model may fail

to capture essential parts of the puzzle that constitute the reality
of public administration
and policy.
If complexity studies are seen as an extension of established
open systems models
then complexity is simply an enhancement to existing
knowledge, rather than a seismic
shift in world views. However, the recognition that the truly
interesting elements of real-
ity are nonlinear and non-Gaussian implies a more general
critique of the value of using
standard statistical techniques to understand complex dynamic
phenomena.
Theories of innovation may also help to add insight into the
evolution of complexity
studies in public administration and policy. For example,
Roger’s (1983) oft-cited model
of innovation reveals a standard sequence of behavior but also
reflects a flattened normal
distribution as a majority of followers allow early adopters to
take on the risk of innova-
tion. While such dynamics are likely to occur, Roger’s model
does not inform us of the
continued process of the evolution of a scientific or
technological innovation.
An evolutionary model of change may, however, provide
insights into the change in
an academic discipline. The model of evolutionary change
offered by Schumacher (1986)
may provide some insight into the evolution of complexity
studies in public administra-
tion and policy. Schumacher’s (1986) model of evolutionary
change provides a means for

assessing the relative maturity of the theory and methods used
to study complex systems
in public administration/policy. Schumacher’s model includes a
four-stage process that is
intended to describe evolutionary processes in all living
systems, including human socio-
technical systems. These four stages progress in a circular and
sequential fashion. These
four sequential stages are emergence, convergence,
proliferance, and divergence.
Article_14-9.indd 72 26-06-2014 17:32:40
L. Douglas Kiel / Complexity Theory and Its Evolution 73
The first stage in Schumacher’s model is emergence. Emergence
refers to the cre-
ation or production of a “new state of matter” (p. 10). Such new
matter, representing nov-
elty, evolves as attractive forces “experiment” with
environmental conditions to determine
adaptive fit. Emergent phenomena may continue to evolve along
uncertain pathways or
may die out if the emergent “mutation” cannot survive in its
environment. High levels of
risk exist for emergent entities and survival is not ensured.
As an emergent entity evolves, forces of repulsion and
attraction serve to determine
whether the entity will die or converge into a coherent form
capable of further reproduc-
tion. This second evolutionary stage is labeled convergence. At
this stage of evolution,
there is coherence with the mutation now being clearly distinct

in its environment. A criti-
cal mass of the entity now exists providing the chance of further
replication.
The converged entity, now with some solid grounding in its
environment, may reach
a stage of environmental fit in which it proliferates. The entity
thus matures to a level that
it fills multiple niches in its environment and may dominate its
environment. This third
evolutionary stage, a period of proliferance, allows the
possibility for increased system
complexity that may generate novelty and eventually new forms
of the entity. Prolifer-
ance expedites complexity and further allows recombinations
that may produce further
complexity.
A fourth state in this evolutionary paradigm is divergence. The
divergent stage evi-
dences novel forms of the proliferant entity as it seeks new
forms of adaptive fit. Divergent
forms may die out with high frequency given the dominance of
the previous form. Eventu-
ally, though, a divergent form creates adequate attraction so that
the cycle continues and a
new emergent form of matter is produced.
An example of this evolutionary model using technological
innovation will serve
to emphasize the relevance of this model. Consider the case of
the evolution of modern
computing technology in the developed world. The emergence
of the mainframe (vacuum
tube technology) in the late 1930’s resulted in the convergence
of the technology and its

organizational applications in the 1950s–1970s. The rise of the
microcomputer and net-
works engendered a proliferant era in which computers
proliferated in the environment.
A new divergent stage has emerged with wearable computing
and sensors that may lead to
new yet unknown emergent technologies.
As a further example of general applicability of this
evolutionary model, let us
examine its relevance to human cultural innovation. Consider
the behavioral revolution
in the social sciences as an evolving phenomenon. Many of the
statistical tools sup-
porting this “revolution” emerged in the early 1900’s (Ziliak &
McCloskey, 2008). The
post-World War II era showed general convergence, as more
social science disciplines
relied on statistical analyses for scientific understanding. The
proliferant period of the
1980s to the present shows an expanding array of statistical
tools and techniques avail-
able to the social scientists. Predicting the forthcomi ng
divergent stage may not be wise
in a nonlinear world, but such a stage may include elements of
capturing big data on
social behavior combined with individual level means for
assessing behavior and health
(Pentland, 2008).
Article_14-9.indd 73 26-06-2014 17:32:40

2. Emergence: 1989–1998
The emergent stage of the application of the complexity
sciences to public admin-
istration and policy studies was in the decade of 1989–1998.
These earliest efforts can be
viewed as testing the relevance of the complexity sciences to
the field of public adminis-
tration and policy studies. These early publications were
generally focused on applications
of dissipative structures Kiel (1989) and notions of self-
organization (Comfort, 1994) to
public organizations. The majority of these early publications is
quite general and defi-
nitional in nature and intended to acquaint the scholarly
community with the terms and
concepts of the complexity sciences. Thus these efforts tested
whether the larger public
administration and policy community would accept the
relevance of the basic concepts of
the complexity sciences. The challenge of identifying the proper
language is evidenced in
works that examined topics ranging from chaos theory to
quantum mechanics (Overman,
1996). This body of literature was metaphorical in tone and
content. In the literature public
organizations were viewed as dissipative self-organizing
structures.
In the emergent stage there were some early time-series
analyses as well. Kiel and
Elliott (1992) used phase diagrams to examine the nature of
change in the U.S. federal
budget. Kiel (1993) applied phase diagrams to spending and
work activity dynamics in a
State government agency. The most sophisticated time-series

analysis during this period
is Kiel and Seldon’s (1998) discovery of nonlinearity and
randomness in time series data
from a police organization.
The emergent stage of applications of the complexity sciences
to public administra-
tion and policy studies was sustained as top journals such as
Public Administration Review
and the Journal of Public Administration Research and Theory
showed willingness to
publish works incorporating the novelty of the complexity
perspective. Kiel’s work (1994)
was the first book-length effort applying the complexity
sciences, at that time generally la-
belled chaos theory, to public administration. The risk, inherent
in all novelty, was reduced
as the primary sources of knowledge dissemination accepted the
value of the complexity
sciences to our field.
3. Convergence: 1999–2002
In the convergent stage, once emergent phenomenon can now
maintain a distinct and
coherent niche in its environment. Such coherence is
exemplified in two seminal works
that evidenced the convergent stage of evolution. These two
seminal works are Comfort’s,
Shared Risk (1999) and Morçöl’s, A New Mind for Policy
Analysis (2002).
Louise Comfort’s book, Shared Risk, is an underappreciated
work. This was the first
book to apply the complexity sciences in a comprehensive and
methodologically rigorous

way to an important public administration and policy
phenomenon. Comfort’s highly de-
tailed analysis used considerable data from actual seismic
events to explore the dynamics
of organizational responses to earthquakes. Comfort showed
that statistical tools such as
nonlinear logistical regression are useful in understanding
responses to extreme events.
Article_14-9.indd 74 26-06-2014 17:32:40
Comfort’s study of organizational responses to extreme events
is also important because it
laid the groundwork for the larger recognition of the importance
of networks when explor-
ing complex governmental phenomena. Comfort’s work (1999)
remains required reading
in the field today.
Göktuğ Morçöl’s A New Mind for Policy Analysis further
evidences the convergent
stage of the evolution of complexity studies in public
administration and policy studies.
Morçöl’s work reveals that the vast scope of the field of policy
studies was also ame-
nable to the epistemology and methods of the complexity
sciences. A New Mind for Policy
Analysis was the first work of philosophical depth to explore
the relevance and need for
applications of complexity to policy studies. His book also fit
complexity studies into the
larger socio-historical body of scientific explanation. He also

examined a large scope of
issues ranging from the relevance of governmental action in a
complex economy to the
relevance of cognitive science to studying complex policies.
I see these two seminal works as the archetypes of the
convergent stage of an aca-
demic field. Morçöl (2002) places the complexity sciences
within the larger evolution
of scientific paradigms. This book provides the theoretical and
philosophical founda-
tions that provide future scholars the foundations for both
applications and further the-
ory development. Comfort’s work (1999) confirmed that the
complexity sciences could
enhance our understanding of complex “real-world”
phenomena. The theory and method-
ological foundations represented in these two works showed that
the field was adequately
stable to expedite a succeeding stage of proliferance.
4. Proliferance: 2003 to the present
The proliferating stage of an evolving phenomenon represents
maturity and fecun-
dity. The convergent stage resolves the risk of emergence,
allowing more attraction and
a proliferating body of knowledge and tools. The proliferant
stage represents a stage of
increasing production.
Applications of network theory and the methods of network
analysis have prolif-
erated in the current era (Kapucu, 2006; Kapucu, Arslan, &
Collins, 2009; Kapucu &
Garayev, 2011; Koliba, Meek, & Zia, 2011). The uncertai nties,

nonlinearities and insta-
bilities often associated with networks coincide with the
behavior of complex systems.
Thus the complexity sciences provide a strong backdrop for
further exploration of public
sector networks. It is not possible here to provide a
comprehensive picture of the scope
and depth of the literature of network analysis applying
complexity principles to public
administration and policy studies. This proliferant literature
though is likely to maintain its
current upward trajectory as we learn more about the challenges
and prospects of complex
governance networks.
Another proliferating method that is associated with the
complexity sciences is
agent-based modeling (ABM). Numerous applications of agent-
based modeling in pub-
lic administration and policy now exist. These applications
range from models of lottery
Article_14-9.indd 75 26-06-2014 17:32:40
markets (Chen & Chie, 2008) to models of humanitarian
interventions in war torn nations
(McCaskill, 2012). The enormity of the potential applications of
ABM to public adminis-
tration and policy studies is clearly evident in this period of
proliferation (See, Dennard,
Richardson, & Morçöl, 2008; Kim & Lee, 2007). The fact that
McCaskill’s (2012) ABM-

based dissertation received the best dissertation award of the
National Association of
Schools of Public Affairs and Administration is clear evidence
that the public adminis-
tration scholarly community is prepared to incorporate ABM
into the body of accepted
research methods.
Further evidence of the proliferation of the applications of the
complexity studies
to public administration and policy is the obvious increase in
the number of scholars
contributing to this literature. Edited volumes by Teisman and
Klijn (2008) and Teisman,
van Buuren, and Gerrits (2009) are evidence of this growing
body of scholars capable
of applying complexity concepts to issues of public concern and
governance. Dennard,
Richardson and Morçöl’s (2008) edited volume also reveals the
expanding body of
methods and scholars capable of applying these methods to the
many complex challenges
of governance.
Three recent works also validate the view that the current
period represents a period
of proliferating knowledge production. Morçöl’s (2012) recent
book represents a new level
of maturity in the field as knowledge of both theory and method
proliferate to ask new
questions and perhaps settle old questions concerning the
potential for building theory of
relevance to complex governance networks. Scholars have
recognized for two decades that
when contending with complexity practitioners and analysts
may, at best, receive guidance

only from heuristics (Kiel, 1994). The proliferant period of
knowledge production reveals
the production of such heuristics. Room (2011) now provides
such a roster of heuristics
applicable to policy guidance and implementation in our
“turbulent world.” Most recently,
Ramalingam’s (2014) application of the complexity sciences to
the challenges of foreign
aid reinforce the dynamics of a proliferating enterprise as the
number of applicable phe-
nomena of relevance expands. Finally, it is worthy of note that
evidence of a proliferating
academic enterprise is the reality of the increasing difficulty in
keeping up with the grow-
ing body of literature.
5. Divergence: the future?
The divergent stage of an evolving phenomena represents a
decoupling, a bifurca-
tion perhaps, from the proliferant form (Schumacher, 1986).
This decoupling may lead to
novelty that creates new emergent forms that again initiate the
process of emergence, con-
vergence, proliferance, and divergence that constitute the cycle
of evolving complexity.
The knowledge artifacts of the divergent stage are generally
difficult to determine
as to specifics since the proliferant period does not provide all
of the information required
to determine which mutations in the process of the knowledge
“speciation” may diverge
to create novel forms. Attempting to define the divergent stage
of evolving nonlinear and
dynamic phenomena, such as knowledge production, may prove

fanciful. However, the
Article_14-9.indd 76 26-06-2014 17:32:40
proliferation of knowledge in several academic fields may add
insights into future devel-
opments in our field.
The vast majority of the literature of the complexity sciences
applied to public ad-
ministration and public policy is focused on the organizational
or institutional level of
analysis. Advances in the cognitive sciences and the
neurosciences, however, may add to
our knowledge of the challenges of complexity at the individual
level. This new knowl-
edge is salient, as it may enhance our knowledge of the
individual level functions and
dysfunctions of coping with rapidly evolving complexity.
Improved understanding of the
individual level cognitive challenges of evolving complexity,
combined with our knowl-
edge of organizations and institutions, may lead to new means
for building human systems
that are both resilient and appreciative of human capabilities
and limitations.
The rise of “big data” and the ability to capture increased detail
in related longitu-
dinal data may also add to our understanding of the temporal
dynamics of complex gov-
ernance systems. Emerging mechanisms for tracking individual

level behavior (Pentland,
2008) may also allow studies of human behavior within
organizational and institutional
contexts that provide greater levels of detail and complexity
than have previously been
available to scholars.
6. Conclusion
A distinct theoretical and methodological foundation for the
study of complex gov-
ernance systems now exists. The theory of the endeavor is now
well established (Koliba,
Meek, & Zia, 2011; Morçöl, 2012) and the methodological
mechanics (Comfort, 1999;
Dennard, Richardson, & Morçöl, 2008) are well defined.
Scholars interested in expanding
the body of knowledge now possess the means for moving the
entire field beyond a stage
of growth and expansion to a stage of great relevance and of
greater support for under-
standing and managing complex human systems.
The success of any academic endeavor is subject to probability
distributions of
many interacting variables, such as timing, environmental
circumstances, and validation.
The trajectory of complexity studies in public administration
and policy engenders con-
fidence that the confluence of these probability distributions
will result in achieving the
goals of informing practice and in developing interdisciplinary
social and administrative
sciences that further knowledge production. In a historical
period typified by increasing
complexity, knowledge of the processes of complexity

generation and the handling of that
complexity are increasingly central to the democratic project, to
the lives of individuals
and to humanity’s future.
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Week2 Management theories and resources
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complexity theory: Richer decision-making in public services.
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com.proxy1.ncu.edu/doi/full/10.1111/padm.12168?sid=EBSCO
%3Aedswss
2. Kwok, A. C. F. (2014). The evolution of management
theories: A literature review. Nang Yan Business Journal, 3(1),
28-40. https://doi.org/10.1515/nybj-2015-0003
3. Matei, A., & Antonie, C. (2014, January). The new public
management within the complexity model. Procedia - Social and
Behavioral Sciences, 109, 112,
https://www.sciencedirect.com/science/article/pii/S1877042813
052385?via%3Dihub
4. U.S. Department of Agriculture. (2018). Search on
management theory [Web resource].
https://usdasearch.usda.gov/search?utf8=%E2%9C%93&affiliate

=usda&query=management+theory&commit=Search
5. Week 2. Management Theories
Public management theories are essential to optimizing
operations and building organizational cultures. Theories serve
as the guiding principles within public organizations.
Management theories can be adopted and evolve into
organizational policies. During Week 2, you will learn about
various theories used in public organizations. You will have an
opportunity to examine real-world examples of these theories in
practice, as management theories serve a vital role in the
government sector. Public policies, theories, and models are
often used to guide federal government agencies regarding
adherence to regulations. Each federal agency is bound by
specific regulations and each agency’s leadership has even more
stringent guidelines as directed by the executive branch.
In order for public organizations to work efficiently, there must
be systems in place to promote collaboration. Therefore, you
will find that public organizations often follow a decentralized
approach to management. Several reasons exist for this
management approach. First, public organizations may have
several missions. With so many missions, it becomes extremely
difficult to manage, and therefore, it must be divided into sub-
agencies, directorates, divisions, and sections with each
component having its own sets of missions and objectives that
tie into the larger organizational objectives. Such a complex
organizational structure requires more than one set of leaders,
theories, principles, concepts, policies, and procedures. The
process has to be compartmentalized. This is where public
management theory plays a crucial role in serving as a guiding
outlet for the various sections within the organization.
This week, you will explore the complexity theory and how it
can be used to address emerging problems in today’s public
sector workforce. For example, each federal department has
multiple agencies, multiple layers, directorates, and divisions.

The U.S. Department of Justice (DOJ) has different agencies
with different missions. DOJ focuses on law enforcement,
crime, safety, and security for the American people. However,
there are over 30 small agencies in that department. Whi le the
overarching mission is on law enforcement, the small agencies
focus on micro-level missions such as the Bureau of Prisons,
Civil Division, Civil Rights Division, Tax Division, and the
U.S. Marshal Service. All these agencies focus on crime, but
have different missions. Likewise, the U.S. Department of
Homeland Security focuses on stopping terrorism and protecting
the U.S. from domestic attacks. Nevertheless, DHS has over 22
large agencies focusing on different aspects of protecting
America such as Immigration and Customs Enforcement (ICE),
Federal Emergency Management Agency (FEMA), U.S. Coast
Guard, Customs and Border Protection (CBP), and many others.
Each agency falls under the DHS umbrella of providing services
to enhance homeland security, but each does that from a
different perspective. FEMA focuses on natural disasters, ICE
and CBP focus on protecting the boarder and ensuring legal
immigration, the Coast Guard protects the shores, and other
agencies play other pivotal roles such as the Office of
Intelligence and Analysis tracking cybersecurity and other
forms of potential terror threats. Each agency also collaborates
with other federal and state agencies on key matters relevant to
their primary and secondary missions.
The complexity theory focuses on system evaluation in public
management. This approach is important in any public
organization especially in terms of dealing with employees and
employee related issues. Managers in public organizations are
faced with a daily bombardment of emerging problems affecting
many Americans, and the actions they take and the decisions
they make affect millions of lives. Given the enormity of these
management decisions, it is critical to have a practical approach
to problem solving. While theory alone is not the solution to the
complex problems in today’s society, having a sound theoretical

approach can help alleviate and eliminate life threatening crises.
The complexity theory is one such approach that can help
strengthen managerial strategic planning through evaluation.
Be sure to review this week's resources carefully. You are
expected to apply the information from these resources when
you prepare your assignments.
Week 3 resources
1. Carey, G., & Friel, S. (2015). Understanding the role of
public administration in implementing action on the social
determinants of health.
http://www.ijhpm.com/pdf_3113_f8c8e769398c68eab31fb15234
e93945.html
2. Centers for Disease Control and Prevention. (2018). Natural
disaster and severe weather [Web resource]
https://ncuone.ncu.edu/d2l/le/content/135194/viewContent/1365
665/View
3. Kiel, L. D. (2014). Complexity theory and its evolution in
public administration and policy studies. Complexity
Governance and Network, 1(1), 71-78 (check pdf for week 3)
4. Week 3. Building Coalition
Collaboration between agencies is a common practice in the
public sector. Depending on the mission and objective of the
organization, public sector managers may find themselves
working on joint initiatives, sometimes involving many
agencies. Collaborating with other government agencies can
sometimes help alleviate organizational strains by eliminating
burdens and reducing workload, which in turn eases stress and
anxiety. Building coalitions is a crucial component of
conducting business within the public sector. Government sector
managers must be prepared to collaborate at a moment’s notice
because of emerging contemporary issues. For example, the
U.S. Department of Labor must be prepared to collaborate with
local employment agencies regarding information sharing. The

U.S. Department of Homeland Security, Office of Intelligence
and Analysis must stand ready to share information with other
agencies within the intelligence community to prevent future
cases of terrorism. The Federal Emergency Management Agency
(FEMA) within DHS must be ready to collaborate with local
emergency management agencies as well as with law
enforcement to ensure rapid response to natural and manmade
disasters such as hurricanes, earthquakes, and active
shooter/mass shooting scenarios.
Building partnerships is critical to having lasting solutions to
evolving and recurring national crises. Without joint efforts,
there would be duplicative work being conducted by multiple
agencies. For example, there are several different federal
agencies that respond to manmade and natural disasters.
Without coordination, these organizations might get in each
other’s way, which would cause a strain on their missions.
In Week 3, you will learn about the necessary intricate details
for building coalition from an information-sharing perspective.
Information sharing is the process of disseminating vital
information among partners to help build defense responses.
Information sharing also entails disclosing information between
public sector and private sector partners to help strengthen
resilience and help protect national interests. Without
information sharing, coalition is not built, thus no joint efforts
are established. Joint efforts are only successful because each
organization has expertise in certain areas and through their
expertise, each is able to share with the others.
You will now examine the coordination theory in respect to
information sharing and building coalition with other
organizations. Sometimes, the hardest part of collaboration is in
the fine print. The easiest part could be agreeing to work
together. Managers must be prepared to build working
relationships with other public and sometimes private entities to

address emerging issues and challenges.
Be sure to review this week's resources carefully. You are
expected to apply the information from these resources when
you prepare your assignments.

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