Lessons learned about planning

The author summarizes the most important
lessons he has learned in more than 40 years
of studying planning from the perspectives
of planning theory, planning methods, and
computer applications in planning. He
suggests that planner’s traditional
professional roles, models, and methods
often fail to adequately consider alternative
futures and unnecessarily restrict meaningful
public participation. He proposes that these
problems can be overcome, but not easily, by
adopting more community-centered
approaches of planning with the public and
using simple, easy-to-use, and
understandable models and methods.
The author recognizes that following his
advice in practice raises many difficult
questions that he cannot currently answer.
Keywords: forecasting, citizen participation,
scenario planning, planning methods,
planning models, planning technology
Research support: None.
About the author: Richard E.
Klosterman (dick.klosterman@gmail.com)
is an emeritus professor of geography,
planning, and urban studies at the University
of Akron. Dr. Klosterman served as JAPA’s
Computer Reports Editor from 1986 to
1998, was on the faculties of Florida State
University and the University of Akron, and
was a visiting professor at the Massachusetts
Institute of Technology, the University of
Illinois Urbana–Champaign, and Rutgers
University. He has used his What if? plan-
ning support system with clients around the
world.
Journal of the American Planning Association,
Vol. 79, No. 2, Spring 2013
DOI 10.1080/01944363.2013.882647
© American Planning Association, Chicago, IL.
Lessons Learned About
Planning
Forecasting, Participation, and Technology
Richard E. Klosterman
I
entered planning in 1971 during an exciting time of social and
technological optimism at what may have been a high-water mark for
public-sector planning in the United States. New academic planning
programs were being created and planning went far beyond its previous focus
on the physical city to include social policy, health, and criminal justice
planning. In the decades since 1976, when I received my doctorate, I have
benefited from an explosion of planning scholarship with the emergence of
new books, new journals, new academic fields, and new organizations. The
resulting body of academic research has dramatically increased our under-
standing of all aspects of planning, including the areas of particular interest to
me: planning theory, planning methods, and computer applications in
planning.
The work of a generation of planning theorists has given me an in-
creased appreciation for the complex reality of planning practice, the
limited role of formal information and quantitative analysis in policymak-
ing, and the vital role that less formal types of information such as stories,
metaphors, and personal experiences play in planning practice (see, e.g.,
Forester, 1989; Healey, 1992; Innes, 1995; Sager, 1994; Throgmorton,
1996; an excellent review is also provided in Healey, 2012). Like other
planners, I have also benefited from the expansion of planners’ repertoire
to include meditation, negotiation, and consensus building (see, e.g.,
Innes, 1996; Innes & Booher, 2010; Susskind & Ozawa, 1984). I played a
small role in these efforts, authoring a widely read defense of planning
(Klosterman, 1985) and editing four decennial surveys of planning theory
education (Klosterman, 2011).
Dramatic advances in information and communication technologies
(ICT) since I first used computers in 1968 have made sophisticated computer
programs and a wealth of spatially referenced data readily available to planners.
It has also allowed them to communicate quickly and easily with clients and
colleagues, anywhere and anytime, and prepare interactive virtual displays that
were unimaginable when I entered planning. I have explored the ways in
which these developments have affected planning as the JAPA’s Computer
Reports Editor, in my publications (e.g., Klosterman, 1992, 1994), and by
combining my interests in planning theory and computer applications in
planning (Klosterman, 1987, 1997, 2007).
161Perspective
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162 Journal of the American Planning Association, Spring 2013, Vol. 79, No. 2
The dramatic advances in computer technology have
made GIS a standard part of planning education and
professional practice and stimulated a revolution in spatial
analysis and urban modeling. I have contributed to these
changes through a widely used textbook (Klosterman,
1990) and developing the What if? planning support
system (Klosterman, 1999, 2008), which uses GIS data
and user-defined assumptions to explore the implications
of different growth assumptions and policy choices.1
This essay combines my understanding of the role that
planning can and should play in helping a community
engage an uncertain future with my realization that
complex planning models and methods are more likely to
reinforce planner-centric, top-down planning than to help
planners work with the communities they serve to develop
a shared vision of their future. However, I also believe that
properly designed, simple planning models and methods
will allow planners to give community members a more
meaningful role in the decisions that affect their lives. This
essay describes the lessons I’ve learned over the last 40 years
that have led me to these conclusions.
The Need for Future-Oriented
Planning
Like others in my generation (Brooks, 1988; Isserman,
1985; Myers, 2001), I believe that planners have all too
often abandoned their traditional role of helping
communities consider what the future may and should be.
Instead, many planners focus their efforts largely on
pragmatic problem solving and more immediate concerns
of community development, code enforcement, and
public-private development. Planners often neglect the
future because the press of daily job requirements and the
desire for job security understandably focus their attention
on short-term concerns with clearly identified results. But I
also know that the widespread adoption of social science
methods and GIS, while greatly enhancing planners’ skill
sets, provides little assistance in helping communities
engage with their collective futures.
As planners, we realize that current actions affect
future outcomes and that taking no action is an action
in itself. Planning assumes that individuals, organiza-
tions, and communities can control their fate and take
action to achieve desired futures and avoid undesirable
ones. Without planning, communities can only stumble
blindly into a future they make no effort to shape. With
planning, the future is the result of the interaction of
underlying trends and the public’s efforts to modify
them (Moore, 2007).
However, we also recognize that there are many
obstacles to public-sector planning for the future. Elected
officials generally focus public attention on issues
immediately at hand. Property owners are understandably
wary of public actions that may affect the value of their
most precious asset. Businesses, large and small, place their
particular interests above those of society at large. The
rhetoric of protecting property rights and promoting
individual freedom disparages government efforts to
address the long-term collective interests of the community
and protect the poor and powerless, while benefiting the
rich and powerful.
The good news is that Americans like planning. They
prepare plans for their personal lives, for the organizations
to which they belong, and for the companies that employ
them. They care deeply about their neighborhoods and
communities and are vitally concerned about the future
they are leaving their children and grandchildren. Given
the opportunity, people turn out night after night to
express their concerns and their hopes for a better future.
Arbitrary-seeming regulations and the lack of a meaningful
role in helping shape their world are what Americans
understandably resist (Warner, 2000).2
The challenge for planning academics and
practitioners alike is to create and use institutions,
procedures, methods, and models that give voice to the
latent demand for planning and address critical issues of
environmental sustainability, deteriorating infrastructure,
and growing disparities of wealth and power. Experience
has proven that this is much more difficult than I once
imagined. However, it has also taught me that appropriate
modes of professional practice and new planning models
and methods can help planners and the public they serve
work together to address their shared futures. I have
learned many lessons about the obstacles to, and promise
for, more effective planning, which I describe below.
You spend your life walking backwards because you
can see the past but not the future; that’s why we trip.
—Maöri proverb3
Forecasting the Future Is Difficult
Forecasting the future has long played a central role in
planning. Like many planners with a technical background,
I assumed for much of my career that the sophisticated
methods of regional science and the policy sciences could be
used to identify past trends and accurately predict the fu-
ture.4 Given this information, I thought that planners could
reliably prepare long- and short-term plans for a community

Klosterman: Lessons Learned About Planning 163
and identify the public infrastructure and services that
would be required to serve its future needs.
I now recognize that individuals and organizations
cannot accurately predict their long-term futures, and there
is no reason to assume that planners can foresee an
unknowable future better than the public they serve. At
best, planners’ models can only provide explicit logical and
mathematical procedures for determining the implications
that can be drawn from limited information about the past
and present and plausible assumptions about the future. To
ask for more is to require the impossible.
The Role of Assumptions
I also realize that the application of any model is
ultimately dependent on the core assumptions that
represent the modelers’ understanding of the future.
These assumptions cannot be derived from a method or
model. On the contrary, a model is just a vehicle for
tracing through the implications of the core assumptions
that are chosen independently from, and prior to, the
model that implements them. When the core
assumptions are valid, the choice of methodology is
either secondary or obvious. And when they are wrong,
the projection methodology rarely makes a difference
(Ascher, 1981).
Beneath their elaborate facades, planners’ supposedly
sophisticated methods and models generally do little more
than extend past trends on the assumption that the future will
be a foreseeable consequence of current conditions and past
trends. This approach is ultimately justified by an assumption
that these trends will continue into the future even though
there is no guarantee they will do so. More important, blindly
extending past trends into the future ignores the role of
planning to help communities achieve desired futures and
avoid undesirable ones. The future that occurs under planning
is the result of underlying trends and planners’ efforts to
modify them. When the projected future is undesirable, the
goal of planning will often be to prevent the most likely future
from occurring. This means that accurate forecasting must
anticipate both the nature and impact of community actions,
part of which may be a response to the forecast. Otherwise,
the future is taken as a given, which planning and society can
accommodate, but cannot help determine (Isserman, 1985,
2007; Wachs, 2001).
The Politics of Forecasting
Like most planners, I have long recognized that plan-
ning is political. However, like many technically oriented
planners, I once believed that planners’ models and meth-
ods could be used to objectively identify and evaluate
alternative public policy means while avoiding all political
questions of defining policy goals and objectives. (The
following discussion draws on Klosterman, 1987; Wachs,
1982, 1989; also see Flyvbjerg, 2005; Flyvbjerg, Mette, &
Buhl, 2002.)
This view is still widely shared by planners, elected
officials, and the public, who often assume that planning
models and methods are unbiased, politically neutral
tools used by technical experts. Portraying forecasts as the
objective outcomes of value-neutral analysis allows ana-
lysts to maintain their professional identities as politically
neutral experts. Advocates of particular policy positions
gain support for their positions by relying on supposedly
unbiased technical analyses that support their positions.
As well, politicians who choose between conflicting policy
proposals can accept forecasts that correspond to their
preconceptions and ignore those that do not. All three
sets of actors (i.e., analysts, advocates, and politicians)
gain by pretending that forecasts are objective, scientific
statements, and not political arguments for a particular
position.
However, I now recognize that value-neutral technical
expertise in the preparation and use of forecasts is more
apparent than real. Forecasts are rarely produced in a
vacuum, but rather are prepared for a client (e.g., a
government agency, an organization, a private firm, or an
interest group) who often has a vested interest in the
projected numbers and request, and even demand,
projections that support public policies and actions that
benefit them. More important, planning models and
forecasts are not based on universally accepted bodies of
theory and uncontested facts, but rather on incomplete
data, partial hypotheses, and assumptions about what the
future will and should be. As a result, the preparation and
use of planning models and methods necessarily involves
numerous choices in the selection of data, the application
of computational procedures, and the analysis,
presentation, and distribution of results. These choices are
inherently political because they can shape the analysis
results, the perception of problems, and the definition of
potential solutions, helping answer the fundamental
political question of who gets what, when, and how.5
Technical Problems of Forecasting
The forecasting task facing planners is further
complicated because they generally work with information
that is out of date, estimated, or collected at the wrong
level of spatial and sectoral aggregation. Predictions in the
natural sciences are based on well-developed bodies of
theory and carefully controlled experiments that are
impossible in the social sphere. Private-sector forecasters
can use large quantities of reliable data and sophisticated

projection techniques that are unavailable to most
planners. As a result, it is not at all surprising that planners’
forecasts for the long-term future of cities and neighbor-
hoods inevitably prove to be wrong.
Another problem is the frequent need to prepare
long-term projections for small areas. Planners can prepare
short-term projections for small areas relatively easily
because demographic and economic changes over a two- or
three-year period are generally small. However, projecting
long-term changes is extremely difﬁcult for sub-county
areas, where new residential developments and employ-
ment centers or changes in local land use policies can lead
to substantial population and employment changes over
relatively short time periods. (These general observations
are supported by empirical tests of forecast accuracy; see,
e.g., S. K. Smith, 1987; S. K. Smith & Shahidullah, 1995).
Scenario: an account or synopsis of a possible course
of action or events. (“Scenario,” 2014)
The Promise of Scenario Planning
I agree with a growing number of scholars who sug-
gest that planners abandon the futile effort to predict
what the future will be and prepare a range of scenarios
suggesting what the future may be (see, e.g., Avin &
Dembner, 2001; Hopkins & Zapata, 2007; Myers &
Kitsuse, 2000; Xiang & Clarke, 2003). Scenarios are
much more than predictions and forecasts that merely
extend past trends to identify a single future. Instead,
they describe a range of plausible and divergent stories
describing how the future may unfold and how it might
be different from the past. Scenarios are not visions,
because not all of the futures may be desirable; they are
not forecasts, because they describe a process of change,
not a single point in time. Scenarios recognize the
uncertainty and complexity of foresight and provide a
way to think carefully about the future without trying to
predict what it will be. (This discussion draws on Avin,
2007; Hopkins & Zapata, 2007; E. Smith, 2007.)
Scenarios are used in the private sector to help organiza-
tions survive and prosper in the face of future contingencies
and competition. In the public sector, a diverse group of
people work together to create a range of different scenarios
that challenges the assumptions individuals may have about
the future and allow them to jointly consider their shared
future. Done well, scenario planning allows community
members to learn about their community, identify what the
community can control, recognize what it cannot control,
and consider the implications of choices they make today.
In current practice, planners select a single preferred
future from a small set of alternative futures, which
generally include high, medium, and low options (a
desired future with two bookends) or two options, one of
which is a baseline business-as-usual option that serves a
foil for the preferred option. Assuming they can control
the future, planners then discard the less-desirable op-
tions and attempt to make the desired future happen.
Under fully realized scenario planning, there are no good
and bad scenarios, but rather a collection of plausible
alternatives, some of which may include new, sometimes
even uncomfortable, possibilities. (Hopkins and Zapata
[2007] provide several excellent examples of scenario
planning in practice.)
Scenario planning can play an important role in at-
tempts to engage the future by providing an opportunity
for planners and the communities they serve to co-write
compelling stories about the past and present and develop
strategies for creating a better future. Compelling,
evidence-based stories about the past, present, and desired
future embedded in scenarios with plausible plot lines can
also set the stage for planning by creating a sense of place
that inspires planning action to achieve a desired future.
Planners can play a central role in this process by helping
communities understand where they are, how they got
there, and where they would like to go in the future
(Isserman, 2007).
I participate; you participate; he participates; we
participate; you participate…They proﬁt.
—Arnstein (1969, p. 387, quoting 1968 French
student poster)
Meaningful Participation Is a Problem
Like all planners, I have long assumed that the
public should have a meaningful role in shaping the
decisions that shape their lives. However, I now realize
that public participation efforts are all too often viewed
by the public and planners alike as perfunctory, going-
through-the-motions rituals conducted primarily to
satisfy legal requirements and obtain public support for
agency proposals.
The plans and proposals that shape policy discussion
are generally prepared by planning staff and consultants
with little input from citizens or stakeholders. While
planners may solicit public comments, distribute attitude
surveys, and appoint volunteer working committees, the
public’s formal role in the planning process is all too often
limited to a public hearing held at the end of the process,

just before the action or policy is approved. Minor changes
may be made in response to the comments made at the
meeting, but the core decision concerning the issues to be
addressed, the alternatives to be considered, and the criteria
to be used in evaluating them will have been made long
before the public is involved.
Under these circumstances, public participation is all
too often little more than a means for ratifying choices
made by professionals pursuing their own perceptions of
the public interest. More troubling, public involvement is
generally limited to an iron triangle of local business and
development interests, public officials, and neighborhood
groups, to the exclusion of the poor and unrepresented
interests (Burby, 2003; Klein, 2000).
I believe that the public’s limited role in planning
practice is due in part to the professional role that has
defined the profession since its inception. The early
planning profession adopted the perspective of planning
as design, which assumed that planning a city is
fundamentally the same as designing a building or
designing a landscape.6 In the mid-20th century the
profession adopted a new ideal of planning as applied
science, which attempted to replace the intuitive designs
of the planner–architects with the scientific methods and
findings of the emerging fields of regional science, urban
economics, and operations research.
Both of these approaches were based on an implicit
model of planning for the public, which assumed that
planners could achieve in the public sector the deliberate
outcomes that are readily accomplished in a private firm
or a centrally controlled government enterprise. (The
following discussion draws on Klosterman, 2008, pp.
86–87.) In these settings, there is generally a single client
with a clearly defined future, well-defined objectives,
long-established means for achieving those objectives, and
centralized control over the resources needed to achieve
them. Together, these factors allow technical experts to
prepare blueprints providing detailed guidance for con-
structing the structure or landscape that will best serve
their client’s needs.
Planners replaced the private sector’s client with the
public and the client’s desires with their own perceptions of
the public interest. The client’s desired future was replaced
by the planners’ projections for the city’s future population,
employment, land uses, and related infrastructure
demands. The designer’s blueprints were replaced by com-
prehensive plans, which were assumed to guide public and
private actions toward a shared image of the desired future.
I now realize that public-sector planning lacks all of
the conditions that characterize private-sector planning.
The public does not share a clearly defined set of objectives
that can guide planning practice. Reliable long-term fore-
casts for small areas are difficult, if not impossible, to
prepare. The norms of professional practice are inadequate
for dealing with the increasingly complex issues facing
planning. The urban fabric is shaped by the actions of a
diverse range of organizations and groups, largely outside
the control of planners.
Fortunately, an alternative ideal of planning with the
public promises to overcome the most important limita-
tions of planning for the public. I believe that computer
modeling and methods can play an important role in
achieving this ideal.
Form follows failure.—Petroski (1992, p. 22)7
The Promise of Planning
With the Public
Traditional Models and New Approaches
Planning with the public is not limited to the formal
models that have been proposed in the academic literature.
Instead, planning with the public requires that, whenever
possible, planners involve as many members of the public,
as completely as possible, in the decisions that affect their
lives. Unlike traditional public hearings conducted at the
end of the planning process, citizens are involved through-
out the planning process in a collective effort to under-
stand the community’s past and present and identify its
preferred future.
Planning with the people who live and work in a
community encourages planners to learn from those most
familiar with local conditions and the realities in which
plans will be implemented. It also helps planners under-
stand the concerns of stakeholders, identify potential
collaborators and latent opposition, and mobilize support
for issues that have traditionally lacked public support
(Healey 2010; Innes & Booher, 2010).
This suggests that planners should strive to help
communities understand where they are, how they got
there, and where they would like to go in the future.
Rather than preparing plans and proposals that they
believe best serve the needs of their clients, planners
should attempt to facilitate and inform a citizen-led
policy process. Instead of blindly extending past trends,
planners should become authorities on the area for
which they are planning, learning about its past and
present, understanding the potential and limitations of
its natural and organizational resources, and identifying
the key factors that will affect its future development
(Isserman, 1984).

The Difficulty of Planning With the Public
I certainly realize that, taken seriously, planning with the
public will be more challenging for planners and the com-
munities they serve than the practices of the past. It will
require much more than the popular regional visioning and
scenario-planning exercises in which a small number of
self-selected citizens gather around tables and devote a few
hours to placing chips or Lego blocks representing planner-
generated land use demands to locations on maps of their
region. Participants find these exercises engaging, empower-
ing, and fun; they readily generate favorable media coverage;
and they often increase public awareness of planning issues.
However, it is unreasonable to assume that a diverse group of
citizens can produce meaningful scenarios in a few hours in
the absence of information on the feasibility and implica-
tions of alternative policy alternatives. As a result, they all
too often serve largely as exercises in unproductive wishful
thinking or as mechanisms for promoting planners’ preferred
options for the future (Bartholomew, 2007; Chakraborty,
2011; Helling, 1998).8
It is also important to recognize that the perceptions of
individuals and groups are inevitably colored by their
personal experiences and their limited understanding of the
past and present. In the face of overwhelming problems and
crippling uncertainty, communities often retreat to an
idealized past rather than looking realistically at their current
conditions and the most likely future (Baum, 1999).9 As a
result, community-based planning and forecasting using
simple analytical models and tools can be extremely helpful
in augmenting the partial, poorly defined, and often faulty
tacit knowledge stored in individuals’ mental models with
explicitly defined public knowledge, helping reduce the
influence of expedient viewpoints and resist overly wishful or
pessimistic thinking (Skaburskis, 1995).
KISS: Keep It Simple, Stupid.10
Use Technology Right
The tremendous advances in computer technology in
the last 40 years have been accompanied by a series of
technologies (large-scale urban models, spreadsheets, expert
systems, GIS, planning support systems [PSS], and the
Internet), each promising to revolutionize planning practice
and research (reviews of these developments are provided in
Klosterman, 1992, 1997). Unfortunately, planners rarely use
computer-based models and methods to help communities
engage the future because the current generation of planning
models and methods generally does not overcome the
realities of planning practice that I identified previously.
The widespread adoption of GIS and office productivity
tools, while extremely useful for facilitating routine manage-
ment tasks, has diverted planners’ attention from their
traditional concern with helping shape the future. Imple-
menting planners’ forecasting methods in a computer does
not make planning any less political or an unknown future
any more certain. Technologies such as public participation
GIS, which uses GIS and the Internet to inform the public
and collect citizen inputs, provide no assurance that the
public’s role in public policymaking will climb above the
lowest rungs of Arnstein’s participation ladder (Arnstein,
1969; McCall & Dunn, 2012).
More important, implementing planning methods in a
computer does not eliminate the need to make assump-
tions and choices. Representing a complex reality in a
computer model inevitably requires choices in specifying
causal relationships, selecting input variables, and adjusting
model outputs so that they are reasonable to modelers and
their clients. Every component of a forecasting model is a
hypothesis that may be valid for short-term forecasts, but is
highly questionable for the 20- to 30-year forecasts plan-
ners routinely prepare. The more complex the model, the
larger the number of (often hidden) assumptions. Com-
puter-based models and methods do not reduce the need
to make assumptions; they only hide them in computer
algorithms, which are unfathomable to outsiders.
In an attempt to mimic engineering prototypes and
architects’ scale models, planners have developed large and
complex models that attempt to capture the city in a
computer. I now recognize that these efforts reflect and
support the profession-centered model of planning that I
critiqued above. Fortunately, these models have been
augmented in recent years by the development of GIS-
based PSS, which use simpler modeling approaches in an
effort to support the future-oriented, community-based
planning efforts that I feel should be the center of planning
practice (see, e.g., Brail, 2008; Geertman, Toppen, &
Stillwell, 2013).
In my opinion, the use of simple planning models is
justified by Box’s Law: “All models are wrong; some models
are useful” (Box & Draper, 1987, p. 424). (The following
discussion draws on Ascher, 1978; Klosterman, 2012).
The first part of the law is true by definition because a
model is a simplification of reality that selectively focuses
attention on some aspects of a complex world and ignores
others. As a result, all models are inevitably wrong in the
sense that they are incomplete and leave out some aspects
of reality. However, Box’s Law also suggests that the ques-
tion isn’t whether a model is correct in some absolute sense,
but rather whether it is useful for a particular purpose. A
model can serve a number of purposes: to develop or test

theory, to provide a sheen of technical sophistication to
public policymaking, to enhance the reputation and in-
come of the modeler, or to support professional practice.
While all of these objectives are valid, computer models’
primary value for planning practice lies in their ability to
help planners and the communities they serve understand
the present and engage the future.
Complex models are large and expensive systems that
provide detailed answers to a wide range of policy questions
and explicitly express causal relationships that are implicitly
retained in the minds of analysts using less complex models.
They are particularly useful for representing interconnected
relationships, feedback loops, and other intricate relation-
ships that are ignored by simpler models. However, as com-
plex models add more variables and longer causal chains,
they often require data that may not be available or mea-
sured correctly. As more variables are added the associated
measurement errors may compound rapidly, overwhelming
the specification errors made by not properly modeling
underlying causal processes (Alonso, 1968; Wachs, 1982).
Analysts who rely on judgment and a simple model
keep the richness of nuance and detail in their heads;
complex model users rely instead on relationships, which
are expressed in formal mathematical relationships they
may not understand. Simple models also promote more
open and democratic policymaking by making explicit the
factual and political assumptions that complex models hide
from view. By involving the public more directly in the
modeling process, simple models not only limit the discre-
tion of professionals but reduce the knowledge differential
between professionals and laymen, generating information
that is more familiar to, and more likely to be accepted by,
policymakers and the public (Klosterman, 1987; Wachs,
2001).
Equally important, I was surprised to discover an
extensive body of research demonstrating convincingly that
complex forecasting models are no more accurate than
simpler models (see, e.g., Makridakis, Hibbon, & Moser,
1979; S. K. Smith, 1987; S. K. Smith & Shahidullah,
1995). This research also suggests that simpler models are
preferable when other factors such as flexibility, ease of use
and interpretation, and the ability to use existing data and
provide timely information are considered. As a result, it
seems clear to me that planners should develop models that
are as simple as possible, but not simpler.11
I believe that planning models and methods should
serve as prostheses for the mind, allowing planners and the
communities that they serve to understand the past and
present, think systematically about the future, and focus
their attention on ways they can work together to prepare
for an uncertain future (Klosterman, 2008; for efforts
along this line, see Isserman, 2007; Klosterman, 2007).
To do this, models like my What if? PSS make explicit the
assumptions about the past, present, future, and alternative
policy choices that underlie the analysis results. They
identify alternative policy options, allow them to be easily
changed, and clearly identify the effects of different policy
choices. Most important, they do not naively assume that
we can predict the future, but instead explicitly
acknowledge that the model results only indicate what
would happen if the underlying modeling assumptions
prove to be correct.
I hope that these simple models will encourage planners
to view forecasts as the results of assumptions that may
reflect the competing perspectives and interests of contend-
ing parties rather than clearly defined foundations for future
action. Viewed in this way, the future is not a single grand
vision or the inevitable consequence of past trends, but
rather an object of public deliberation. Appropriate planning
models and methods can play an essential role here by
testing a range of forecasts and policy proposals derived from
the policy preferences and assumptions of different segments
of the population (Wachs, 2001).
Much More to Learn
It is understandable that planners of the past adapted
the profession-centered model of planning for the public
from architecture and engineering, which ignored the intel-
lectual and political realities of helping communities engage
an unknown and politically contentious future. However, I
believe that it is time for the profession to combine modes of
professional practice that directly involve stakeholders and
the public with simple and easy-to-understand models and
methods in a collective effort to determine what the future
may and should be.
I realize that my recommendations raise a number of
important questions for planning practice and theory: Can
the simple, easy-to-use, and understandable planning
models and methods that I believe should be used in public
discourse deal adequately with the inherently wicked
problems of an increasingly complex urban world? How
can planners help multiple constituencies act strategically
in the face of alternative visions of the future? How can
planning models and methods best inform and improve
collaborative planning processes? How can the tremendous
potential of computer visualization and communication be
used to promote meaningful participation? Most impor-
tant, how can planners’ attempts to plan with the public
best help communities better engage an unknown and
politically contentious future?

These questions and a host of others of which I am not
aware make one thing clear: After 40 years of study, I still
have many lessons to learn about planning.
Acknowledgments
The author would like to thank Dick Bolan, Uri Avin, Howie
Baum, and the anonymous reviewers for many extremely helpful
suggestions that stimulated substantial improvements to earlier
versions of this essay.
Notes
1. My efforts in these areas continue. A revised edition of the methods text
that incorporates the lessons described in this essay is currently being
prepared, and I hope to develop a collection of open-source online
applications that implement the methods in the book. The desktop
version of the What if? PSS is no longer available, but an online, open-
source version is currently being prepared in conjunction with the
Australian Urban Research Infrastructure Network (https://aurin.org.au/).
2. The appeal of community-based planning is confirmed by a survey
conducted by the American Planning Association (2012) in which more
than 60% of the respondents from all political parties, in urban, subur-
ban, and rural areas, believed that their communities need more planning,
and more than one half of the respondents wanted to participate in
community planning efforts.
3. The Ma¯ori are the indigenous Polynesian people of New Zealand.
The quotation is from Lau (2004).
4. This view is regrettably reflected in the current version of my meth-
ods text (Klosterman, 1990).
5. As Innes (1998) points out, even apparently neutral requirements
to collect particular kinds of information can fundamentally shape
policymakers’ perceptions of public policy issues and their responses
to them.
6. The conception of planning as design has been recently been resurrected
under that label of geodesign (McElvaney, 2012; Steinitz, 2012), which
unfortunately shares many of the limitations of its previous incarnation.
7. This principle recognizes that all innovation is driven by the real or
perceived shortcomings of things as they currently exist (i.e., their failure
to function properly).
8. An alternative approach for using scenarios to create robust and contin-
gent plans is described in Chakraborty, Kaza, Knaap, and Deal (2011).
9. Community visioning exercises all too often compound this problem
by presenting imagined futures that do not recognize current conditions
and political realities (Helling, 1998; Myers & Kitsuse, 2000).
10. The acronym was coined by Kelly Johnson (1910–1990), lead
engineer at the Lockheed Skunk Works, creators of the Lockheed U-2
and SR-71 Blackbird spy planes, among many others. While popular
usage translates it as “keep it simple, stupid,” Johnson translated it as
“keep it simple [and] stupid.” There was no implicit meaning that an
engineer was stupid; just the opposite. The principle is best exemplified
by the story of Johnson handing a team of design engineers a handful of
tools, with the challenge that the jet aircraft they were designing must be
repairable by an average mechanic in the field under combat conditions
with only these tools. Hence, the stupid refers to the relationship
between the way things break and the sophistication available to fix
them (“KISS Principle,” 2012).
11. Like religious precepts, this principle is much easier to espouse than
to practice, but is still useful as a guide to practice. It is generally
attributed to Albert Einstein, but its originator isn’t clear.
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