A diagnostic approach for going beyond panaceas
Elinor Ostrom*
Center for the Study of Institutions, Population, and Environmental Change, Indiana University, 408 North Indiana Avenue,
Bloomington, IN 47408; Workshop in Political Theory and Policy Analysis, Indiana University, 513 North Park, Bloomington,
IN 47408; and Center for the Study of Institutional Diversity, School of Human Evolution and Social Change, Arizona State
University, Tempe, AZ 85287-2402
Edited by B. L. Turner II, Clark University, Worcester, MA, and approved July 11, 2007 (received for review March 12, 2007)
The articles in this special feature challenge the presumption that scholars can make simple, predictive models of social–ecological
systems (SESs) and deduce universal solutions, panaceas, to problems of overuse or destruction of resources. Moving beyond pana-
ceas to develop cumulative capacities to diagnose the problems and potentialities of linked SESs requires serious study of complex,
multivariable, nonlinear, cross-scale, and changing systems. Many variables have been identified by researchers as affecting the pat-
terns of interactions and outcomes observed in empirical studies of SESs. A step toward developing a diagnostic method is taken by
organizing these variables in a nested, multitier framework. The framework enables scholars to organize analyses of how attributes
of (i) a resource system (e.g., fishery, lake, grazing area), (ii) the resource units generated by that system (e.g., fish, water, fodder),
(iii) the users of that system, and (iv) the governance system jointly affect and are indirectly affected by interactions and resulting
outcomes achieved at a particular time and place. The framework also enables us to organize how these attributes may affect and
be affected by larger socioeconomic, political, and ecological settings in which they are embedded, as well as smaller ones. The
framework is intended to be a step toward building a strong interdisciplinary science of complex, multilevel systems that will enable
future diagnosticians to match governance arrangements to specific problems embedded in a social–ecological context.
commons
complexity
governance
interdisciplinary research
sustainability science
What Can Be Done?
I
n the introduction to this special
feature, we call attention to per-
verse and extensive uses of policy
panaceas in misguided efforts to
make social–ecological systems (SESs),
also called human–environment systems,
sustainable over time. It is not enough,
however, just to call attention to the
inadequacy of the panaceas that are pre-
scribed as simple solutions to complex
SESs. Korten (1) long ago identified the
danger of blueprint approaches to the
governance of tough social–ecological
problems and urged that policy makers
adopt a learning process rather than
imposing final solutions. Korten’s advice
is similar to that of Walters (2, 3) and
the emphasis on adaptive management
in contemporary analyses of complex
adaptive systems (4–6). Unfortunately,
the preference for simple solutions to
complex governance problems continues
to be strong (7).
To move beyond panaceas and build a
solid field of sustainability science (8, 9),
one needs to build on the work of schol-
ars who have undertaken careful, well
documented and theoretically sound
studies of ecological systems, socioeco-
nomic systems, and linked SESs (10–17).
We should stop striving for simple an-
swers to solve complex problems (18).
The problems of overharvesting and
misuse of ecological systems are rarely
attributable to a single cause (19).
Holling et al. (ref. 20, p. 352) identified
the structure of the problems involved:
The answers are not simple because
we have just begun to develop the
concepts, technology and methods
that can address the generic nature
of the problems. Characteristically,
these problems tend to be systems
problems, where aspects of behaviour
are complex and unpredictable and
where causes, while at times simple
(when finally understood), are always
multiple. They are non-linear in na-
ture, cross-scale in time and in space,
and have an evolutionary character.
This is true for both natural and so-
cial systems. In fact, they are one sys-
tem, with critical feedbacks across
temporal and spatial scales.
The conceptual structure of these
problems is a rugged landscape with
many peaks and valleys. Finding higher
peaks when the number of potential so-
lutions is drastically reduced to a few
‘‘optimal’’ strategies is grossly inade-
quate for reaching creative and produc-
tive solutions to challenging problems
(21). One can become fixated on a low
conceptual hill by trying to optimize
specific variables while overlooking bet-
ter solutions involving ignored variables.
Instead, we need to recognize and un-
derstand the complexity to develop
diagnostic methods to identify combina-
tions of variables that affect the incen-
tives and actions of actors under diverse
governance systems (22). To do this we
need to examine the nested attributes of
a resource system and the resource units
generated by that system that jointly af-
fect the incentives of users within a set
of rules crafted by local, distal, or
nested governance systems to affect in-
teractions and outcomes over time (see
Fig. 1). Furthermore, we need to enable
resource users and their officials to ex-
periment with adaptive policies so as to
gain feedback from a changing SES be-
fore a severe transformation adversely
overcomes them (23, 24).
A Nested Framework for Analyzing
Interactions and Outcomes of
Linked SESs
Moving beyond panaceas to develop
cumulative capacities to diagnose the
problems and potentialities of linked
SESs requires serious study of the com-
plex, multivariable, nonlinear, cross-
scale, and changing SESs described by
Holling et al. (20). We need to clarify
the structure of an SES so we under-
stand the niche involved and how a par-
ticular solution may help to improve
outcomes or make them worse. Also,
solutions may not work the same way
over time. As structural variables
change, participants need to have ways
of learning and adapting to these
changes.
Many variables affect the patterns
of interactions and outcomes observed
in empirical studies. After undertaking
a careful analysis of the research
examining the factors likely to affect
self-organization and robustness of
common-property regimes, Agrawal
(25) identified
30 variables that had
Author contributions: E.O. contributed new reagents/
analytic tools, analyzed data, and wrote the article.
The author declares no conflict of interest.
This article is a PNAS Direct Submission.
Abbreviations: GS, governance system; RS, resource system;
RU, resource user; SES, social–ecological system; U, user.
*E-mail: ostrom@indiana.edu
© 2007 by The National Academy of Sciences of the USA
www.pnas.orgcgidoi10.1073pnas.0702288104 PNAS
September 25, 2007
vol. 104
no. 39
15181–15187
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been posited in major theoretical work
to affect incentives, actions, and out-
comes related to sustainable resource
governance. Agrawal raises challenging
questions about how research can be
conducted in a cumulative and rigorous
fashion if this many variables need to
be identified in every study. Whereas
scholars do need to learn how to iden-
tify and measure the variables that
Agrawal identified, and an even larger
number as shown in Table 1, all of
these variables are not relevant in ev-
ery study, because SESs are partially
decomposable systems.
Decomposable Systems. Scientific progress
has been achieved in the past when
scholars have recognized that complex
systems are partially decomposable in
their structure (26–29). Simon (ref. 30,
p. 753) describes nearly decomposable
systems as being ‘‘arranged in levels, the
elements at each lower level being sub-
divisions of the elements at the level
above....Multicelled organisms are
composed of organs, organs of tissues,
tissues of cells.’’ Holland (31) has exam-
ined the parallel processes present in
decomposable systems for balancing ex-
ploitation and exploration of adaptive
systems.
Three aspects of decomposability of
complex subsystems are important for
achieving a better understanding of
complex SESs and crafting ways to im-
prove their performance. The first as-
pect is the conceptual partitioning of
variables into classes and subclasses. The
second aspect is the existence of rela-
tively separable subsystems that are
independent of each other in the ac-
complishment of many functions and
development but eventually affect each
other’s performance. The third aspect is
that complex systems are greater than
the sum of their parts.
The first aspect, variables that are
composed of classes and subclasses,
must be understood to build coherent
and cumulative scientific understanding
(see Fig. 1 and Table 1). The second
aspect, parallel functionality and adapt-
ability, is essential for enabling long-
term solutions to complex SESs. Policies
can be explored in one part of a system
without imposing uniform formulas on
the larger system that might lead to a
large-scale collapse. The third aspect
makes it essential for scholars to recog-
nize that combining variables, for in-
stance A, B, and C, can lead to a system
with emergent properties that differ sub-
stantially from combining two of the
original variables with a different one,
say A, B, and D.
Developing the Nested Conceptual Maps.
Let us now address the importance of
identifying the conceptual tiers and link-
ages among variables that constitute an
SES as it affects and is affected by larger
and smaller SESs. At the broadest con-
ceptual level, one can posit a general
framework, a conceptual map, that can be
used as the starting point for conducting
the study of linked SESs. Fig. 1 presents a
simple, very general framework for what I
hope captures the highest-tier variables
that scholars must analyze when examin-
ing linked SESs.
†
At this broad level, one
can begin to organize an analysis of how
attributes of (i) a resource system (e.g.,
fishery, lake, grazing area), (ii) the re-
source units generated by that system
(e.g., fish, water, fodder), (iii) the users
of that system, and (iv) the governance
system jointly affect and are indirectly
affected by interactions and resulting out-
comes achieved at a particular time and
place. Using such a framework also en-
ables one to organize how these attributes
may affect and be affected by the larger
socioeconomic, political, and ecological
settings in which they are embedded, as
well as smaller ones.
Each of the eight broad variables
shown in Fig. 1 can be unpacked and
further unpacked into multiple concep-
tual tiers.
‡
How far down or up a con-
ceptual hierarchy a researcher needs to
proceed depends on the specific empiri-
cal or policy question under investiga-
tion. If a researcher wishes to address
the ‘‘regulating services’’ examined by
the Millennium Assessment, the related
ecosystem (ECO) variables would need
to be further unpacked. Furthermore,
many interactions and outcomes depend
on the specific combination of several
variables at one or multiple tiers (36–
39). The direction and strength of im-
pact of one-variable frequently depend
on the other variables present (40, 41)
and the past history of processes in the
SES. Further use and development of
this framework will hopefully enable
researchers to develop cumulative, co-
herent, and empirically supported an-
swers to three broad questions:
1. What patterns of interactions and out-
comes, such as overuse, conflict, col-
lapse, stability, and increasing returns,
are likely to result from using a partic-
ular set of rules for the governance,
ownership, and use of a resource sys-
tem and specific resource units in a
specific technological, socioeconomic,
and political environment?
2. What is the likely endogenous devel-
opment of different governance
arrangements, use patterns, and out-
comes with or without external finan-
cial inducements or imposed rules?
3. How robust and sustainable is a par-
ticular configuration of users, re-
source system, resource units, and
governance system to external and
internal disturbances?
Because this is a decomposable sys-
tem, each of the highest-tier conceptual
variables in Fig. 1 can be unpacked and
related to other unpacked variables in
testable theories relating the outcomes
of human use of the diverse types of
SESs. Table 1 lists major second-tier
variables that have been shown in
†
This framework further elaborates the Institutional Anal-
ysis and Development (IAD) framework developed by
scholars at Indiana University (32) and the framework
developed by Anderies et al. (33) for examining the ro-
bustness of SESs. See Meinzen-Dick (34) for a further elu-
cidation of the general variables presented in the above
framework (Table 1) for analyzing irrigation institutions
and the greatly expanded and general version of this
framework contained in the supporting information of
Brock and Carpenter (35).
‡
The task of identifying which variations are subcategories
of a more general variable is not to identify the relative
importance of a variable in a particular setting. Some
crucial variables used in the design of successful gover-
nance systems are third- and fourth-tier variables that are
important in these, but not in all, SESs.
Resource
System
(RS)
Resource Units
(RU)
Interactions (I) Outcomes (O)
Governance
System
(GS)
Users
(U)
Social, Economic, and Political Settings (S)
Related Ecosystems (ECO)
Direct causal link Feedback
Fig. 1. A multitier framework for analyzing an SES.
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