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.org cgi doi 10.1073 pnas.0702288104 PNAS September 25, 2007 vol. 104 no. 39 15181���15187 SPECIAL FEATURE: PERSPECTIVE

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. 15182 www.pnas.org cgi doi 10.1073 pnas.0702288104 Ostrom