5.6 Systems approach
5.6.3 Resilience theory
Table 5.5.Forrester’s seven properties of complex adaptive systems 1. They are counterintuitive
2. Complex systems are remarkably insensitive to changes in many system parameters
3. Complex systems counteract and compensate for externally applied corrective efforts
4. Complex systems resist most policy changes
5. Complex systems contain influential pressure points often in unexpected places from which forces will radiate to alter system balance
6. Complex systems often react to policy change in the long run in a way opposite to how they react in the short run. Worse before better makes beneficial policies hard to implement and maintain to the point where they bear fruit.
Better-before-worse makes policies that are detrimental in the long run hard to abandon.
7. Complex social systems tend towards a condition of poor performance.
Source:Forrester (1961)
practice of adaptive management in the 1990s in the WA agricultural region (C. Keating, personal communication August 2003), as discussed in Chapter 4.
There is a special class of complex systems that are termed adaptive which have been described by seven properties (Table 5.5) (Forrester, 1961). The unique feature that distinguishes these from other kinds of complex systems is that adaptive systems in some way interact with their environment and change in response to environmental change. This potential for systems to be adaptive and self-organising is responsible for many natural resource problems and policy resistance, which are discussed below and exemplified in the case study in Chapters 2, 6 and 7.
Davidson-Hunt and Berkes, 2003) and the health sciences (particularly mental health) (Bonannoet al., 2001; Olssonet al., 2003) literatures. Resilience and the related construct of robustness are widely used in the natural and social literatures, in association with the central construct of stability (or constancy) (Hansson and Helgesson, 2003).
There are two basic types of stability that draw attention to the tension created between efficiency on the one hand and persistence on the other, or between constancy and change, or between predictability and unpredictability (Holling, 1973; Hansson and Helgesson, 2003), a tension also noted in the organisational analysis debate (discussed in Section 4.4). The first definition refers to actual absence of change (or constancy) with a focus on the objec- tive or ‘ends’ of optimal performance. The second definition refers to how a system copes with disturbances focussing on persistence, variability and unpredictability, covered by the notions of resilience and its limiting case robustness (Holling, 1973; Hansson and Helgesson, 2003). The use of the terms resilience and robustness by Hansson and Helgesson (2003) equates to the constructs of engineering resilience and ecological resilience identified by Holling (1973). In the literatures of natural science, social science, engi- neering and health science some terms have been used interchangeably with resilience, for example, ‘robustness’, ‘stability’, ‘reliability’, ‘persistence’,
‘survivability’. Examples of these uses are being collated and clarified in a joint project of the Resilience Alliance and the Santa Fe Institute’s Robustness Program (Resilience Alliance, 2002). The use of the term resilience in this book is consistent with Holling’s ‘ecological resilience’, which is taken to mean the way to understand how ecosystems maintain themselves, or adapt, following perturbation or rapid change (Gunderson and Holling, 2002).
In recent years resilience theory has received considerable development in addressing two paradoxes identified by Hollinget al. (2002b). In an attempt to resolve the paradoxes, four provisional propositions, six assumptions and twelve conclusions have been reported. In the quest for a theory of adaptive change, Holling et al. (2002b) examined many case studies and identified two paradoxes (Table 5.6) that prevented any quick and easy predictions about the potential for a system to collapse. The first was the Paradox of the Pathology of Regional Resources and Ecosystem Management and the second was the Trap of the Expert. In addition Gundersonet al. (2002b) made four provisional propositions about the behaviour of large-scale systems, based on a review of ecological processes, with the proviso that they may not be appropriate for other disciplines (Table 5.7). Walkeret al. (2002) proposed six assumptions about systems made up of humans and nature (Table 5.8) and Holling (2000) made twelve conclusions from empirical examples, models and
Table 5.6.Two paradoxes of regional resource management Paradox 1. The pathology of regional resource and ecosystem management
Observation: New policies and development usually succeed initially, but they lead to agencies that gradually become rigid and myopic, economic sectors that become slavishly dependent, ecosystems that are more fragile and a public that loses trust in governance.
The paradox: If that is as common as it appears, why are we still here? Why has there not been a profound collapse of exploited renewable resources and the ecological services upon which human survival and development depend?
Paradox 2. The trap of the expert
Observation: In every example of crisis and regional development we have studied, both the natural system and the economic components can be explained by a small set of variables and critical processes. The great complexity, diversity, and opportunity in complex regional systems emerge from a handful of critical variables and processes that operate over distinctly different scales in space and time.
The paradox: If that is the case, why does expert advice so often create crisis and contribute to political gridlock? Why, in so many places, does science have a bad name?
Source:Hollinget al. (2002b)
Table 5.7.Four provisional propositions about large-scale systems 1. The organisation of regional resource systems emerges from the interaction
of a few variables. The essential structure and dynamics of complex systems are produced by the interactions of at least three, but no more than six, variables that operate at spatial and temporal scales that differ by approximately an order of magnitude.
2. Complex systems have multiple stable states. Complex systems can exhibit alternative stable organizations. Transitions between different organisations are due to changes in the interaction of structuring variables. Change often occurs when gradual change in a slow variable alters the interactions among fast variables.
3. Resilience derives from functional reinforcement across scales and functional overlap within scales. Resilience derives from both a duplication of function across a range of spatial and temporal scales and a diversity of different functions operating at each scale.
4. Vulnerability increases as sources of novelty are eliminated and as functional diversity and cross-scale functional replication are reduced.
Diminished sources of novelty reduce the ability of a system to recover from disturbances. The elimination of structuring species or processes can cause an ecosystem to reorganise. A reduction in functional diversity and duplication of functions reduces the ability of a system to persist.
Source:Gundersonet al. (2002b)
Table 5.8.Assumptions of systems under resilience theory Resilience assumptions
1. The existence of thresholds and hysteretic effects should be assumed.
2. Assumes dynamic and unknown probabilities.
3. It is based on imperfect knowledge, and utility depends on social context.
4. Market imperfections are the norm and market-based evaluations are the norm.
5. Agents hold preferences over outcomes, social, economic and political processes.
6. Expert solutions do not maximise legitimacy.
Source:Walkeret al. (2002).
Walkeret al. (2002) used the term ‘social-ecological systems’ (SESs) for large-scale regional systems made up of humans and nature.
tests (Table 5.9). These paradoxes, propositions, assumptions and conclusions are tested in Chapters 6 and 7 for their applicability to the WA agricultural region.
In its current form resilience theory aims to understand three fundamental themes (Gunderson et al., 2002a). The first considers the characteristics of stability, resilience and change from one state to another in systems with multiple stable states. The second considers cross-scale interactions, and the third is one of adaptive change and learning using the heuristic model or metaphor of the adaptive cycle (Section 5.6.4). The two aims of resilience management are (1) to prevent the system from moving to unintended system configurations in the face of external stresses and disturbance; and (2) to nurture and preserve the elements that enable the system to renew and reor- ganise itself following a massive change (Walker et al., 2002). Resilience theory also gives us a new language and concepts to describe and help under- stand phenomena in the process of dynamic change in linked SESs.