expensive or time consuming to resolve, as well as others that are not resolvable due to the inherent uncertainty and unpredictability of nature (Wilson, this volume:Chapter 10).
The rationale for the consideration of uncertainty comes mainly from the recognition that natural systems and social systems are seldom linear and predictable, and from systems theory that emphasizes connectedness, context, and feedback. Processes in ecology, economics, and many other areas are dominated by nonlinear phenomena and an essential quality of uncertainty. These observations have led to the notion of complexity, developed through the work of many people and groups, notably the Santa Fe Institute (2001). In complex systems, small changes can magnify quickly and flip a system into one of many alternative paths. Such systems organize around one of several possible equilibrium states or attractors. When conditions change, the system’s feedback loops tend to maintain its current state—up to a point. At a certain level of change in conditions (threshold), the system can change rapidly and catastrophically. Just when such a flip may occur and the state into which the system will change are rarely predictable (Holling, 1986).
Turning to the issue of learning, steps (3), (5), and (6) of adaptive management require that managers learn from the outcome of the decisions made. Adaptive management emphasizes learning by doing, and this is accomplished by treating policies as hypotheses and management as experiments from which managers can learn. Organizations and institutions can learn as individuals do, and hence adaptive management is based on social learning. Lee (1993) details such social learning based on the extensive experience with the Columbia River basin, a region full of cross-scale institutions. By emphasizing the interaction between management institutions and the biophysical system, Lee (1993) argues that one cannot expect to manage the environment unless one understands the effects of this interaction.
The goal of adaptive management is different from conventional management. In adaptive management, the goal is not to produce the highest biological or economic yield, but to understand the system and to learn more about uncertainties by probing the system. Feedback from management outcomes provides for corrections to avoid thresholds that may threaten the ecosystem and the social and economic system based on it. Thus, adaptive management depends on feedbacks from the environment in shaping policy, followed by further systematic experimentation to shape subsequent policy, and so on; the process is iterative (Holling, 1986; Holling et al., 1998).
Adaptive management is an understudied area in commons research, except perhaps in fisheries. Lee’s (1993) work shows how the study of institutions and participatory processes can be combined with research on adaptive management. Many interdisciplinary scholars are looking for adaptive management-style alternatives to conventional scientific approaches in dealing with problems of complex systems. For example, in the area of sustainability, Kates et al. (2001) argue