As previously discussed, the driving assumption underlying AEAM is that uncertainty is inevitable, because the behavior of natural resource systems is only partly knowable.
Based on the assumption that structured learning is better than trial and error, AEAM is based on a process of Integrated Learning (Figure 2).
In this way, AEAM views policies as hypotheses, therefore management actions become treatments in an experiment.
We shall then describe one example of AEAM as applied in a wetland savanna ecosystem in Florida.
Rather than dodging uncertainty with simplifying assumptions or rationalizations, the AEAM process focuses on uncertainty from the very beginning, utilizing disagreements to reveal and highlight gaps in understanding and other sources of uncertainty.
This trust is one way in which the AEAM process addresses the refusal to share information, a frequent source of gridlock in environmental decision processes.
Instead of pursuing the `correct' policy as a solution to problems, AEAM differs from traditional engines of policy by looking for policy that addresses other social objectives as well as the need to learn in the face of uncertainty (Gunderson 1998).
The AEAM process strives to avoid this pathology by broadening implementation to mean the testing and evaluating of hypotheses--or policies.
The AEAM process develops these hypotheses as a suite of alternative explanations about the behavior of the resource.
In an AEAM process, convened in 1989, a number of alternative hypotheses were posed to explain these population declines (Light et al.
Walters (1986) introduced three concepts of how to structure management approaches in the AEAM process: