This contribution describes a quantitative tool designed to support resource management decision-making. The Hierarchical Model of Resource Management is based on the assumption that the publics’ objectives for resource management are a function of their values and further, that influence flows down, whereas information with respect to the state of the world and the consequences of fulfilling objectives flows back up. The purpose of the Hierarchical Model is to facilitate the public participation process. It can be quantified with either a measurement approach or a preference approach. The measurement model provides a means for linking values to behaviors and has been derived from the traditional values-attitudes-behaviors framework widely accepted in social psychology and consumer behavior literature. Data are collected via survey and analyzed using a variety of mathematical and statistical methods, including structural equations, clustering and descriptive statistics. The preference model provides a means for linking objectives for natural resource management to choices among potential implementation strategies. The approach is based on decision theoretic methods that are widely used to evaluate alternatives involving multiple objectives. This approach follows a path from strategic objectives to fundamental and means objectives, to preference functions, ending with preference ordering and choice. The measurement and preference paths run in parallel, but have significant differences that are explained in the text. Finally, the Hierarchical model is placed within a recursive decision framework that is based on the principles of adaptive management. The range of information provided by the two approaches for quantifying the Hierarchical Model provide information that will be essential if public preferences are to incorporated into resource management decision making.
Weitere Kapitel dieses Buchs durch Wischen aufrufen
- A Hierarchical Model of Collaborative Resource Management
Deborah J. Shields
- Springer Netherlands
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