Use of Causal Modeling with Bayesian Networks to Inform Policy Options for Sustainable Resource Management

Abstract

The central challenge of resource management is to ensure a sufficient but sustainable supply of goods and services from ecosystems to meet the economic, recreational, and cultural needs of society. Because ecosystems are complex, the consequences of resource use and extraction are often difficult to predict. Large-scale and properly controlled policy experiments are infeasible, and modeling approaches have generally focused on advancing ecological theory rather than management practice. As a result, resource management is frequently dominated by simple heuristics based on untested assumptions. The natural resilience of ecosystems provides positive feedback for many of these heuristics until systems are pushed beyond their sustainable capacity and collapse to a different state. I present a case study of using causal modeling with Bayesian networks to inform policy options for managing populations of moose in British Columbia, where declines of this iconic species are challenging existing management strategies and practices.

Presentation Video

About the Presenter

Steven F. Wilson, Ph.D.
Standpoint Decision Support, Inc.

Steve Wilson has more than 25 years’ experience working at technical and professional levels in strategic and operational planning for public and private-sector clients. He specializes in quantitative approaches to decision support and policy analysis. Steve holds a Ph.D. in wildlife ecology from the University of British Columbia in Vancouver.