๐จ๐ฆApplication of Bayesian Belief Networks to Evidence-Based Policy Development
Abstract
The natural resource management domain sits at the intersection of incomplete knowledge of the natural world and diverse and often competing societal expectations. While governments strive to develop policy that is โevidence-basedโ achieving that goal can be challenging. Multiple sources of evidence (e.g. expert knowledge), and competing interpretations of that evidence, can lead to disagreements and ineffective policy. This presentation describes an example of using Bayesian belief networks to combine expert and empirical knowledge to develop forest management policy on public lands in Ontario, Canada. Expert knowledge based BBNs were developed through a series of workshops, validated and updated using literature and monitoring data, from which sensitivity and scenario analysis were used to extract policy options. While this approach to policy development required an initial time investment above the norm the result has paid dividends including detailed documentation of the knowledge base for future revisions, increased transparency and public acceptance of the policy development process, and an objective means to identify key uncertainties to apply limited research and monitoring resources. Current and future development focuses on machine learning from research and monitoring databases.
About the Presenter
Mike Brienesse is a Policy Advisor with the Ministry of Natural Resources and Forestry in Sault Ste. Marie, Ontario, Canada. He has worked for both government and forest industry in diverse roles including forest operations, management planning, resource analysis, and policy development. He graduated with an HBScF from Lakehead University, completed an executive MBA with McGill University, and holds a certificate in Strategic Decisions and Risk Management from Stanford University. His professional interests include knowledge systems, decision analysis, and non-timber forest products.
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