This video shows the Bayesian Belief Networks - training given on March 20, 2015 during the plenary meeting in Padova, Italy. The traning was led by Dr. Claire Quinn of the University of Leeds.
In the RECARE project, WP7 will study the cost-effectiveness of the implemented measures. The main objectives are to:
1. Define an appropriate multi-methods approach to assess costs and benefits at the implementation level and
entire Case Study level, taking into account the impacts of the measures on ecosystem functions and services,
2. Implement the approach for each Case Study with attention to scale, timing and dosage,
3. Explore Case Study level preferences for remediation options and evaluation.
Task 7.2 involves the participatory model development for evaluation of remediation options and is lead by partner 15 (University of Leeds).
The impact assessment methodology developed in Task 7.1 is indicator-based. However, temporal and
spatial dynamics require scenario modelling. In this task we will: i) develop Bayesian belief networks (BBN)
about the functioning of different remediation options, including the pace of change and dosage-effectiveness
relationships where appropriate, and ii) examine motivations (including beliefs, attitudes and cultural aspects)
that underlie the decisions stakeholders make over different options and the likelihood of motivations being
translated into practice.