Expert Modelling

This page focuses on expert models, which are computer-based models that can mimic (or outperform) the decisions of a human expert. Expert models belong to the field of artificial intelligence and essentially use inference techniques to deduce novel information of the system under study. The content on this page discusses Bayesian networks and fuzzy cognitive maps.

The Chapter summary video gives a brief introduction and summary of this group of methods, what SES problems/questions they are useful for, and key resources needed to conduct the methods. The methods video/s introduce specific methods, including their origin and broad purpose, what SES problems/questions the specific method is useful for, examples of the method in use and key resources needed. The Case Studies demonstrate the method in action in more detail, including an introduction to the context and issue, how the method was used, the outcomes of the process and the challenges of implementing the method. The labs/activities give an example of a teaching activity relating to this group of methods, including the objectives of the activity, resources needed, steps to follow and outcomes/evaluation options.

More details can be found in Chapter 16 of the Routledge Handbook of Research Methods for Social-Ecological Systems.

Chapter summary:

This video introduces the concept of Expert Modelling.

Method Summaries

There are no method summaries for Expert Modelling yet. Please consider contributing.

Case Studies

Using Fuzzy Cognitive Mapping to investigate bushmeat hunting in Malawi

van Velden, J. (2022)

Lab teaching/ activity

There are no lab teachings or activities for Expert Modelling yet. Please consider contributing.

Tips and Tricks

There are no tips and tricks for Expert Modelling yet. Please consider contributing.
Key Publications related to Expert Modelling:
  • Gray, S.A., S. Gray, J.L. de Kok, A.E.R. Helfgott, B. O’Dwyer, R. Jordan, and A. Nyaki. 2015. ‘Using Fuzzy Cognitive Mapping as a Participatory Approach to Analyze Change, Preferred States, and Perceived Resilience of Social-Ecological Systems.’ Ecology and Society 20(2): 11. doi:10.5751/ES – 07396 -20 0211.
  • Marcot, B.  G., R.S.  Holthausen M.G.  Rowland, and M.J.  Wisdom. 2001. ‘Using Bayesian Belief Networks to Evaluate Fish and Wildlife Population Viability under Land Management Alternatives from an Environmental Impact Statement.’ Forest Ecology and Management 153: 29– 42. doi:10.1016/ S0378-1127(01)00452-2.
  • Rumpff, L., D.H. Duncan, P.A. Vesk, D.A. Keith, and B.A. Wintle. 2011. ‘State-and-transition Modelling for Adaptive Management of Native Woodlands.’ Biological Conservation 144: 1224–1236.