Presenter/Author Information

R. Farmani
Dragan Savic

Keywords

system dynamics, feedback loop, evolutionary algorithms, bayesian belief network, adaptive management, uncertainty, learning

Start Date

1-7-2008 12:00 AM

Description

This paper presents an approach for constructing and testing a decision analysis process for adaptive water management under uncertainty. Water resources management as a complex dynamic system contains nonlinearities, feedback loops, and delays. Qualitative system dynamics modelling (e.g. causal loop diagram) is employed within a participatory integrated framework (integrating social, environmental and economic elements) to identify major drivers and their trends, potential evolutionary paths and their interdependencies, and also possible actions that can be taken to reduce impact of these drivers. An evolutionary Bayesian belief network-based methodology is developed to guide stepwise decision making during the transition process taking into account key uncertainties. Causal loop diagrams, as directed graphs, have no restrictions with feedback loops. Loops in causal maps are usually the result of dynamic relationships between variables across multiple time periods. However, Bayesian belief networks are hierarchical acyclic graphs, therefore have no means of handling feedback loops. The proposed methodology addresses this major shortcoming of Bayesian belief networks.

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Jul 1st, 12:00 AM

An Evolutionary Bayesian Belief Networkbased Methodology for Adaptive Water Management

This paper presents an approach for constructing and testing a decision analysis process for adaptive water management under uncertainty. Water resources management as a complex dynamic system contains nonlinearities, feedback loops, and delays. Qualitative system dynamics modelling (e.g. causal loop diagram) is employed within a participatory integrated framework (integrating social, environmental and economic elements) to identify major drivers and their trends, potential evolutionary paths and their interdependencies, and also possible actions that can be taken to reduce impact of these drivers. An evolutionary Bayesian belief network-based methodology is developed to guide stepwise decision making during the transition process taking into account key uncertainties. Causal loop diagrams, as directed graphs, have no restrictions with feedback loops. Loops in causal maps are usually the result of dynamic relationships between variables across multiple time periods. However, Bayesian belief networks are hierarchical acyclic graphs, therefore have no means of handling feedback loops. The proposed methodology addresses this major shortcoming of Bayesian belief networks.