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Bayesian Networks: a Guide for Their Application in Natural Resource Management and Policy March 2010

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Bayesian Networks: a Guide for Their Application in Natural Resource Management and Policy March 2010 Technical Report No. 14 Bayesian networks: A guide for their application in natural resource management and policy March 2010 What is the objective of the model? Conceptual Testing model model of how scenarios the system works Evaluation of Transform models conceptual model (sensitivity and into influence accuracy) diagram Parameterise Describe the model model variables (quantitative and (assign states) qualitative) Published March 2010 This publication is available for download as a PDF from www.landscapelogic.org.au Cover: Steps used to build a Bayesian network LANDSCAPE LOGIC is a research hub under the Commonwealth Environmental Research Facilities scheme, managed by the Department of Environment, Water Heritage and the Arts. It is a partnership between: • six regional organisations – the North Central, North East & Goulburn–Broken Catchment Management Authorities in Victoria and the North, South and Cradle Coast Natural Resource Management organisations in Tasmania; • five research institutions – University of Tasmania, Australian National University, RMIT University, Charles Sturt University and NORTH CENTRAL Catchment CSIRO; and Management Authority • state land management agencies in Tasmania and Victoria – the Tasmanian Department of Primary Industries & Water, Forestry Tasmania and the Victorian Department of Sustainability & Environment. The purpose of Landscape Logic is to work in partnership with regional natural resource managers to develop decision-making approaches that improve the effectiveness of environmental management. Landscape Logic aims to: 1. Develop better ways to organise existing knowledge and assumptions about links between land and water management and environmental outcomes. 2. Improve our understanding of the links between land management and environmental outcomes through historical studies of private and public investment into water quality and native vegetation condition. Bayesian networks: A guide for their application in natural resource management and policy Carmel A. Pollino and Christian Henderson Integrated Catchment Assessment and Management Centre, Fenner School of Environment and Society, Australian National University, Canberra. Executive summary Bayesian networks have been successfully used to assist problem solving in a wide range of disci- plines including information technology, engineering, medicine, and more recently biology and ecology. There is growing interest in Australia in the application of Bayesian network modeling to natu- ral resource management (NRM) and policy. Bayesian networks offer assistance to decision-makers working in complex and uncertain domains by assembling disparate information in a consistent and coherent framework and incorporating the uncertainties inherent in natural systems and decision- making. Bayesian networks as modeling tools have been shown to fulfill the following needs: Integration –of models, data types and qualitative information; Prioritisation – through cost benefit analysis and ranking variables against a stated objective; Flexibility – as they can be modified to suit the context in which they are applied and can be updated as new knowledge is obtained; and Communication – as they are graphically based and allow explicit documentation of assumptions and uncertainties, making them easier to understand and use than most modeling frameworks. A key feature of the successful adoption of Bayesian networks as a modelling tool in decision-mak- ing is their relative simplicity when compared with other modelling approaches. They are graphical models, capturing cause and effect relationships through influence diagrams. The use of probabilities to characterise the strengths of linkages between variables means that these can be defined using both quantitative and qualitative information while the use of Bayes’ theorem (see Section 2.3.2) pro- vides a formalised process to update models as new knowledge or data becomes available. Being probabilistic, Bayesian networks can readily incorporate uncertain information, with these uncertain- ties being reflected in model outputs. Sensitivity analysis tools allow characterisation of uncertainties so that key causal factors and knowledge gaps can be identified. Model outcomes are testable, both quantitatively and through formal review processes. However, despite their advantages, it is important to be aware of several limitations. In their com- mon form, Bayesian networks only poorly represent dynamic processes as continuous probability distributions require conversion into an equivalent discrete space for the purposes of easier calcula- tion. Also exact algorithms are used for probability propagation which limits their representation of uncertainties, while complex networks are very data hungry. While their ability to incorporate qualita- tive (and possibly subjective) information is often seen as an advantage, the use of expert opinion is a potential source of bias and there is a tendancy to be overenthusiastic in the inclusion of such detail when data and knowledge is limited. This report builds on an earlier report (Henderson et al. 2008). It overviews the role of models within environmental management (Section 1), the key components of a Bayesian network (Section 2), their benefits (Section 3) and limitations (Section 4), reviews past applications (Section 5) and dis- cusses the potential roles for Bayesian networks in NRM and policy development (Section 6). Bayesian networks: A guide for their application in natural resource management and policy 3 Contents 1. The Context: Natural Resource Management 5 2. What is a Bayesian network? 6 2.1 How to build a BN 7 2.2 Structure of a Bayesian network 8 2.3 Conditional probability tables 10 2.4 Evaluation 13 3. Benefits of Bayesian networks 17 3.1 Complexity 17 3.2 Bayesian Decision Networks 18 3.3 Adoption, Communication, Participation 21 4. Limitations of Bayesian networks: Description and solutions 22 4.1 Dynamics 22 4.2 Limitations in defining probabilities 25 4.3 Subjective input into BNs 28 5. Applications of Bayesian networks 31 5.1 Assessment frameworks 31 6. Using Bayesian networks for decision-making 42 6.1 Frameworks for decision-making in NRM: The Landscape Logic experience 42 7. Concluding remarks 44 Endnotes 45 References 46 4 Landscape Logic Technical Report No. 14 1. The Context: Natural Resource Management A regional-scale structure is used in Australia to monitoring programs that are dedicated to detecting plan, promote and deliver on natural resource a response. These factors make NRM a classic management (NRM) priorities. This arrangement example of a ‘wicked problem’ where diverse was formalised in 2000 with the formation of 56 interests, evolving understanding of a problem and regional bodies across Australia. The purpose of its complexity combine to make problem resolution regionalisation was to facilitate a greater commu- a challenging process (Rittel and Webber, 1973). nity involvement in NRM planning, priority setting Uncertainties in environmental policy and and investment. Regional plans now form the basis management are usually addressed via one of the for investment, with a focus on target setting, imple- following five approaches (Peterman and Anderson mentation and cooperative arrangements for 1999): catchment-wide activities. Plans address a broad 1. Using best estimates (usually point estimates) range of issues including land, water and vegetation for parameters and state variables, ignoring management, biodiversity conservation, and sus- uncertainties; tainable agriculture. 2. Uncertainties are acknowledged and used to To develop and meet NRM objectives, the justify status quo management actions because Australian government has invested almost $6 billion the outcomes of actions are uncertain; between 1996 and 2007. Despite this significant 3. Aggressive policies are introduced, e.g. for investment, delivery of tangible impacts through harvesting or pollutant release, as negative regional arrangements has proved difficult. Audits of consequences cannot be demonstrated with public environmental programs (ANAO 2001, 2007, certainty; 2008) concluded that it was not possible to gauge 4. Arbitrary safety factors are applied that can over the effectiveness of investment as there had been no or underestimate reality; or provision for adequate monitoring of change on the 5. Explicitly considering and quantifying ground. As reviewed in Hajkowicz (2009), although uncertainties. this outcome was not unique to Australia, it fell well While approach five is the most sensible, at pres- short of community and government expectations. ent, there are few tools that can assist in planning, In Australian landscapes demonstrating monitoring and evaluating the success of invest- a measurable change in the health of natural ments in an uncertain environment. Such tools are resources as a consequence of public investment is needed to better focus investments, more efficiently exacerbated by: the ‘tyranny of size’, where physical allocate scare resources and allow for ongoing area is large and dollars invested per unit area is improvements in resource condition through low (Hajkowicz 2009); furthermore, it is difficult to adaptive management. A modelling approach that is gauge effectiveness of actions when there are long increasingly being regarded as useful in NRM, and time lags in response; and the most insurmountable which is explored further in this report, is Bayesian limitation is the general lack of any long-term networks (BNs).
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