Chapter 2 focuses on the method : Bayesian network modelling that was applied and evaluated during the case study field work and begins with a contemporary review of the use of computer-based support tools in water management to support the reader in contextualising the relevance of the research reported in the middle chapters.
Bayesian calculus and Bayesian networks are then described in detail and a review of reported applications from the academic literature is given. The final section of Chapter 2 describes the individual components of the case study field work.
Chapter 3 begins with a description of water stress issues in the case study region:
the Upper Iskar River and city of Sofia in south-western Bulgaria. The results of knowledge elicitation interviews with ten informed practitioners working in the water sector in Sofia, and who are involved in implementing demand management, are then described. Results include a description of the current policy process in Sofia and identification of constraints to implementation in the Upper Iskar case. The process of knowledge elicitation to support model development is identified as an important and effective activity in model development that addresses a specific science-policy interface: balancing issue- and curiosity-driven science and the articulation of knowledge for decision-making processes. The research questions that formed the focus of the model development and technical evaluation are presented in Table 3.5 at the end of Chapter 3.
Chapter 4 describes data collection activities to address structural and parameter uncertainties in a conceptual Bayesian network model for WDM legislation for the Sofia case. Collection and modelling of water supply and demand data, and information on economic indicators are described.
Chapter 5 provides a technical evaluation of the use of Bns to support WDM legislation decisions. Instantiation of the conceptual model applied to the Upper Iskar case is described and modelling issues and remaining knowledge gaps are discussed. Conclusions are that Bn modelling is applicable to policy problems where decisions can be ordered in sequence, even over multiple time steps. The process of model development is also beneficial in clarifying and examining the decision process and determining research priorities.
Until recently limitations have existed with modeling feedback cycles using Bayesian networks due to the necessary calculus not being developed (Jensen, 2001). Recent developments (e.g. Montani et al, 2008; Neil et al, 2008), however, mean that there is now scope to use Bns in domains where feedback cycles exist.
Chapter 6 reports the application of Bns to support water conservation programme design. The development and practical implications of four models, which utilise data collected from social surveys in the city of Sofia, are presented and discussed. The three issues addressed are: (i) water conservation behaviour (i.e. constraints, attitudes etc), (ii) implementation conditions described through uptake and water saving potential, and (iii) estimating the value of collecting data prior to implementation. Conclusions are that WDM programme design involves intensive and potentially costly data collection. Collecting the right type and amount of data to support targeting of implementation effort and to reduce uncertainty of programme impacts, is a challenging task. Bayesian networks supported analysis of household survey data and showed potential for further use in addressing sampling issues such as missing or incomplete data. Value of information analysis also shows potential for directing data collection effort to reduce uncertainty about WDM programme impacts..
Chapter 7 presents the result of an end-user evaluation of the use of Bayesian networks to support cross-sectoral planning. The end-user evaluation involved nine individuals at different stages of the WDM implementation process in Sofia testing Bn models during a one-day workshop. The aim of the evaluation was to elicit end-user’s perceptions of the effectiveness of Bns in the context of their individual and collective roles as decision-makers, and thereby evaluate the use of Bns in supporting decisions processes requiring collaboration and understanding between multiple decision-makers or organisations. Three research hypotheses were tested by collecting end-users perceptions of the support tools effectiveness following the workshop. Results indicate that Bns perform particularly well in terms of technical suitability and transparency. Policy makers perceived effectiveness scores were highest across five of the seven factors included in the evaluation instrument and were significantly higher (p=<0.05) than engineers and water company employees.
The validity of results may be affected by the evaluation instrument design which leaves scope for discussion.
Chapter 8 is a discussion of the use of Bayesian network models in WDM planning and implementation. Results from the end-user evaluation are considered in light of conclusions from the technical evaluations. The role of Bns in legitimisation and validation of information is discussed and issues of evaluating the accuracy and precision of their outputs in problem domains characterised by non-repeatable decisions is discussed. The roles of Bns in systemising decision analysis and evaluation design for WDM during the implementation process and facilitating exchanges between science and practice are also discussed. The methodological lessons about the applicability of Bns to the WDM problem domain and their applicability in terms of transparency and technical suitability elicited during the end-user evaluation compose the main contribution to knowledge in this thesis.
Chapter 9 identifies areas of future research and include developing methods to combine Bns and other modelling approaches and their application in specific areas of the WDM problem domain, i.e. legislation and design. It is suggesting that if Bns are to be widely accepted for policy modelling, methods for (i) parameter estimation, i.e. populating conditional probabilities tables, and, (ii) calculating or eliciting utilities, require further evaluation and systemisation.