Glossary of Terms
Chapter 3: Methods Review
3.3 Complexity modelling
3.3.2 Bayesian Networks
86 Models, it seems that the technology is now moving towards a mature stage as indicated by moves towards a community framework for Agent Based Modelling (Janssen et al. 2008) that would incorporate what is needed to make the ABM community more efficient and aligned with other disciplines:
• A unified protocol for describing ABMs,
• An online model archive,
• A shared library of model components,
• Shared test beds for models creating the ability for improved validation and testing,
• Better GIS-ABM integration and
• More formal and standardised training and education in ABM.
87 diagram and thereby complete the BNs, including maximum likelihood methods and Bayesian adjustment. Various types of input data can be used, including expert judgment, output from simulation models, as well as actual estimated probabilities.
There are two types of inferences made in Bayesian Networks:
Using deductive reasoning allows the analyst to express the likelihood of an output in cases where the input is true, and in cases where the input is false. Using abductive reasoning allows the analyst to express the likelihood of the information in cases where the result would be true, and when the result is false. Deductive reasoning is most commonly used when the input is considered to be a driver or influence with respect to the result. Abductive reasoning is most commonly used when the input is an indicator or symptom of the result (Pope 2008: 2).
Boulanger and Brechet (2005) have identified Bayesian Networks (BNs) as particularly useful for understanding risk and uncertainties in planning and policy analysis in general. Similarly, BNs have been identified as particularly suitable for integrated assessments and modelling in the area of water resource planning and management, when dealing with high levels of uncertainty and complex relationships (Jakeman et al. 2007). In fact, Bayesian models have been used in many different water related contexts, as is clear from a relatively recent special issue of Ecology and Society (Volume 22, Issue 8) on BNs in water resource modelling and management. In most of the applications described in this special issue, BNs have been used to model the whole water system, but occasionally BNs have been used to model only a particular component (Castelletti and Soncini-Sessa 2007c). Bromley and colleagues (2005), Cain and colleagues (2003) and Ticehurst and colleagues (2007) have also shown through case study applications how BNs are useful for considering multiple dimensions in assessments such as environmental, technical, social and financial aspects. BNs are also useful for visualizing the outputs of more complex models (Castelletti and Soncini-Sessa 2007c) such as Agent Based or Hydrological models. Additionally, BNs are also useful for aiding decision making when they are used as Decision Support Systems (Castelletti and Soncini-Sessa 2007c). Cain and
88 colleagues (2003) describe how this was done in a case study in Sri Lanka where agricultural policy makers co-designed and used the model to identify problems and potential solutions for a river basin. In a similar case study in Denmark, BNs were used in an adaptive management framework of groundwater with full stakeholder involvement (Henriksen et al. 2007).
Figure 3-4: Example BN influence diagram Source: Ticehurst et al. 2007: 1131.
89 Table 3-4: Examples of applications of BNs in urban water contexts
Description of application of BNs in urban water contexts Reference
Combining expert knowledge and physical knowledge for evaluating the system risk of pipe bursts.
Babovic et al. 2002
Examining the link between land use and stormwater quality. Ha and Stenström 2003
Simulating integrated urban drainage systems to evaluate measures to improve water quality in receiving waters.
Achleitner et al. 2007
Using BNs in a decision support methodology in order to optimise the number and placement of sensors for monitoring industrial waste water.
Dupuit et al. 2007
Table 3-5: Examples of BNs in general water related contexts
Description of application of BNs in other water related contexts Reference
Assessing the impact of climate change on surface waters in Finland Varis and Kuikka, 1997
Within integrated water resource management, using BNs to facilitate the consideration of economic, social and political impacts; and the active participation of stakeholders in the decision making process.
Bromley et al. 2005
Sustainable planning of the management of an aquifer in Spain de Santa Olalla et al. 2007
Institutional analysis of interactions, actors and rules in water management in an Indian village
Saravanan, 2008
Ecological risk assessment in a catchment Pollino et al. 2007
Assessing the sustainability of lakes in Australia; with a case study of Lake Cudgen Ticehurst et al. 2007
Examples of applications of BNs in the urban water context are shown in Table 3-4; but BNs have also been applied in other water-related contexts, as described in Table 3-5.
The benefits of the BNs are many, as described by Uusitalo (2007) they:
• Are suitable for small and incomplete data sets and BNs have demonstrated good prediction accuracy despite small sample sizes
• Support ‘structural learning’, i.e. methods for improving the structure of the BN using computational methods
• Allow for combining different sources of knowledge and data, including expert knowledge and narrative information
90
• Can provide fast responses to queries once models have been set up.
On the other hand, the disadvantages described by Uusitalo (2007) are that:
• They often require discretisation of continuous variables, which is not straightforward and a potential source of errors
• It is often difficult to convert information from stakeholders and experts to a form that is suitable for BNs
• They do not support feedback loops because they are acyclic, and are hence often unlikely to accurately model the long term dynamic system behaviour.
Several of these points have related to the type of input data that is possible, giving a mixed picture. However, Bromley and colleagues (2005) considered BN as very flexible in its input requirements reporting to have been able to use all data that is available. However Varis and Kuikka (1999), who have considerable experience with BNs (Varis et al. 1990, 1994; Varis 1995, 1997; Varis and Kuikka 1997; Varis and Somlyody 1997), point out that while the methodology is very useful and promising, application in real cases is complicated and often long-winded as the model needs to be comprehended and accepted by many stakeholders who are typically unfamiliar with the approach. Varis and Kuikka (1999) have reported that the attitudes towards BNs vary considerably. In their experience while university students and Finnish national institutes have been generally positive to the BN approach, international institutes have been relatively conservative in their attitudes. They further speculate that the attitudes have to do with a combination of the level of methodological risk aversion and the methodological orientation amongst stakeholders.