Bayesian belief modeling of climate change impacts for informing regional adaptation options

Abstract

A sequential approach to combining two established modeling techniques (systems thinking and Bayesian Belief Networks; BBNs) was developed and applied to climate change adaptation research within the South East Queensland Climate Adaptation Research Initiative (SEQ-CARI). Six participatory workshops involving 66 stakeholders based within SEQ produced six system conceptualizations and 22 alpha-level BBNs. The outcomes of the initial systems modeling exercise successfully allowed the selection of critical determinants of key response variables for in depth analysis within more homogeneous, sector-based groups of participants. Using two cases, this article focuses on the processes and methodological issues relating to the use of the BBN modeling technique when the data are based on expert opinion.The study expected to find both generic and specific determinants of adaptive capacity based on the perceptions of the stakeholders involved. While generic determinants were found (e.g. funding and awareness levels), sensitivity analysis identified the importance of pragmatic, context-based determinants, which also had methodological implications. The article raises questions about the most appropriate scale at which the methodology applied can be used to identify useful generic determinants of adaptive capacity when, at the scale used, the most useful determinants were sector specific. Comparisons between individual BBN conditional probabilities identified diverging and converging beliefs, and that the sensitivity of response variables to direct descendant nodes was not always perceived consistently. It was often the accompanying narrative that provided important contextual information that explained observed differences, highlighting the benefits of using critical narrative with modeling tools.

Building adaptive capacity in South East Queensland, Australia

“…Through an iterative cycle, the BBNs identified the following: (1) community wellbeing/resilience/support; (2) adequate funding; and (3) and proactive policies, as central determinants in shaping the adaptive capacity context. However, as observed by Richards et al (2012), the influence of generic determinants was moderated by sector-specific determinants, confirmed in post-workshop interviews with stakeholders.…”

“…For example, participants were instructed to identify primary variables that directly influence their capacity to manage their group's priority issues, and in turn, to identify the variables that directly influence the primary variables. The process followed for developing causal, hierarchical layers in the BBN structures, for assigning conditional probabilities to each causal relationship and to capturing narrative insights into participants' understanding of relationships between variables, is described in detail in Richards et al (2012).…”

“…The process of synthesising data generated from the four methods described involved tabulating constraints on adaptive capacity identified in each of the four reports Richards et al 2012;Roiko et al 2012;Sano et al 2012) and generating options for enhancing adaptive capacity. These constraints and options were categorised according to three general determinants frequently identified in the BBNs: community resilience and well-being, institutions and policy, and finance.…”

Global change impacts on the Upper Danube Catchment (Central Europe): a study of participatory modeling

“…These usually have a narrow focus, e.g., on adaptation, knowledge sharing or the technical design and implementation of participatory approaches (e.g., Bartels et al 2013;de la Vega-Leinert et al 2008;Faysse et al 2014;Huntjens et al 2010). PM as a tool to assist the development of regional models is presented for example by Richards et al (2013), Carmona et al (2013a, b) and Holman et al (2008). The combination of global change impact assessment as the problem context, integrated models as the tool, and the focus on a regional scale provides a specific problem setting for PM.…”

Interdisciplinarity as an Emergent Property: The Research Project “CINTERA” and the Study of Marine Eutrophication

“…The natural science team provided much more specialized knowledge about which nutrients would be studied, the potential impact of these on the plankton of the fjord, and they explained their research design. The social science team could explain the concept of the stakeholder workshops and the methods used-the Systems Thinking (ST), Conceptual Probability Tables (CPT), and Bayesian Belief Network (BBN) methods [42] as well as how their case selection corresponded to the natural science side. Each work package spoke clearly to how the work related to the central research question.…”