Representing storylines with causal networks to support decision making

Abstract

<p>Physical climate storylines, which are physically self-consistent unfoldings of events, have been powerful tools in understanding regional climate impacts. We show how embedding physical climate storylines into a causal network framework allows user value judgments to be incorporated into the storyline in the form of probabilistic Bayesian priors, and can support decision making through inspection of the causal network outputs.</p><p>We exemplify this through a specific storyline, namely a storyline on the impacts of tropical cyclones on the European Union Solidarity Fund. We outline how the constructed causal network can incorporate value judgments, particularly the prospects on climate change and its impact on cyclone intensity increase, and on economic growth. We also explore how the causal network responds to policy options chosen by the user. The resulting output from the network leads to individualized policy recommendations, allowing the causal network to be used as a possible interface for policy exploration in stakeholder engagements. </p>

Integrating climate model projections into environmental risk assessment: A probabilistic modeling approach

“…In local contexts, the uncertainties associated with global and downscaled climate projections may still be too large to be of practical use for decision‐making. Therefore, alternative approaches such as physical climate storylines can be supplementary tools for incorporating value judgments and responses to policy options (Kunimitsu et al, 2023).…”

Varieties of approaches to constructing physical climate storylines: A review

“…PCS are constructed by inferring the ways in which a subset of these counterfactuals (upward or downward) can be considered analogous to similar events occurring in past or future climatic conditions. This use of counterfactuals, when represented through a causal network, can be particularly useful when analyzing value attitudes of those using PCS to support decision-making (Kunimitsu et al, 2023).…”

“…So, they provide alternative storylines that explore the relation between hazardous events, impact propagation, and financial cost to the European Union. This particular way of constructing PCS allows for an explicit consideration of values both in building the storyline, and in the kind of policy options that one might choose depending on one's risk aversion (Kunimitsu et al, 2023).…”

“…Non‐cognitive values can also influence implicit methodological choices in the PCS approach. For example, Ciullo et al (2021) (see also Kunimitsu et al, 2023) construct “downward counterfactuals” by identifying those unrealized predictions that could have had a more devastating impact on the target region, and therefore on the EU Solidarity Fund. In this case, social and ethical considerations about highlighting key vulnerabilities of the region impacted by a climate hazard drive the methodological choice of what kind of counterfactual to consider.…”

“…Woo (2019Woo ( , 2021 extends this reasoning to extreme events. When applied to PCSs, counterfactual reasoning makes the conditional nature of PCSs explicit, can be used to introduce temporal elements of PCS (Section 3), and helps make value choices on the part of modelers and decision makers more explicit (Kunimitsu et al, 2023), an issue we address in detail in Section 4.…”