Building a Values-Informed Mental Model for New Orleans Climate Risk Management.

Individuals use values to frame their beliefs and simplify their understanding when confronted with complex and uncertain situations. The high complexity and deep uncertainty involved in climate risk management (CRM) lead to individuals' values likely being coupled to and contributing to their understanding of specific climate risk factors and management strategies. Most mental model approaches, however, which are commonly used to inform our understanding of people's beliefs, ignore values. In response, we developed a "Values-informed Mental Model" research approach, or ViMM, to elicit individuals' values alongside their beliefs and determine which values people use to understand and assess specific climate risk factors and CRM strategies. Our results show that participants consistently used one of three values to frame their understanding of risk factors and CRM strategies in New Orleans: (1) fostering a healthy economy, wealth, and job creation, (2) protecting and promoting healthy ecosystems and biodiversity, and (3) preserving New Orleans' unique culture, traditions, and historically significant neighborhoods. While the first value frame is common in analyses of CRM strategies, the latter two are often ignored, despite their mirroring commonly accepted pillars of sustainability. Other values like distributive justice and fairness were prioritized differently depending on the risk factor or strategy being discussed. These results suggest that the ViMM method could be a critical first step in CRM decision-support processes and may encourage adoption of CRM strategies more in line with stakeholders' values.

Computer models and the evidence of anthropogenic climate change: An epistemology of variety-of-evidence inferences and robustness analysis.

To study climate change, scientists employ computer models, which approximate target systems with various levels of skill. Given the imperfection of climate models, how do scientists use simulations to generate knowledge about the causes of observed climate change? Addressing a similar question in the context of biological modelling, Levins (1966) proposed an account grounded in robustness analysis. Recent philosophical discussions dispute the confirmatory power of robustness, raising the question of how the results of computer modelling studies contribute to the body of evidence supporting hypotheses about climate change. Expanding on Staley's (2004) distinction between evidential strength and security, and Lloyd's (2015) argument connecting variety-of-evidence inferences and robustness analysis, I address this question with respect to recent challenges to the epistemology robustness analysis. Applying this epistemology to case studies of climate change, I argue that, despite imperfections in climate models, and epistemic constraints on variety-of-evidence reasoning and robustness analysis, this framework accounts for the strength and security of evidence supporting climatological inferences, including the finding that global warming is occurring and its primary causes are anthropogenic.