Understanding Scientists’ Computational Modeling Decisions About Climate Risk Management Strategies Using Values-Informed Mental Models
This work was partially supported by the National Science Foundation through the Network for Sustainable Climate Risk Management (SCRiM) under NSF cooperative agreement GEO-1240507.
Global Environmental Change
Climatic changes -- Risk management, Environmental engineering, Climate change mitigation, Decision making, Humanitarianism
When developing computational models to analyze the tradeoffs between climate risk management strategies (i.e., mitigation, adaptation, or geoengineering), scientists make explicit and implicit decisions that are influenced by their beliefs, values and preferences. Model descriptions typically include only the explicit decisions and are silent on value judgments that may explain these decisions. Eliciting scientists’ mental models, a systematic approach to determining how they think about climate risk management, can help to gain a clearer understanding of their modeling decisions. In order to identify and represent the role of values, beliefs and preferences on decisions, we used an augmented mental models research approach, namely values-informed mental models (ViMM). We conducted and qualitatively analyzed interviews with eleven climate risk management scientists. Our results suggest that these scientists use a similar decision framework to each other to think about modeling climate risk management tradeoffs, including eight specific decisions ranging from defining the model objectives to evaluating the model’s results. The influence of values on these decisions varied between our scientists and between the specific decisions. For instance, scientists invoked ethical values (e.g., concerns about human welfare) when defining objectives, but epistemic values (e.g., concerns about model consistency) were more influential when evaluating model results. ViMM can (i) enable insights that can inform the design of new computational models and (ii) make value judgments explicit and more inclusive of relevant values. This transparency can help model users to better discern the relevance of model results to their own decision framing and concerns.
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Mayer, L. A., Loa, K., Cwik, B., Tuana, N., Keller, K., Gonnerman, C., ... & Lempert, R. J. (2017). Understanding scientists’ computational modeling decisions about climate risk management strategies using values-informed mental models. Global Environmental Change, 42, 107-116.