Structuring and evaluating decision support processes to enhance the robustness of complex human–natural systems

• Published in: Environmental modelling & software : with environment data news 2020-01, Vol.123, p.104551, Article 104551
• Main Authors: Moallemi, Enayat A., Zare, Fateme, Reed, Patrick M., Elsawah, Sondoss, Ryan, Michael J., Bryan, Brett A.
• Format: Article
• Language: English
• Subjects: Climate change; Cognitive ability; Cognitive biases and heuristics; Decision analysis; Decision making; Decision support systems; Deep uncertainty; Exploratory modelling; Human behavior; Human factors; Robustness; Scenario discovery; System effectiveness; Uncertainty
• ISSN: 1364-8152; 1873-6726
• DOI: 10.1016/j.envsoft.2019.104551

Decision-making in the context of complex human–natural systems requires a transition towards robust model-based inferences which are effective despite uncertainties of human and climate driven change. Supporting robust decision-making needs a sequence of interactive methodological choices for setting the problem context, framing the decision problem, evaluating possible solutions, and making recommendations. These methodological choices are influenced by a variety of human factors, originating from cognitive, behavioural, and mental frameworks of stakeholders. We review a broad array of methodological constructs to better emphasise the choices that are most appropriate given different levels of knowledge. Consideration of these methodological constructs clarifies how problems can be perceived and framed in rival decision support paths emerging from the cumulative effects of individual methodological choices and the challenging human factors that shape decision-making under deep uncertainty. We conclude that the careful consideration of rival decision support paths can enhance the confidence in decision recommendations and illuminate sensitivities to the methodological choices. •Achieving sustainability under global change requires robust decisions.•We review a broad array of methodological constructs in robust decision making.•We analyse human factors that can lead to biased choices and misleading inferences.•We analyse the combined effects of methods and human factors on robust inferences.