"To progress our understanding, learning and decision making on major socio-environmental issues using advances in model-grounded processes that engage with institutional and governance contexts, cross-sectoral and scale challenges, and stakeholder perspectives."

Fit-for-purpose problem framing, model development and evaluation as well as eclectic uncertainty analysis are stressed so that the advantages and limitations of model-related assumptions are transparent. The aim is to advance model-grounded, learning and decision processes and their wider application to a new level that leads to innovations in thinking and practice to support resolution of grand challenge problems; including generating policy insights and evidence, and reducing and managing critical uncertainties (assumptions, model structure, parameterizations, inputs including future drivers, and boundary conditions).

Papers may address how science can help identify and provide germane information and support required by managers, decision-makers and society at large. Of special interest is quantitative and/or qualitative consideration of why and in what circumstances uncertainty matters: uncertainty aspects not just in the modeling but in the human processes it grounds for addressing a problem. Modern environmental literature contains a large number of technical approaches to estimating and managing uncertainty, but relatively few insights into how these estimates are used to solve practical problems, and about how the needs of decision making processes can be used to drive the tradeoffs that are inherent in any technical exercise in uncertainty analysis or accounting. Toward this objective, this journal solicits, for example, papers related to the following:

  • Frameworks, methodologies, and/or case studies that help us discover evidence for workable solutions to socio-environmental challenges, recognizing that these will emerge from social, health and other science perspectives.
  • Methods that help us understand which uncertainties are important and to track uncertainties that arise from things like problem framing, modelling, and model evaluation through to the end user; especially methods that simultaneously and separately consider identifying and managing uncertainty from many sources and facets of the problem.
  • Analysis of existing decision-support models - such as integrated assessment models, domain models in water and air quality - to assess weaknesses and guide improvements; establish gains from adding increased complexity to established models.
  • Insights into how users and decision makers can be engaged to influence technical efforts
  • Quantitative and qualitative methodologies and ontologies for all parts of the modeling process from problem framing and conceptual modeling through to evaluation of options and communicating tradeoffs among potential solutions that allow for bi/tri-directional feedback between scientists, decision-makers and the wider public. These may include decision-support systems, model coupling and integration techniques, web interfaces, big data analysis, optimization, stakeholder engagement methods, visioning techniques, the use of narrative, and visualization techniques.