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Problem-Focused Learning

Decision support tools are often tacked onto the back end of models as an afterthought. There is an inadequate bridge built between scientists, modelers, policy-makers, and other users. Cross-disciplinary language and procedural barriers are inadequately addressed. What is needed is a procedure to evolve a decision support framework so that it can grow as it integrates a range of models, enabling cross-disciplinary teams to build it as they use it to make decisions requiring cross-disciplinary participation. The traditional sequence of
  1. science modeling and measurement/ data assimilation;
  2. backend decision support tools;
  3. systems, and assessments; and finally,
  4. management decisions and policy directives
neglects the need to enlist downstream users in collaborative problem-solving teams. The users then decide what decision support frameworks they need, so that they share ownership and commitment to use those systems. User Learning occurs concurrently with Tapping User Expertise. The process of integrating decision support capability is then tightly coupled with the process of working with domain experts, who simultaneously learn to use the tools as they provide feedback to grow and evolve decision support capability to address their particular needs. Nor can decision support occur after the fact of data collection, since users determine what data should be collected.

Each team combines learning with problem-solving. Problem-solving processes align with the five key components of the NASA Earth Science Research Strategy, and its hierarchy of scientific questions:

Variability of key systems: water cycles, ocean, ecosystems, atmosphere, ice cover, earth motions
Interventions: changes in land use, atmospheric constituents, Earth’s surface
Responses: Earth system response to human-induced changes
Consequences: Consequences of Earth system changes for human civilization
Prediction: Prediction and meta-prediction (estimates of our capacity to anticipate future changes)

Overlaid on these categories are procedural meta-tags, e.g. geospatial, time, user identity, and tags to one or more project teams using the data.


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