definition? How—if at all—did the ultimate problem definition differ from initial formulation by scientists and decision makers, respectively?


2. Program management


Tentative finding: Successful efforts to develop programs linking knowledge with action generally adopt a project orientation and organization, with dynamic leaders accountable for achieving use-driven goals and targets. They avoid the pitfall of letting study of the problem displace creation of solutions as the program goal.


Question: Was your program developed in such a project mode? Did it have specific, measurable goals and targets? If so, what? To what extent and in what ways was goal and target definition driven by scientists or decision makers, or both? To what extent and in what ways were program leaders held accountable for achieving those goals and targets?


3. Program organization


Tentative finding: Successful programs linking knowledge with action include boundary organizations committed to building bridges between the research community on the one hand, and the user community on the other. These boundary organizations often construct informal and sometimes even partially hidden spaces in which project managers can foster user-producer dialogues, joint product definition, and end-to-end system building free from distorting dominance by groups committed to the status quo. In order to maintain balance, most effective boundary organizations make themselves jointly accountable to both the science and user communities.


Question: Did your program involve a boundary spanning function or organization? If not, how did you organize the dialogue between producers and users? If so, where and how was the boundary organization or function created? What did it do? To what extent was it accountable to both users and producers for achieving its goals?


4. The decision-support system


Tentative finding: Successful programs linking knowledge with action create end-to-end, integrated systems that connect basic scientific predictions or observations to decision-relevant impacts and options. They avoid the pitfall of assuming that a single piece of the chain (e.g., a climate prediction) can be useful on its own, or will be taken care of by someone else.


Question: To what extent is the decision-support system developed by your program an end-to-end system? What are its discrete elements (e.g., a climate forecast, an impact model converting climate forecasts into yield forecasts required by decision makers)? Which were the hardest elements to put in place? Why? What changes in research, decision making, or both have occurred as a result of the system?



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