DOCUMENT TAGS Anticipation
Start Fund Learning Framework: Crisis Anticipation Window
24 April 2018
The Start Fund anticipation window seeks to mitigate harm and loss for communities at risk of crisis. It does so by enabling and incentivising StartNetwork members to monitor risk and act on the basis of forecasts. Throughthe Start Fund anticipation window, Non-Governmental Organisationscan respond to shifts in risk, suchas a forecast of extreme rainfall or likely political crisis. A key element of thisapproach requires collective sense-making, or collaborative risk analysis, aroundthe situation forecasted and its potential humanitarian impact.
This framework addresses three primary needs for learning and evidence generation. These are the use of learning to help measure our performance, develop knowledge and drive adaptation
where appropriate. Meeting these needs will help us to ensure high levels of accountability to our key stakeholders; our members, donors, the communities our members work with. This framework can be used by members and donors seeking to understand how we learn from and evidence anticipation. It can also be used by other forecast-based early action practitioners seeking to develop approaches to measuring early action.
Focusing on performance ensures that our intended results are clearly articulated and enables us to understand whether our activities and those of our members and partners are effective.
Our approach to building knowledge acknowledges that the forecast-based early action is a new practice and there is much to explore and learn. We anticipate unexpected outcomes and have designed our processes to capture these. At the same time, the Start Network is intentionally seeking to contribute to evidence base around forecast-based action, such as the in relation to impact of uncertainty on decision making.
Adaptation enables us to drive continuous improvement in both the management of the Anticipation Window and in forecast-based programming. By measuring our performance in an ongoing fashion and ensuring learning loops are in place, we can adjust and adapt accordingly.