A metric for the prognostic outreach of scenarios: Learning from the past to establish a standard in applied systems analysis

Jonas M ORCID: https://orcid.org/0000-0003-1269-4145, Żebrowski P, & Rovenskaya E (2015). A metric for the prognostic outreach of scenarios: Learning from the past to establish a standard in applied systems analysis. In: Proceedings, 4th International Workshop on Uncertainty in Atmospheric Emissions, 7-9 October 2015, Krakow, Poland. pp. 78-89 Warsaw, Poland: Systems Research Institute, Polish Academy of Sciences. ISBN 83-894-7557-X

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Abstract

Our study concerns retrospective learning, the characteristic feature of which is that prognostic uncertainty increases the more the further we look into the future. RL seeks to establish a metric for the outreach of prognostic scenarios. The purpose behind RL is to provide an easy-to-apply indicator, which informs non-experts about the time in the future at which a prognostic scenario ceases to be in accordance (for whatever reasons) with the system’s past. Ideally, this indicator should be derived concomitantly with building a prognostic model. RL concerns the limitations of predictions and prognostic scenarios.

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