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. ORCID: https://orcid.org/0000-0001-5283-8049, & Rovenskaya, E. ORCID: https://orcid.org/0000-0002-2761-3443 (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

Preview	Slideshow policy_impacts_1.pdf - Presentation Download (1MB) | Preview
Preview	Text 4thWorkshopProceedings.pdf - Published Version Download (14MB) | Preview

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.

Item Type:	Book Section
Uncontrolled Keywords:	4th International Workshop on Uncertainty in Atmospheric Emissions
Research Programs:	Advanced Systems Analysis (ASA)
Depositing User:	IIASA Import
Date Deposited:	15 Jan 2016 08:54
Last Modified:	27 Aug 2021 17:25
URI:	https://pure.iiasa.ac.at/11662

Actions (login required)

View Item