TY - JOUR
T1 - Exploring Deep Uncertainty Approaches for Application in Life Cycle Engineering
AU - Tegeltija, Miroslava
AU - Oehmen, Josef
AU - Kozin, Igor
AU - Kwakkel, Jan
PY - 2018
Y1 - 2018
N2 - Uncertainty assessment and management, as well as the associated decision making are increasingly important in a variety of scientific fields. While uncertainty analysis has a long tradition, meeting sustainable development goals through long-term Life Cycle Engineering (LCE) decision making demands addressing Deep Uncertainty (DU). DU characterizes situations where there is no agreement on exact causal structures, let alone probabilities. In this case traditional, probability based approaches cannot produce reliable results, as there is a lack of information and experts are unlikely to agree upon probabilities. Due to the nature of LCE, this paper argues that methods to better cope with DU can make a significant contribution to the management of LCE. We introduce a set of methods that use computational experiments to analyze DU and have been successfully applied in other fields. We describe Robust Decision Making (RDM) as the most promising approach for addressing DU challenges in LCE. We then illustrate the difference between applying traditional risk management approaches and RDM through an example, complemented with the interview findings from a company using RDM. We conclude with a discussion on future research directions.
AB - Uncertainty assessment and management, as well as the associated decision making are increasingly important in a variety of scientific fields. While uncertainty analysis has a long tradition, meeting sustainable development goals through long-term Life Cycle Engineering (LCE) decision making demands addressing Deep Uncertainty (DU). DU characterizes situations where there is no agreement on exact causal structures, let alone probabilities. In this case traditional, probability based approaches cannot produce reliable results, as there is a lack of information and experts are unlikely to agree upon probabilities. Due to the nature of LCE, this paper argues that methods to better cope with DU can make a significant contribution to the management of LCE. We introduce a set of methods that use computational experiments to analyze DU and have been successfully applied in other fields. We describe Robust Decision Making (RDM) as the most promising approach for addressing DU challenges in LCE. We then illustrate the difference between applying traditional risk management approaches and RDM through an example, complemented with the interview findings from a company using RDM. We conclude with a discussion on future research directions.
KW - deep uncertainty
KW - life cycle engineering
KW - long-term planning
KW - risk
KW - sustainability
UR - http://www.scopus.com/inward/record.url?scp=85047064833&partnerID=8YFLogxK
U2 - 10.1016/j.procir.2017.12.006
DO - 10.1016/j.procir.2017.12.006
M3 - Conference article
AN - SCOPUS:85047064833
SN - 2212-8271
VL - 69
SP - 457
EP - 462
JO - Procedia CIRP
JF - Procedia CIRP
ER -