Nuclear Waste Depository Option Analysis under Uncertainty

1. DASIG Workshop 2016, AMBS Nuclear Waste Depository Option Analysis under Uncertainty Jian-Bo Yang and Dong-Ling Xu Alliance Manchester Business School The University of Manchester, UK Email: jian-bo.yang@mamchester.ac.uk

2. Outline of Presentation • Assessment of Nuclear Waste Repository Options • Overview of Evidential Reasoning (ER) Approach (2 slides) • Modelling of MCDA Problems under Uncertainty (5 slides) • Subjective Opinions • Probability Uncertainty • Interval Uncertainty • Ignorance • Applications (9 Slides) • Concluding Remarks

3. Problem OutlineNuclear Waste Repository Option Analysis under Uncertainty • Two Options: • Deep • Surface • Diversity • Multiple Criteria • Different measurement scale • Different opinions on each options and criteria importance • Uncertainty • Distributed assessment data • Interval uncertainty in attribute weights • A MCDA Problem

4. Brief Overview of the ER Approach (1 of 2) What is Evidential Reasoning (ER) Approach • A utility function approach for Multiple Criteria Decision Analysis (MCDA) • Unique uncertainty modelling capability • Helping participants to gain more insight into each alternative • Supported by the IDS (Intelligent Decision System) Software Demo Version from: www.manchester.ac.uk/personal/staff/jian-bo.yang

5. Brief Overview of the ER Approach(2 of 2) Why the ER Approach is Different • Based on belief decision matrix • Using the evidential reasoning rule for criteria aggregation • Multi-disciplinary research outcome • Outcomes including performance distribution, utility scores and combined effects of uncertainties in data • Decision Theory - Utility Theory • Systems Engineering • Statistical Analysis • Artificial Intelligence • Rule-Based Inference • Computer Technology

6. …… Attribute 1 Attribute 2 Attribute n A1n A12 A11 Alternative 1 A2n A22 A21 Alternative 2 …… Amn Am2 Alternative m Am1 Uncertainty Modelling in the ER Framework (1 of 5)Conventional MCDM Modelling Decision Matrix • Traditional Decision Matrix – Average Point Assessment It uses average numbers to assess each alternative on each criteria

7. Heaton Moor Heaton Moor East Didsbury East Didsbury House House Heaton Mercy Heaton Mercy Altrincham Altrincham Criteria Criteria {(G, 0.5), (E, 0.5)} {(G, 0.5)} {(A, 0.2), (G, 0.8)} {(G, 0.2), (E, 0.8)} Location Distance 7 5 6 5.5 113,000 110,000 118,000 150,000 Price {(VP, 0.05), (G, 0.35), (E, 0.60)} Attractiveness {(A, 0.4), (G, 0.6)} {(G, 0.3), (E, 0.7)} {(G, 0.6), (E, 0.4)} Excellent Good Good Excellent Location Distance 7 5 6 5.5 113,000 110,000 118,000 150,000 Price Excellent Good Excellent Good Attractiveness Uncertainty Modelling in the ER Framework (2 of 5) Decision Matrix and Belief Decision Matrix

8. …… Attribute n Attribute 1 {[A11, α1], …, [K11, β]} {[A1n, η], …, [K1n, θ]} Alternative 1 …… Alternative 2 {[A21, γ], …, [K21, δ]} …… {[A2n, ι ], …, [K2n, κ]} …… …… …… Alternative m {[Am1, ε], … ,[Km1, ζ]} …… {[Amn, λ], … ,[Kmn, μ]} Uncertainty Modelling in the ER Framework (3 of 5) Belief Decision Matrix • Belief Decision Matrix – Belief Distribution Assessment It can represent precise numbers for all criteria on each alternative It can represent subjective judgements It can represent ignorance explicitly

9. Uncertainty Modelling in the ER Framework (4 of 5) Belief Decision Matrix in Applications

10. Uncertainty Modelling in the ER Framework (5 of 5) Uncertainties Modelled by Belief Decision Matrix • Group subjective opinions An repository option on heath risk {(High, 1/16), (Medium, 4/16), (Low, 11/16)} • Random data Potential impact estimation via simulation {(£30m, 25%), (£50m, 50%), (£100m, 25%)} • Data with ignorance (partial or complete) Safety evaluation of different options {(excellent, 30%), (good, 50%), (unknown, 20%)} - unknown 20% - Partial - unknown 100% - Complete • Data with interval uncertainties {(excellent - Good, 50%)}; {(excellent, 20 - 40%)}; {(excellent - Good, 0 - 20%)}

11. Applications of ER Approach (1 of 9)Individual Opinions Modelled by IDS

12. Applications (2 of 9)Example of Group Opinion Aggregation with support fromIntelligent Decision System

13. Applications (3 of 9)Group Opinion Distribution Generated byIntelligent Decision System

14. Applications (4 of 9)Group Opinion Distribution on the Example

15. Evidential Reasoning MCDAModelling structure and graphic interpretation Health risk Use ER to generate overall beliefdegrees 0.006 0.014 0.228 … … Grade 0 Grade 50 Grade 100 Combine evidence 0 0 0.25 0.19 βin βi1 βiN 0 0 Hazard perception Toxicityevaluation Impact of worst case Multiple Criteria Decision Analysis

16. Applications (5 of 9)Aggregated Group Opinions Generated by ER Algorithmwith Support from Intelligent Decision System

17. Applications (6 of 9)Risk Scores Generated from Aggregated Distribution with Support from Intelligent Decision System

18. Applications (7 of 9)Sensitivity Analysis on Uncertainty in Attribute Weights

19. Applications (8 of 9)Sensitivity Analysis on Uncertainty in Attribute Weights Effect of the uncertainty

20. Concluding Remarks • Belief Decision Matrix: A flexible framework for modelling MCDA problems with uncertainty • The ER Algorithm: A rational information aggregation process • Can handle different types of uncertainties in the same framework • Generate combined effects of uncertainty • Help decision makers to gain more insight into decision options