Analysing Evacuation Decisions using Multi-Attribute Utility Analysis (MAUT)

1. Analysing Evacuation Decisions using Multi-Attribute Utility Analysis (MAUT) Paul Kailiponi CRISIS Centre Aston Business School Aston University

2. Outline • ERGO Project • The Evacuation Problem • Decision Components • Objective Function • Probability Function • Illustrative Example (identifying risk thresholds) • Substantive Uses of Decision Model • Future Improvements to Model

3. Evacuation Responsiveness by Government Organizations (ERGO) • European Commission Project (JLS/2007/CIPS/025) • Project Goals • Models for public preparation • Analytical Models • Substantive (real) aids for Evacuation • Explicit Practitioner Participation

4. ERGO (cont.) 80 interviews, approximately 150 documents, other media data

5. The Evacuation Problem • Evacuation Decision - When do we start evacuating an area?- • How long does it take to evacuate? • Oak Ridge Evacuation Modeling System (OREMS) • Configurable Emergency Management & Planning System (CEMPS) (Pidd et al., 1996) • Examples from ERGO Countries • Spain, Japan, Iceland • When is the risk of a hazard high enough to call for an evacuation? • Hurricane Evacuation Decisions (Regnier, 2008) • Decision Analysis

6. Decision Analysis • Multi-Attribute Utility Theory (MAUT) • Evacuation Decision-making Characteristics • Multiple, Conflicting Objectives • Uncertain Outcomes • Decision Model Creation • Objective Function • Probability Function

7. Objective Function Assessment • What do emergency managers care about when faced with potentially catastrophic disasters? • Elicitation Process • Broad range of stakeholder participants • Maximize confidence that all values are identified (Bond, 2007) • Utility assessment for each objective • Weights created for the importance of each objective • Identification of Objective Trade-offs • Multi-Attribute Utility Function created from preliminary utility assessments

8. Probability Assessment • Hazard Profile • Region and hazard specific • Casualty rates due to hazard • Evacuation Behaviour • Official orders/information (Burnside, 2007) • Visual Clues (Perry, 1983) • Probability Function for Example Model • Storm surge probability taken from Hamburg during ERGO data-gathering visits • Forecasts at 12 & 9 hours normally distributed with S.D. Of 50cm and 30cm respectively • Public Reaction to Evacuation Orders drawn from limited assessments

9. Illustrative Evacuation Decision Model • Identify Risk Thresholds • Four Evacuation Strategies • No Action, Advisory, Mild Evacuation Order, Urgent Evacuation Order • Strategy chosen affects the percentage of the public that evacuates • Strategy chosen affects the economic/organizational costs • Casualty rates affect the percentage of public that DO NOT evacuate & lead to life costs • Optimal Decisions at 12 & 9 hour forecasts • Flood defences • Dykes at 8 metres

10. Example Influence Diagram

11. Results – 12 hour forecast

12. Results – 9 hour forecast

13. Sensitivity Analysis • Parameters where slight variation in values leads to changes in the optimal decision • Key parameters in Example Evacuation Decision Model • Objective weight (life costs) • Non-evacuee casualty rates • Represent areas in which the respective assessments must be verified

14. Substantive Benefits of MAUT Process • Explicit identification of objectives • Value-focused creation of strategies • Scenario building • Quantitative assessment of trade-offs between objectives • Identification of risk thresholds • Evaluation of evacuation mitigation policies • A model based on expert participation throughout the process

15. Conclusions • MAUT process is appropriate for any decision with multiple conflicting objectives and uncertainty • Nuclear Disaster (French, 1996) • Anti-Terrorist Analysis (Keeney, 2007) • Fire Service Analysis (Swersey, 1982) • Dependent on participation by decision-makers • Application to Evacuation Decisions