Transcript Slide 1

Analysing Evacuation Decisions using
Multi-Attribute Utility Analysis
(MAUT)
Paul Kailiponi
CRISIS Centre
Aston Business School
Aston University
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
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
ERGO (cont.)
80 interviews, approximately 150 documents, other media data
The Evacuation Problem
• Evacuation Decision - When do we start evacuating an
area?1. How long does it take to evacuate?
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Oak Ridge Evacuation Modeling System (OREMS)
Configurable Emergency Management & Planning System
(CEMPS) (Pidd et al., 1996)
Examples from ERGO Countries
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Spain, Japan, Iceland
2. When is the risk of a hazard high enough to call for an
evacuation?
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Hurricane Evacuation Decisions (Regnier, 2008)
Decision Analysis
Decision Analysis
• Multi-Attribute Utility Theory (MAUT)
• Evacuation Decision-making Characteristics
– Multiple, Conflicting Objectives
– Uncertain Outcomes
• Decision Model Creation
– Objective Function
– Probability Function
Objective Function Assessment
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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)
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Utility assessment for each
objective
Weights created for the
importance of each objective
Identification of Objective Tradeoffs
Multi-Attribute Utility Function
created from preliminary utility
assessments
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 datagathering 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
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
Example Influence Diagram
Results – 12 hour forecast
Results – 9 hour forecast
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
Substantive Benefits of MAUT Process
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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
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 decisionmakers
• Application to Evacuation Decisions