social science integration. hurricane forecasts as an element of generalized risk management system...
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Social Science Integration
Hurricane Forecasts as an Element of Generalized Risk Management System
Public
Emergency Planners
Forecast Products
Public Risk Perception
Measurement,Modeling and Forecasts Capabilities
Long-range
1-5 days
Short-term, high-resolution
Transmission by Media
Preparedness Decisions
Encoded by Emergency Managers
EM Decisions
ψ(M)
The Social Science Research Mission
• To understand how residents, businesses, and emergency managers comprehend and utilize hurricane forecast information when making preparedness decisions, and how preparedness can be enhanced through a blend of next-generation forecast products, improved communication, and education.
Specific Near-Term Research Goals
1. Advance current knowledge of the drivers of short-term hurricane risk perception and preparedness decisions by community stakeholders
2.Assess the economic and psychological value of alternative improved forecast capabilities; and
3. Use (1) and (2) as the basis of architectures for improved communication, education, and support systems for hurricane emergency management
Risk Perception and Preparedness as a Context-Dependent Dynamic System
Forecast (t)
Web TV Radio
WOM
Expected Hazard Impact (t) (Wind, Water, timing, etc,)
Background Context (e.g., location, past experience)
Preparedness Actions (t)
Observed-Expected Impact (t)
Time=t+1
Expected actions of other decision makers
Specific Research Questions
• Comprehension. In the future residents may be exposed to a wide array of forecast products distributed through a diversity of media channels, some potentially conflicting.– What perceptual biases arise when individuals are
presented with complex probabilistic information about storm threats?
– What is the relative importance of different media in disseminating information about storms, and how do residents integrate information across multiple media channels?
Specific Research Questions (continued)
• Perception. Upon receiving a forecast communication individuals must translate this to a belief about likely personal risk. – What drives individual differences in beliefs about the
form of hurricane effects (i.e., how accurate are mental models of impacts)?
– How are beliefs about risk influenced by the expected timing of effects (i.e., the role of inter-temporal discounting)?
– How are risk perceptions influenced by situational and personal factors such as community location and past experience?
Specific Research Questions (continued)
• Utilization. Given forecasts and personal expectations of impact, residents must then translate this to decisions about personal action.– How is forecast knowledge used to decide when
and how to prepare?– What role do social networks and imitation play in
preparedness (including evacuation) decisions?
Specific Research Questions (continued)
• Learning. Because forecasts are inherently probabilistic, experienced storm effects will rarely match expected or forewarned effects.– How do false positive and negative warnings
affect subsequent preparedness decisions?
Methods
• These issues will be explored via a blend of methodologies including– Laboratory experiments and simulations (using
information acceleration tools)– Field (and web-based) surveys conducted on
cross-sectional samples and longitudinal panels – In-depth interviews
Information Acceleration
• A web-based research tool that allows participants to “virtually” experience the approach of a hurricane, during which they can– Gather information from various realistic sources
(e.g., television broadcasts, word of mouth)– Make simulated preparedness decisions
• Provides a laboratory for testing hypotheses about how different information formats and sequences affects comprehension and preparedness
Field Surveys and Panels
• Laboratory work will be augmented with more conventional field surveys and interviews to provide a means of cross-validation and extension to under-represented groups
• A possibility: a “cell phone panel” of coastal residents that can be used to measure how risk perceptions vary in real time as a storm approaches a coastal area
Valuation
• Goals: – To assess the subjective value different
stakeholders place on different improved forecasting abilities (e.g., longer horizons versus higher resolution)
– To provide a quantitative basis for assessing the objective cost of forecast errors in terms of false positive and false negative alarms
Method
• Stated Choice Experiments (NCAR, Lazo)– Attribute valuation is assessed by asking participants
to make choices among pairs of potential forecast capabilities, each having an associated cost
– Choices are analyzed by a random utility model that allows the recovery of an implicit willingness-to-pay for various forecast improvements
– Results can be cross-validated with insights about forecast-attribute utilization yielded by information accelerators and field surveys
Education and Decision Support
– Decision Support/Communication:• As an outgrowth of EM interviews, suggested
Architectures– e.g., Modules for storm-specific scenario planning
• Protocols for the timing and content of hurricane warnings• Methods for coordination information flows across
media– Education• Training in both hurricane preparedness and human
decision biases.
Other Possible SS Partners
– The Penn-Wharton Risk and Decision Processes Center
– The Florida Catastrophic Storm Risk Management Center (Jay Baker in risk perception)
– Centre for the Study of Choice at University of Technology, Sydney
– International Hurricane Center– Texas A&M
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