Social Science Integration

1. Social Science Integration

2. Hurricane Forecasts as an Element of Generalized Risk Management System ψ(M) Transmission by Media Measurement, Modeling and Forecasts Capabilities Public Risk Perception Forecast Products Public EM Decisions Long-range Emergency Planners Preparedness Decisions 1-5 days Short-term, high-resolution Encoded by Emergency Managers

3. 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.

4. 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

5. Risk Perception and Preparedness as a Context-Dependent Dynamic System Forecast (t) Time=t+1 Web TV Radio WOM Background Context (e.g., location, past experience) Expected Hazard Impact (t) (Wind, Water, timing, etc,) Observed-Expected Impact (t) Expected actions of other decision makers Preparedness Actions (t)

6. 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?

7. 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?

14. 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?

15. 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?

16. 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

17. 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

21. 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

22. 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

23. 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

24. 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.

25. 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