Lab Simulation Studies of Response to Natural Hazards Robert Meyer Wharton Center for Risk and Decision Processes

September, 2004: Category-4 Hurricane Ivan threatens New Orleans

Mayor Nagin said he would "aggressively recommend" people evacuate, but that it would be difficult to order them to, because at least 100,000 in the city rely on public transportation and have no way to leave. Despite the potential need for emergency housing, no shelters had been opened in the city as of Tuesday night. Nagin said the city was working on setting up a shelter of "last resort" and added that the Superdome might be used, but a spokesman for the stadium said earlier Tuesday that it was not equipped as a shelter.

Good News: the storm never hits. Q: What did New Orleans and FEMA conclude from this close call with Ivan? a) That the city was fortunate to have averted a catastrophe, hence immediate steps should be taken to remedy the evacuation problems; b) The city would be safe for another 40 years c) The city is inherently lucky d) Actions should be taken, but since the next hurricane season is months off, what’s the hurry?

Hurricane Katrina, 2005

Then…a month later Houston Braces for Hurricane Rita

This time, a prepared city 1.5 million Texans in Galveston/Houston ordered to evacuate via a careful staged plan developed and refined in the 80’s.

Unexpected problem: 2.8 million, not 1.5 million, try to leave. Result: a human catastrophe worse than the storm

Why do we have such a hard time learning? Problem with real-world analyses: Natural hazards occur with in sufficient frequency to allow longitudinal panel analysis Control that would allow one to tease apart alternative explanations for apparent mitigation errors

Example: Hurricane Wilma October 2006: For the sixth time in 2 years Hurricane warnings were posted in extreme South Florida Public awareness of storms and basic preparations could not be higher (e.g., the need to stock up on gas and water) Q: How prepared were residents?

The answer

The Multitude of Explanations People underestimated the odds that the storm would actually hit People figured the government would bail them out if things got bad Lack of knowledge, lack of funds Recent near-misses taught them that storms are quite survivable without preparations

The Hurricane Simulation Respondents were endowed with a residence of known value, and were paid at the end of the simulation the difference between this endowment and the cost of mitigation and storm repairs. Mitigation measures do not improve the value of the home--they only reduce storm losses. At the start respondents are told their expected length of tenure in the home and its location Respondents can gather information about hurricanes, mitigation, and make mitigation purchases by clicking control buttons in the simulation

The Optimal Policy In almost all cases it’s all or nothing Learning should have been easy; Ss could make decisions up the last second Ss were given full-information about what would have happened had they not invested in protection

The result: like in the real world, subjects under- invested in mitigation, and investments were cyclical

The “Learning” Process In the absence of an unambiguous correct course of action, mitigation decisions were driven by short-run negative feedback. The lack of damage at time t caused a reduction in mitigation in time t+1…even when the cause of reduced damage was investments in mitigation! In time these lag-damage effects vanished and investments reached an equilibrium—but well below optimum.

Other research Community effects—does living with and observing others naturally improve mitigation decisions (answer: no) Third-party decisions—what happens when one is making decisions for others (the FEMA game; answer: even lower mitigation investments) The dynamics of information search in advance (and after) hurricanes Hurricane Katrina case study Hourly web-surfing habits of 65,000 residents of Fla, La, and Miss in August, 2005