1. Why We Under Prepare for Hazards Robert J. Meyer The Wharton School University of Pennsylvania

2. An Eternal Problem: Minimizing the Societal Impact of Natural Disasters  A modern dilemma: advanced scientific knowledge of the processes that generate natural disasters and means to protect against them has done little to reduce their damaging impact.  2004 Tsunami (est. 224,000 dead); 2005 Hurricane Katrina (100bn loss, 1300 dead): 2005 Earthquake (Pakistan): 79,000 killed; 1970 Cyclone, Bay of Bengal: 300,000 killed; 1995 Kobe Earthquake (Japan): 6,000 killed, 80bn loss.

3. Why were these tragedies so bad?  In almost all cases post-event analyses suggest that the events need not have been as a damaging as they were  Decision makers knew they were living in risk-prone areas, knew what steps to take to mitigate losses, and, often, could afford to undertake them.

4. Example: New Orleans’ Close Call with Hurricane Ivan, 2004

5. Example  September 13, 2004: Category-5 Hurricane Ivan is near the West Coast of Cuba heading NW into the Gulf, and 3 of 6 computer models predict a direct hit on New Orleans in 3 days  Likely consequence: catastrophe

7. 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. September 14: Mayor Orders General Evacuation, but discovers major flaws in evacuation system

8. Good News  Ivan spares New Orleans (coastal Alabamians and Floridians not real happy, though).  New Orleans breathes sigh of relief

9. Quiz  If you were Ray Nagin, what should you have learned from this close call? –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 is safe for another 40 years –c) The city is inherently lucky –d) What close call?

10. One year later…

11. Two Months Later: Wilma  October 2005: Wilma becomes strongest hurricane ever recorded in Atlantic basin, threatens South Florida  South Floridians ordered to stock up (for the 4 th time that year)  Q: What did residents learn from their own earlier bout with Katrina and other storms?

12. Apparently, very little

14. So why?  Ultimately, decisions to undertake mitigation are made by individuals for whom the best course of personal action is highly uncertain –While one may be aware of aggregate risk, how this translates to individual circumstances is often ambiguous; –There is inherent uncertainty about the cost- effectiveness of mitigation investments, which compete with other expenditures –The processes that allow us to make good decisions in most walks of life fail when applied to low-probability, high-consequence events

15. The bottom line: why we under- prepare  We have limited abilities to recall the past, have limited abilities to foresee the future, and make mitigation decisions by imitating the behavior of neighbors who are equally myopic

16. Biases in learning from the past  For most human endeavors, learning by trial-and-error is an efficient way to develop survival skills  The problem: when T&E processes are applied to learning about mitigation in low- probability, high-consequence, settings, it will lead us to the wrong behaviors more often than the right ones.

17. The reasons  One rarely sees positive benefits of investments in mitigation (most experiences are false alarms);  When hazards are encountered, the implications they hold for optimal mitigation will tend to be ambiguous

18. Two major consequences  Rapid extinguishing of normative mitigation behaviors; and  The prolonged persistence of superstitious beliefs about mitigation

19. Example: Rapid forgetting and the Rebuilding of Pass Christian, MS after Hurricane Camille

20. Richelieu Apartments, Pass Christian, Mississippi, August 1969

21. Same Location after Hurricane Katrina (former Pass Christian Shopping Center

22. Example: the flip side of recency: learning too much from recent disasters

23. September 2005: Houston Braces for Hurricane Rita

24. FEMA, State vow not to allow this to be another Katrina  Action: 1.5 million Texans in Galveston/Houston ordered to evacuate via staged plan

25. Slight problem  2.8 million, not 1.5 million, try to leave.  Takes up to 13 hours to drive 45 miles  Problem exacerbated by broken down cars, need to send relief supplies to people in cars  More die during evacuation than storm

26. How observing past outcomes can be misleading

27. The hurricane-proof “Dome Home” Pensacola Beach, FL 2003

28. The Dome Home after Ivan, September 2004

30. The Persistence of Mitigation Myths

31.  A tornado is approaching your house. The best way to prevent the house from suffering damage is: –Close all the doors and windows to create a tight seal; –Open a few windows to relieve pressure when the funnel passes near or over; –Neither of these actions will have any effect on reducing damage

32.  Opinions (95 Pennsylvanians): –Close all the doors and windows (15%) –Open a few windows (55%); –Neither of these actions will have any effect on reducing damage (30%)

33. Hurricanes in the Lab

34. 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 could gather information about hurricanes, mitigation, and make mitigation purchases by clicking control buttons in the simulation

37. The Explanation  In the absence of an unambiguous correct course of action, mitigation decisions were driven by short-run negative feedback  There was no evidence of learning either from observing the misfortunes of others or close-call encounters—the damage had to be real  In time lag effects vanished, but investments remained well below optimum.

38. Biases in seeing into the future  As bad as we are at learning from the past, we seem to be worse at accurately anticipating the future consequences of current behaviors

39. Key biases  Projection bias: we have a hard time envisioning future hedonic states that are different from the one we are in;  Optimism Bias: we are prone to imagine the are prone to the best rather than worst-case scenarios, causing errors in protective planning

40. Examples: New Orleans post 2004 Hurricane planning, failure to evacuate in the face of Hurricane Katrina

41. Optimistic Planning and the 1935 Labor Day Hurricane

42. September 2,1935 (Labor Day)  675 WWI vets are in make-shift camps in the Fla Keys, working to build a highway to Key West  7 AM: Weather Bureau warns there is a CHANCE that a hurricane MIGHT affect the area that night or early Tuesday—but it looks to be heading to Cuba

43. The decision  The only way to evacuate the Vets is by a train from Miami  No train had been scheduled because of the holiday; a special one would have to be ordered.  the FERA supervisor in Jacksonville must decide whether and when to order an evacuation

44. The Decision  The calculation: it usually takes 2.5 hours to ready a train and reach the camps  Hence, no need for an immediate evacuation; if the threat looks real come noon/early afternoon, send the train (better be safe than sorry).

45. What happened  1:30 PM: Weather service revises forecast…gales to begin soon, hurricane conditions late that night  2PM: Evacuation Train ordered  Problem: Cars are in Miami, Engine in Homestead  Engine is Pointed in the Wrong Direction  Train does not leave Homestead until 5PM

46. 5 PM

47. 7PM

48. 8PM; no further progress

49. 10 PM; Landfall Long Key; 200+ mph 26.35”

50. Morning: 452 Dead; 279 VFW Camp Workers

51. Biases in leaning from Others  Given the tremendous uncertainty that surrounds mitigation decisions, many homeowners tend to make decisions by imitating the decisions of others or following social norms  The problem, of course, is that such a heuristic works only if the norms are rational

52. Example: the Wharton Earthquake Simulations

53. Procedure  Participants played a series of real-time games in which they lived with other players in a hypothetical country prone to earthquakes.  They could make investments in permanent improvements that reduced damage from quakes  They were paid based on the initial value of their home plus earnings minus earthquake damage and mitigation investments

54. The Screen Layout

55. The Manipulations  For half of all communities mitigation was ineffective (optimal investment=0), for half it was highly effective (optimal=100)  Ss played 3 blocks of 10-minute games  After 1 warm up game, 1 player in each community was secretly informed of the true effectiveness. Other players knew that the community had an informed player, but his/her identity was not revealed

58. Informed player is told that mitigation is highly effective

61. So why don’t we prepare?  As human decision makers we have evolved to be quite skilled at learning quickly from frequent, unambiguous, feedback, and planning for the short term  Problem: effective mitigation decisions requires skills that are just the opposite to that; for example, a willingness to persistently invest in costly actions that do not have an observable positive payoff

62. Solutions: the obvious  Legislation: policies need to be put into place that protect policy makers and residents from themselves; e.g. through building codes, long-term commitments to funding, required hazard-response plans  Education: residents need to be taught not just about hazard risks, but also trained to be better long-term decision makers

63. Solutions, the less obvious  Problem: forming effective legislation and education programs requires us to know much more than we currently do about human decision making in low-probability, high-consequence settings. While we know much about the physical science of hazards, we know much less about the associated psychological science. Bridging this gap should be a major goal of research funding in the natural hazards area in the years to come.