Professor Patricia Longstaff
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This is a presentation by Professor Patricia Longstaff at the RUSI Resilience Conference 2014.
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Professor Patricia Longstaff
- 1. International cooperation for RUSI P.H. Longstaff 2014 Resilience P. H. Longstaff Syracuse University
- 2. Disaster Cooperation requires trust • Necessary (but not necessarily sufficient) Robert Aexelrod • Long-term relationship • Frequent contact with opportunity to cheat/cooperate • Recognize other’s agents and interests • Institutional memory to keep track of cheating (e.g., corruption) RUSI P.H. Longstaff 2014 and cooperating • Maybe industry or government or NGO • Between • Donor and Donor • Donor and Recipient
- 3. We do not need to all agree • We do need to expect that there will be surprises in complex, unpredictable environments • “Disaster” (without a star) RUSI P.H. Longstaff 2014 • We need to know the other’s interests - what will guide them • Regime A (SOP) • Regime B (disaster plans) • Regime C (everybody for themselves or a guiding star)
- 4. Interests/Goals • Resistance (The Citadel) • In battle, surprise or superior force reduces ability to resist • Tendency to fail catastrophically RUSI P.H. Longstaff 2014 • Trust high until failure • Resilience (Surviving to operate another day) • Static: Bouncing back – return to “normal” • Adaptive: Bouncing forward • Trust built and reinforced often
- 5. Some current research efforts for resilience • NSF: Microscope v. Kaleidoscope • Defining, Measuring and Predicting • Trusted sources of information • Citizens as assets • Citizen Sensors then and now RUSI P.H. Longstaff 2014 • Citizens as responders/organizers: Panic or partner • Adaptive regulation/control • For crisis • Empowering Improvisation: human and technical • For creeping disasters: Set up sensors that indicate when • adaptive mechanisms are failing (e.g. Challenges cascade) • tipping points are near • buffers/reserves are near exhaustion
- 6. Breaking resilience: Introduce surprises that are • Too novel • Black Swans and Unknown-Unknowns • Too fast • No connection to fast scale • Slow to adapt (tight coupling, llooww lleeaarrnniinngg)) • Too many • Not enough diversity or enough weak ties to resources to deal with MULTIPLE TYPES • A few surprises (experience) is good but too many is bad