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					Catastrophe, Social Collapse,
   and Human Extinction

              Robin Hanson
   Assoc. Professor, Economics, G.M.U.
    Research Associate, F.H.I., Oxford
~0.45 wars/year with
 >1000 deaths
                       War Death Stats

                           1820-1997
                           L. Cederman (2003)
                           “Modeling the Size of Wars”
                           Am. Pol. Sci. Rev.
                                                                              α = 0.5
                                                          Severity
                                                                       105
Disaster Power Laws                                                    104

                                                                       103

α < 1 : most harm in biggest                                           102
                                                                                                         α=2
Cite       Phenomena      Outcome         α                            101
                                                                             101 102 103      104 105    α=1
Watts      measles        sick          0.13
                                                                                                 Frequency
Watts      pertussis      sick          0.16
                                               α ≥ 1 : most harm in smallest
Rhodes     pertussis      sick          0.26
Rhodes     measles        sick          0.27   Cite       Phenomena                Outcome        α
Turcotte   forest fires   area          0.40   Turcotte   earthquakes              area                 1.00
Turcotte   game of life   dead          0.40   Sanders    Pacific hurricanes       energy               1.04

Cederman   war            dead          0.41   Turcotte   marine families          extinctions          1.20

Barton     earthquakes    dead          0.41   Barton     floods                   dead                 1.35
                                               Barton     tornados                 dead                 1.39
Barton     earthquakes    dollar loss   0.41
                                               Clauset    terror attack            injuries             1.39
Rhodes     mumps          sick          0.45
                                               Turcotte   landslides               area                 2.00
Barton     hurricanes     dead          0.58
                                               Sanders    Atlantic hurricanes      energy               2.30
Sanders    forest fires   energy        0.66
                                               Sanders    tornados                 energy               2.72
Barton     hurricanes     dollar loss   0.98
                                               Sanders    windstorms               energy           11.87
   Black Swan Disasters
• α < 1, e.g., epidemic, war, quake
• Most expected harm from most-die events
  – If worry about any events, worry about biggest
• Harm history underrates! (median < mean)
  Small vs. Big Disaster Trades
• War: Nukes deter small, make big worse
• Tower Fire: Wait in small, leave in big
• Forrest Fire: Prevent small, causes big
• Quake: Under desk in small, by files in big
• Infection: Antibiotics stop small, risks large
• Official disaster advice, plans neglect big!
Population

       Current Total Population
 109                                 ??                D  max( S , T )
                                                              Epidemic
          Naive                                      WWI      (infected projected)
                                                                100yr


 106
        Projection                                           Dead
                                  Alive
                                                  α = 0.41
                                                                             Quake
                                                                               500 yr
 103
                 But then how                                War
                 have we survived                            178yr

                 for 10,000 years?


  1
                    10 -6                 10 -3                      1       Events/Year
             Fermi’s Question
Out of a billion trillion planets, ….




 Why do we see no one transforming it all?
                 The Great Filter
1020      Life      Multi-cellular   Civilization
                                                        Colonize   0
Planets                                                 Universe   Now




                                                    ?
Population

       Current Total Population
 109
                                                   D  max( S , T )

          Naive                                   WWI


 106
        Projection                                      Dead
                                  Alive

 103




  1
                    10 -6                 10 -3           1   Events/Year
Population

       Current Total Population
                                                           1    1        1
 109
                                                      D T S
        Smooth
 106
       Projection


 103
                                      ~70 people colonized
                                      Polynesia & New World
       Humanity Survives Threshold?   (but they were together)




  1
                    10 -6              10 -3                     1    Events/Year
          Famous Collapses
•   Nafufians of S.W. Asia, 6400BC
•   Sumeria, 3000BC
•   Mesopotamia, Egypt, Greece 2200BC
•   Roman Empire, 400AD
•   Moche in Peru, 700AD
•   Mayan, 900AD
•   Tiwanaku in Andes, 1100AD
•   Anasazi in Pueblos, 1300AD
     The Wisdom of Stairs

“The reason to be careful when you walk up stairs
is not that you might slip and have to retrace one
step, but that the first slip might cause a second,
and so on until you break your neck.”

“A judge who would not normally take a bribe may
when his life is at stake, letting others hope to get
away with theft, leading still others to avoid making
investments that might be stolen.”
       Social Collapse
= Disproportionate social impact
• Intricate coordination disrupted
  – Capital mismatch, less transport & travel
  – Intricate division of labor => disaster risk
• Lose trust need for coordination
  – High stakes tempts betrayal
  – Inequality up, so more envy
  – Authorities hide big problems
Population

       Current Total Population
                                                     1       1          1
 109
                                                    D T S
                                                     Post-Collapse
        Collapse                                        Deaths


 106
           Direct                          Direct
          Left Alive                       Deaths



                                  Post-Collapse
 103                                Left Alive


       Humanity Survives Threshold



  1
                    10 -6                  10 -3              1      Events/Year
   Ways To Prevent Extinction
• For disasters of all sizes, uniformly reduce
  – Raw effect, or
  – Social collapse
• Focusing on the largest disasters, reduce
  – Raw effect, or
  – Social collapse
     • Correlate location of some survivors
     • Correlate them with reserve resources   Refuge!
     • Isolate them from unstable others
                   Refuges
• Deep mines? Secret location?
• Assume only this one survives
  – Enough resources for long wait
    • Recycle air? Nuke power? Grow own food?
  – Enough people for viable population genetics
    • Long-lasting artificial insemination cheaper?
  – Revert to hunting, simplest farming
    • Accept it: industry gone for millennia!
    • Use real subsistence hunter-gatherers, farmers
          Buy Low, Sell High
                Will price
                rise or fall?
 “Pays $1 if
Obama wins”        sell
                                E[ price change | ?? ]
                buy
  price
                sell              Lots of ?? get tried,
                   buy             price includes all!
  Today’s Current Event Prices
   67% Obama next US president
 9-27% Bird Flu confirmed in US by 2009
  7-9% 9.0 Richter Earthquake by 2009
32-40% US offshore oil ban gone by 2009
40-60% Bank United Financial to Fail by 2009
17-20% Bin Laden caught by 4/2009
38-39% US or Israel air strike on Iran by 4/2009
20-59% US agree to 2025 10% CO2 cut by 2011
20-30% Any nation drop Euro by 2011
21-28% China war act on Taiwan by 2011
60-85% Higgs Boson seen by 2011
       Beats Alternatives
• Vs. Public Opinion
   – I.E.M. beat presidential election polls 451/596 (Berg et al ‘01)
   – Re NFL, beat ave., rank 7 vs. 39 of 1947 (Pennock et al ’04)
• Vs. Public Experts
   – Racetrack odds beat weighed track experts (Figlewski ‘79)
        • If anything, track odds weigh experts too much!
   –   OJ futures improve weather forecast (Roll ‘84)
   –   Stocks beat Challenger panel (Maloney & Mulherin ‘03)
   –   Gas demand markets beat experts (Spencer ‘04)
   –   Econ stat markets beat experts 2/3 (Wolfers & Zitzewitz ‘04)
• Vs. Private Experts
   – HP market beat official forecast 6/8 (Plott ‘00)
   – Eli Lily markets beat official 6/9 (Servan-Schreiber ’05)
   – Microsoft project markets beat managers (Proebsting ’05)
   – XPree beat corp error, 3.5 vs 6.6%
          Near Miss Markets
• “Pays $1 if >D dead by year Y via event T”
  – T = War, plague, quake, asteroid, terror, …
  – Better: bet given policies might avert
  – Suffers “death bet” taboo
• Non-death event size indicators
  – E.g., # infected in plague, # troops in war
• Earlier milestones
  – E.g., # nations w/ nukes, # gene machines
               Refuge Markets?
• How bet on disaster if unlikely paid? Solution:
   –   Refuge holds experts, amateurs
   –   Auction amateur slot tickets to qualified public
   –   Refuge locked down for X day periods
   –   Ticket owner locked Y days before (so can get there)
   –   Before that, open trading in tickets & derivatives
• Derivatives reveal info!
   – “Ticket to enter on date T” shows time profile of risk
   – “Ticket if S” and “Pays $1 if S” shows chance & risk of
     S = situation S applies on date T-Y-J

				
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posted:7/19/2012
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