46 lines
1.6 KiB
Plaintext
46 lines
1.6 KiB
Plaintext
// Helpers
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ss(arr) = SampleSet.fromList(arr)
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// Nuclear ukraine
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rusiaUsesNuclearWeaponsInUkraine = ss([0.27, 0.04, 0.02, 0.001, 0.09, 0.08, 0.07])// <- fill-in
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// Note that the period of time is left unspecified
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// Nuclear NATO
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escalationOutsideUkraineGivenUkraineWasNuked = ss([0.15, 0.09, 0.0013, 10^(-5), 0.01, 0.3, 0.05])// <- fill-in
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escalationToNATOUnconditional = rusiaUsesNuclearWeaponsInUkraine *
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escalationOutsideUkraineGivenUkraineWasNuked
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// Nuclear NATO to nuclear London/Washington
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bigUKUSCityNukedGivenEscalationOutsideUkraine = ss([0.4, 0.15, 0.9985, 0.05, 0.02, 0.002, 0.5])// <- fill-in
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bigUKUSCityUnconditional = escalationToNATOUnconditional *
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bigUKUSCityNukedGivenEscalationOutsideUkraine
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// Impact in lost hours
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remainlingLifeExpectancyInYears = 40 to 60 // <- change
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daysInYear= 365
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productiveHoursInDay = 6 to 18 // <- change
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ableToEscapeBefore = 0.5// <- fill-in
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proportionOfPeopleInLondonWhoDie = 0.7
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expectedLostHours = bigUKUSCityUnconditional *
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(1 - ableToEscapeBefore) *
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proportionOfPeopleInLondonWhoDie *
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remainlingLifeExpectancyInYears *
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daysInYear *
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productiveHoursInDay
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// Probably good to also estimate idiosyncratic factors such as
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// - Increased or decreased productivity in a city
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// - Increased or decreased impact in a city
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// - Value assigned to surviving in a world after a nuclear winter
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// - ...
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// Display
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{
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rusiaUsesNuclearWeaponsInUkraine: rusiaUsesNuclearWeaponsInUkraine,
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escalationToNATOUnconditional: escalationToNATOUnconditional,
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bigUKUSCityUnconditional: bigUKUSCityUnconditional,
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expectedLostHours: expectedLostHours
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}
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