// Helpers
ss(arr) = SampleSet.fromList(arr)

// Nuclear ukraine
rusiaUsesNuclearWeaponsInUkraine = ss([0.27, 0.04, 0.02, 0.001, 0.09, 0.08, 0.07])// <- fill-in
// Note that the period of time is left unspecified

// Nuclear NATO
escalationOutsideUkraineGivenUkraineWasNuked = ss([0.15, 0.09, 0.0013, 10^(-5), 0.01, 0.3, 0.05])// <- fill-in
escalationToNATOUnconditional = rusiaUsesNuclearWeaponsInUkraine *
  escalationOutsideUkraineGivenUkraineWasNuked

// Nuclear NATO to nuclear London/Washington
bigUKUSCityNukedGivenEscalationOutsideUkraine = ss([0.4, 0.15, 0.9985, 0.05, 0.02, 0.002, 0.5])// <- fill-in
bigUKUSCityUnconditional = escalationToNATOUnconditional * 
  bigUKUSCityNukedGivenEscalationOutsideUkraine

// Impact in lost hours
remainlingLifeExpectancyInYears = 40 to 60 // <- change
daysInYear= 365
productiveHoursInDay = 6 to 18 // <- change
ableToEscapeBefore = 0.5// <- fill-in
proportionOfPeopleInLondonWhoDie = 0.7
expectedLostHours = bigUKUSCityUnconditional *
  (1 - ableToEscapeBefore) * 
  proportionOfPeopleInLondonWhoDie * 
  remainlingLifeExpectancyInYears *
  daysInYear * 
  productiveHoursInDay
  
// Probably good to also estimate idiosyncratic factors such as 
// - Increased or decreased productivity in a city
// - Increased or decreased impact in a city
// - Value assigned to surviving in a world after a nuclear winter
// - ...


// Display
{
    rusiaUsesNuclearWeaponsInUkraine: rusiaUsesNuclearWeaponsInUkraine,
    escalationToNATOUnconditional: escalationToNATOUnconditional,
    bigUKUSCityUnconditional: bigUKUSCityUnconditional,
    expectedLostHours: expectedLostHours
    
}