Models by Luca

survey_2024 = 485
survey_growth = 0.5 to 3
survey_2025 = survey_2024 * survey_growth

peer_2024 = 15
peer_growth = (1 to 1.5) * (2 to 4)
peer_2025 = peer_2024 * peer_growth

botec_2024 = 15
botec_growth = 0.5 to 4

k = 10^3
m = 10^6
i_true = mixture(100*k,1*m,10*m, [0.5,0.4,0.1])
ii_most_common = 100*k to 1*m
iii_naive_lognormal = 100 * k to 10 * m 
iv_trim_lognormal = truncateRight(iii_naive_lognormal,10*m)
v_worst_case = mixture(1,10*m, [0.9,0.1])

b=10^9
likelihood = 1% to 5%
duration = 2
annual_likelihood = 1-(1-likelihood)^(1/duration)
damages = (10*b to 100*b)*1.3
global_multiplier = 1/3.3%
// moderate_multiplier = 2
// severity_multiplier = 2 to 100

// h/t to Ozzie!

@name("Naive Population Probability (Assuming Homogeneity)")
@doc("Assumes all individuals have the same probability of performing action Z")
@startClosed
naivePopulationProbability(individualProbability, populationSize) = 1 -
  (1 - individualProbability) ^ populationSize

@name("Simulated Population Probability (Accounting for Heterogeneity)")
@doc(

million = 10^6
billion = 10^9

a = 250 * million to 25 * billion
b = 200 * million to 7.5 * billion
c = 2 to 4
d = 1 to 4

benefit = (a+b)*c*d

/* Unit conversion assumption */

seconds_per_hour = 60 * 60
hours_per_month = 24*30
months_per_year = 12
seconds_per_year = seconds_per_hour * hours_per_month * months_per_year

/* Start-up cost of set-up */

h100_needed_for_set_up = 200 to 1000

b = 10^9
bps = 10000
ai_bar_bps_per_bn = 0.5 to 50

bio_risk_per_year = 0.001% to 0.1%
// assuming biorisk is 0.1%-10% during the time of perils

yr_speed_up = 2 to 20

account_no = 3000

account_share_HK = 0.02 to 0.1
t_per_a_HK = 5 to 25
t_per_c_HK = 1 to 3
c_per_a_HK = t_per_a_HK / t_per_c_HK
crimes_HK = account_no * account_share_HK * c_per_a_HK
d_per_crime_HK = 260000
d_HK = crimes_HK * d_per_crime_HK

K = 10^3
M = 10^6
B = 10^9

US_mules = 200*K to 2*M
fraction_active = 10% to 90%
t_per_a = 1 to 10
a_per_c = 1 to 5
c_per_t = t_per_a / a_per_c
dollars_per_crime = 10*K to 50*K

K = 1000

revenue_scaler = PointSet.makeContinuous([
  {x: 1, y: 1},
  {x: 10000, y: 10000},
])

FBI_victim_dist = 4.7 to 470
FBI_victim = PointSet.fromDist(FBI_victim_dist)
FBI_revenue = revenue_scaler .* FBI_victim

/*
Describe your code here
*/

B = 10^9

fbi_baseline = 2.5*B
growth = 1 to 1.4
recovered = 0.5 to 0.9
american = 0.6 to 1