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// Cost-effectiveness of saving to buy galaxies — Squiggle model // Cost-effectiveness of saving $1M to buy distant galaxies vs AI safety philanthropy // Wealth as fraction of global wealth post-ASI globalWealth = 5e14 // $500T // A. Probability you can buy galaxies (no AI takeover × galaxies for sale) aPCanBuy = 0.6 * 0.2 // B. Your returns as a multiple of global wealth growth
/* Describe your code here */ max_years_until_impact = 60 // Idea: It feels like AI taking off makes the concept of time less useful as a variable in the scenarios where we get meaningful speedup in research before doom. In that case perhaps it would make sense to model it such as if AI is coming later in "progress years" so to the extend that it is possible to do faster bio research for those periods, it might be sensible to think of it in the same way as it does to think of it as having more time? future_mass_before_agi(x) = 1 - cdf(Sym.lognormal(2.2, 2), x) years_until_super_babies = Sym.lognormal(3, 0.7) super_baby_growup = Sym.normal(18, 2) grown_up_super_babies(x) = cdf(years_until_super_babies + super_baby_growup, x)
p_drafted = 0.05 to 0.30
p_die_given_drafted = 0.05 to 0.30
p_death = p_drafted * p_die_given_drafted
russian_deaths = 1.5e5 to 5e5
russia_population = 1.25e8 to 1.55e8
share_male = 0.5
share_fighting_age = beta(40, 60)
p_death_sempere = russian_deaths / russia_population / share_male / share_fighting_age
// Total population
tcd_pop = normal({p5:16343231,p95:22494774})
caf_pop = normal({p5:5346925,p95:7814208})
ner_pop = normal({p5:23264141,p95:30200196})
mli_pop = normal({p5:22371158,p95:26038039})
sle_pop = normal({p5:7396538,p95:8648290})
ssd_pop = normal({p5:8293052,p95:11407307})
bdi_pop = normal({p5:12024009,p95:16677804})
som_pop = normal({p5:15525542,p95:27842362})
mdg_pop = normal({p5:27873947,p95:33799461})/* LBE_1 Notebook Dashboard UI */ // --- 1. IP & MARKET BASE --- tax_rate = 0.30 // Corporate Tax Rate ip_royalty_rate = 0.30 // 30% off Top-Line Gross Revenue ticket_price = 60 to 90 // Premium pricing justified by Anime IP marginal_cost_pp = 2 to 5
import "hub:ozziegooen/sTest" as sTest import "hub:AI-safety/p-solve-alignment" as generalAlignmentCosts /* Cost-Effectiveness Analysis for ASI Alignment to Animal Welfare */ /* Simplified version of https://squigglehub.org/models/AI-for-animals/alignment-to-animals-EV Unlike the more complicated version, this model does not consider spending-so-far on alignment, it just considers total cost; and the probability of solving alignment is a user input instead of being a derived parameter.
/* Generated by Squiggle AI. Workflow ID: ce07efa6-e1cb-4c49-a387-a659e4ee7b6a */ // Golden Rice Counterfactual Model // Estimates lives saved if Golden Rice had been approved globally in 2005 import "hub:ozziegooen/sTest" as sTest // == Model Parameters ==
// Golden Rice Counterfactual Model // Estimates lives saved if Golden Rice had been approved globally in 2005 // Forked from https://squigglehub.org/models/Abi/goldenrice import "hub:ozziegooen/sTest" as sTest // == Model Parameters == // == Model Parameters ==
/* A model for the probability of solving AI alignment The basic setup: 1. It will cost some amount to solve alignment; the cost is distributed over multiple orders of magnitude. 2. Some amount has already been spent, and some amount will be spent in the future. 3. If the amount spent exceeds the cost, then alignment is solved. */
import "hub:ozziegooen/sTest" as sTest import "hub:AI-safety/p-solve-alignment" as generalAlignmentCosts /* Cost-Effectiveness Analysis for ASI Alignment to Animal Welfare */ /* In favor of alignment-to-animals: 1. scale of animal welfare is much larger (that's a controversial vale judgment) 2. cost to solve alignment-to-animals is possibly much lower, and unlikely to be higher 3. greater field-building effect of research due to the field being newer
/* Expected value of influencing USAID dollars via campaigning for the Presidential candidate who's more pro-USAID, compared to donating to GiveWell top charities. */ // actual budget 2025 was $34B pre-cuts usaid_dollars = normal(34B, 10B) num_voters = 150M // relative to GiveWell
// How many rocks on earth?
sigmoid(x) = 1/(1+exp(-x))
logit(x) = log(x/(1-x))
demoSigmoid = {|t| sigmoid(t)}/* Comparing the Cost-Effectiveness of generic SysInn FOs situated in EU and US */ // Ignoring all variables that do not discriminate between EU and US // EU COST-EFFECTIVENESS philtracteu = 0.25 to 0.5 // 1/centralization
/* Describe your code here */ // assumption: revenue = api revenue + revenue from enterprise seats + consumer revenue //////// enterprise //////// // key uncertainties:
// assumption: revenue = api revenue + revenue from enterprise seats + consumer revenue //////// enterprise //////// // key uncertainties: // - what is the pricing for these 8M enterprise seats? // - any other kinds of enterprise deals that this does not account for num_seats = 8M // Q4 2025 earnings
/* Generated by Squiggle AI. Workflow ID: e3c09385-d27b-4836-95d9-d0afa5684643 */ // Cost-Effectiveness Analysis: ITN Distribution for Malaria Reduction in Kenya // This model analyzes the cost-effectiveness of distributing long-lasting insecticide-treated nets (LLINs) import "hub:ozziegooen/sTest" as sTest // == Core Epidemiological and Demographic Inputs ==
/*
Describe your code here
*/
/** Wu & Khlangwiset baseline cost per DALY ($/DALY) */
wk_baseline_cpdaly = uniform(0.21, 2.08)
/** Real-world adoption rate
Point estimate: 7.2% repeat use in Malawi (Nakoma Ngoma et al. 2025)
Uncertainty: plausibly 5-15% */