Models

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

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.

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

/* 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.

*/

/*
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
*/

a = normal(2, 5)

//Input parameters
prob_can_buy_galaxies_point_estimate = 3%
fraction_global_wealth_on_galaxies_low = 0.02%
fraction_global_wealth_on_galaxies_middle = 16%
fraction_global_wealth_on_galaxies_high = 100%
fraction_global_wealth_on_galaxies_loguniform = truncate(10 ^ uniform(-3.7, 0),0,1) // Attempt at log-uniform distribution. See right pane for descriptive stats.
fraction_global_wealth_on_galaxies_lognormal = truncate(0.001 to 1, 0, 1) //Log-normal distribution, truncated at 1. Increased lower bound to increase mean/median after truncation. See right pane for descriptive stats.

global_wealth = 878T
donor_wealth = 1B

/*
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% */

/*
Capturing countervailing dynamics that affect the relative tractabilities of Innovation advocacy in US vs EU
*/

// EU relative strengths
stability = 1.3 to 2
neglectedness = 1.125 to 2
funding_additionality = 1.125 to 2
policy_surface = truncate(normal({p5: 2, p95: 6}), 0.6, 7.4)
activity_additionality =  1.3333 to 2.0833 // AKA grantee influence

/*
Generated by Squiggle AI. Workflow ID: ca63c333-eaa6-40be-a4b5-798a2ba5e8d4
David Reinstein -- edited from this starting point. The original model started with https://acbmcostcalculator.ucdavis.edu/ (Risner et al 2021)
*/
/// Initial code generated by GPT5 based on conversation at https://chatgpt.com/share/68b9b37b-810c-8002-9300-1f6c6a8da252 on 4 Sep 2025.
/// [NB: that link stopped working -- https://chatgpt.com/c/68e39548-8240-8011-b6d0-865fb359bd23 is the internal link that moves that ACBM model

/// Cultured Meat Cost (CM-COGS) — Simple Scenario Model (v0.1)
/// Audience: economists & non-engineers
/// Epistemic status: starter scaffold with reasonable ranges; replace with your own data.

/*
Describe your code here
*/

a = normal(2, 5)

/*
Describe your code here
*/

// 1. SCALE PARAMETERS
chefs_trained = 50
meals_per_chef = 5000
impact_years = 10
total_meals_per_year = chefs_trained * meals_per_chef

import "hub:ozziegooen/sTest" as sTest

// Model: Cost-Effectiveness of Institutional Meat Reduction

@name("Inputs")
inputs = {
  @name("Meals Replaced in 2024")
  @doc("Total number of meals replaced across all institutions in 2024")
  mealsReplaced2024 = 2474256

import "hub:ozziegooen/sTest" as sTest

// Model for cost-effectiveness of cage-free corporate campaign
@name("Key Inputs")
inputs = {
  @name("NPV of Hens Affected by All Commitments")
  @doc(
    "Total number of hens that would be affected if all corporate commitments are fulfilled"
  )
  npvHensAffected = 90932653

// ==========================================
// FUNDING SCENARIOS
// ==========================================

// --- SHARED PARAMETERS ---
baseSalaryY1 = 175k
baseSalaryY2 = 182k
benefitsBurden = 0.4
fullyLoadedY1 = baseSalaryY1 * (1 + benefitsBurden)
fullyLoadedY2 = baseSalaryY2 * (1 + benefitsBurden)

/*
Describe your code here
*/

a = normal(2, 5)