Models

// Cost-effectiveness of saving money to buy distant galaxies vs x-risk grants
// All $ are in units of billions
// Use sample count of 10000 and coordinate count of 50 in menu>configuration

pScenario = 0.1 // probability galaxies can be bought at all
w0 = 470000 // initial world financial wealth in billions
returns = 2 to 200 // growth in our fraction of world wealth during IE
bid = truncate(0.005 to 0.5, 0.001, 1) // fraction of world's wealth bid on distant galaxies
moralTrade = 2 // gains from trade from owning some galaxies
valueDefault = min(exponential(3),0.999) // value of a default galaxy relative to our galaxy

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.

/*
Describe your code here
*/

a = normal(2, 5)

/*
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
*/

a = normal(2, 5)

/*
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