Models

// I am deciding between eating mussels and eating tofu, with omega 3 supplements. I end up with a conclusion that a mix is reasonable, because 500g of mussels is more than I can feasibly eat a week, so supplementing omega 3 and b12 on top should put me in a healthy range. Results below generated by iterating with claude opus 4.8 until I felt i had removed all its mistakes. I believe the results are reasonable. I feel that mussels are plausibly sentient but springtails are more plausibly sentient, and springtails are killed much more frequently when farming soybean than mussels are when farming mussels. I fix them equal for this aanalysis to be sure my conclusion is not unfair to the mussels. The bottome line I draw is it is probably slightly better morally speaking to eat organic mussels as a vegan in the EU, but it's really not clear in the end.
//
// === Mussels vs Tofu — sentience-weighted expected deaths for 500g of mussels and equivalent tofu ===
//
// Compares 500 g cooked mussels vs a CALORIE-EQUIVALENT serving of cooked tofu.
// Output unit: sentience-weighted welfare units per serving (human welfare-range = 1.0).
//
// All figures cite the local fact sheet:
//   /home/dmrivers/mussels_vs_tofu/Mussels vs. Soy Plus Algae-Oil_ Cost-Effectiveness Input Fact Sh.md (pasted at end of file)
// with additional numbers from the prior conversation research (Potapov 2023,

/*
--
*/

// ExpectedTPPower_{region}
etpp_US = normal({p5: .36, p95: .6})
etpp_EU = normal({p5: .23, p95: .47})
etpp_Japan = normal({p5: .67, p95: .91})

// AdminPowerModifier_{region}

/*
Describe your code here
*/

a = normal(2, 5)

/*
Describe your code here
*/

a = normal(2, 5)

// EA Indonesia — EG pilot, 2-yr forecast
// fractions -> beta (bounded 0–1); large leg -> per-prospect sum (lumpy)
// hi to low is p5 - p95 (90% CI)

// ---------- Leg 1: broad / pledge (large N -> smooth product is fine) ----------
reachedNet  = 600 to 2400
pledgeConv  = beta({ mean: 0.11, stdev: 0.05 })   // ~0.05–0.20
newPledgers = reachedNet * pledgeConv

grossPledge = 40 to 300                            // USD/yr, unbounded above -> lognormal

/*
Describe your code here
*/

a = normal(2, 5)

// 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
*/

a = normal(2, 5)

/*
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.

/*
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)}