Models

// Model for the expected value of donations to a US House of Representatives campaign. Based on Eric Neyman's model of the expected value of donating to Alex Bores' 2026 campaign: https://ericneyman.wordpress.com/2025/10/20/consider-donating-to-alex-bores-author-of-the-raise-act/

@name("Key Inputs")
inputs = {
  @name("Dollars per Vote ($)")
  @doc(
    "Cost per vote in a House race. Eric Neyman assumed a typical campaign costs $100 per vote based mainly on 'numbers thrown around casually by experts', then multiplied by 3 because New York has higher costs.
  
  I spent 15 minutes looking for literature and I found:

/*
Make a model trying to quantify the amount of uncertainty we might have in our forecasts of how a technology will progress

*/

a = normal(2, 5)

/*
Generated by Squiggle AI. Workflow ID: 4314add4-2a10-4053-b14f-e9f5de966369
*/
// Choose scenario: "small", "medium", or "large"
scenario = "medium"

// Scenario-specific parameters
params = {
  small: {
    durationMonths: 12,

/*
Describe your code here
*/

a = normal(2, 5)

/*
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/
*/
/// 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.

// MODELING THE RELATIVE COST-EFFECTIVENESSES OF DIFFERENT CLEAN ENERGY TECHNOLOGIES

// Innovation data with correlated lognormal distributions
// Source: https://docs.google.com/spreadsheets/d/1DKomktGl1JxDRpALD8euBf2Ybrnuofb3vMOZ0hNsvZA/edit?usp=sharing
techData = [
  {name: "SHR Geothermal", weight: 117, min: 0.43, max: 8.82},
  {name: "SMR", weight: 966, min: 0.29, max: 7.64},
  {name: "EGS", weight: 170, min: 0.22, max: 4.41},
  {name: "Plant-Based Protein", weight: 153, min: 0.18, max: 4.04},
  {name: "Cultivated Protein", weight: 117, min: 0.00001, max: 0.07}, // Avoid zero for lognormal

/*
Generated by Squiggle AI. Workflow ID: c45c9658-b3f0-4fcd-bd39-ea10b971c6c5
*/
// Cost-Effectiveness Estimate for The Mission Motor's "Just Organizations" One-on-One Support (2025)

import "hub:ozziegooen/sTest" as sTest

// == COST SIDE ==

@name("TMM 2025 Inputs")

/*
Reproduction of Eric Neyman's model for the expected value of electing Alex Bores to US Congress.

source: https://www.lesswrong.com/posts/TbsdA7wG9TvMQYMZj/consider-donating-to-alex-bores-author-of-the-raise-act-1#Comparison_to_non_AI_safety_opportunities
*/

@name("ΔP(Doom) If USA Behaves Wisely")
@format(".0%")
@doc("Absolute change in P(doom) given that the USA behaves wisely rather than unwisely.")
dp_doom_given_usa_wise = -5%

// 2060 EGS Capacity GW
capacity_35 = truncateRight(10 to 1000, 2000) // 35 years from 2025

capacity(t) = max((t - 10) * capacity_35 / 25, 0)

// Global annual decline in carbon intensity of electricity after 2025
annual_carbon_intensity_decline = 0.5% to 7.5%

// Clobal annual increase in climate resilience after 2025
annual_increase_in_resilience = 0.5% to 8%

/*
 * Cost-effectiveness of DOE funding based on case studies of Enhanced Geothermal Systems (EGS) and Fracking
 */

// ===== EGS Model 1 =====
@name("EGS Model 1 Inputs")
egs1_inputs = {
  @name("Impact of 300GW EGS by 2050 (GT averted)")
  @doc("Estimated gigatonnes of CO2 averted in a scenario with 300GW EGS deployment by 2050")
  impact_300gw = normal(5, 1.24)

// Model estimating the change in probability of AI doom from electing a pro-AI-safety representative to the U.S. House.

// TODO: AI wrote all the docstrings (based on my suggestions), I have not reviewed for accuracy, all I did was remove the four million "this value is highly uncertain" tics that the AI loves.

@name("Include Weak-Bill Path?")
@doc("If 0, zero out the weak-bill path to impact. If 1, include it.")
include_weak_bill_path = 1

@name("Include Strong-Bill Path?")
@doc("If 0, zero out the strong-bill path to impact. If 1, include it. Removing the strong-bill path still allows strong bills to be passed via weak bills or via warning shots, but not directly via voting/advocacy.")

import "hub:ozziegooen/sTest" as sTest

// Model for estimating blades of grass on an American football field

@name("Field Dimensions")
fieldDimensions = {
  @name("Field Length (m)")
  @doc(
    "American football field is 120 yards (109.73m) including end zones, or 100 yards (91.44m) without"
  )

// Alternative for the expected value of donations to a US House of Representatives campaign. 
// Original model here: https://squigglehub.org/models/mdickens/congressional-campaign-donations

@name("Key Inputs")
inputs = {
  @name("Dollars per Vote ($)")
  @doc(
    "Cost per vote in a House race. Eric Neyman assumed a typical campaign costs $100 per vote based mainly on 'numbers thrown around casually by experts', then multiplied by 3 because New York has higher costs.
  
  I spent 15 minutes looking for literature and I found:

/*
Describe your code here
*/

a = normal(2, 5)

// Cost-effectiveness of messaging campaigns directed at elected representatives. 
// I created the core model and then ran it through Squiggle AI to create the boilerplate. Then I wrote all the docstrings.

// ==== Inputs ====

inputs = {
  @name("Proportion of Votes Within 1% (%)")
  @doc(
    "Proportion of vote outcomes that fall within one percentage point of the pass threshold. Based on historical voting records. The number has varied over time—it was 2.2% from 2000 to 2010, and 5.1% from 2015 to 2025.
  

/*
Nov 2025
*/

boost = 1 + (0.1 to 1)

// Under the simple version, all orgs get the same boost.
// to test the max difference this could make,
// compare (1) longterm FB boost
// and (2) Gen Capacity boost, which should be the lowest

voll = lognormal({p5: 10.6, p95:18.7}) // $/kWh
//via ERCOT/Brattle — their 95CI fitted to 90% CI due to squiggle idiosyncracies
threshold = 1*10^11 // $
lost_load_kWh = threshold / voll
lost_load_TWh = lost_load_kWh / 10^9
total_us_consumption_TWh = 4070


duration_multiplier = lognormal({p25:2.3, p75:3.2})
//via 

/*
Describe your code here
*/

maxFoodPerPerson = 1

defaultWesternMan = .25 to maxFoodPerPerson
defaultIndianMan = .9 to maxFoodPerPerson

womanFactor = triangular(0.75, 0.85, .95)

/*
Generated by Squiggle AI. Workflow ID: f0421d48-236b-4158-b2d9-6c830c768e48
*/
// Cost-Benefit Analysis for Bubble Tea Store in Berkeley over 5 years

import "hub:ozziegooen/sTest" as sTest

// == Time Parameters ==

@name("Analysis Period (months)")

// 2026 Fundraising Budget

// 1. Foresight Team
forecasters = 4 to 6
forecaster_salary = 25k to 60k
foresight_team_total = forecaster_salary * forecasters

// 2. OSINT and Surveillance Technology
software_engineer_osint = 90k to 250k