Models

/*
Generated by Squiggle AI. Workflow ID: 60eefacd-2bb4-4371-b8e0-277627606855
*/
import "hub:ozziegooen/sTest" as sTest

// LearnToGive: Cost-Benefit Analysis Model
// Calculates the impact of a nonprofit providing scholarships, computer equipment, and education in Thailand

@name("Key Input Parameters")
inputs = {

/*
Calculate a CBA of my nonprofit, LearnToGive, which raises money for 500 baht scholarships for students in Northern and Northeastern Thailand via tutoring of other students. We have raised 40k baht, enough for nearly 60 scholarships for severly underfunded and undersupported students. Additional money raised will be given to the school to buy computers (only two in the school are working), and I will be spending two weeks teaching english at the school myself (as they do not have actual english teachers). Calculate, considering PPP and the impact on ourselves as tutors with Feynman technique, impact on students we teach, and impact on scholarship students, the overall CBA and impact of the nonprofit.
*/

a = normal(2, 5)

/*
What should the annual time discount be?
In this version we only include factors that are (A) changing in one direction over time and (B) common across all FOs
*/

// Presentation: https://docs.google.com/presentation/d/1R1EXDYWq2_HL-p8cdlX0s50PTGx1WwgoGRg2XiFrWaM/edit?usp=sharing

// Deep Research: https://chatgpt.com/share/682c94f4-a088-8002-93c5-fd0287d500b0

// Discount due to waiting 5 years from 2025 to 2030 (pessimistic)

// BASED ON INN/AE WEIGHTS MODEL, EXTRACTED 8 JUL 2025
// Recreating innovation capacity and affectable emissions without the time adjustments, which can be made in the impact model

/*
Climate Policy vs Innovation Effectiveness Model
This model compares the relative effectiveness of domestic policy and innovation
on reducing emissions over time, based on historical data and future projections.
*/

// ==================== INNOVATION PARAMETERS ====================

// Climate damage model with temperature, vulnerability, and damage over time
@name("Delay (years)")
@doc("Number of years to delay the start of the intervention")
delay = 20

@name("Model Parameters")
inputs = {
  @name("Vulnerability Decline Rate (% per year)")
  @doc("Annual percentage decline in vulnerability, modeled as uncertain between 2% and 6%")
  @format(".1%")

// Innovation Capacity and Affectable Emissions Model for US and EU

// ===== Impact Differentiators =====

// OC ^ SOW weight type
hedginess = 0.5 to 2
neglectedness = 0.5 to 2
orgStrength = 0.2 to 1
activityAdditionality = normal({p5: 0.3, p95: 0.9})

/*
Describe your code here
*/

a = normal(2, 5)

/*
1. Following Gelman et al., I model the vote margin as a normal distribution, with the mean coming from current prediction markets
1.1. NB: I don't think primary elections are actually well modeled as having normally distributed vote margins? Often there is one very distant frontrunner. But maybe highly contested primaries are well modeled like this and this will be a highly contested primary? caveat emptor
2. Dollars per vote: I loosely based this on a document from Dan Eth about Jay Shooster's campaign but kind of made it up. Better numbers appreciated
3. Primary: 
3.1 Default odds of winning: 40%, per Jay Shooster
3.2 Sd of margin: made up by me saying it will be 10% of total votes
4. General:
4.1 Default odds of winning: Jay suggests that the democrat will win this district iff democrats control the house, and probability for the latter is currently ~75% on prediction markets
4.2 Sd of margin: last year was 2.5k, so I vaguely guessed 5k

import "hub:ozziegooen/sTest" as sTest

// Cost-Benefit Analysis of OTC Statins in the US (5-year model)
// This model estimates lives saved and QALYs gained from making statins available over-the-counter

@name("Model Inputs")
inputs = {
  @name("US Adults 40-75 years old")
  @doc("Based on 2020 ACS table S0101")
  usAdults4075 = 129M

meet_annually = 20 to 300
interest_ratio = 0.02 to 0.5
boyfriend_rate = meet_annually * interest_ratio / 15 * (1 / 365)
t_days = exponential(boyfriend_rate)

uplim=5000

// Model Parameters
@name("Cost Parameters")
costs = {
  @doc("Opportunity cost per hour.")
  hourlyOpportunityCost = 12.5 to 50

  @doc("Cost per date, observationally")
  costPerDate = 5 to 20

//Simple correlation coefficient calculator
// Define X
x = 0.25 to 40 // US tractability
// Define Y
y = x * (0.3 to 5) // EU tractability
// Define Z
z = x * (0.1 to 5/3) // China tractability

// Calculate correlation coefficient
calculateCorrelation(listX, listY) = {

// Is it valid to define Tractability{CLI} in relation to the magnitude of Tractability{InnSys}?

tractabilityInnSys = 1% to 5%

tractabilityCLI = 5^-0.5*((0.04 to 5)*5^0.5)^0.885 * (1% to 5%)

ratio = tractabilityCLI / tractabilityInnSys

compare = Plot.dists(
{

/*
My own version of AI 2027 forecast, leaning on Titotal's critique
*/

// Current time horizon, hours (length of task that can be completed by AI with 80% accuracy)
horizon_2025 = (3 to 12) / 60

// Current time horizon doubling time (years)
t_0 = {
  horizon_2020 = (0.5 / 60 to 2 / 60) / 60

/*
Generated by Squiggle AI. Workflow ID: a1c362d8-dd87-4bed-8e91-dfa6b85810db
*/
//import "hub:ozziegooen/helpers" as h

// Base inputs and assumptions
inputs = {
  @name("Shoelace users")
  @doc(
    "Number of people using shoes with shoelaces ~daily."

import "hub:ozziegooen/sTest" as sTest

// Model for analyzing rationalist community employment and income statistics
@name("Rationalist Community Income Analysis")
@doc(
  "This model analyzes income distributions of the rationalist community compared to demographic and professional counterparts, using data from the 2024 LessWrong survey and U.S. income statistics."
)
inputs = {
  @name("Rationalist Community Income Data ($)")
  @doc("Income distribution for rationalist community members based on 2024 LessWrong survey, with median $65,000 and mean $127,570")

/*
Describe your code here
*/

a = normal(2, 5)

/*
Generated by Squiggle AI. Workflow ID: c7d7e695-dd3d-4ba5-96bf-fbd980580a22
*/
import "hub:ozziegooen/sTest" as sTest

// Statins OTC Impact Model: Estimating lives saved and QALYs gained from over-the-counter statin availability in the US

@name("Population and Uptake")
@doc("Key demographic inputs and estimated uptake rates for OTC statins")
inputs = {

/*
Generated by Squiggle AI. Workflow ID: 593ecf75-b82a-47a3-b2a3-c3da65fab405
*/
import "hub:ozziegooen/sTest" as sTest

@name("Over-the-counter Statins: Lives Saved Estimation")
@doc(
  "This model estimates the potential number of lives that could be saved annually by making statins available over-the-counter in the United States."
)
inputs = {

baseline_hedginess = 1.2
hedginess_exponent = 0.5 to 2
hedginess = baseline_hedginess ^ hedginess_exponent

ratio = 1.34 to 3.33
median_ratio_1 = Math.sqrt(1.34*3.33)
median_ratio_2 = exp(Math.sqrt(log(1.34)*log(3.33)))

p5_1 = log(1.34)/log(median_ratio_1) // should be close to 0.5
p95_1 = log(3.33)/log(median_ratio_1) // should be close to 2