Models

/*
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

field_size_1 = 3

rho = 0.1 to 0.8

neglectedness_1 = field_size_1^-rho
r5 = quantile(rho, 0.05)
r95 = quantile(rho, 0.95)
n50 = quantile(neglectedness_1, 0.5) // n50 is neglectedness under median Rho assumption

approx_1 = n50^(rho/(r5*r95)^0.5)

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

/*
Describe your code here
*/

a = normal(2, 5)

/*
Estimating the cost-effectiveness of DOE spending by combining four approaches
*/

// COST-EFFECTIVENESS in tCO2e per USD

// Based on the fracking case study

// APPROACH 1
// Incrementalist interpretation

/*
Generated by Squiggle AI. Workflow ID: 3fc0f5eb-3c5e-45db-a40e-011a064c195c
*/
import "hub:ozziegooen/sTest" as sTest

// Orexin-A sleep need reduction experiment expected value model
// Evaluates a small pilot RCT to test if intranasal orexin-A can reduce sleep needs
// Estimates QALY value, economic impact, and ROI for funding application

// == Experimental Outcomes ==

/*
Describe your code here
*/

a = normal(2, 5)

/*
Generated by Squiggle AI. Workflow ID: 08a75b0f-81e9-4c92-ae3e-10abf9d13bb7
*/
import "hub:ozziegooen/sTest" as sTest

// == Input Variables (Stochastic Nodes) ==

@name("Annual Progress in Core LLM Intelligence")
@doc(
  "Rate of improvement in core LLM capabilities year-over-year. Represents a mixture of normal progress with occasional larger jumps."

/*
Does it make sense to spend down 100% under the most extreme assumptions?
*/


h = 10

a = 1 to h
b = 1 to h
c = 1 to h

// GDP method

output_loss = lognormal({p10: 0.59, p90:1.7}) 

us_gdp = 27000000000000
scenario = 100000000000
/// whole US version
gdp_day = (us_gdp/365)
damages_per_day = gdp_day*output_loss
outage_days = scenario / damages_per_day

voll = lognormal({p10: 10.6, p90:18.7}) // $/kWh
threshold = 1*10^11 // $
lost_load_kWh = threshold / voll
lost_load_TWh = lost_load_kWh / 10^9
total_us_consumption_TWh = 4070
duration_multiplier = 2 to 5

total_us_blackout_duration = lost_load_TWh/total_us_consumption_TWh * 365 // outage-days

/*
Generated by Squiggle AI. Workflow ID: 4fd6cfbf-5a3e-446f-8757-2dc42891af90
*/
// LandBnB Marketplace Dynamics Model (Monthly Snapshot - Bangalore Niche)
// Simple model to explore potential scale, revenue, and utilization based on initial assumptions.

// == Inputs ==
// Grouping inputs makes the model easier to read and modify.
inputs = {
  // --- Supply Side (Venues) ---

/*
Generated by Squiggle AI. Workflow ID: b451f629-dfc2-4997-ba5c-fb6001d4b885
*/
import "hub:ozziegooen/sTest" as sTest

// === Model Inputs ===

@name("Automated Cooking System Parameters")
@doc(
  "Core parameters for modeling an automated cooking system's information processing capabilities and failure rates"

/*
Generated by Squiggle AI. Workflow ID: 6f3499c3-3d19-4cc8-9d37-677c35a21cfe
*/
// Model estimating the risk of human extinction due to AI in the next 50 years

import "hub:ozziegooen/sTest" as sTest

@name("AI Extinction Risk Model")
@doc(
  "A model estimating the probability of human extinction due to AI within the next 50 years (2024-2074)"

/*
Generated by Squiggle AI. Workflow ID: e233ef09-428d-4a38-8b65-60f318d7d928
*/
import "hub:ozziegooen/sTest" as sTest

// AI Extinction Risk Model
// This model estimates the risk of human extinction due to AI in the next 10 years

@name("Model Inputs")
@doc("Key parameters for estimating AI extinction risk")

/*
2000-2020 innovation benefits DECOMPOSITION APPROACH
*/

// DECOMPOSITION APPROACH

// Calculate excess warming between 2010 and 2100 under WEO 2010 (NPS) and WEO 2023 (STEPS) forecasts
warming_pre_2010 = 0.8 to 1
temp_inc_2010 = 3.5 - warming_pre_2010 // Excess 2010-2100 warming under WEO 2010 (NPS)
temp_inc_2023 = 2.4 - warming_pre_2010 // Excess 2010-2100 warming under WEO 2023 (STEPS)

/*
Describe your code here
*/

sum(l)=List.reduce(l, l[0], {|acc, el| acc+el})
product(l)=List.reduce(l, l[0], {|acc, el| acc*el})

product_of_sums(size_base, size_partition, base_dist)={
  indices=List.upTo(0, (size_base/size_partition)-1)
  base_pos = List.make(size_base, {||base_dist})

/* EVENT ADVERTISING ROI CALCULATOR
This model calculates how much you can justifiably spend per application
for event advertising based on your event's value and conversion rates. */

/* USER INSTRUCTIONS:
1. Enter your specific event data in the "Key inputs" section below
2. Adjust the ranges based on your historical data or best estimates
3. The model will calculate your willingness to pay per application
4. Review the results in the "summary" section at the bottom
*/