Models

/*
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)

/*
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

/*
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
*/

/*
Is it a good idea for US actors to accelerate an AI arms race with China?
*/

// Probability that an aligned AI has good values regardless of what its creator wants, i.e., an aligned Chinese-built AI will refuse to let China take over the world.
p_aligned_ai_enforces_good_values = 0.1

// Probability that China would take over the world if it could.
p_china_would_take_over_the_world = 0.6

// Correlated Normal Random Variables with Pre-Scaled Weights

// ===== Inputs =====

@name("Base RV Parameters")
baseParams = {
  @name("Mean")
  mean = 1  // Midpoint of 0.5 and 1.5
  
  @name("Standard Deviation")

/*
Describe your code here
*/

a = normal(2, 5)

/*
How valuable are CFP grants compared to in 2022/3?
*/

// DESCRIPTION

// OVERVIEW
// We model the probability of that CFP technologies are sufficiently incubated in the US to reach commercial scale
// (or in the case of nuclear, trigger a fresh wave of nuclear fission).

/*
 * EV of AI safety technical research vs. policy advocacy, in 
 * terms of change in probability of extinction.
*/

@doc(
  "Open Phil granted $69M on AI safety in 2023; a LW post[1] says $43M of that was on technical AI safety; but most of that was not directly funding alignment research (generously, only $28M was alignment research, the rest being forecasting/field-building/etc.). There are some other philanthropic funders, plus for-profit companies do some internal safety research. Internal research is the bulk of the money but I expect most of it is bad ex ante (e.g. fake safetywashing research). It would be feasible to estimate spending on AI 'safety' research, but harder to estimate how much of that spending is real, so I kinda just made up a number range.

[1] https://www.lesswrong.com/posts/adzfKEW98TswZEA6T/brief-analysis-of-op-technical-ai-safety-funding"
)

/*
BOTEC model to evaluate benefits from water quality forecasts and optimal trade-off between precision and recall.
*/

@name("Model Inputs")
@doc("Key input parameters for the aquaculture prediction model")
inputs = {
  @name("True Rate of Poor Water Quality")
  @doc("The actual occurrence rate of poor water quality in aquaculture farms")
  @format(".1%")

/*
Generated by Squiggle AI. Workflow ID: dd6b98ba-7143-423f-b6bc-01f9f58d4dbf
*/
import "hub:ozziegooen/sTest" as sTest

// Model for estimating the number of C code bugs in the world
@name("Number of C Code Bugs in the World")
@doc(
  "This model estimates the total number of C code bugs in the world by combining estimates of total C code lines (both public and private) with bug density estimates."