Models by benthamite

/*
Describe your code here
*/

// SAMSHA via perplexity
cocaine_users = {
    'Year': [2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022],
    'Users_Millions': [4.55, 4.8, 5.05, 5.9, 5.5, 5.45, 5.2, 4.8, 5.274]
}

/*
Describe your code here
*/

a = normal(2, 5)

/*
Describe your code here
*/

a = normal(2, 5)

/*
1. Following Gelman et al., model the vote margin in each swing state as a normal distribution, with the mean coming from current prediction markets
1.1. NB: Squiggle doesn't have t distributions, just doing normal for now
1.2. NB2: Prediction market numbers seem sus, I added my own estimate whcih seems more reasonable to me
2. Consider the value of getting N additional voters in a given swing state with total voting population P as increasing the mean of this distribution by N/P
3. Assume that the number of additional voters we generate is 32% of our number of users (per Coppock), and that our number of users is linearly proportional to our budget
4. Sample from this distribution a bunch of times with different budget sizes, and look at the probability that Biden gets >= 270 electoral votes with each budget size
*/

/*
  Estimates advantage of Biden by:

  1. Estimating the probability that proposals of given values will be produced
  2. Estimating the increased likelihood of the proposal being implemented under Biden
  3. Multiplying the above two numbers together

*/

/*
Describe your code here
*/

a = normal(2, 5)

/*
Goal: estimate how many dollars in donations to LTFF are equivalent to one 
hour spent by a CEA employee

LTFF kindly published their marginal grants, and one of them was a (hypothetical)
grant for people to travel to a biosecurity workshop. Fortuitously, CEA staff have
recently spent some time helping with a biosecurity workshop.

This means we can estimate how many dollars the LTFF has to spend in order to 
improve a biosecurity workshop as much as CEA staff improves it by working one hour. 

/*
  ==== Costs of replacing an employee ====

  This model attempts to estimate the cost of replacing an employee.
  Numbers are in months of lost productivity, e.g. if total_cost = 5
  that means that replacing an employee cost the equivalent of 5 months of their labor
  disappearing.

  These numbers are lower than the ones I usually see (6-9 months, https://lrshrm.shrm.org/blog/2017/10/essential-elements-employee-retention).
  I think it's because that study is low-quality and made up  but feel free

/*
Describe your code here
*/

a = normal(2, 5)