/* 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] }
/* 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 */
/* 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