Models in Politics

// ExpectedTPPower_{region}
etpp_US = normal({p5: .36, p95: .6})
etpp_EU = normal({p5: .23, p95: .47})
etpp_Brazil = normal({p5: .32, p95: .56})
etpp_Mexico = normal({p5: .05, p95: .29})
etpp_Korea = normal({p5: .16, p95: .4})
etpp_India = normal({p5: .57, p95: .81})
etpp_Japan = normal({p5: .67, p95: .91})

// AdminPowerModifier_{region}

a = 0.5 // EU
x = lognormal({mean: 0.18+a, stdev: 0.34}) - a // EU Correlation

u0 = cdf(x, 0) // Probability that EU correlation is negative
u0pt5 = cdf(x, 0.5) - cdf(x, 0) // Probability that EU correlation is between 0 and 0.5
u1 = cdf(x, 1) - cdf(x, .5) // Probability that EU correlation is between 0.5 and 1
u1plus = 1 - cdf(x, 1) // Probability that EU correlation is between 0.5 and 1

b = 0.5 // China

/*
Describe your code here
*/

a = normal(2, 5)

/*
Assessing how Trump's second election affects US innovation prospects
*/

// Using values from here: https://docs.google.com/document/d/1Mfwr9K8vVAczjl5yx8oWtaX6ufkQqYNfPo4MrYk76TQ/edit?usp=sharing

// Outcome probabilities
// These will be normalized later

dem_win = normal(0.525, 0.04)