Last active
April 6, 2018 06:17
-
-
Save ceshine/89dad3422dab752c93beeac88bb0f82f to your computer and use it in GitHub Desktop.
Talent vs Luck simulation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
library(checkpoint) | |
checkpoint("2018-02-25") | |
library(ggplot2) | |
# number of people | |
N <- 1000 | |
# probability of event interception | |
P_E <- 0.075 | |
# probability of lucky event | |
P_L <- 0.5 | |
# initial capital | |
C_0 <- 10. | |
# total time steps | |
T_ <- 80 | |
# talent mean | |
M_T <- 0.6 | |
# talent standard deviation | |
SD_T <- 0.1 | |
talents <- rnorm(N, M_T, SD_T) | |
c_all <- matrix(0, T_+1, N) | |
c_all[1, ] <- rep(C_0, times=N) | |
event_all <- matrix(0, T_, N) | |
for(i in seq(T_)){ | |
# whether an event happens | |
event_happens <- rbinom(N, 1, P_E) | |
# whether an event is lucky/good | |
event_is_good <- rbinom(N, 1, P_L) | |
# whether a person can profit from a lucky event | |
event_good_profited <- rbinom(N, 1, talents) | |
# The effect of the hypothetical event | |
event_effect_hypo <- ((event_is_good & event_good_profited) * 3 + 1) / 2 | |
# The actual effect at this time step | |
event_effect_actual <- event_effect_hypo * event_happens | |
event_effect_actual[event_effect_actual==0] <- 1 | |
event_all[i, ] <- event_effect_actual | |
c_all[(i+1),] <- c_all[i,] * event_effect_actual | |
} | |
ggplot(data.frame(talents=talents), aes(x=talents)) + geom_histogram(binwidth=0.02) + | |
geom_vline(xintercept=0.6, linetype=2) + geom_vline(xintercept=0.7, linetype=3) + | |
ylab("number of individuals") + | |
geom_vline(xintercept=0.5, linetype=3) + theme_bw() + theme(text=element_text(size=14)) | |
# bins <- cut(c_all[T_+1,], breaks=1000, include.lowest = T, right=FALSE) | |
c_hist <- hist(c_all[T_+1,], breaks=100, plot=F) | |
plot( | |
(c_hist$breaks[1:(length(c_hist$breaks)-1)] + c_hist$breaks[2:length(c_hist$breaks)])/2, | |
c_hist$counts, log="y", type='h', lwd=2, lend=2, | |
xlab="capital/success", | |
ylab="number of individuals") | |
min(log10(c_all[T_+1,])) | |
max(log10(c_all[T_+1,])) | |
c_hist <- hist(pmax(log10(c_all[T_+1,]),-2), | |
breaks=c( | |
floor(min(log10(c_all[T_+1,]))), | |
seq(1, ceiling(max(log10(c_all[T_+1,]))), 0.5)), plot=F) | |
ggplot(data.frame( | |
x=seq(1, ceiling(max(log10(c_all[T_+1,]))), 0.5), cnt=log10(c_hist$count) | |
), aes(x=x, y=cnt)) + | |
geom_point() + | |
geom_smooth(method = "lm", se = T, col="red") + | |
scale_x_continuous("capital/success", seq(1, ceiling(max(log10(c_all[T_+1,]))), 1), | |
labels=10 ^ seq(1, ceiling(max(log10(c_all[T_+1,]))), 1)) + | |
scale_y_continuous("number of individuals", seq(0, 3, 1), | |
labels=10 ^ seq(0, 3, 1)) + | |
theme_bw() + theme(text=element_text(size=14)) | |
ggplot(data.frame(x=c_all[T_+1,], y=talents), aes(x=x, y=y)) + | |
geom_point(alpha=0.25) + ylab("talent") + xlab("capital/success") + ylim(0, 1) + | |
scale_x_log10(breaks=c(1, 10, 100, 1000)) + theme_bw() + | |
theme(text=element_text(size=14)) | |
ggplot(data.frame(y=c_all[T_+1,], x=talents), aes(x=x, y=y)) + | |
geom_point(alpha=0.5) + xlab("talent") + ylab("capital/success") + theme_bw() + | |
theme(text=element_text(size=14)) | |
ggplot(data.frame(y=c_all[T_+1,], x=talents), aes(x=x, y=y)) + | |
geom_point(alpha=0.5) + xlab("talent") + ylab("capital/success") + | |
scale_y_log10(breaks=c(1, 10, 100)) + theme_bw() + | |
theme(text=element_text(size=14)) | |
c_final_sorted <- sort(c_all[T_,]) | |
sum(c_final_sorted) | |
sum(c_final_sorted[1:(N*0.8)]) / sum(c_final_sorted) | |
sum(c_final_sorted[(N*0.8):N]) / sum(c_final_sorted) | |
# Performance of the Talented | |
talented <- c_all[T_,][talents > 0.7] | |
length(talented) | |
sum(talented > 10) / length(talented) | |
sum(c_all[T_,] > 10) / N |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment