Talent vs Luck: the role of randomness in success and failure

Some Quick Thoughts On this Paper

Ceshine Lee
Mar 10, 2018 · 6 min read
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Photo by Aleksejs Bergmanis from Pexels

A Simplified Model

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A Square World
# 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
}
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The talent distribution
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The capital distribution after 80 time steps (y axis in log scale)
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Fitting the “power law” (x axis and y axis in log scale)
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Final capital(log scale) versus talent
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Talent vs final capital
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Talent vs final capital(log scale)

Source Code of the Simplified Model

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Towards human-centered AI. https://veritable.pw

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