Well, the US forces actually attempted not to rig them.
No need to, the locals can do everything necessary. The US forces just provided the money and prevented the "undesirables" from playing.
Whichever laws you invoked when you said implied that "old-style colonialism won't work in our time" is a reasonable hypothesis.
I did not invoke any laws of nature. I think that in the current social, political, informational, military, etc. global environment the old-style colonialism is highly unlikely to work. No laws of nature are involved in this assertion.
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If I understand Taleb correctly, his objection is that if X's distribution's upper tail tends to a power law with small enough (negated) exponent α, then sample proportions of X going to the distribution's top end are inconsistent under aggregation, and suffer a bias that decreases with sample size. And since the Gini coefficient is such a measure, it has these problems.
However, Taleb & Douady give me the impression that the quantitative effect of these problems is substantial only when α is appreciably less than 2. (The sole graphical example for which T&D mention a specific α, their figure 1, uses α = 1.1). But I have a hard time seeing how α can really be that small for income & wealth, because that'd imply mean income & mean wealth aren't well-defined in the population, which must be false because no one actually has, or is earning, infinitely many dollars or euros.
[Edit after E_N's response: changed "a bias that rises with sample size" to "a bias that decreases with sample size", I got that the wrong way round.]
Um no. They're not well defined over the distribution, they will certainly be well defined over a finite population.
You seem to be confused about how distributions with infinite means work. Here's a good exercise: get some coins and flip them to obtain data in a St. Petersburg distribution notice that even though the distribution has infinite mean all your data points are still finite (and quite small).