Unlearned lessons are those insights missed from a past situation. When we do not learn from experiences, we continue to make the same decisions in similar situations. In the case of the United States, unlearned lessons undermine the future security and prosperity of democracies. The results of unlearned lessons can be the individual’s free choice, but others, including some facing us now, heavily burden the future with the collective history of other prior choices. More volatile times face open societies globally. As Nassim Taleb observes, when the tails of a probability distribution get fatter, the predictable becomes a function of the distribution's extreme values and only those extreme values. In multiple publications, Taleb argues that the world is "undergoing a switch between continuous low-grade volatility to a process moving by jumps, with less and less variations outside of jumps." The faster the rate of systems change, the heavier become those tails, due mostly to the growth of unrecognized interdependence between the moving parts. In the statistical analysis of systems, if one is uncertain about the tails of the data, then one is uncertain about the mean as well. Yet, the faster the rate of systems change, the heavier become those tails, due mostly to the growth of unrecognized interdependence between the moving parts, and thus the less useful for learning are their means. Such a situation requires the prudent person to plan for maximal damage scenarios, not for most probable scenarios, and to ensure that the choices they make along the way offer reasonable and secure alternatives when the worst scenarios emerge.
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