The recommendation is to move from the Fourth Quadrant into the third one. It is not possible to change the distribution; it is possible to change the exposure, as will be discussed in the next section. What I can rapidly say about the Fourth Quadrant is that all the skepticism associated with the Black Swan problem should be focused there. A general principle is that, while in the first three quadrants you can use the best model or theory you can find, and rely on it, doing so is dangerous in the Fourth Quadrant: no theory or model should be better than just any theory or model. In other words, the Fourth Quadrant is where the difference between absence of evidence and evidence of absence becomes acute. The most obvious way to exit the Fourth Quadrant is by “truncating,” cutting certain exposures by purchasing insurance, when available, putting oneself in the “barbell” situation described in Chapter 13. But if you are not able to barbell, and cannot avoid the exposure, as with, say, climate notions, exposure to epidemics, and similar items from the previous table, then we can subscribe to the following rules of “wisdom” to increase robustness: first, have respect for time and nondemonstrative knowledge, meaning that ideas and practices that have survived long stretches of history carry an implicit tested reliability that a clever new theory has not yet earned; second, avoid optimization and learn to love redundancy, since a system trimmed down to maximum efficiency has no slack left to absorb a shock, while spare capacity, though wasteful-looking in normal times, is what allows survival when something unexpected happens; third, avoid prediction of small-probability payoffs, though not necessarily of ordinary ones, because our estimates of rare-event probabilities and their impact are especially unreliable, even though everyday, well-behaved events remain reasonably predictable; fourth, beware the atypicality of remote events, since the largest or most extreme instance of a phenomenon is rarely just a bigger version of the typical case and often behaves according to different rules entirely; fifth, beware the moral hazard with bonus payments, because compensation structures that reward short-term gains without correspondingly penalizing later losses encourage people to take on hidden tail risk that someone else ends up paying for; sixth, avoid some risk metrics, since tools like standard deviation or Value-at-Risk are built on assumptions of tame, well-behaved randomness and give a false sense of precision when applied to environments where extreme events dominate; seventh, ask whether a potential Black Swan is positive or negative, since your strategy should differ depending on whether you are exposed to harmful uncertainty you want to shield against or beneficial uncertainty you want to expose yourself to; eighth, do not confuse absence of volatility with absence of risk, because a period of apparent calm can simply mean that risk is being suppressed or hidden rather than eliminated, and it can resurface all at once later; and ninth, beware presentations of risk numbers, since the way a risk figure is framed or displayed can make a dangerous exposure look deceptively safe, so the presentation itself deserves as much scrutiny as the underlying calculation.
Related: Antifragility means benefiting from volatility · Inversion solves problems by avoiding failure