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The passage below is accompanied by four questions. Based on the passage, choose the best answer for each question.
Understanding the key properties of complex systems can help us clarify and deal with many new and existing global challenges, from pandemics to poverty . . . A recent study in Nature Physics found transitions to orderly states such as schooling in fish (all fish swimming in the same direction), can be caused, paradoxically, by randomness, or 'noise' feeding back on itself. That is, a misalignment among the fish causes further misalignment, eventually inducing a transition to schooling. Most of us wouldn't guess that noise can produce predictable behaviour. The result invites us to consider how technology such as contact-tracing apps, although informing us locally, might negatively impact our collective movement. If each of us changes our behaviour to avoid the infected, we might generate a collective pattern we had aimed to avoid: higher levels of interaction between the infected and susceptible, or high levels of interaction among the asymptomatic.
Complex systems also suffer from a special vulnerability to events that don't follow a normal distribution or 'bell curve'. When events are distributed normally, most outcomes are familiar and don't seem particularly striking. Height is a good example: it's pretty unusual for a man to be over 7 feet tall; most adults are between 5 and 6 feet, and there is no known person over 9 feet tall. But in collective settings where contagion shapes behaviour - a run on the banks, a scramble to buy toilet paper - the probability distributions for possible events are often heavy-tailed. There is a much higher probability of extreme events, such as a stock market crash or a massive surge in infections. These events are still unlikely, but they occur more frequently and are larger than would be expected under normal distributions.
What's more, once a rare but hugely significant 'tail' event takes place, this raises the probability of further tail events. We might call them second-order tail events; they include stock market gyrations after a big fall and earthquake aftershocks. The initial probability of second-order tail events is so tiny it's almost impossible to calculate - but once a first-order tail event occurs, the rules change, and the probability of a second-order tail event increases.
The dynamics of tail events are complicated by the fact that they result from cascades of other unlikely events. When COVID-19 first struck, the stock market suffered stunning losses followed by an equally stunning recovery. Some of these dynamics are potentially attributable to former sports bettors, with no sports to bet on, entering the market as speculators rather than investors. The arrival of these new players might have increased inefficiencies and allowed savvy long-term investors to gain an edge over bettors with different goals. . . .
One reason a first-order tail event can induce further tail events is that it changes the perceived costs of our actions and changes the rules that we play by. This game-change is an example of another key complex systems concept: nonstationarity. A second, canonical example of nonstationarity is adaptation, as illustrated by the arms race involved in the coevolution of hosts and parasites [in which] each has to 'run' faster, just to keep up with the novel solutions the other one presents as they battle it out in evolutionary time.
We evaluate each option individually:
Option A is supported because the passage clearly describes a cause-and-effect process. When contagion shapes behavior, individual choices feed back into the system and lead to unexpected group outcomes. The passage says that “in collective settings where contagion shapes behaviour,” events follow heavy-tailed distributions, like “a run on the banks” and “a scramble to buy toilet paper.” It also mentions that local changes in behavior can “generate a collective pattern we had aimed to avoid.” These points together show a clear link from individual decisions, through feedback loops, to surprising system-wide effects.
Option B is supported because the passage directly explains how a first-order tail event changes the system itself, which then raises the probability of further shocks (second-order tail events). It states that once a rare but significant tail event occurs, “the rules change,” and that this happens because such events “change the perceived costs of our actions.” This shift in incentives leads to changes in behavior, which is why more big events can happen later. The inference that learning or adapting changes outcomes (reshapes payoffs) and increases subsequent disruptions follows directly from this process.
Option C is supported as well, because the passage compares normal and heavy-tailed distributions in a way that affects predictability. With heavy-tailed distributions, there is “a much higher probability of extreme events,” which “occur more frequently and are larger than would be expected under normal distributions.” This means that usual expectations based on bell curves do not work in these systems, so forecasting and managing risk are harder in group settings shaped by contagion and imitation.
Option D does not follow the careful approach to causality in the passage. The text is cautious in explaining the COVID-era market rebound, saying that “some of these dynamics are potentially attributable to former sports bettors… entering the market as speculators.” This shows only a partial contribution, not the only cause. The passage never claims there is just one main reason, or that the rebound is “solely” or “overridingly” due to displaced bettors. By leaving out these words, option D gives a single-cause explanation for the market rebound that the passage avoids.
So, the only inference the passage does not support is option D.
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