DIRECTIONS for questions 21-24: 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.
All of the following inferences are supported by the passage EXCEPT that:
Correct Answer :
heavy-tailed events make extreme outcomes more frequent and larger than bell curve expectations. This complicates forecasting and risk management in collective settings shaped by contagion and copying behaviour.
Solution :
The correct option is: heavy-tailed events make extreme outcomes more frequent and larger than bell curve expectations. This complicates forecasting and risk management in collective settings shaped by contagion and copying behaviour.
To arrive at the correct answer, we must evaluate which of the given inferences is NOT supported by the provided text, as the question asks us to identify the exception.
Let us analyze the passage's discussion of heavy-tailed events. In the second paragraph, the author explains that in collective settings driven by contagion (such as runs on banks or surges in buying toilet paper), probability distributions are often heavy-tailed. The text explicitly states: "There is a much higher probability of extreme events... These events are still unlikely, but they occur more frequently and are larger than would be expected under normal distributions."
While the first half of the chosen option correctly reflects the passage's statement that heavy-tailed events make extreme outcomes more frequent and larger than expected under a normal bell curve, the second half introduces entirely new concepts. The option goes on to claim that this "complicates forecasting and risk management in collective settings shaped by contagion and copying behaviour."
If we carefully scan the entirety of the passage, there is absolutely no mention of "forecasting" or "risk management." The author extensively discusses the dynamics, probabilities, and shifting rules of complex systems, but deliberately does not make any claims about how these systems affect formal forecasting or risk management processes. Because this part of the statement relies on outside assumptions and extrapolation rather than information provided in the text itself, it cannot be considered an inference supported by the passage.
Therefore, because it contains unsupported claims, this is the correct answer to the "EXCEPT" question.
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