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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.
The passage suggests that contact tracing apps could inadvertently raise risky interactions by altering local behaviour. Which one of the assumptions below is most necessary for that suggestion to hold?
The passage explains that when people make sensible choices individually, these actions can add up and create unexpected group results. For contact-tracing apps to increase risky interactions, people’s actions need to affect each other rather than being independent. To check if an assumption is necessary for this argument to hold true, we can use the negation test.
Option A says most users stop using the app quickly, so only highly exposed users remain, which stops any big changes in movement patterns. Even if this happens, the main concern of the passage still stands, because the argument is about how people’s behaviours interact, not about participation bias cancelling effects. The claim does not depend on early uninstallation stopping feedback, so this assumption is not necessary.
Option B says people change their movement partly because of infection information and partly because of what others do, so many small actions add up. If this were not true, and people made decisions on their own without reacting to others, then local changes from the app would not build into the group patterns the passage warns about. The whole process described in the passage would not work. So, this assumption is necessary.
Option C says the app alerts are always perfectly accurate and real-time for everyone. The passage does not rely on perfect technology; it relies on how people react. Even if the information is not exact or is delayed, it can still affect movement choices and create feedback effects. So, the argument does not need this condition to be true, and hence it isn't a necessary assumption.
Option D says that urban movement is uniform and predictable, leaving little room for interdependent behaviour. If this were true, cascading effects would be unlikely, but the passage clearly assumes the opposite: that behaviour is responsive and interlinked. Since the argument does not require uniformity and would actually be undermined by it, this assumption is also not necessary.
Hence, option B is the correct choice.
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