Comprehension

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.

Question: 1

All of the following inferences are supported by the passage EXCEPT that:

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For EXCEPT questions, identify three options clearly grounded in the text. The remaining choice will either overstate, distort, or add claims the passage never makes.
Updated On: Jul 1, 2026
  • examples like runs on banks and toilet paper scrambles illustrate how contagion can amplify local choices into system-wide cascades that surprise participants and lead to patterns they did not intend to create.
  • learning can change the rules that actors face. So, a rare shock can alter payoffs and raise the odds of subsequent large disturbances within the same system, which supports the idea of second-order tail events.
  • 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.
  • the text attributes the COVID-19 pandemic rebound in financial markets solely to displaced sports bettors and treats their entry as the overriding cause of the rapid recovery across assets and time horizons.
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The Correct Option is D

Solution and Explanation

Approach: Instead of validating all four, run a quick "qualifier audit" — compare the strength of language in each option against the passage's own hedging. The option that is far more certain than the text is the unsupported one.

Step 1: The author's tone throughout the bettor passage is cautious: "Some of these dynamics are potentially attributable...," "might have increased inefficiencies," "might have... allowed savvy investors to gain an edge." Three soft qualifiers in two sentences. The author is floating a possibility, not asserting a cause.

Step 2: Options 1, 2 and 3 each carry matching tentativeness or are flat descriptions of mechanisms the passage states as fact (cascades from contagion, rule-changing shocks raising later-disturbance odds, heavy tails making extremes more frequent and larger). None of them outruns the text.

Step 3: Option 4 swaps every qualifier for certainty: "solely," "the overriding cause," applied "across assets and time horizons." A reader could not derive "the single, dominant cause of the recovery" from "some dynamics are potentially attributable." The strength mismatch alone disqualifies it.

Step 4: Since the question asks which inference is NOT supported, the over-strong option is the one we want.

Answer: Option 4.
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Question: 2

Which one of the options below best summarises the passage?

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A good summary option captures all major themes without exaggerating or omitting key ideas. Reject choices that distort or overgeneralise examples used briefly in the passage.
Updated On: Jul 1, 2026
  • The passage explains how social outcomes generally follow normal distributions. So, extreme events are negligible, and policy should stabilise averages rather than learn from large shocks in fast-changing collective settings.
  • The passage explains how noise can create order, then shows why complex systems with contagion are vulnerable to heavy-tailed cascades. It also explains why early shocks change rules through nonstationarity with a market illustration during the COVID-19 disruption.
  • The passage explains how speculative entrants always produce inefficiency after health shocks. Therefore, long-term investors invariably profit when new participants push prices away from fundamentals under pandemic conditions and comparable crises.
  • The passage explains how nonstationarity works in evolutionary biology and rejects applications in markets or public health because adaptation is exclusive to parasite-host systems and cannot arise in technology-mediated social dynamics.
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The Correct Option is B

Solution and Explanation

Approach: Use the "coverage and contradiction" filter. A correct summary must (i) include the opening idea and the closing idea, and (ii) never assert something the passage denies. Score each option on both.

Step 1: Note the passage's bookends. It opens with noise producing order (fish schooling) and closes on nonstationarity / adaptation (host-parasite arms race, COVID market rule-change). Any summary that ignores either bookend is incomplete.

Step 2: Option 1 fails the contradiction test instantly — it says extreme events are negligible and outcomes are normal, while the whole middle of the passage is about heavy tails. Eliminated.

Step 3: Option 3 and option 4 both fail coverage: each clamps onto one slice (option 3 on the bettor/inefficiency aside; option 4 on the biology example) and then over-generalises it into an "always / never" rule. Neither captures the noise-to-order opening, and both add claims the text hedges or denies. Eliminated.

Step 4: Only option 2 carries both bookends — noise creating order at the start, nonstationarity with the COVID market illustration at the end — with the heavy-tailed cascade as the connective middle. Full coverage, zero contradiction.

Answer: Option 2.
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Question: 3

Which one of the following observations would most strengthen the passage’s claim that a first-order tail event raises the probability of further tail events in complex systems?

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To strengthen a claim about second-order tail events, look for evidence that extreme events cluster — that one big shock increases the likelihood of more shocks.
Updated On: Jul 1, 2026
  • In epidemic networks, initial super-spreading episodes are isolated spikes after which outbreak sizes match the baseline distribution from independent contact models across comparable cities with no rise in the frequency or size of later extreme clusters.
  • River discharge records show water levels fit a normal distribution with thin tails that match laboratory data, regardless of storms or floods.
  • After a major equity crash, researchers find dense clusters of large daily moves for several weeks, with extreme days occurring far more often than in normal circumstances for assets with customarily low volatility profiles.
  • Following large earthquakes, regional seismic activity returns to baseline within hours with no aftershock sequence once data are adjusted for reporting effects, which suggests independence across events rather than any elevation in subsequent tail probabilities.
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The Correct Option is C

Solution and Explanation

Approach: Sort the four options by a single yes/no test — "Does a big event here make later big events MORE likely?" Only a "yes" can strengthen. Three options answer "no" (independence or thin tails); the survivor is the answer.

Step 1: The passage's mechanism is positive feedback in time: a shock changes the rules and seeds more shocks (aftershocks, post-crash gyrations). So I am looking specifically for temporal clustering of extremes.

Step 2: Scan for the word that signals clustering versus the words that signal independence. Option 1 = "match the baseline... no rise" (independence, no). Option 4 = "returns to baseline... no aftershock sequence... independence" (no). Option 2 = "normal distribution... thin tails" (no extremes at all, no).

Step 3: Option 3 alone says "dense clusters of large daily moves for several weeks" right after a crash, with extreme days "far more often than in normal circumstances." That is the only "yes" — the first big move is followed by a cluster of further big moves.

Step 4: A strengthener for an empirical claim is the option that reproduces the claimed pattern in fresh data. Option 3 reproduces "tail event raises probability of further tail events" precisely.

Answer: Option 3.
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Question: 4

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?

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For “necessary assumption” questions, look for the option without which the argument collapses. Here, the claim relies on interdependent behaviour—small local decisions must scale into collective patterns.
Updated On: Jul 1, 2026
  • Most users uninstall apps within a week, which leaves only highly exposed individuals participating. This neutralises any systematic bias in routing decisions and prevents any predictable change in aggregate contact patterns.
  • Individuals base movement choices partly on observed infections and on the behaviour of others. So, local responses interact, which turns many small adjustments into large scale patterns that can frustrate the intended aim of risk reduction.
  • App alerts always include precise location to within one metre and deliver real time updates for all users, which ensures that the data feed is perfectly accurate regardless of privacy settings, power limits, or network conditions.
  • Urban networks have uniform traffic conditions at all hours, which allows perfectly predictable routing independent of personal choices, social signals, or crowd reactions and, therefore, makes interdependence negligible in city movement decisions.
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The Correct Option is B

Solution and Explanation

Approach: Treat it as a load-bearing-brick test. Remove each option from under the argument; the one whose removal makes the conclusion fall is the necessary assumption. The conclusion is "apps could raise risky interactions by altering local behaviour."

Step 1: The hidden requirement behind any "local change produces an unwanted collective outcome" claim is coupling — one person's adjustment must influence or collide with others'. Without coupling, you just get many private, non-interacting changes and no emergent backfire.

Step 2: Option 2 states exactly this coupling: choices depend on observed infections and on others, so responses interact and many small adjustments aggregate into a large-scale pattern. Pull this brick out and the conclusion has nothing to stand on. That makes it the assumption.

Step 3: The other three are not bricks under this argument; they are wrecking balls. Option 1 (mass uninstalls neutralise aggregate change), option 3 only adds data accuracy which is beside the point, and option 4 (uniform traffic makes interdependence negligible) each remove or ignore the coupling. They weaken or fail to support the very claim.

Step 4: The brick whose absence topples the argument is option 2.

Answer: Option 2.
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