Question

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

• A. 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.
• B. 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.
• C. 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.
• D. 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.
• A. 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.
• B. 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.
• C. 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.
• D. 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.
• A. 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.
• B. River discharge records show water levels fit a normal distribution with thin tails that match laboratory data, regardless of storms or floods.
• C. 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.
• D. 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.
• A. 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.
• B. 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.
• C. 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.
• D. 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.

Answer 1

The Correct Option is D

Step 1: Understand what the passage actually says about each option.
Option (1): Supported.
The passage explicitly uses runs on banks and toilet paper buying to illustrate contagion-driven cascades that produce extreme, unintended system-wide behaviour.
Hence, this inference is supported.
Option (2): Supported.
The passage discusses nonstationarity — how a first-order tail event changes the rules of the system, altering perceived costs and raising the probability of a second-order tail event.
This matches the inference stated.
Option (3): Supported.
The passage stresses that heavy-tailed distributions produce more frequent and larger extreme outcomes than normal distributions, especially in contagion-driven systems.
This is directly stated and therefore supported. Option (4): Not supported (EXCEPT).
The passage gives the example that former sports bettors might have contributed to market inefficiencies and movements during the COVID–19 rebound.
It does not claim:

that they were the sole cause, nor
that their entry was the overriding cause of market recovery.
The authors clearly treat this factor as one potential contributor, not the decisive explanation.
Thus, Option (4) states something the passage does not support and is therefore the correct answer to an EXCEPT question.

Answer 2

Step 1: Identify the major themes of the passage.
The passage covers three core ideas:

Noise (randomness) in complex systems can surprisingly create orderly collective behaviour.
Complex systems with contagion dynamics are prone to heavy-tailed cascades and extreme events.
Nonstationarity explains how early shocks change the rules of the system, illustrated with stock-market behaviour during COVID-19.
A correct summary must incorporate all three ideas.
Step 2: Evaluate each option.
Option (1): Incorrect.
This contradicts the passage. The passage explicitly argues that complex systems do not follow normal distributions and that extreme events are important, not negligible.
Option (2): Correct.
This option accurately reflects:

the surprising emergence of order from noise,
the vulnerability of contagion-driven systems to heavy-tailed events,
the idea of nonstationarity and how early shocks change rules,
the COVID-19 market example used in the passage.
It is the only option that captures the full scope of the passage.
Option (3): Incorrect.
This overstates the passage. The text says speculative entrants might have contributed to market movements; it emphatically does not say that speculative entrants always cause inefficiency or that long-term investors always profit.
Option (4): Incorrect.
This misrepresents the passage entirely. The passage does not reject applying nonstationarity to markets or public health; in fact, it explicitly applies it to those contexts. The parasite–host example is merely an analogy.
Thus, the best summary is Option (2).

Answer 3

Step 1: Recall the passage’s claim.
The passage states that:

A first-order tail event (a large, rare shock)
raises the probability of second-order tail events,
meaning that after the initial shock, extreme events become more frequent.
Thus, we must choose the option showing clusters of extreme events after an initial extreme event.
Step 2: Evaluate each option.

Option (1): Weakens the claim.
Says tail events remain isolated with no increase afterward — the opposite of what we want.
Option (2): Irrelevant.
Describes a normal distribution with thin tails; nothing about successive extreme events.
Option (3): Strongly supports the claim.
After a major stock market crash, there are:

dense clusters of large daily moves,
extreme events appearing far more often,
a sustained period of elevated tail risk.
This directly confirms that a first-order tail event increases the probability of further tail events.
Option (4): Weakens the claim.
Says seismic activity returns to baseline with no aftershocks — contradicting the idea of second-order tail events.
Thus the best answer is Option (3).

Answer 4

Step 1: Recall the passage’s claim. & nbsp;

The passage argues that contact-tracing apps, though designed to help individuals avoid risk, could inadvertently create collective patterns that increase risky interactions.

This can only happen if:

• Individual behavioural changes interact, and
• Small local adjustments cascade into large-scale patterns.

This is the hallmark of a complex system with interdependent behaviour.

Step 2: Identify the assumption required for this mechanism to work.

Option (2) states exactly this assumption:

\(\textit{Individuals change behaviour based on infection data and the behaviour of others, and these interactions scale up.}\)

Without this interdependence, individual actions would stay local, and no large-scale unintended pattern could emerge — which the passage says can happen.

Thus, (2) is the necessary assumption.

Why the others are wrong:

• Option (1): Talks about uninstalling; irrelevant to behavioural cascades.
• Option (3): Assumes perfect accuracy of apps; the passage never requires this.
• Option (4): Assumes uniform traffic and no interdependence — this contradicts the very idea needed for the unintended collective pattern.

Conclusion:

Therefore, the assumption most necessary for the passage’s argument is Option (2).