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

Approach: This is an EXCEPT inference question, so three options will be faithful paraphrases of the passage and one will overstate or distort what is actually said. Hunt for the option with an absolute word the author never used.

Step 1: Test option 1. The passage explicitly lists "a run on the banks, a scramble to buy toilet paper" as collective settings where contagion shapes behaviour and produces "a collective pattern we had aimed to avoid." So local choices cascading into surprising system-wide patterns is directly supported. Keep it.

Step 2: Test option 2. The passage says a first-order tail event "changes the perceived costs of our actions and changes the rules that we play by" (nonstationarity), and that this raises the probability of second-order tail events. "Learning can change the rules" plus "rare shock raises odds of later disturbances" is a clean restatement. Supported.

Step 3: Test option 3. The passage says heavy-tailed distributions carry "a much higher probability of extreme events" that "occur more frequently and are larger than would be expected under normal distributions," in contagion-driven collective settings. Option 3 mirrors this almost word for word. Supported.

Step 4: Test option 4. The passage is deliberately tentative about the sports bettors: "Some of these dynamics are potentially attributable to former sports bettors" and the entry "might have increased inefficiencies." Option 4 claims the rebound is attributed "solely" to displaced bettors and treats them as "the overriding cause." That hardens a hedged, partial conjecture into a single total cause. The passage does not support that.

Why the trap works: Words like "solely," "overriding," and "the" single cause are classic CAT distortion markers. The author used "some," "potentially," "might" deliberately.

Answer: Option 4.

Answer 2

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 no stationarity — 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 3

Approach: A best-summary option must cover the passage's full arc and contradict none of it. Map the passage into its movements first, then reject any option that drops a movement or adds a claim the author never made.

Step 1: Trace the four movements. (a) Noise can paradoxically create order — fish schooling from misalignment. (b) Complex systems with contagion are vulnerable to heavy-tailed events, not bell curves. (c) A first-order tail event raises the odds of further (second-order) tail events. (d) This works through nonstationarity — rules change — illustrated by the COVID market episode and host-parasite coevolution.

Step 2: Reject option 1. It claims social outcomes "generally follow normal distributions" and extreme events are "negligible." That is the exact opposite of the heavy-tailed argument. Out.

Step 3: Reject option 3. The sports-bettor / market-inefficiency point is one illustrative detail, not the thesis, and the passage hedges it ("potentially," "might"). This option inflates a sub-example into an absolute law ("always," "invariably profit"). Out.

Step 4: Reject option 4. The passage applies nonstationarity to markets and public health; it does not "reject applications in markets" or confine adaptation to parasite-host systems. It directly contradicts the text. Out.

Step 5: Confirm option 2. "Noise can create order, then... complex systems with contagion are vulnerable to heavy-tailed cascades... early shocks change rules through nonstationarity with a market illustration during COVID-19." That hits movements (a), (b), (c) and (d) in order, without distortion.

Answer: Option 2.

Answer 4

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 5

Approach: The claim to strengthen is: one extreme event makes more extreme events likely afterwards (serial dependence / clustering of tails). The strengthening observation must show that after a big shock, further big events cluster — not return to baseline. Two options describe independence and one describes thin tails; those weaken or are irrelevant. Find the one showing post-shock clustering.

Step 1: Restate the target claim precisely. "Once a... 'tail' event takes place, this raises the probability of further tail events" — the example given is aftershocks and post-crash market gyrations. We need evidence of dependence: extreme begets extreme.

Step 2: Option 1 says after super-spreading spikes, outbreak sizes return to baseline "with no rise in the frequency or size of later extreme clusters." That is independence — it directly weakens the claim. Reject.

Step 3: Option 2 describes river levels fitting a normal distribution with thin tails. No extreme events, no clustering — irrelevant to a claim about heavy-tailed cascades. Reject.

Step 4: Option 4 says after big earthquakes activity returns to baseline within hours with "no aftershock sequence," implying "independence across events." This is the textbook counterexample to the claim — it weakens it. Reject.

Step 5: Option 3 says after a major equity crash there are "dense clusters of large daily moves for several weeks, with extreme days occurring far more often than in normal circumstances." A first-order tail event (crash) is followed by a burst of further extreme moves — exactly the serial dependence the passage asserts. This strengthens the claim.

Answer: Option 3.

Answer 6

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 7

Approach: This is a necessary-assumption question. The argument is: apps inform people locally, people change behaviour, and the aggregate result is more risky interaction, not less. For that chain to work, individual choices must feed into each other so small local moves build into a collective pattern. The correct assumption is the bridge that makes interdependence possible; the wrong options each break the chain by making behaviour independent or the apps irrelevant.

Step 1: Pin the logic. The passage says "If each of us changes our behaviour to avoid the infected, we might generate a collective pattern we had aimed to avoid." The leap is from individual avoidance to an emergent collective outcome. That leap needs people's responses to interact.

Step 2: Apply the negation test to option 2. It assumes individuals base movement on observed infections and on others' behaviour, so local responses interact and aggregate into large patterns. Negate it — suppose choices were fully independent and never reacted to others. Then there is no feedback, small adjustments stay small, and the collective backfire cannot arise. The argument collapses. So option 2 is necessary.

Step 3: Reject option 1. If most users uninstall within a week and this "prevents any predictable change in aggregate contact patterns," the very effect the passage describes is neutralised. It undercuts the suggestion rather than supporting it.

Step 4: Reject option 3. Perfect one-metre accuracy and real-time updates concern data precision, not whether behaviour changes interact. The suggestion can hold with imperfect data and fail with perfect data, so this is not necessary.

Step 5: Reject option 4. Uniform traffic that makes routing "independent of personal choices, social signals, or crowd reactions" makes interdependence "negligible" — again killing the feedback the argument needs.

Answer: Option 2.

Answer 8

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).