CAT 2025 Slot 1 VARC Question & Solution

Reading ComprehensionMedium

Passage

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.

Question 1

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

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.

Solution:

Main Idea of the Passage

The passage explains how collective behaviour shaped by contagion and imitation can produce unexpected and extreme outcomes. It focuses on:

Feedback loops between individual choices and group-level effects
The role of heavy-tailed distributions in making extreme events more likely
How major shocks can change incentives, leading to further disruptions
A cautious approach to explaining real-world phenomena, avoiding single-cause claims

Evaluation of the Options

Why Options A, B, and C Are Supported

Option A is supported because the passage clearly describes a cause-and-effect process.
- In collective settings shaped by contagion, individual decisions feed back into the system
- Examples like bank runs and panic buying show how local behaviour can generate large, unintended outcomes
- The passage explicitly notes that such feedback can create collective patterns people were trying to avoid
Option B is supported because the passage explains how a first-order tail event alters the system itself.
- Once a major shock occurs, “the rules change”
- This happens because such events change perceived costs and incentives
- As people adapt their behaviour, the probability of further large disruptions (second-order tail events) increases
Option C is supported because the passage contrasts normal distributions with heavy-tailed distributions.
- Heavy-tailed systems have a higher probability of extreme events
- These events occur more frequently and with greater magnitude than expected under a bell curve
- This makes prediction and risk management much harder in contagion-driven group settings

Why Option D Is Not Supported

Option D overstates causality.
- The passage discusses the COVID-era market rebound cautiously
- It says some dynamics are “potentially attributable” to former sports bettors entering markets
- This wording indicates a partial contribution, not a single or dominant cause

By presenting a one-cause explanation, option D goes beyond what the passage claims.

Final Answer

Inference NOT Supported by the Passage: Option D

Question 2

Which one of the options below best summarises the passage?

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.

Solution:

Main Idea of the Passage

The passage explains how complex systems behave under uncertainty and contagion. It develops its argument in a clear sequence:

Randomness (“noise”) can create order, as seen in examples like fish schools
Systems shaped by imitation or contagion tend to produce heavy-tailed outcomes, where extreme events occur more often than a normal distribution predicts
A single extreme event can change incentives, risk perceptions, and behaviour, making further shocks more likely
This dynamic is captured by the idea of non-stationarity, illustrated through examples from:
- Pandemics
- Financial markets during COVID-19
- Technology-driven social systems

The passage focuses on how shocks reshape the system itself, rather than treating extreme events as isolated anomalies.

Explanation of the Correct Answer

Why Option B Is Correct

Option B best summarizes the passage because it:

Includes the opening idea that noise can generate order
Captures the central argument about heavy-tailed events in contagion-driven systems
Notes that early shocks change the rules of the system through non-stationarity
Uses the COVID-era market as an illustrative example, not as the main claim

This mirrors both the structure and the emphasis of the passage.

Why the Other Options Are Incorrect

Option A:
- Claims that social outcomes usually follow normal distributions
- Suggests extreme events are rare
- This directly contradicts the passage’s emphasis on heavy-tailed behaviour
Option C:
- Over generalises the example of displaced sports bettors
- The passage only suggests they may have contributed to market dynamics
- It does not claim they always cause inefficiency or guarantee profits
- It also incorrectly treats this example as the main argument, rather than supporting evidence
Option D:
- Misapplies non-stationarity to evolutionary biology
- The passage uses non-stationarity in the context of:
  - Markets
  - Pandemics
  - Technology-driven social systems
- This goes beyond the passage’s scope

Final Answer

Correct Answer: Option B

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?

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.

Solution:

Main Idea of the Passage

The passage argues that in complex systems, a rare but extreme event can fundamentally change how the system behaves. After such a shock:

Incentives, behaviour, or constraints shift
The system becomes nonstationary
As a result, further extreme events become more likely

In other words, a first-order tail event can raise the probability of second-order tail events.

Explanation of the Correct Answer

Why Option C Best Supports the Claim

Option C provides direct evidence for the passage’s argument:

A major equity crash serves as the first-order tail event
In the weeks that follow, extreme price movements cluster densely
These extreme outcomes occur far more frequently than under normal conditions

This clearly shows that the initial shock changes the system’s dynamics, making further extreme events more likely—exactly the mechanism described in the passage.

Why the Other Options Do Not Support the Claim

Option A:
- States that extreme events are isolated spikes
- Says later outcomes return to baseline distributions
- This directly contradicts the passage’s claim
Option B:
- Describes a system where outcomes remain stable and normal
- Shows no link between one extreme event and subsequent extremes
- Therefore, it is irrelevant to the argument
Option D:
- Claims that activity returns to normal with no aftershocks
- Suggests events are independent
- This again goes against the idea of cascading tail events

Final Answer

Correct Answer: Option C

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?

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.

Solution:

Main Idea of the Passage

The passage argues that individually sensible choices can combine to produce unexpected collective outcomes. In systems shaped by interaction and contagion, people’s decisions do not remain isolated. Instead:

Individuals respond to information and to each other
Local changes in behaviour can cascade
These cascades can create system-wide patterns that no one intended

For contact-tracing apps to increase risky interactions, behaviour must be interdependent, not independent.

Explanation of the Correct Answer

Why Option B Is a Necessary Assumption

Option B states that people change their movement partly based on infection information and partly in response to others’ behaviour.

This assumption is essential because:
- The passage’s argument depends on feedback loops
- Individual actions must influence and be influenced by others
Applying the negation test:
- If people acted independently and did not react to others
- Then local changes caused by the app would not aggregate into risky group-level outcomes

Without this assumption, the mechanism described in the passage would collapse.

Why the Other Options Are Not Necessary

Option A:
- Talks about early app abandonment and participation bias
- Even if true, the core argument about interacting behaviours still holds
- Hence, it is not required for the reasoning
Option C:
- Assumes perfect, real-time accuracy of app alerts
- The passage does not rely on technological perfection
- Behavioural reactions can still produce feedback even with imperfect information
Option D:
- Assumes urban movement is uniform and predictable
- The passage implicitly assumes the opposite
- Uniformity would weaken the argument rather than support it