Human decision-making relies on cognitive shortcuts known as heuristics. While these shortcuts allow rapid decisions in uncertain situations, they also cause predictable errors. Understanding how heuristics shape judgment can help in designing better decision-making environments.
What is the main idea?
Step 1: Identify the central theme of the passage. The passage explains what heuristics are, how they help, how they cause errors, and why understanding them is valuable.
Step 2: Choose the option that captures the overall message. Option (C) summarizes the passage: understanding heuristics helps improve decision-making systems.
The given sentence is missing in the paragraph below. Decide where it best fits among the options 1, 2, 3, or 4 indicated in the paragraph.
Sentence: Productivity gains, once expected to feed through to broader living standards, now primarily serve to enhance returns to wealth.
Paragraph: Economists now argue that inequality is no longer a by-product of growth but a condition of it. ____ (1)____. Unlike wages, wealth reflects not just income but also access to assets, favourable institutional conditions—such as low interest rates—and public policies like low taxes and housing shortages. ____ (2)____. In other words, wealth depends on political choices in ways that income currently does not. It’s not just the inequality itself that is the issue but the erosion of mechanisms that once constrained it. ____ (3)____. Wealth and income inequality are linked, but where wages have stagnated and collective bargaining has weakened, capital income—derived from profits, rents and interest—has been boosted by design. ____ (4)____.
Para-Jumble Arrange the sentences in a coherent order:
Introducing new technology in workplaces often fails not because it is inefficient but because it disrupts informal social norms that shape cooperation and workflow. Workers resist changes that alter these unwritten norms even when the technology itself may be superior.
Which of the following can be inferred from the passage?
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.