영어 모의고사 (자동 편성)

Suppose you meet a quiet, gentle man ①who speaks softly, tends a small garden, and organizes his bookshelf with meticulous care. ②Ask whether he is more likely a florist or a truck driver, most people instinctively choose florist, since his personality matches the stereotype so neatly. This judgment, however, ③ignores a crucial fact: truck drivers vastly outnumber florists in almost every country, which means that, statistically, he is far more likely to belong to the larger group regardless of how well he fits the flower-shop image. Relying on resemblance to a familiar category rather than on underlying numerical proportions, ④known among researchers as representativeness, leads people to overlook these base rates almost automatically. Even trained professionals, ⑤who should know better, often fall into the same trap when a vivid description feels more convincing than plain statistics.

Facing a shortage of caregivers and a rapidly aging population, several countries have begun ①deploying robots designed to remind elderly residents to take medication, monitor for falls, and even offer simple conversation throughout the day. Early trials show that many residents report ②reduced loneliness, particularly when a robot's presence allows an overworked human staff member to spend a few extra minutes with each patient rather than ③rushing between rooms. Critics, however, worry that this framing quietly shifts the goal of care: instead of asking how technology can support human connection, institutions may come to treat a robot's companionship as an acceptable substitute for it, especially in facilities already ④struggle with tight budgets. Advocates for the elderly argue that robots should be evaluated strictly as tools that free up human time for human contact, never as a lower-cost replacement for the contact itself, which ⑤remains irreplaceable.

Factories that once employed hundreds of workers on an assembly line now run with a fraction of that staff, as robotic arms handle tasks ①that used to require human hands. Economists have long debated whether this shift destroys jobs or simply ②transforms them. Historical evidence offers some comfort: previous waves of automation eventually created new categories of work that no one ③had predicted. However, critics point out that earlier transitions unfolded over generations, ④given workers time to retrain, whereas today's robots are being deployed at a pace that leaves little room for adjustment. For this reason, many economists argue that the real challenge is not the robots themselves, but the speed at which society ⑤is expected to adapt to them.

Standard economic theory assumes that money is fungible, meaning that a dollar earned as salary is functionally ①identical to a dollar received as a tax refund or won in a lottery. In practice, however, people routinely sort their finances into separate mental accounts, treating money differently depending on its source or intended purpose. A household that would never dip into its retirement savings to pay for a vacation might nonetheless spend an unexpected bonus on that very trip without ②hesitation, even though both sums are, in strictly financial terms, equally available for either purpose. This ③compartmentalization can produce decisions that appear ④consistent when viewed through a purely rational lens: the same individual who diligently saves a portion of every paycheck may simultaneously carry high-interest credit card debt, effectively paying more to borrow than they earn on their savings. Mental accounting is not always harmful, however; by assigning specific categories to specific funds, individuals can also impose useful ⑤discipline on their spending, preventing money earmarked for essential needs from being diverted toward impulsive purchases.

Individuals confronted with a set of options frequently default to whichever alternative requires no active decision at all. Decision-makers often treat the current arrangement as a reasonable ①baseline simply because it already exists, even when switching would plainly serve their interests better. When employees must actively opt into a retirement savings plan, participation rates remain stubbornly ②low; yet when the same plan is presented as automatic unless an employee opts out, participation rises ③dramatically, despite the fact that the underlying financial terms are identical in both cases. Two forces appear to drive this asymmetry: first, the cognitive effort required to evaluate alternatives and initiate change, and second, an aversion to the potential regret that might follow an active decision that turns out poorly. Because inaction carries no such vivid regret, the status quo comes to feel ④riskier than it objectively is, illustrating how mere inertia can ⑤masquerade as a deliberate preference.

A few years ago, spotting a manipulated video required a trained eye and ①specialized software. Today, freely available applications allow almost anyone to swap a face or fabricate a voice ②convincingly enough to fool casual viewers within minutes. This shift has quietly ③reinforced one of the oldest assumptions in human communication: that seeing and hearing something is a reasonably reliable way to confirm it happened. Journalists now face the burden of verifying footage that once would have been accepted as ④self-evident proof. Perhaps more troubling than any single fake video is the broader effect on public trust, in which genuine footage can be ⑤dismissed as fabricated simply because deepfakes exist.

Military researchers have developed weapons systems capable of identifying and engaging targets without a human pressing the final button. Advocates argue that autonomous systems can react faster than any human operator. Human rights organizations counter that removing a person from the final decision strips away moral responsibility, since _______________. Diplomatic negotiations toward an international treaty have repeatedly stalled, partly because military powers disagree on how to define autonomy precisely enough to regulate it.

Hyperbolic discounting has direct implications for how retirement savings programs ought to be designed. (A) If employees are asked at the moment of hiring whether they wish to increase their contribution rate at a specific point in the future, many agree readily, since the sacrifice feels distant and therefore painless. (B) When that future date eventually arrives, however, the automatic increase proceeds smoothly precisely because the decision was locked in before the immediate discomfort of a smaller paycheck became salient. (C) This design, sometimes described as committing one's future self in advance, effectively exploits the very inconsistency that hyperbolic discounting predicts, turning a psychological weakness into a practical advantage.

In other words, people want the safest outcome for society in theory, but the safest outcome for themselves in practice. When engineers design the software that controls a self-driving car, they inevitably face a question that philosophers have debated for decades. ( ① ) If a crash becomes unavoidable, should the vehicle protect its own passenger at all costs, or should it be programmed to minimize total harm, even if that means steering toward the person inside? ( ② ) Surveys reveal an uncomfortable contradiction: most people say a car should be programmed to save the greater number of lives. ( ③ ) Yet the same people admit they would never buy a vehicle designed to sacrifice its own owner. ( ④ ) This gap between the ideal and the personal choice suggests that ethical programming cannot be settled by public opinion alone. ( ⑤ ) Manufacturers, regulators, and philosophers must instead agree on transparent rules before autonomous vehicles are allowed to make such decisions.

Consequently, even seasoned professionals who believe themselves immune to such influence often fail to notice how much their final judgment has shifted. The anchoring effect can be surprisingly resistant to conscious correction. ( ① ) Even when individuals are explicitly warned in advance that a number they are about to see is randomly generated and therefore meaningless, their subsequent estimates still drift toward that arbitrary figure. ( ② ) This occurs because the mind uses the initial number as a starting point and then adjusts insufficiently away from it, a process that leaves the final judgment biased toward the anchor. ( ③ ) Experimental studies have repeatedly demonstrated this effect across a wide range of tasks, from estimating historical dates to appraising real estate. ( ④ ) The bias persists not because people lack expertise, but because the adjustment process itself is inherently incomplete. ( ⑤ ) Awareness of the anchoring effect, therefore, does not guarantee immunity from it, which is precisely what makes the bias so difficult to counteract in practice.