영어 모의고사 (자동 편성)

When individuals face uncertainty about how to behave, they frequently look to the actions of others for guidance, ①assuming that a behavior adopted by many people is more likely to be correct or appropriate than one adopted by few, a tendency known as social proof. This reliance on collective behavior can be remarkably efficient, since it allows individuals ②to benefit from the accumulated judgment of a crowd without having to gather and evaluate every relevant fact themselves. However, social proof can also lead entire groups astray when the initial signal that ③trigger imitation is itself unreliable or coincidental. A restaurant that happens to attract a long line early in the evening may continue attracting customers throughout the night simply because passersby ④interpret the queue as evidence of superior quality. Financial markets exhibit an analogous pattern: investors who observe others buying a particular asset often join the buying themselves, ⑤reasoning that so many participants cannot all be mistaken.

Individuals confronted with a set of options frequently ①default to whichever alternative requires no active decision at all, a tendency scholars term the status quo bias. Rather than weighing the merits of each available choice afresh, decision-makers often treat the current arrangement as a reasonable baseline simply because it already exists, even when ②switching would plainly serve their interests better. This pattern becomes especially apparent in contexts where enrollment in a program ③depend on a default setting. When employees must actively opt into a retirement savings plan, participation rates remain stubbornly low; yet when the same plan is presented as automatic unless an employee opts out, participation rises dramatically, despite the fact that the underlying financial terms ④are identical in both cases. Two forces appear to drive this asymmetry: first, the cognitive effort required to evaluate alternatives, and second, an aversion to the potential regret that ⑤might follow an active decision that turns out poorly.

Suppose you meet a quiet, ①gentle man who speaks softly, tends a small garden, and organizes his bookshelf with meticulous care. Asked whether he is more likely a florist or a truck driver, most people ②instinctively choose florist, since his personality matches the stereotype so neatly. This judgment, however, ignores a crucial fact: truck drivers vastly ③trail 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, known among researchers as representativeness, leads people to overlook these base rates almost ④automatically. Even trained professionals, who should know better, often fall into the same trap when a vivid description feels more ⑤convincing than plain statistics.

A striking proportion of individual investors believe their own stock-picking ability to be above average, a statistical ①impossibility given that only half of any population can genuinely outperform the rest, yet a pattern that persists with remarkable consistency across surveys. This tendency, known as overconfidence bias, leads investors to trade more ②frequently than a more cautious assessment of their own skill would justify. Extensive research comparing the trading records of numerous individual accounts has ③demonstrated that investors who trade most frequently tend to earn substantially ④higher returns than those who trade rarely, largely because transaction costs accumulate and because frequent trading often reflects overconfident reactions to short-term noise rather than genuine insight. Overconfidence also manifests in the tendency to underestimate the range of possible outcomes, causing investors to concentrate their holdings in a small number of assets they feel certain about rather than diversifying across a broader portfolio. Paradoxically, then, the very confidence that motivates investors to act decisively often ⑤undermines the outcomes they are trying to achieve.

Individuals rarely evaluate their own lives in isolation; instead, they measure their achievements, possessions, and status against a reference group, a set of people they use as a standard for comparison. This group need not be one to which a person actually belongs. Sociologists distinguish between normative reference groups, which shape a person's values and behavior, and comparative reference groups, which merely provide a benchmark for self-assessment. Robert Merton showed that soldiers who compared their promotion chances to peers within their own slow-moving unit often felt more satisfied than soldiers in units with faster promotion rates, because _______________. This counterintuitive finding demonstrates that satisfaction depends less on objective circumstances than on the group against which those circumstances are measured.

Traditional industrial systems have relied on a linear model of production, in which raw materials are extracted, transformed into goods, and eventually discarded as waste. This approach treats natural resources as if unlimited and disposal as if costless. A circular economy proposes an alternative framework in which _______________, thereby reducing the extraction of virgin resources. Central to this model is the notion that waste, rather than being an inevitable byproduct, can be redesigned as a resource for another process, much as fallen leaves nourish the trees that shed them. For example, manufacturers design products so that components can be disassembled and reused in new items once their original function ends. Some companies also lease equipment instead of selling it outright, retaining responsibility for refurbishment. Because such practices lessen dependence on finite raw materials, they reduce the environmental damage caused by mining. Consequently, the circular economy is increasingly regarded as a viable economic strategy that conserves resources for future generations.

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

Few of these later buyers, however, possess any independent basis for judging whether the asset's price still bears any relation to its underlying value. A speculative bubble typically begins with a genuine, defensible reason for optimism, such as a promising new technology or an unusually favorable economic environment. ( ① ) Early investors who recognize this genuine opportunity purchase the asset and, as expected, often see its price rise accordingly. ( ② ) As word of these early gains spreads, a second wave of investors enters the market, drawn less by independent analysis than by the simple observation that the price has already climbed. ( ③ ) They are, in effect, purchasing confidence rather than value, trusting that the crowd's continued enthusiasm will sustain or further elevate the price. ( ④ ) This reliance on collective momentum rather than fundamental analysis is precisely what transforms a reasonable initial rally into an unsustainable bubble. ( ⑤ ) When the momentum eventually falters, as it inevitably must, the absence of any solid fundamental foundation leaves the price with nothing to support it.

Until clearer rules emerge, artists, developers, and publishers remain locked in a legal gray zone that satisfies almost no one. Generative AI systems learn to write, draw, and compose by studying enormous collections of existing human work, much of which was created without the artists' knowledge or consent. ( ① ) Supporters of the technology argue that this process resembles how a human student learns by studying the masters. ( ② ) Critics respond that the comparison breaks down at scale, since a single AI model can absorb millions of works in days. ( ③ ) Courts around the world are now being asked to decide whether training an algorithm on copyrighted material counts as fair use or as infringement. ( ④ ) This is a question the law was never designed to answer. ( ⑤ )

This reveals an uncomfortable truth about the underlying business model itself. Social media platforms rarely show users a neutral stream of information. ( ① ) Instead, recommendation algorithms are optimized to maximize engagement, and content that provokes strong emotion tends to hold attention longer than balanced reporting ever could. ( ② ) Researchers describe this as a distorted mirror: the platform reflects not society as it truly is, but a version shaped by whatever keeps people scrolling. ( ③ ) Some platforms have experimented with reducing the visibility of outrage-driven content. ( ④ ) Yet these experiments consistently saw user engagement drop. ( ⑤ )