영어 모의고사 (자동 편성)

Sensors buried in the soil now report moisture levels in real time, drones survey entire fields for signs of disease within minutes, and automated systems adjust irrigation without a farmer ever ① walking the rows. For large operations with the capital ② to invest, these tools have measurably increased yields while reducing water waste, addressing two problems that traditional farming has struggled with for generations. Smaller family farms, however, often cannot afford the upfront cost of this equipment, and some worry that the gap between high-tech and traditional operations will only ③ widen as the technology advances further. There is also a quieter cultural cost that rarely appears in productivity reports: generations of farming knowledge, passed down through direct observation of soil, weather, and crops, risk ④ being replaced by dashboards and automated alerts that few young farmers ⑤ teaching to question.

Many large companies now rely on artificial intelligence ① to screen job applications before a human ever sees them. These systems promise efficiency, scanning thousands of resumes in seconds and ranking candidates according to patterns ② drawn from previous hiring decisions. However, this reliance on historical data creates a troubling risk. If a company's past hiring favored a particular group, the algorithm may learn to treat that pattern as a marker of success and quietly reproduce it, even when no one ③ intending discrimination. Unlike a human interviewer, the system offers no explanation for its choices, which makes the bias difficult ④ to detect until an outside audit reveals it. Some researchers argue that fairness cannot be added afterward; it must ⑤ be built into the system's design from the very beginning.

Among the many tools available to those who design choice environments, few are as quietly ①powerful as the default option, the alternative that takes effect automatically unless a person actively selects something else. Because changing a default requires ②deliberate effort, many individuals simply accept whatever arrangement has been preselected for them, regardless of whether that arrangement actually serves their interests best. Countries that automatically enroll citizens in organ donation programs, requiring an explicit request to opt out, consistently report far ③lower donor registration rates than countries that require citizens to actively opt in. Employers who automatically enroll new hires in retirement savings plans likewise see participation rates climb dramatically compared with employers who leave enrollment entirely voluntary. What makes default options so influential is not merely convenience; a default also functions as an implicit signal about what is considered ④normal or recommended, subtly shaping perceptions even among individuals who believe themselves to be deciding ⑤independently.

Traditional economic models predict that a rational individual offered any positive sum of money, however small, should ①accept it without hesitation, since even a modest gain is preferable to receiving nothing at all. When the proposer offers a division perceived as ②unfair, such as retaining ninety percent of the sum for themselves, the second participant frequently rejects the offer entirely, leaving both parties with nothing rather than accepting an outcome viewed as insulting. This behavior indicates that individuals value fairness as an end in itself, sometimes willingly ③sacrificing material gain to punish perceived exploitation. Neuroscientific investigations complement this finding, revealing that unfair offers activate brain regions associated with ④pleasure and negative emotion, suggesting that the rejection of unfairness is not merely a calculated strategic move but also an emotionally driven response. Such evidence challenges the assumption that economic actors pursue only their own material self-interest, demonstrating instead that considerations of equity can ⑤override the straightforward pursuit of maximum individual gain.

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.

When engineers design the software that controls a self-driving car, they inevitably face a question that philosophers have debated for decades: who should the vehicle protect in an unavoidable crash? (A) This gap between the ideal and the personal choice suggests that ethical programming cannot be settled by public opinion alone. (B) Surveys reveal an uncomfortable contradiction: most people say a car should minimize total harm, yet the same people admit they would never buy a vehicle designed to sacrifice its own owner. (C) Manufacturers, regulators, and philosophers must instead agree on transparent rules before autonomous vehicles are allowed to make such decisions on the road.

The discovery of CRISPR gene-editing technology gave scientists a tool precise enough to correct a single mistaken letter in the human genome, raising hope for curing inherited diseases. (A) Scientific organizations around the world have therefore drawn a careful line, permitting gene editing to treat disease in existing patients while banning any edit that would be inherited by their descendants. (B) Yet editing a gene in a sperm, egg, or early embryo does not simply change one patient; it alters the genetic code passed down to every future generation. (C) Critics worry, however, that as the technology becomes cheaper and more widely available, this line will become harder to enforce than it is to draw.

This admission, however, is precisely what the sunk cost fallacy makes so difficult to accept. Consider a construction firm that has spent several years and a substantial budget building a bridge, only to discover partway through that a cheaper and safer alternative route has since become available. ( ① ) A purely forward-looking analysis would compare the remaining cost of finishing the original bridge against the total cost of switching to the new route, ignoring whatever has already been spent. ( ② ) If the new route is genuinely cheaper going forward, the rational choice is to abandon the original project regardless of how much money it has already consumed. ( ③ ) Abandoning the project, however, requires the firm's leadership to publicly acknowledge that a significant portion of the original investment produced no lasting value. ( ④ ) As a result, firms often continue projects long after the numbers clearly favor stopping, simply to avoid confronting this uncomfortable truth. ( ⑤ ) The bridge, in such cases, becomes less a matter of engineering than a monument to the difficulty of admitting a costly mistake.

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