영어 모의고사 (자동 편성)

Anyone who has ever promised to finish a project "by Friday," only ①to still being working on it weeks later, has experienced the planning fallacy firsthand. This term describes the persistent tendency of individuals and organizations ②to underestimate how long a task will take, how much it will cost, and how many complications will inevitably arise. What makes this bias particularly stubborn is that awareness of past failures rarely leads to more accurate predictions; a construction firm that ③finished its last three projects late will often insist, with genuine confidence, that this next project will be different. Researchers argue that planners focus almost exclusively on the specific steps of the current task while ④ignoring the broader statistical record of similar projects that came before. Until such external correction becomes standard practice, ambitious deadlines will likely continue to be ⑤broken with remarkable consistency.

When workers receive a five percent raise in a year when prices have ①climbed five percent as well, their real purchasing power has not changed at all, yet many still report feeling noticeably better off. This tendency to judge economic well-being by nominal figures ②printing on a paycheck, rather than by what that money can actually purchase, is known among economists as money illusion. Employers exploit this pattern by ③favoring modest nominal raises over deeper structural adjustments, since a visible increase on a payslip satisfies employees more effectively than an explanation about inflation could. The illusion persists even among people who claim to understand inflation well, which suggests that the bias ④operates at an intuitive level that formal knowledge does not easily override. Some economists ⑤argue that policymakers should exploit this illusion strategically, since workers resist nominal wage cuts far more fiercely than they resist the equivalent erosion of real wages.

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

Every time a smartphone connects to a cell tower or a Wi-Fi network, it quietly records a data point that can later ①be pieced together into a detailed map of where its owner has been. Most users agree to this tracking without ②reading the fine print, assuming that a single company's database poses little threat. The real danger, however, emerges when location data from multiple sources ③combining. A pattern that reveals a person's home, workplace, and daily route can expose far more than any single app ever intended to share. Companies often argue that anonymized data cannot be traced back to individuals, yet researchers have repeatedly ④shown that a handful of location points is enough to identify a specific person. As a result, privacy experts increasingly insist that location data ⑤deserves the same legal protection as medical records.

Generative AI systems learn to write, draw, and compose by studying ①enormous collections of existing human work, much of which was created without the artists' knowledge or consent. Supporters of the technology argue that this process resembles how a human student learns by studying the masters, ②absorbing style and technique rather than copying specific pieces. Critics respond that the comparison ③holds up perfectly at scale, since a single AI model can absorb millions of works in days. Courts around the world are now being asked to decide whether training an algorithm on copyrighted material counts as fair use or as ④infringement. Until clearer rules emerge, artists, developers, and publishers remain locked in a legal ⑤gray zone that satisfies almost no one.

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

For decades, memory researchers assumed that repetition alone determined how well information would be retained, an assumption that an influential theory proposed by Craik and Lockhart eventually overturned. According to their framework, what matters most is _______________. Shallow processing, which involves attending merely to superficial features such as the shape of letters or the sound of a word, produces memory traces that fade quickly and remain vulnerable to interference. Elaborative processing, by contrast, requires connecting new information to existing knowledge, generating meaningful associations, and analyzing semantic content, thereby producing traces that are considerably more durable. Consequently, a student who merely repeats a definition aloud without considering its implications is less likely to retain it than one who relates the concept to a personal example. This distinction reshaped educational practice, encouraging teachers to design tasks that demand comprehension rather than mechanical repetition, since durable memory depends on the depth, not the frequency, of cognitive engagement.

When people face a difficult decision, they often weigh not only the expected outcome of each option but also how painful it would feel to discover later that a different choice had been better. (A) Researchers have found that people feel sharper regret over losses that resulted from taking action than over identical losses that resulted from doing nothing. (B) Investors, for example, sometimes continue holding a declining stock rather than selling it, because selling would force them to confront the possibility of having made a clear mistake. (C) This helps explain why so many individuals default to inaction whenever a decision feels uncertain, even when remaining passive is not actually the safer choice.

Prospect theory proposes that the subjective value of money is not a straight line but a curved function, differing sharply from the assumptions of classical economic models. (A) Understanding this curvature allows economists to predict decisions that a simple, linear model of value could never anticipate. (B) This tendency, known as diminishing sensitivity, explains why a shopper barely notices a ten dollar increase on a two thousand dollar purchase, though the same ten dollars would matter enormously as the price of a meal. (C) In the domain of gains, the function bends downward as amounts increase, and a parallel pattern appears among losses, where the curve bends in the opposite direction, yet sensitivity still fades as the amount grows larger.

Confirmation bias becomes particularly consequential in political discourse, where citizens must evaluate competing claims about complex issues. (A) As a result, exposure to more information does not necessarily produce more accurate beliefs; it can instead deepen existing divisions by giving each side additional material that appears to validate its own position. (B) Voters who favor a particular policy tend to seek out news sources and commentators who share that view, while largely avoiding those who might challenge it. (C) This selective consumption means that two people examining ostensibly the same news environment can end up with radically different, mutually reinforcing interpretations of events.