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The Smithian model of innate human disposition—as entirely self-interested, gleaning motivation only from personal profit—has long underpinned modern economic theory. One proponent of the person-as-selfish-agent model is Samantha T. Cleary, an economist who argues that this view of human nature remains the most useful basis for advancing behavioral economics and opposes more complex algorithms that take into account such complicating factors as altruism and social pressure. Economics is a purely statistical field of study concerned with a society's most common motivators, not every individual's. The field can operate most efficiently by using straightforward models that Cleary poetically describes as elegant.
Yet how will economics strengthen its predictive powers if not by increasing the sophistication of its models, using swaths of data to inform analysis of the many processes that drive the aggregation of human economic decisions? How can behavioral economics develop without this step? If a behavior follows a any consistent pattern across a large cross section of the population, economists should be able to measure and predict it, at least theoretically. But even if an algorithm could account for complex motivations and apparently irrational decisions, Cleary argues that such an approach to studying economic behavior would remain greatly vulnerable to misinterpretation, bias, and hyperlocal preferences. Furthermore, this complex algorithm would only introduce a greater margin of error, while being so complex and situation-specific as to be useless for rendering any long-term or generalizable predictions. It might have the capacity to describe the behaviors of a small pocket of people, but couldn't contribute to broader economic theory in any meaningful way.
Imagine, though, that an intricate algorithm were able to accurately predict behaviors based on many more inputs than self-interest. Whether the prediction confirmed or denied established economic theory, it would still contribute to the growing body of data in behavioral economics. People, unlike machines, do not operate according to simple psychological rules, and if we can account for this to some extent in our work, it is hard to envision a reason why we would not accept the challenge of doing so. Economies, after all, depend ultimately on one thing: human decision-making. Work based on the assumption that personal choices obey centuries-old economic theories is willfully limited in scope. To be aligned with the arguments of purists like Cleary, one would have to dismiss the ultimate purpose of economic study: to understand and predict the workings of real economies.

Which of the following is most consistent with Cleary’s reasoning as described in the text?

A
Economic models should apply to as many contexts as possible.
B
Economies can only be described in the present, not predicted.
C
Complex models of human behavior are essentially equivalent to economic laws.
D
Economists cannot model the most complex, realistic situations when they use “elegant” mathematical tools.
E
Mathematical equations can reliably account for a broad range of human behavioral patterns.

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正确答案: A

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